Tag: Future of Work

From Spreadsheets to Strategy: How to Move Beyond Excel and Into Real Data Analytics

Posted on by Marisa Crous

If you’re the person everyone comes to when the numbers need untangling, you’re already closer to a data analytics career than you think.

Maybe you’ve built five-tab Excel dashboards for your team. Maybe you live inside pivot tables, VLOOKUPs and forecasting spreadsheets. Maybe your role title says “operations manager”, “finance lead” or “marketing executive”, but half your job already revolves around data.

And yet, when organisations hire “data analysts”, the role often sits elsewhere.

That gap is becoming more visible as businesses shift from spreadsheets to scalable analytics, AI-assisted workflows and real-time decision-making. Data roles have grown significantly faster than the wider job market, while employers increasingly expect analysts to work across SQL, visualisation tools, Python and GenAI-enabled workflows.

The good news is this: moving beyond Excel does not mean starting your career over. In fact, your existing business experience may be your biggest advantage.

As Luis Cantu, an alumnus of the LSE Data Analytics Online Career Accelerator, puts it:

“What I was looking for was to reposition myself, leveraging my commercial background, but building a set of tools that would help me provide commercial teams with better insights.”

This article walks through a practical four-step path to make that transition – from diagnosing the Excel ceiling to building a portfolio and positioning yourself for a real analytics role.

The Excel ceiling – and why it holds so many careers back

Excel is still one of the most important business tools in the world. It teaches people how data is structured, how analysis works and how decisions get made.

But there comes a point where Excel stops scaling.

You feel it when spreadsheets start crashing under large datasets. When version control turns into “Final_v7_REALfinal.xlsx”. When every weekly report depends on you manually refreshing formulas. When teams can’t collaborate cleanly across systems. When repetitive work eats up hours that should be spent solving problems.

That’s the operational ceiling. Then there’s the career ceiling.

Today, “advanced Excel skills” alone rarely signal analyst-level capability to employers. SQL now appears in up to 60% of data analyst job postings, while Python and visualisation platforms increasingly sit alongside it in role requirements.

This matters especially for professionals trying to make a data analyst career change without “official” analyst experience. Many people already do analytical work every day – they just lack the technical stack and portfolio evidence that hiring managers recognise.

As Harry Kelleher, Course Facilitator on the LSE Data Analytics Online Career Accelerator, explained during a recent webinar:

“The analyst role has become a lot broader… not just doing your technical job very well, but having a very good understanding of the business problems that you’re investigating.”

Your Excel experience already gives you the business foundation. Modern analytics tools help you scale that impact across larger datasets, faster workflows and more strategic decisions. 

For a broader beginner’s view of the data analyst career path, our guide on how to become a data analyst covers the foundations – this article picks up where that one ends, for readers already comfortable in Excel.

And the market shift is structural, not cyclical – there are clear reasons why now is the best time to pursue a career in data, regardless of where you’re starting from.

Step 1 – Recognise when Excel stops scaling

The first step in any data analyst learning path is recognising the signs that your current workflow has outgrown spreadsheets.

There are usually five clear indicators:

1. Your datasets are becoming too large

Excel works brilliantly – until it doesn’t. Large operational datasets quickly become slow, unstable and difficult to manage.

2. Collaboration becomes messy

Spreadsheets are notoriously difficult to govern across teams. Different versions, conflicting formulas and unclear ownership create friction fast.

3. You become the bottleneck

If every dashboard refresh depends on you manually updating formulas or cleaning data, the process cannot scale.

4. Automation is limited

Modern analytics workflows rely on repeatability. Excel often struggles with robust automation, especially when pulling from multiple systems.

5. Insight delivery becomes reactive

You spend more time maintaining reports than solving strategic problems. That’s often the point where professionals start looking beyond spreadsheets.

Excel users already bring valuable business context, stakeholder awareness and operational understanding. The next step is building the technical stack that helps you scale that impact.

Step 2 – Learn the stack that replaces Excel at scale

The transition from spreadsheet user to analyst is less about learning “coding” and more about learning the right workflow.

Strong data analyst skills today are typically built in layers – each tool solving a problem Excel cannot solve efficiently at scale.

Start with SQL – the single most in-demand data skill

If you’re moving from Excel to SQL, the good news is this: the leap is smaller than most people expect.

SQL is essentially the language of business databases. It allows analysts to filter, join, aggregate and analyse millions of rows directly from the source.

For Excel users, the mental model already exists.

As Harry Kelleher explained:

“A really important foundation for anyone working with AI is knowledge in the subject matter, and therefore knowing your basics in coding, and being able to understand the data analytics lifecycle.”

Think of it this way:

  • Filters become WHERE
  • VLOOKUPs become JOINs
  • Pivot tables become GROUP BY

A good first milestone? Replicate one Excel report in SQL within two weeks.

Add visualisation – Tableau or Power BI

Excel charts communicate information. Tableau and Power BI communicate decisions.

Modern organisations increasingly expect dashboards to be interactive, shareable and connected to live data sources. Analysts are now expected to build tools businesses actually use – not static screenshots pasted into PowerPoint decks.

Luis Cantu, now a Data and Insights Analyst at Leeds United Football Club, moved into data analytics from a career in independent commercial consulting. In his current role, he has seen firsthand how effective data visualisation can influence decision-making:

“Showing them dashboards that come from the analysis… and condensing it into a concrete, well-presented dashboard… helped me feel confident going in front of directors.”

The programme includes dedicated visualisation training using Tableau, helping learners move from “reporting numbers” to telling business stories.

Layer in Python – for automation and real analytics

Python is often the point where Excel users stop repeating manual work and start automating it.

Instead of spending hours cleaning spreadsheets every week, Python allows analysts to automate workflows, process large datasets and perform analysis at scale.

It also unlocks capabilities that Excel simply cannot support effectively:

  • Predictive modelling
  • Sentiment analysis
  • API integration
  • Unstructured data analysis
  • Scalable automation

Python is often the point where Excel users start automating repetitive work. 

As Luis Cantu explained:

“Using advanced statistical tools like R and Python to really dig into the data using just a few lines of code is really powerful.”

Don’t skip GenAI fluency

The future-thinking analyst works alongside AI, using it to solve problems faster and more strategically. 

During the webinar, Harry Kelleher described it this way:

“We’re going to be writing less code, and we’re going to be more orchestrating the use of AI, and doing a lot more strategic thinking.”

That means analysts increasingly need to know:

  • How to prompt effectively
  • How to validate outputs
  • How to identify hallucinations or bias
  • How to apply AI within business context
  • How to communicate findings responsibly

The LSE research on data literacy in the AI era reinforces this change: the competitive advantage is moving away from pure tool execution and towards problem framing, strategic thinking and human judgement.

This stack opens up a much wider career surface than Excel alone – see the full range of careers you can pursue with data analytics skills for a sense of what becomes possible.

Step 3 – Build a portfolio that proves you can solve business problems

Hiring managers increasingly want proof that you can apply data tools to real business problems. 

That means building a data analyst portfolio.

The strongest portfolios are not built around generic datasets like Titanic survival predictions. They’re built around business problems that feel commercially real.

Good portfolio projects usually demonstrate:

  • A clear business problem
  • A structured analytical process
  • Appropriate tool selection
  • Actionable insight
  • Communication clarity

This is where Excel users often have a hidden advantage. You already understand how businesses operate. You know what stakeholders care about. You know what “good enough” looks like in a commercial environment.

Structured programmes can accelerate this process significantly. The LSE Data Analytics Career Accelerator culminates in a live Employer Project where learners solve real business challenges with organisations, including EdPlace and GAEA AI. 

Karen Munro, a Career Coach on the programme, described the transition this way:

“Coaching helps learners realise that they’re not starting from zero… helping them weave together previous experience with their new data skills.”

Emma Roberts’ mid-career shift from engineering to data analytics – and now to running her own analytics consultancy – shows what becomes possible when applied learning replaces theoretical study.

Step 4 – Position yourself for the role (not the restart)

One of the biggest mistakes career switchers make is positioning themselves as beginners. You are probably not a “junior analyst with no experience”.

You are more likely:

  • A marketer with analytics capability
  • An operations professional with automation skills
  • A finance lead with data visualisation capability
  • A commercial manager who can translate insight into revenue impact

That combination is extremely valuable.

As Luis Cantu explained during the webinar:

“We’re going to be measured not on how well we solve problems, but how much value we’re adding.”

Hiring managers increasingly want domain-fluent analysts – people who understand both the technical workflow and the commercial environment.

This changes how you position yourself:

  • Lead your CV with business outcomes, not tasks
  • Quantify impact wherever possible
  • Bring portfolio projects into interviews
  • Use existing professional networks
  • Reframe your experience as analytical, not administrative

This is also where coaching comes into play. Karen Munro described one of the biggest mindset shifts this way:

“Shifting from ‘I’m a student’ to ‘I’m a data professional who solves problems.’”

That reframing is often what unlocks the transition. And the outcomes can be significant. Career Accelerator graduates reported an average salary uplift of 31% after completion (FourthRev Completers’ Survey).

For a sense of what’s actually achievable in this transition, the real-world results of our LSE learners show what Career Accelerator alumni have gone on to do.

Make your move from Excel to analytics

The move from spreadsheets to analytics builds on the experience you already have.

The professionals best prepared for the future combine business understanding with modern analytics skills. 

The roadmap is straightforward:

  1. Diagnose the Excel ceiling
  2. Learn the modern analytics stack
  3. Build portfolio evidence
  4. Position yourself around business impact

The LSE Data Analytics Career Accelerator is built for exactly this transition. 

Over six months, learners build practical fluency in SQL, Python, Tableau, GenAI tools and data storytelling, culminating in a live Employer Project. Learners earn a Certificate of Competence from LSE and receive up to 12 months of 1:1 career coaching.

To learn more about the LSE Data Analytics Career Accelerator, download the programme brochure.

FAQs

Can I become a data analyst with just Excel?

Excel is an excellent foundation, but most modern analyst roles now also expect SQL, visualisation tools and some familiarity with Python or AI-enabled workflows.

How long does it take to move from Excel to data analytics?

That depends on your starting point and study intensity, but many professionals can build job-ready analytics capability within six to 12 months through structured, portfolio-led learning.

Do I need a degree to move into data analytics?

Not always. Many employers prioritise practical skills, portfolios and commercial experience. Structured programmes may also offer aptitude-based entry routes for experienced professionals without formal quantitative degrees.

Solutions for Bridging the Gender Gap in Tech

Posted on by Marisa Crous

What’s changed since 2024?

When this article was first published in January 2024, the conversation around women in tech felt cautiously optimistic: representation was inching upward, big companies were publishing diversity data, and the Alan Turing Institute was leading research on women in AI. A lot has changed.

The gender pay gap in UK tech has widened sharply, from 6% in 2023 to 12% in 2024, moving in the wrong direction for the first time in years. Google, Meta and Microsoft confirmed in 2025 that they would no longer publish DEI and workforce diversity data, making it harder than ever to hold the industry to account. The Alan Turing Institute paused its dedicated Women in Data Science and AI programme, also in 2025. And AI specialisms – where most of the new, high-paid roles are being created – remain among the most male-dominated areas in the sector.

This refresh updates every statistic and source in the original article to reflect where things actually stand in 2026.

The challenges, solutions, and strategies for bridging the gender gap in tech

In 2024, 441,000 women worked as IT specialists in the UK, still just 22% of the sector – a figure that has barely shifted in a decade. If gender representation in IT matched the wider workforce, there would be over 530,000 more women in tech roles. It’s well-established that diversity can fuel innovation and growth, and research continues to show that diversity drives measurably better business outcomes. As we move deeper into an age of AI and technology, that case has never been stronger.

So why are there still so few women in tech, and what’s stopping them from entering – and staying in – the industry?

A quick look at the current gender divide in tech

Nothing has historically been more telling about the real numbers of women in tech than looking at the GAFAM companies: Google, Apple, Facebook, Amazon, and Microsoft. Until recently, annual workforce reports gave a benchmark for how little had changed. But in 2025, Google, Meta and Microsoft confirmed they would no longer publish diversity, equity and inclusion data, making direct comparison impossible going forward. The most recent available figures show women’s representation in tech roles ranging from 29% of roles at Microsoft to 45% at Amazon – though Amazon has never broken out numbers specifically for tech roles. The withdrawal of this data is itself a significant story: it removes public accountability at exactly the moment it’s needed most.

In AI specifically, the picture is sharper still. The Stanford AI Index 2025 puts women at 26% of AI and machine learning roles globally, while the World Economic Forum reports women hold 30% of data science roles, the highest representation in any AI-adjacent profession, but still well below parity. The Alan Turing Institute, which long led UK research in this area, discontinued its dedicated Women in Data Science and AI programme in 2025, a loss the sector is still absorbing.

In product management (PM), women hold around 30–37% of positions depending on the dataset, but only 36% of senior-level PM roles, and the wage gap persists. In 2022, women entering PM roles earned 94% of what men in equivalent positions earned. This wasn’t always the case: since 2005, tech companies have started introducing technical PM specialisations and new entry requirements, like a computer science degree, and the industry has been moving away from gender-balanced PM teams ever since.

As of 2025, 40.4% of UX design roles are held by women per Zippia data, making the career an outlier in tech. This could be because product design is a more accessible career, diverse backgrounds and experience are welcomed, and degrees in psychology, visual arts, or marketing can all be relevant. However, only 27.5% of senior UX designer roles are held by women, meaning the imbalance returns the higher up the ladder you go.

Reasons for the gender gap in tech

Research by Accenture and Girls Who Code identified non-inclusive company culture as the biggest cause of the gender tech imbalance, and 37% of women in tech cite it as the leading reason they leave. In these workplaces, women feel overlooked, discriminated against, and discouraged by the lack of flexibility and access to women role models. Women of colour and lesbian, bisexual, and transgender women face even greater barriers.

The tech gender pay gap has not just persisted, it has worsened. The 2025 BCS Gender Diversity Report found that women in UK IT roles earned 12% less per hour than men in 2024, up from a 6% gap in 2023. UKTN’s analysis of 50 major UK tech firms puts the median gap at 17.5%, and Channel Web reporting shows the IT-sector gender pay gap is now at a nine-year high. The pay gap has been widening, not closing, since 2022. Recruitment processes also continue to disadvantage women, with job adverts and assessment criteria that tend to favour male candidates.

How can we close the tech gender gap?

School

The tech gender imbalance starts early, in school and university. To create inclusivity, there first needs to be access to computing classes in school and, secondly, the encouragement to join. If young girls and women feel comfortable engaging in technical subjects and have a sense of belonging, they’ll establish an interest that could lead to tech studies.

University

In university, there needs to be clear pathways from studying to working. Again, the culture matters: a more inclusive university culture impacts a woman’s decision to look for a tech career. Men also benefit from an inclusive environment, with their likelihood of looking for tech careers also increasing.

Recruitment

While more companies are actively taking steps to build a pipeline of female talent at all hiring levels, businesses could go further by hiring women who may lack a traditional computer science degree but have the right transferable skills – and investing in the technical training they need.

Policy and leadership

Companies should set diversity targets, introduce full pay transparency, and consider the impact of technology on gender equality, such as when gender biases are baked into digital services and products. Having a diverse leadership group will also establish an inclusive culture and drive opportunities. If you see women leading, you’ll be more empowered to do the same.

Opportunity

Companies can forge connections with groups such as Girls Who Code, Stemettes, and Code First Girls to give girls, young women, and non-binary individuals a chance to attend workshops and join mentoring programmes with technology and engineering companies.

Where AI fits in

The original version of this article was written before generative AI reshaped the tech landscape. The AI gender gap is now one of the most pressing dimensions of the broader problem and one with long-term consequences.

The Stanford AI Index 2025 reports that women hold just 26% of AI and machine learning roles globally. These are among the fastest-growing and highest-paid positions in tech, which means the under-representation of women in AI today will compound the pay and seniority gaps of tomorrow. Worse, AI systems built without diverse teams are more likely to encode existing biases: research indicates gender-diverse AI teams produce significantly fewer bias-related errors in their products.

The UK has taken a step backwards here: the Alan Turing Institute, which ran one of the few dedicated programmes researching women’s participation in data science and AI, ended that work in 2025. The gap it leaves is real.

FourthRev’s LSE AI Leadership Career Accelerator was built with this problem in mind, giving people from non-traditional backgrounds a structured path into AI leadership roles.

Women are needed in the tech industry

Women have unique and distinct strengths to offer the tech industry.

Take product management. Marty Cagan, Silicon Valley’s “guru of PM,” argues that while being smart, creative, and persistent are qualities all PMs need, women make excellent PMs because they often possess a higher level of emotional intelligence, humility, and stamina, all essential to excel in the job.

Women and minorities are critical to the fields of data analysis and data science, where unconscious bias can impact data sets and algorithms, leading to inaccurate conclusions and harmful policy decisions. A good read on this is Caroline Criado-Perez’s Invisible Women: Exposing Data Bias in a World Designed for Men.

In her article Why Are Women Encouraged to Go Into UX?, UX writer Daley Wilhelm says UX requires empathy and involves “cleaning up and caregiving,” traits that help in understanding and predicting user behaviour. To explore the dangers of making assumptions about users, read Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher.

Gender diversity also has a significant impact on company performance and success; tech companies with a gender-diverse workforce see higher share prices. And the AI dimension makes representation even more critical: gender-diverse AI teams have been shown to produce significantly fewer bias-related errors in their products – a finding backed by the Stanford AI Index 2025.

Thriving in a male-dominated workplace

Some industries have a long way to go before they reach equal gender representation. But that shouldn’t stop women from entering their desired field.

Look for the right culture

To ensure you’ve chosen a business committed to equality and fairness, look for companies with diversity, equity, and inclusion as part of their core values, and check for equitable parental leave policies to avoid the “motherhood penalty.”

Demand equal pay for your work

The pay gender gap in tech is real. Research shows that women are significantly less likely to get a raise even though they ask for them just as often as men, and McKinsey’s Women in the Workplace research points to the broken rung between entry-level and manager as a key structural barrier that compounds over a career.

Use sites like Glassdoor, Levels, and CWJobs to see what you should be earning. For UK tech specifically, Hired.com’s State of Software Engineers and the ONS Gender Pay Gap bulletin give a more granular picture of where the gaps sit by role and sector. And read this article on how to go about salary negotiation as a woman in tech.

Find a mentor or coach

Female mentors can help you navigate your career journey and expand your network while prompting conversations that can break down barriers and remove stigmas or biases. You could also engage a sponsor who will advocate for you and ensure your work gets seen in the right places, at the right time, by the right people.

Career coaching

Working one-on-one with a career or executive coach can help you achieve your long-term goals. It can also help you realise your strengths and potential and give you the confidence to get the results you want.

King’s Product Management Career Accelerator learner, Iryna, assumed she’d be starting as an intern because she had no previous PM experience. Her confidence was transformed in just one session with her FourthRev Career Coach – and that work motivated her to aim higher, helping her land the sole product manager role in an exciting startup.

LSE Data Analytics Career Accelerator learner Elodie Hudson made the transition from teacher to data analyst with her coach’s support.

The highlight of my learning journey was the realisation that I could acquire skills that I previously thought were beyond my reach. It’s been life-changing.”

Elodie Hudson
Lead Data Scientist at AssessTech Analytics

Network with other women in tech

If you work in UI/UX, consider joining Ladies That UX, where you can share knowledge and connect with fellow UX designers from across the globe.

Women working in data analysis or science can explore the global communities at Women in Data, and if your passion is product management, join the Women In Product community of over 30,000 women and non-binary people.

For a broader UK-focused network covering all tech disciplines, Women in Tech UK and the Women in Tech Network are both active, well-resourced communities worth bookmarking.

Turn your ambitions into reality

We’ve been working to bridge the gender gap in education since we first launched our Career Accelerators in January 2022.

Women make up 47% of all learners enrolled on a FourthRev Career Accelerator since 2022.[1]

We’d love to help you make your tech career a reality. Browse the Career Accelerator portfolio and unlock your potential in a high-growth digital career.

[1]Female learners enrolled on a Career Accelerator as of June 2026.

An Introductory Guide to GenAI for Product Managers

Posted on by FourthRev Team

Generative AI has moved from buzzword to baseline skill for product managers. Here’s how PMs are using AI tools in 2026, the productivity payoffs, and how to become an AI-literate product manager.

Generative AI is no longer an emerging trend – it’s embedded in how modern product teams work. In 2026, 91% of businesses use AI in at least one capacity, up from 78% in 2024 and 55% in 2023. For product managers specifically, adoption has been even faster: a 2025 Productboard survey found that 94% of enterprise product professionals rely on AI tools daily.

The productivity payoffs are real and measurable:

  • Workers using GenAI save an average of 5.4% of their work hours weekly. Product managers report saving an average of four hours per task on functions like drafting PRDs, competitive analysis, and building roadmaps.
  • Businesses report an average 24.69% increase in productivity and 15.7% in cost savings from AI adoption.
  • Chief AI Officer roles are now present in 61% of enterprises – a signal that AI strategy sits at the leadership table.

The question for product managers is no longer “should I learn this?” but “how do I use this effectively?” To help you answer that, the King’s Product Management Career Accelerator now features a dedicated AI and Product Management Learning Track.

Become an AI-literate product manager

To give you a glimpse into the series that learners get full access to, you can watch the first Masterclass hosted by AI expert Alexander Cohen. You’ll learn about the foundations of AI and LLMs and the implications of AI on product management.

When Alex presented this Masterclass, he predicted a fundamental shift in how products get built. That prediction has proven remarkably accurate:

“Within five years, AI is going to make a fundamental change in the way that we produce products. One of those fundamental changes is that AI will in many respects replace human engineers. As such, we’ll get to a place where product managers are going to be responsible for defining what goes into a product and what it should do, and then an AI will execute on that.”– Alexander Cohen

In 2026, this is already happening. AI-assisted coding (“vibe coding”) has made building software dramatically cheaper and faster. The most expensive thing a team can do now is build the wrong thing, which puts the product manager squarely at the centre of the business.

Cheat sheet: An intro to GenAI

Following the Masterclass, Alex has created a guide to AI that you can refer to on the job and with your colleagues to help you understand what AI is, why it’s important, and how to use it in your product management career.

Tokens

What it is

Tokens are the fragments of text that AI models use to analyse and generate language. For example, the sentence “Artificial intelligence is fascinating” might be broken down into tokens like [“Artificial”, “intelligence”, “is”, “fascinating”].

Why it’s important

  • The way text is tokenised influences the model’s ability to understand and respond accurately. Proper tokenisation ensures the model captures the nuances of language, which is vital for applications like chatbots, translation services, and content creation.
  • For product managers, understanding tokens helps in designing AI applications that require precise language processing, ensuring that the outputs are relevant and coherent.
  • Token limits have expanded dramatically since 2024. GPT-4o now supports 128k token context windows. Claude supports 200k tokens. Google’s Gemini 1.5 Pro supports up to 1 million tokens. This changes what’s possible for PMs working with long documents, product specs, and research.

Try it yourself

Context windows

What it is

A context window is the span of text an AI model can consider at once. In 2024, a typical context window was 4,000–8,000 tokens. In 2026, leading models have expanded this dramatically:

  • GPT-4o: 128,000 tokens
  • Claude (Anthropic): 200,000 tokens
  • Gemini 1.5 Pro (Google): up to 1,000,000 tokens

To put this in perspective, 200,000 tokens is roughly equivalent to a 500-page book. You can now feed an entire product specification, user research report, or competitor analysis into an LLM and get structured output in a single pass. This was impossible two years ago.

Why it’s important

  • Larger context windows are crucial for maintaining coherence in long-form work. For product managers, this means you can use AI for tasks like summarising extensive user research, analysing full product backlogs, reviewing entire codebases, or processing hours of meeting transcripts – all without losing context.
  • When selecting AI tools for your workflow, understanding context window size helps you choose the right model for the task. A quick competitive question might only need a small window, but reviewing a 60-page product brief requires a large one.

The implications of AI on product management

AI isn’t just playing a supporting role in product management, it’s reshaping the discipline. Over 73% of product managers now use at least one AI-powered tool in their daily workflow, nearly double the 45% adoption rate in 2024. For PMs, AI won’t take away your job, but it is fundamentally redefining what’s achievable in your work.

Use AI for market insights and opportunities

  • AI-driven analytics provide deep insights into market trends and customer behaviours, enabling data-informed decision-making. This helps in crafting precise value propositions and aligning product vision with market needs.
  • Predictive analytics and machine learning models assist in anticipating customer needs and identifying opportunities for innovation, enhancing strategic planning and competitive positioning.

Use AI for routine and repetitive tasks

  • AI automates routine tasks like scoping, documentation, and backlog management, improving efficiency and accuracy. Natural language processing (NLP) tools can generate detailed reports and documentation based on minimal input.
  • With AI handling repetitive tasks, product managers can focus on their softer skills, such as effective communication, stakeholder management, and team leadership, fostering better collaboration and aligning teams towards common goals.

“As AI starts to take over the heavy lifting of how products get built, we’re going to start to see this onus coming back to product managers on what actually goes into a product.”– Alexander Cohen

AI tools product managers are using in 2026

The AI tool landscape for product managers has matured significantly. Here are the categories and tools PMs are using most in 2026:

CategoryToolsWhat it does
PRD and documentationChatPRD, Notion AI, ChatGPTPMs report saving 4+ hours per PRD by using AI to draft, structure, and refine product requirements documents.
User research and feedback analysisZeda.io, Dovetail, BuildBetterAI-powered tagging, categorising, and synthesising qualitative user feedback. Turns hours of interview transcripts into structured insights in minutes.
Roadmapping and prioritisationProductboard AI, Airfocus, Aha!AI-assisted feature scoring, opportunity assessment, and roadmap generation based on data rather than gut feel.
Competitive intelligenceCrayon, KlueAutomated competitor tracking, pricing analysis, and market movement alerts.
Analytics and data queryingJulius, Amplitude AINatural language querying of product data. Ask a question in plain English, get a chart or insight back.

The key takeaway: AI isn’t a single tool you adopt. It’s a layer across your entire workflow. The most effective PMs in 2026 use AI at every stage of the product lifecycle – from discovery and research through to delivery and measurement.

The rise of AI agents and what it means for PMs

The biggest change since this article was first published in 2024 is the rise of agentic AI – AI systems that don’t just respond to prompts but take autonomous, multi-step actions to complete tasks.

The numbers tell the story:

  • Gartner projects that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.
  • 80% of enterprises deploying AI agents report measurable economic benefits, including increased throughput, lower operational costs, and faster product release cycles.
  • A February 2026 Harvard Business Review article argued that product management skills are now central to successful AI deployment across organisations.

For product managers, this creates a new set of responsibilities:

  • Defining what AI agents should and shouldn’t do (guardrails and scope)
  • Designing human-in-the-loop controls for high-stakes decisions
  • Managing the behaviour of AI features within your product
  • Understanding how agentic workflows change user expectations

If you’re building products in 2026, you’re almost certainly building products that include AI agents. Understanding how they work isn’t optional – it’s core to the PM role.

From coordination to strategy

AI-assisted coding tools like Cursor and GitHub Copilot have dramatically reduced the cost of building software. When it’s cheap to build, the most expensive thing you can do is build the wrong thing.

This puts the PM at the centre of the business. The role is shifting from coordination (“managing the backlog, running standups, writing tickets”) to strategy (“determining what to build, for whom, and why it’s important”). AI handles more of the execution and PMs focus on customer understanding, strategic judgement, and ensuring the right outcomes.

“We’re going to see that subject matter expertise, that domain knowledge, that real-world understanding of how things actually happen for real people, that’s going to be incredibly valuable and a real, great differentiator.”– Alexander Cohen

What is an AI product manager?

As AI becomes embedded in more products, a distinct role is emerging: the AI product manager. While every PM now needs AI literacy, an AI PM specialises in building and managing products where AI is the core capability – not just a feature.

How an AI PM differs from a traditional PM

  • A traditional PM defines features and user flows. An AI PM also manages model behaviour, data quality, and output reliability.
  • A traditional PM writes acceptance criteria. An AI PM also defines what “good” looks like for probabilistic outputs that may vary each time.
  • A traditional PM manages a development roadmap. An AI PM also manages model retraining cycles, bias monitoring, and ethical considerations.

Skills unique to AI product managers

  • Understanding how LLMs work (tokens, context windows, fine-tuning, RAG)
  • Prompt engineering and evaluation
  • Data quality assessment and pipeline awareness
  • AI ethics: bias, fairness, transparency, and explainability
  • Managing AI agent behaviour and guardrails

The career path

Traditional PM → AI-literate PM (uses AI tools in daily work) → AI Product Manager (builds AI-powered products).

The King’s Product Management Career Accelerator, with its dedicated AI Learning Track, is designed to help you take that first step from traditional PM to AI-literate PM.

Get hands-on with the concepts

Train a neural network: TensorFlow Playground

TensorFlow Playground is an interactive web-based tool designed to help users understand the basics of neural networks. It provides a visual and hands-on way to experiment with different neural network architectures and parameters.

Features

  • Interactive visualisation: Allows users to see how neural networks learn and adjust weights in real-time.
  • Adjustable parameters: Users can modify parameters such as learning rate, number of neurons, and activation functions to observe their impact on the model’s performance.
  • Educational tool: Ideal for beginners to get a grasp of fundamental concepts in machine learning and neural networks without requiring any programming knowledge.

Why it’s important

Understanding how neural networks work at a basic level helps product managers and developers make informed decisions about integrating AI into their products. It demystifies the learning process of AI models and provides a foundation for more advanced AI concepts.

Try it yourself

Play around with TensorFlow Playground right now.

Run a custom LLM model: Ollama

Ollama is a platform that allows users to run open-source AI models locally on their own hardware. Since this article was first published, Ollama has grown to support a much wider range of models – including Llama 3, Mistral, Phi-3, Gemma, and dozens of others – making it the go-to tool for running local LLMs.

Features

  • Local model deployment: Users can deploy and run AI models directly on their machines, ensuring data privacy and security.
  • Support for a wide range of models: Compatible with the latest open-source models, enabling users to choose the best fit for their needs.
  • Customisability: Users can experiment with different model configurations and parameters to tailor the AI’s performance to specific tasks.

Why it’s important

Running AI models locally with Ollama empowers product managers and developers to have full control over their AI applications. It enhances data privacy, reduces dependency on cloud services, and allows for more experimentation and customisation of AI models. In 2026, with enterprise data sensitivity at an all-time high, local deployment is increasingly attractive.

Try it yourself

Download Ollama here to create and customise your own LLM.

Talk to an LLM with emotions: Hume.ai

Hume.ai is a platform focused on creating empathic voice interfaces that can understand and respond to human emotions. Since the original article was published, Hume has released EVI 2 and EVI 3 – their latest voice-to-voice foundation model that responds in under 300 milliseconds with advanced personality mimicry.

Features

  • Emotion recognition: Uses AI to detect and interpret a wide range of human emotions from voice inputs, analysing vocal modulations including tune, rhythm, and timbre.
  • Empathic responses: Generates responses that are emotionally appropriate, enhancing user engagement and satisfaction.
  • Ultra-low latency: EVI 3 responds faster than most humans do in real conversation.
  • Full SDK support: Developers can integrate EVI into applications with just a few lines of code (React, TypeScript, Python, .NET, Swift, and more).

Why it’s important

Understanding and responding to human emotions is crucial for creating engaging and effective AI-driven interactions. Hume.ai enables product managers to develop more intuitive and empathetic user interfaces, improving customer experiences in areas like customer service, virtual assistants, healthcare, and interactive entertainment.

Try it yourself

Start a conversation with Hume’s Empathic Voice Interface.

Draft a product requirements document: ChatPRD

ChatPRD acts as an on-demand Chief Product Officer, helping product managers draft detailed product requirements documents that meet high-quality standards. It’s one of the most popular PM-specific AI tools in 2026.

Features

  • PRD generation: Produce structured, detailed PRDs from a brief description or conversation.
  • Strategy coaching: Get AI-powered feedback on your product thinking, assumptions, and priorities.
  • Template flexibility: Works with your existing PRD structure or suggests best-practice formats.

Why it’s important

Writing PRDs is one of the most time-consuming parts of a PM’s job. ChatPRD reduces drafting time significantly while maintaining quality, freeing you to spend more time on strategy, research, and stakeholder alignment.

Try it yourself

Try ChatPRD.

Analyse long documents and research: Claude

Claude by Anthropic is a large language model with a 200,000-token context window – one of the largest available. This makes it exceptionally useful for product managers who need to process, analyse, and synthesise large volumes of information.

Features

  • Extended context: Process entire product specs, user research reports, or competitive analyses in a single pass.
  • Structured output: Ask Claude to summarise, extract themes, identify risks, or create action items from lengthy documents.
  • Nuanced reasoning: Particularly strong at tasks requiring careful analysis and balanced judgement.

Why it’s important

PMs deal with information overload daily. Claude’s ability to ingest and reason over very long documents means you can go from “60-page research report” to “structured brief with key insights” in minutes rather than hours.

Try it yourself

Try Claude at claude.ai.

Understand (or write) code as a PM: Cursor

Cursor is an AI-powered code editor that has become popular among non-technical product people exploring what’s being called “vibe coding” – using natural language to build prototypes and understand codebases without being an engineer.

Features

  • Natural language coding: Describe what you want to build in plain English and Cursor generates the code.
  • Codebase understanding: Ask questions about an existing codebase and get plain-language explanations.
  • Rapid prototyping: Build quick proofs-of-concept to validate ideas before involving engineering.

Why it’s important

In 2026, the line between “technical” and “non-technical” PMs is blurring. Tools like Cursor let product managers prototype ideas, understand technical constraints, and have more informed conversations with engineers – without needing to become developers themselves.

Try it yourself

Download Cursor.

Prompt engineering basics for product managers

Knowing that AI tools exist is one thing. Getting great outputs from them is another. Here are practical prompting techniques PMs can use immediately:

Drafting a PRD

Instead of: “Write a PRD for a notification feature.”

Try: “You are a senior product manager at a B2B SaaS company. Draft a PRD for an in-app notification system. Include: user problem, success metrics, scope (in and out), user stories, and technical considerations. The audience for this PRD is the engineering team and the VP of Product.”

Why it works: Giving the AI a role, context, structure, and audience produces dramatically better output.

Analysing user feedback

Try: “Here are 50 pieces of user feedback from our NPS survey. Group them into themes, rank the themes by frequency, and for each theme suggest one potential product improvement. Present the output as a table.”

Why it works: Structured instructions produce structured output. Specifying the format (table) prevents vague paragraph responses.

Running competitive analysis

Try: “Compare [Competitor A] and [Competitor B] across these dimensions: target audience, pricing model, key features, recent launches, and market positioning. Present as a comparison table. Then suggest three opportunities where our product could differentiate.”

Why it works: Breaking a complex task into dimensions and asking for a specific format gives you an output you can actually use in a strategy deck.

Common mistakes to avoid

  • Being too vague (“help me with my product”) – always provide context, role, and desired format.
  • Not iterating – treat AI like a collaborative partner, not a one-shot oracle. Refine your prompt based on the first output.
  • Trusting outputs without verification – AI can hallucinate facts, especially about competitors and market data. Always verify.
  • Ignoring your own expertise – AI is a tool to accelerate your thinking, not replace it.

Experience the full AI Learning Track

Learners on the King’s Product Management Career Accelerator get full access to the AI and Product Management Learning Track, which features expert-led live Masterclasses designed to keep pace with how AI is evolving.

The Learning Track is designed to enhance the Career Accelerator’s curriculum and adapt as technology evolves, giving learners the latest tips and techniques to apply AI to their product management work. You’ll also earn a LinkedIn digital badge to showcase your AI literacy to employers and your professional network.

To find out more about the Career Accelerator and Learning Track, download the programme brochure.

Frequently Asked Questions

What is generative AI in product management?

Generative AI in product management refers to using large language models and AI tools to enhance product strategy, research, documentation, and decision-making. This includes drafting PRDs, synthesising user feedback, running competitive analysis, and building prototypes.

How do product managers use AI?

PMs use AI across the product lifecycle: for discovery (research synthesis, competitive analysis), definition (PRD drafting, user story generation), delivery (sprint planning, code review), and measurement (data querying, experiment analysis). Over 73% of PMs use at least one AI tool daily.

What are the best AI tools for product managers in 2026?

Popular tools include ChatPRD (PRD drafting), Claude and ChatGPT (document analysis and research), Productboard AI (roadmapping), Dovetail and Zeda.io (feedback analysis), Julius (data querying), and Cursor (prototyping and code understanding).

Do product managers need to learn to code to use AI?

No, but understanding how LLMs work, how to write effective prompts, and how to evaluate AI outputs is increasingly expected. Tools like Cursor make it possible to prototype with code using natural language, blurring the line between technical and non-technical PMs.

What is an AI product manager?

An AI product manager specialises in building and managing products where AI is the core capability. This requires understanding model behaviour, data quality, prompt engineering, and ethical AI – in addition to traditional PM skills.

Is AI going to replace product managers?

No – but it’s redefining the role. AI handles more execution and routine tasks; product managers focus on strategy, customer understanding, stakeholder alignment, and deciding what to build. The PMs who thrive in 2026 are those who use AI as a force multiplier.

What AI skills should product managers learn?

Start with prompt engineering, understanding tokens and context windows, and familiarity with the major LLMs (GPT-4, Claude, Gemini). Then build toward AI ethics, agent design, and tool evaluation. Programmes like the King’s Product Management Career Accelerator include a dedicated AI Learning Track to build these skills.

The Head Start Most Career Changers Don’t Know Exists

Posted on by Marisa Crous

Most courses teach theory. FourthRev’s Career Accelerators go further, with a real-world Employer Project that gives you hands-on work experience before you’re even hired. Here’s how it works and why it matters.

There is a familiar frustration at the centre of almost every career change.

You apply for a role. The employer asks for experience. But without experience, getting hired in the first place can feel impossible. 

For many people considering switching career paths, this is the moment momentum stalls. You can study independently. You can complete online courses. You can earn certificates. Yet when it comes to interviews, employers still ask the same question: “Have you actually done the work?”

That distinction matters more than ever. In a crowded market, the difference between someone who understands a subject and someone who can demonstrate practical ability is often the difference between being qualified and being hireable.

Most online learning programmes don’t close that gap. They teach theory, then leave learners to work out how to translate knowledge into professional credibility on their own.

What if, by the time you finished a programme, you had already worked on a real business challenge for a real organisation, collaborated as part of a team and received feedback from industry professionals?

That is exactly what FourthRev’s Career Accelerators are designed to deliver: real-world experience before you are hired.

The experience gap that holds career changers back

Why qualifications alone aren’t enough

A qualification shows employers what you’ve learned; practical work shows them what you can actually do. 

That distinction becomes decisive when recruiters are comparing candidates with similar educational backgrounds. Employers are rarely looking for theoretical understanding alone. They want evidence that you can apply knowledge in messy, real-world situations.

One past learner described the difference clearly:

“The practical experience is probably the reason why I got through the job interview process. Having that tangible evidence… is something I could really talk about in my job interview.” – Jon Minto, LSE Data Analytics Career Accelerator past learner

The issue is not that qualifications lack value. It’s that, on their own, they often fail to answer the employer’s most important question: Can this person perform in practice?

What employers are actually looking for

Hiring managers consistently prioritise demonstrated capability over abstract potential.

That means evidence of problem-solving, communication, collaboration and decision-making carries real weight. Increasingly, recruiters point directly to project-based experience as the deciding factor.

One learner, Sheldon Kemper, received feedback from a recruiter after completing the Cambridge PACE Data Science Career Accelerator:

“I’ve actually had feedback from the recruiter: they were very impressed with my competitive knowledge and technical capabilities, particularly noting the value of my AI/ML course at Cambridge. They highlighted that as very specific as to why they’ve taken me on.”

Employers do not simply want to hear that you studied analytics, product design or digital marketing. They want evidence that you can apply those skills in a real working environment. 

The real problem most courses don’t solve

Traditional online courses often end with an assignment or multiple-choice assessment. You complete the material, receive a certificate and are then expected to bridge the gap between learning and employability yourself.

That gap is where many career changers get stuck.

Completing a course is not the same thing as demonstrating professional readiness. And without practical examples to discuss in interviews, many learners struggle to translate new knowledge into job opportunities.

That is precisely how project-based learning can accelerate your career, which has become such an important conversation.

How FourthRev builds real work experience into every programme

A programme designed around doing, not just learning

At the heart of FourthRev’s Career Accelerators is a simple idea: real confidence comes from applying what you learn. 

Learners begin with three structured courses focused on technical and professional skills. Across these courses, they complete practical assignments designed to build a portfolio of work.

Then comes the final stage. In the last six weeks of the programme, learners work on a live business brief set by a real company. FourthRev calls this the Employer Project. But for most learners, it feels less like studying and more like doing the job for real. 

This isn’t a simulation: it’s a real business challenge

The Employer Project is not a fictional case study created for educational purposes.

The business challenge comes directly from an employer partner. The company provides the brief, context and feedback. Learners work in teams, present recommendations and receive evaluation from professionals connected to the organisation itself.

For Olga Sachura, who completed the LSE Digital Marketing Career Accelerator, that realism transformed the experience:

“The real-life project for the employer is next level, and you get to work with other people. It suddenly becomes real because you have a team. You have dedicated roles. You have feedback. You have to organise your time. You have to project manage. It’s great.”

The calibre of companies you’ll work with

Employer Projects span industries, sectors and business challenges. Recent partners have included:

The broader employer network has also included organisations such as Creative Navy, DocuSign and Sky. 

You can explore our employer partners to see the breadth of industries involved.

Employers themselves often emphasise the value of collaboration: 

“For organisations like Cancer Support Netherlands… initiatives like this bring fresh perspectives and innovative ideas that we could not realistically commission or pay for in the real world.” – Jill Murray, Board member, Cancer Support Netherlands

“Practical projects like these are priceless. They help learners decide if digital marketing is the right career for them, while giving them real-world experience that theory alone can’t provide.” – Gabriela Lazova, CEO at Lean

What you actually walk away with

A portfolio piece with a real company’s name on it

Learners finish with a deliverable connected to a genuine organisation rather than a hypothetical assignment.

That matters in interviews. It matters on CVs. And it matters when trying to stand out from candidates whose only examples come from isolated coursework.

Learners such as Eleonora Bacchi have used portfolio work from the LSE Data Analytics Career Accelerator to support successful transitions into research analyst roles shortly after completing the programme.

Proof you can work in a team

The Employer Project is designed to be collaborative. Learners work in small groups, take responsibility for different tasks and experience the same teamwork and communication challenges found in real workplaces.

For employers, that offers evidence of more than just technical skills.

As Rubi Rodriguez, a King’s Product Management Career Accelerator past learner, put it:

“It felt like we were colleagues and they were supporting us and our learning process. They were open to all of the questions… really warm and clear and also very honest.”

Direct feedback from industry professionals

Employer partners stay involved throughout the project. Learners present their work at different stages, receive feedback and refine their recommendations as the project develops. Final submissions are assessed with input from professionals connected to the business challenge itself. 

Occasionally, learner recommendations are even adopted by the company. That creates something far more powerful than a course certificate alone: a concrete professional achievement.

The confidence to say “I belong in this field”

Many people entering a new industry struggle because they are still building the confidence to see themselves as part of the field. 

The Employer Project can change that perception.

“The Employer Project reassured me that I made the right choice in changing careers. It was the first moment I felt I could contribute something meaningful in a new industry, and that gave me a sense of confidence I needed moving forward.” – Rie, Career Accelerator past learner

A ready-made answer to interview questions

Behavioural interview questions can be difficult for career changers because they require real examples from the industry. The Employer Project gives learners recent experience they can draw on when discussing teamwork, deadlines and problem-solving in interviews.

As Jon Minto explained:

“Having that tangible evidence… is something I could really talk about in my job interview.”

For a deeper breakdown, read everything you need to know about the Employer Project.

Real people who got the head start

  • Olga Sachura returned to marketing after a career break and partnered with EdPlace on a live marketing strategy project. She now works as Head of Marketing and Advisor at Balentic and credits both the Employer Project and career coaching with accelerating her return to the industry.
  • Two learners on the King’s UX & UI Product Design Career Accelerator transformed their Employer Project experience into consulting opportunities within six months of completing the programme.
  • Megan, an LSE Data Analytics Career Accelerator past learner, used her Employer Project experience directly in interviews to help secure a role.
  • And Sheldon Kemper’s recruiter explicitly cited his practical project work as a major reason for hiring him.

You can read more FourthRev impact stories to see how learners have applied the experience in practice.

You’re not doing it alone

Career coaching that goes beyond the classroom

Working on a live business challenge can sound intimidating, particularly for people changing careers.

That is why support is built into the programme itself. Each learner works with a dedicated Career Coach who helps with positioning, interview preparation, LinkedIn strategy and career planning.

For Olga Sachura, this support became one of the defining parts of the experience:

“I’m very tempted to say that, for me, perhaps the career coaching was the most valuable part of the programme.”

A Success Manager and a community of peers

Alongside career coaching, learners are supported by a Success Manager and a wider peer community throughout the programme.

“The student community is a very important part of the experience as a learner. There’s a lot of encouragement from the course coordinators for the students to engage with each other and learn from each other.” – Andreas Yiallouros, LSE Data Analytics Career Accelerator past learner

You can also explore additional advice from FourthRev career coaches.

Your career doesn’t have to start from zero

The Employer Project gives learners a stronger starting point. By the time they complete the programme, they already have practical project work, professional feedback, team experience and real examples to discuss with employers. 

The average salary increase reported by learners after completing a Career Accelerator is 21.9% (FourthRev 2023/24 Completers’ Survey). It’s learning translated into practical, measurable career outcomes. 

You can see how FourthRev’s programmes are structured to understand how the Employer Project fits into the broader learning journey.

For professionals wondering how to get work experience before landing a role, the answer may not be waiting for someone to give you a chance. It may be choosing a programme designed to let you prove yourself before you are hired.

Explore FourthRev’s Career Accelerators to see which programme could give you that head start.

Frequently Asked Questions

What is an Employer Project?

An Employer Project is a live, real-world capstone project completed during the final six weeks of a FourthRev Career Accelerator. Learners work on a business challenge set by an employer partner and receive feedback throughout the process.

How is this different from a regular course assignment?

Unlike a traditional assignment, the Employer Project involves a real company, a genuine business brief, collaborative teamwork and direct employer feedback. It’s designed to replicate professional working conditions rather than simulate them.

What companies have set Employer Projects?

Recent employer partners have included Bank of England, GAEA AI, Cancer Support Netherlands, DocuSign, Sky, Creative Navy, EdPlace, Salient Bioscience and Lean.

Can I put the Employer Project on my CV?

Yes. The Employer Project is intended to be portfolio-worthy and professionally relevant. Some projects may involve confidentiality agreements, but Career Coaches help learners present the experience effectively.

Do I need prior experience to do the Employer Project?

No. The programme’s earlier courses are designed to build the technical and professional skills needed before learners begin the project itself.

How does the Employer Project help me get hired?

The project gives learners portfolio evidence, team-based experience, employer feedback and concrete examples for interviews. 

How Olena Built Executive Judgement for AI Adoption

Posted on by Marisa Crous

For many senior executives, periods between leadership roles become opportunities to reassess direction, sharpen focus and invest in the capabilities that will define the next phase of their career. For Olena Bilan, that investment centred on AI leadership. 

An operations and transformation executive with more than 15 years of experience, Olena has worked across finance, operations and AI-enabled transformation. She has held Group CFO and COO roles and worked at the intersection of enterprise analytics and AI adoption.

Following her relocation to the UK and between executive roles, she made a deliberate investment in her next chapter – building sharper executive judgement around AI adoption, governance and business value before returning to a senior role.

That investment included the LSE AI Leadership Accelerator. Her goal was to build executive-grade judgement in a field where clarity and discipline matter.

“Before, I evaluated AI as an interested executive. The programme equipped me to lead it as an accountable one.”

A strategic investment in AI leadership skills

Before joining the LSE Accelerator, Olena had already seen the gap between AI ambition and organisational reality playing out first-hand:

“AI capability is advancing rapidly, but in many organisations, the ability to translate that capability into operating reality is moving much more slowly. That gap is not closing. In many cases, it’s widening.”

Recognising this gap, she made a focused decision about the kind of learning she needed.

“I was not looking for an AI course at the level of curiosity, trend awareness, or tool familiarity. I was looking for a structured executive framework that could help me understand how to approach AI more seriously, form my own judgement around it, understand how it becomes business value, why it so often does not, and what has to change inside an organisation for adoption to become real.”

With the LSE AI Leadership Accelerator, she set out to build sharper executive judgement for evaluating, prioritising and leading AI initiatives. 

The reframe that changed how she diagnoses organisations

One of the most important shifts in Olena’s thinking came early on the programme:

“Most organisations are still treating AI adoption as a technology rollout, when in reality it’s an operating model problem that happens to involve technology.”

This distinction changes how leaders understand and diagnose organisational failure. 

“Before the programme, what concerned me was how often AI decisions are delegated by leadership to technical teams, who then become, in practice, vendor selectors. That is the wrong ownership model. AI decisions sit at the intersection of value, risk, governance and operating design – they are leadership decisions, not procurement decisions.”

Rather than asking whether the organisation has access to AI, the more relevant question becomes whether decision flows, incentives, governance and workflows are aligned to support it.

The LSE Accelerator builds exactly this capability – equipping leaders to assess organisational readiness, map AI to business value, and design the governance and change frameworks that determine whether adoption becomes real.

The moment it clicked

For Olena, the most tangible moment on the Accelerator came not from theory, but from a peer discussion.

A fellow participant described being tasked by her boss to implement AI everywhere. It’s a familiar directive in many organisations.

What clicked for Olena was not a critique of the directive, but a clearer view of where AI initiatives actually succeed or fail:

“In most organisations, the leader issuing an AI directive is also the sponsor of the change – they hold the budget, shape the brief, and decide where people’s time and focus go. The quality of those decisions depends on how well that leader understands what AI can and cannot do, and on their ability to translate that into the right task for the right person, framed in a way that person can actually act on. Without it, you get unrealistic tasks, badly scoped problems, or spend driven by trend rather than value.”

The wider point, for her, is about how leaders work with their teams:

“Part of the job is to facilitate the expertise already in the team – to recognise who is best placed to take which question, in what form, and with what context. The more people across a team understand the capabilities, limits and interdependencies of AI, the better that work becomes. That is a leadership skill in its own right: navigating the noise, framing the work clearly, and reading what is realistic against the people and the technology in front of you.”

AI adoption, in that sense, is not only about choosing the right tools. It is about whether the people sponsoring, leading and executing the work understand enough to make good decisions together.

“For me, cutting through AI hype is not scepticism. It is a leadership responsibility. If senior leaders are making investment, operating-model and people decisions around AI, they need enough fluency and judgement to understand where AI creates value, where it creates risk, and where it is simply noise. That judgement is no longer an optional add-on to executive work – it is part of it.”

What shifted in practice

Following the programme, Olena’s approach to AI became more precise and more financially grounded.

“I think much more explicitly now about where AI genuinely moves the needle on margin rather than where it merely looks interesting.”

In other words: Will it deliver real financial impact?

“The programme materially strengthened my ability to articulate AI value to different stakeholder groups – not as a technology story, but as a business transformation story tied to ROI, governance, stakeholder alignment and implementation realities.”

She also describes how the Accelerator taught her deeper integration between technical capability and organisational reality:

“I now have a much more integrated view of AI and organisational change. Before, I saw the familiar components. Now I see much more clearly the structural gap between technical capability and organisational adoption, and why that gap persists.”

Olena is equally clear about what this means for her professional positioning going forward:

“I connect technical teams to business value, translating capability into operating reality and commercial outcomes, without losing either side in the process. That includes the discipline to evaluate vendor claims, implementation proposals, and adoption signals against the actual problem we are trying to solve. Cutting through the noise is not a defensive posture – it’s a board-level skill, and arguably one of the most useful executive contributions to AI adoption right now.”

What the learning actually felt like

Olena had high expectations of the programme and a quiet uncertainty: would it match the level of rigour her background demanded, and would her commercial and operating experience translate naturally into an AI leadership environment? 

“I was deliberate about which programme I chose. I needed executive depth, not trend awareness, and I wanted to verify that before investing time in it. My career has been built in finance, operations, and the commercial side of business, and I was looking for a programme built for executive decision-makers – for leaders who also have to drive the projects, teams and partnerships that turn AI ambition into real, sustained business change, rather than a tool tour.”

 

The programme addressed both. On depth, her curriculum assessment was direct:

“The course is very content-heavy, but the material remains accessible without becoming simplistic. It gives enough structure to make the subject intelligible for business leaders, while still preserving the complexity of what implementation actually involves.”

She also highlights the importance of the programme’s expert industry practitioner input:

“I also found the Masterclasses especially valuable. Hearing from industry experts and facilitators with real business experience added an important practical dimension to the programme.”

From awareness to judgement

“I no longer react to AI. I evaluate it – and judgement now includes deciding what to act on, what to wait on, and what to leave alone.”

This is not about fluency in tools or terminology. It’s about judgement under uncertainty – the core capability required of senior leaders navigating AI.

She reinforces this further:

“That is a real strength of the programme, and I believe it’s part of its uniqueness: it equips people with a practical way to approach AI and related issues, think clearly, navigate complexity, exercise judgement, lead, and act.”

A broader lens on change

Another important dimension of Olena’s LSE Accelerator experience is the human side of transformation.

She notes that team and individual resistance is rarely about tools:

“Resistance is rooted in fear: fear of losing relevance, losing competence, losing control, losing status, or being exposed in areas where confidence is still fragile.”

Understanding this changes how leaders approach AI implementation, requiring empathy, communication and alignment across stakeholders. 

This is where many initiatives stall, even when the underlying technology is sound.

Who this is programme for

She’s clear about who will benefit most from the LSE AI Leadership Accelerator:

“For leaders who want to approach AI with executive maturity and discipline – not as a surface-level add-on — the programme delivers real value. I would recommend it equally to senior leaders from technical and non-technical backgrounds: AI sponsorship is a leadership skill, not a technical one, and both groups gain by sharpening their judgement around it.” 

The LSE AI Leadership Accelerator is built for leaders who are ready to move beyond pilots and position statements and take responsibility for making AI work inside their organisation.

A strategic investment in leadership

For Olena, the programme was not an isolated learning experience. It was part of a broader repositioning, connecting her past experience in business transformation with the growing importance of AI in executive decision-making.

As she puts it, the value goes beyond credentials:

“The LSE name carries credibility, of course, but what matters most to me is that the programme helped deepen substance, not just status.”

 

A leader who can connect the technical, operational, commercial and governance layers of AI, and translate credibly across all of them, is exactly the seat most enterprises are currently missing. That seat is what the programme was designed to build.

“The AI we have today is the weakest version of it we will ever see again. Every generation of AI will arrive faster than the last. The competence that matters is not mastering the current one. It’s the discipline to keep architecting, governing and leading through every generation that follows.”

 

Download the programme brochure to explore how the LSE AI Leadership Accelerator can help you build the frameworks, judgement and leadership capability to turn AI ambition into business value.

 

 

Why Now Is The Best Time To Pursue A Career in Data

Posted on by Summer White

The data industry has never been more accessible or more rewarding. Whether you are considering a career change into data analytics, exploring data science as a first career, or looking to upskill in a data-related role, the timing could not be better.

As data continues to become more important to businesses, organisations, and society as a whole, the need for data scientists, analysts, engineers, and other data-related roles has increased significantly.

The areas where data and analytics can be applied have expanded rapidly, and new tools and methods are continuously being developed to extract more complex and relevant information.

We look at the current state of data-related jobs, the demand, salaries, specialisations, and why now is the best time to pursue a career in data.

What you’ll learn in this guide

  • The current demand for data professionals and the global talent shortage
  • Five compelling reasons to become a data scientist or analyst
  • Real-world applications of data skills across industries
  • How to tell different data roles apart (analyst vs. scientist vs. engineer)
  • Data professional salaries in the UK
  • Career progression paths and how to get started
  • Key technical and business skills employers look for

The current demand for data professionals

In 2019 the World Economic Forum (WEF) called data the “new oil” of the global economy and data professionals the “talent that provides the ability to extract, refine, and deploy this new source of value.” Since then, data science has been listed among the best jobs based on salary, career opportunities, and job satisfaction.

A data career also has significant long-term growth and opportunity; the latest Future of Jobs Report 2023 found that the roles of data analysts and data scientists are among the 10 jobs expected to grow the fastest between 2023 and 2027.

Yet, there is a dire data talent shortage across the world. The UK government’s Quantifying the UK Data Skills Gap report found almost half of the businesses were recruiting for roles that require hard data skills (programming, analysis, data visualisation, machine learning (ML), data communication, knowledge of emerging technologies and solutions), but almost just as many struggled to recruit for these roles. At the time of this research in 2021, there were between 178,000 and 234,000 ads for people with hard data skills. 

Global hiring platforms continue to report steady year-on-year growth in demand for analytics, artificial intelligence and machine learning roles. Organisations across finance, healthcare, retail, logistics and government are putting data at the centre of how they operate and compete.
The shift is broad and sustained. For professionals entering the field today, that points to long-term career resilience rather than a passing trend.

According to the U.S. Bureau of Labor Statistics, employment for data scientists is projected to grow 35% from 2022 to 2032 –  significantly faster than the average for all occupations. In the UK, data-related job postings have grown by over 200% since 2015, with demand accelerating further as organisations invest in artificial intelligence (AI) and machine learning infrastructure. This makes a career in data analytics one of the most future-proof choices available today.

This industry snapshot reveals multiple reasons why you should pursue a career in data science and analytics. Now, let’s focus on the career itself and what it can offer you – both professionally and personally. 

5 Reasons to become a data scientist or analyst

We’ve listed our top five reasons why data science or analysis is a good career for you in 2024: 

  1. Social impact: You can positively impact your company or the planet by solving real-world problems through data. From predicting disease outbreaks to optimising renewable energy distribution, data professionals are at the forefront of solving humanity’s biggest challenges. Organisations like the United Nations and the World Health Organisation actively recruit data talent to support sustainable development goals.
  2. High salary: With a shortage of talent, businesses are willing to pay for the right people with the right skills. In the UK, entry-level data analysts can expect to earn between £38,000 and £53,000, while experienced data scientists and engineers regularly command salaries exceeding £80,000. In competitive markets like London, senior data roles can reach six figures, making it one of the highest-paying career paths in tech.
  3. Future-proofing: Data is an ever-evolving field, allowing you to stay at the forefront of industries, trends, and innovation. As AI and automation reshape the workplace, data literacy is becoming a baseline requirement across virtually every role. If you build your career in data science now, you will be well placed to lead the next wave of digital transformation.
  4. Business diversity: You aren’t siloed to a particular business or sector, almost every business is moving towards data-driven operations. Data professionals are in demand across industries from healthcare and fintech to entertainment and government, which means you can move between sectors over time without having to retrain from scratch.
  5. Global opportunity: Data skills are in demand worldwide, so you could work remotely on international projects or pursue opportunities in different countries.

Unique ways to put your data skills to use

To some, data may seem like a lot of numbers, systems, and processes. But these techniques are being applied to some of the most relevant and urgent challenges we face as a society. These are just some of the solutions you could work on:

  • If you’re passionate about fighting climate change, you could build climate models and weather prediction technology, helping to save lives affected by floods, drought, and other extreme weather phenomena. You could also work on cutting-edge public transportation projects that help reduce CO2 emissions.
  • If food security interests you, you could analyse crop yields and agricultural practices to help farmers increase their food output. 
  • If health is your domain, you could work on fighting cancer by using AI to develop new medicines and medical technologies or to identify patterns from imaging scans not easily detected by humans.
  • If good governance and development is your passion, you can use data to combat corruption, increase citizen engagement, or drive budget transparency and decision-making. In the developing world, it can be used to develop solutions to local problems around safety, women’s health, education, and economic empowerment.
  • If you’re interested in combating misinformation and preventing cyber hacks, you can use the formidable set of tools and techniques available to predict risks and prevent the spread of fake news.  
  • If you’re business-minded, you can help companies drive efficiencies, glean deep operational insights, and generate more revenue.

Untangling the different roles in data

The broad scope and overlap between roles have led to confusion around the tasks and responsibilities of data professionals. Recruiters often use the term ‘data analyst’ loosely, with some jobs advertising for ‘data unicorns’ – those rare people simultaneously skilled in statistics, analysis, data engineering, systems development, people management, interfacing with business and tech stakeholders, and developing and deploying algorithms… 

Looking for unicorns is unrealistic and has led to the Harvard Data Science Review (HDSR) and the Department for Digital, Culture, Media & Sport (DCMS) calling for the standardisation of the roles. Harvard suggested that roles be classified into three key role families: data analyst, data scientist, and data engineer. 

From here, more specialised or domain-specific roles can be established, e.g., ML and AI engineer, big data analyst or engineer, analytics and AI translators, data-oriented product managers, and insights interpreters.

For those who are just starting in their careers or transitioning into data from another field, a data analyst role gives you the core foundation and experience you need, before you decide to specialise. 

Data Analyst vs. Data Scientist vs. Data Engineer: The key differences

 

Data Analyst

Data Scientist

Data Engineer

Primary focus

Interpreting data to inform decisions

Building predictive models and algorithms

Designing data infrastructure and pipelines

Key tools

SQL, Excel, Tableau, Power BI

Python, R, TensorFlow, Jupyter

Spark, Hadoop, AWS, Airflow

Typical rntry path

Career Accelerator or bootcamp

Advanced degree or specialised programme

Software engineering background

UK salary range

£38k – £53k

£58k – £74k

£52k – £82k

Many of our learners on the LSE Data Analytics Career Accelerator started the programme with little-to-no data experience. Our Career Coaches play a key role in preparing our learners for their new careers, like plotting development plans and preparing for interviews.

How much can you earn as a data professional? 

Data science is one of the highest-paying jobs in tech right now. Data analysts tend to earn slightly less, and data engineers and specialists a little more. Those working as a consultant or who are paid project-to-project can command considerable fees. 

As you build experience and specialise, earning potential in data increases – particularly for professionals who combine technical skills with domain expertise like finance or healthcare.

Here are the average salaries across data roles in the UK: 

Data Role

UK Salary Range

Data Architect

£81,000 – £107,000

Data Engineer

£52,000 – £82,000

Data Scientist

£58,000 – £74,000

Data Analyst

£38,000 – £53,000

Machine Learning Engineer

£65,000 – £95,000

Head of Data / Chief Data Officer

£100,000 – £160,000+

Salaries vary significantly by location, with London-based data roles typically paying 15–25% more than equivalent positions elsewhere in the UK. Remote roles with international companies can also offer competitive compensation regardless of where you are based.

For more info on data roles, read our guide on how to become a data analyst and explore our new Career Accelerator in data science from the University of Cambridge Professional and Continuing Education (PACE).

Career track and progression

Generally, there are three main data paths to explore. You could choose the business analytics and intelligence route, where you’ll use data to make business decisions and help solve problems. 

Alternatively, you could focus on the technological theory of data science and specialise in algorithms and big data infrastructure. Or, you could go into data engineering and warehousing, designing and managing big data warehouses and optimising data collection, processing, and analysing. 

When it comes to career progression in data, you can start as an analyst and move up to become a senior or principal analyst, data scientist, or engineer, and eventually a chief data officer. Other options are in academic research and teaching or working as a data consultant.

Data expertise is not confined to analyst roles. It increasingly serves as a gateway to more strategic positions: analytics leadership, AI product management and digital transformation consulting. In these roles, technical fluency becomes a platform for influence. Data informs capital allocation, shapes product strategy and underpins executive decision-making.

For those who combine analytical depth with commercial judgement, the trajectory can extend well beyond execution into leadership.

Typical data career progression path

  • Entry Level (0–2 years): Junior Data Analyst → Data Analyst → Senior Data Analyst
  • Mid Level (3–5 years): Lead Analyst → Data Scientist → Data Engineer → ML Engineer
  • Senior Level (5–10 years): Principal Data Scientist → Head of Analytics → Director of Data
  • Executive Level (10+ years): VP of Data → Chief Data Officer → Chief Analytics Officer

How to start a career as a data professional

Breaking into data does not demand a traditional technical background. It does, however, require rigour: structured learning, applied practice and evidence of capability. A disciplined path tends to include five steps.

Learn the fundamentals

Build a working grasp of statistics, data literacy, spreadsheets and core programming concepts. Fluency in first principles underpins everything that follows.

Free resources such as Khan Academy (statistics), Google’s Data Analytics Certificate, and freeCodeCamp (Python basics) are excellent starting points. Dedicate at least 5–10 hours per week to build a consistent learning habit before moving to more advanced material.

Develop technical skills

Focus on tools such as SQL, Python, R, Power BI, and Tableau. SQL is the single most in-demand skill in data analyst job listings. Pair it with Python for data manipulation (using pandas and NumPy) and a visualisation tool like Tableau or Power BI to cover the core technical requirements of most entry-level data roles.

Build a credible portfolio

Produce end-to-end projects using real datasets. A strong portfolio signals readiness more convincingly than certificates alone. Host your projects on GitHub and create a simple portfolio website. Focus on 3–5 well-documented projects that demonstrate different skills: data cleaning, exploratory analysis, visualisation, and a predictive modelling project. Use publicly available datasets from Kaggle, the UK Data Service, or Google Dataset Search.

Gain practical experience 

Internships, freelance work, and employer-driven learning programmes help strengthen employability. Volunteering your data skills for non-profits through platforms like DataKind or Statistics Without Borders is another way to gain real-world experience. Many employers value this kind of applied, impact-driven project work alongside traditional internships.

Apply for entry-level roles

Junior analyst or reporting analyst roles are strong entry points into the field. When applying, tailor your CV to each role by mirroring the language used in the job description. Highlight specific tools (SQL, Python, Tableau) and quantifiable outcomes from your portfolio projects. Many hiring managers scan for keywords before reading in detail, so make your technical skills immediately visible.

Key Skills Employers Look for in Data Professionals

Technical skills

  • SQL and database querying
  • Python or R programming
  • Data visualisation tools
  • Excel modelling
  • Machine learning fundamentals
  • Cloud platforms (AWS, Google Cloud, Azure) –  increasingly required for mid-level and senior role
  • Version control with Git –  essential for collaborative data projects

Business skills

  • Problem-solving and critical thinking
  • Data storytelling
  • Understanding KPIs and business metrics
  • Stakeholder management

Soft skills

  • Communication
  • Collaboration
  • Strategic thinking
  • Intellectual curiosity –  the best data professionals constantly ask “why?” and explore data beyond the brief
  • Adaptability –  tools and technologies evolve rapidly. Showing a willingness to learn continuously is critical.

As a data professional, you’ll be navigating technical problems that need strategic thinking and collaborative problem-solving. Because it’s a highly practical job that has the potential to truly impact a business, employers will want proof that you’re capable of doing the work. 

Our data-focused Career Accelerators give you the university recognition and practical experience that employers are looking for. While each programme is unique, they all focus on applying the skills to practical projects. 

In the end, you walk away with a portfolio of work and a globally-recognised university certificate that assures employers you’re ready for a new career. 

Frequently Asked Questions About Careers in Data

Is a career in data analytics a good choice?
Yes. Data careers offer strong salary growth, long-term job security, and opportunities across nearly every industry. Data analytics has been ranked among the top 10 most in-demand careers globally for four consecutive years. With organisations investing billions in data infrastructure, the demand for skilled analysts is expected to remain strong well into the 2030s.

Do you need a degree to become a data analyst?
Many employers prioritise practical skills and portfolio experience over traditional degrees. Programmes like the LSE Data Analytics Career Accelerator are designed to give career changers the practical skills and recognised credentials to break into data without a traditional computer science degree. Employers increasingly value demonstrated ability over academic background.

How long does it take to transition into a data career?
Many professionals become job-ready within 6–12 months with structured learning. The timeline depends on your starting point and time commitment. Those with some analytical background (e.g. finance, marketing, research) can often transition faster, while complete beginners typically need 9–12 months of focused study and portfolio-building.

Which industries hire data professionals?
Finance, healthcare, retail, logistics, technology, and government sectors all employ data specialists.

What is the difference between data science and data analytics?

Data analytics focuses on examining existing data to identify trends and inform decisions, typically using tools like SQL, Excel, and Tableau. Data science goes further by building predictive models and algorithms using programming languages like Python and R. Both are valuable career paths, but data science generally requires more advanced statistical and machine learning knowledge.

Why data careers are growing faster than ever

The global economy is now decisively data-driven. Organisations are investing heavily in artificial intelligence, predictive analytics and automation to sharpen decisions and stay competitive. As a result, professionals who can interpret, manage and operationalise data are no longer niche specialists – they are central to performance across sectors.

Entering the field today places you at the heart of this shift. Demand for applied analytics capability continues to grow, offering durable career prospects, strong earning potential and the opportunity to tackle complex commercial and societal challenges with evidence rather than instinct.

Whether you are looking to become a data analyst in the UK, transition into data science from another career, or simply future-proof your skill set, there has never been a better time to start. The combination of high demand, competitive salaries, flexible working, and meaningful impact makes a career in data one of the smartest professional investments you can make in 2024 and beyond.

If you’re interested in transitioning or advancing your career in data analytics, explore the LSE Data Analytics Career Accelerator. 

To learn advanced data science skills and tools, such as machine learning concepts, deep learning, and NLP, download the brochure for the Data Science Career Accelerator.

How To Change Careers Online: A Step-By-Step Plan

Posted on by FourthRev Team

No experience? No problem. Follow this 7-step plan to change careers online, from identifying your transferable skills to landing a new role.

You keep getting the Sunday Scaries. That sinking feeling in your stomach as Monday approaches. The sense that your current role no longer fits who you are or where you want to go.

You scroll through job listings on LinkedIn, explore new industries and imagine a different path. Then reality sets in. You lack direct experience. You are unsure where to start. The sheer number of online courses only adds to the confusion.

This is where a lot of people get stuck. You know you want something different, but you do not really have a clear plan for how to get there. And without that, it is all too easy to spend time and money on the wrong things.

The good news is that structured online programmes like a Career Accelerator are making career change more achievable than ever. In fact, 87.5% of learners achieved a desired career goal within six months (FourthRev 2023/24 Completers’ Survey).

This guide gives you a clear, 7-step plan to help you change careers online, even if you are starting with no direct experience.

Step 1: Get clear on why you want to change

Before you choose a new career path, it’s worth taking a step back and getting clear on your why.

Are you trying to move away from something that is not working, or towards something that actually excites you?

Ask yourself:

  • What kind of work energises me?
  • Which skills do I want to use every day?
  • What type of environment suits me best?
  • What does success look like for me?

Getting clear on this early makes everything else easier. It shapes the decisions you make and helps you choose a path that actually fits.

It’s completely normal to have doubts at this stage. Most people changing careers wonder if they can really make it work in a new field. If that sounds like you, you are definitely not alone. Here is a deeper look at overcoming imposter syndrome during a career change.

Structured programmes often include career coaching from the start, helping you define goals with confidence and direction.

Step 2: Identify your transferable skills

One of the biggest misconceptions about a career change is the idea that you are starting from zero.

In reality, most professionals already have valuable skills that apply across industries. This is why transferable skills are the key to success – something many people underestimate.

Examples include:

  • Communication and stakeholder management
  • Problem-solving and analytical thinking
  • Project management and organisation
  • Creativity and strategic thinking

Take a moment to think about how the skills you already have could translate into a different role.

For example:

  • A teacher can apply structured thinking and data interpretation to analytics
  • A designer can move into UX with strong visual communication
  • A marketer can transition into product roles through strategic planning

Take Elodie, who went from teacher to data scientist in three months by building on skills she already had.

A quick skills audit can really help you connect the dots between what you have already done and where you want to go next.

Step 3: Research high-growth career paths you can enter online

Some careers are more accessible than others for career changers. Focus on fields that value skills, portfolios, and practical ability.

For a deeper view of market demand, explore the top 10 most in-demand tech careers.

Data analytics
This is all about turning data into insights that help businesses make better decisions. It is in demand across almost every industry, and it suits people who enjoy problem-solving and working with information.

UX and UI design
This focuses on how people interact with digital products. It is one of the most common entry points for career changers, with many people coming from non-technical backgrounds. If you enjoy creativity and user behaviour, this could be a strong fit. You can also explore how to become a UX designer in more detail. 

Product management
Think of this as the role that connects business, design and technology. Product managers shape what gets built and why. It is a great option if you have strong communication skills and enjoy working across teams.

Digital marketing
This is about driving growth through online channels like social media, search and email. It has become much more strategic and data-driven, which makes it a natural transition for people with creative or business backgrounds.

Data science
A step deeper into data, this field uses machine learning and AI to solve complex problems. It can take more time to build up the required skills, but the demand and earning potential are strong.

Cybersecurity
With more businesses operating online, protecting data and systems has become critical. This field offers clear pathways for career changers, especially those who enjoy structured problem-solving.

AI leadership
An emerging space focused on helping organisations adopt and manage AI at a strategic level. This tends to suit more experienced professionals who want to move into forward-looking roles.

The key thing to remember is this: you do not need to tick every box before you start. These fields are open to career changers who can show skills, practical work and a willingness to learn.

Step 4: Choose the right online learning path

Learning options vary widely, and choosing the right format can make a big difference.

Free courses vs bootcamps vs Career Accelerators vs degrees

Free courses give you flexibility and a low-pressure way to explore a subject, while bootcamps focus on building specific technical skills quickly. Degrees offer deeper academic learning over a longer period, often with a broader foundation. 

Career Accelerators sit somewhere in between, combining structured learning, hands-on projects, and career support in a shorter, more focused format.

If you are new to this model, here are 10 things to know about Career Accelerators.

Key questions to ask

  • Does the programme include real projects?
  • Is career coaching available?
  • Will you gain a recognised credential?
  • Can you study alongside work?

What makes a Career Accelerator different

Career Accelerators stand out because they bring everything together in one clear, structured path. Instead of piecing things together yourself, you build practical skills, work on real projects, and get ongoing career support along the way.

For example, the LSE Data Analytics Career Accelerator offers a six-month, part-time structure with practical tools such as Excel, SQL, Python, and Tableau, combined with coaching and an Employer Project.

If product strategy interests you, explore the King’s Product Management Career Accelerator.

You can also review the frequently asked questions for more details.

Step 5: Build proof of your skills

Employers want to see evidence of what you can do.

Why evidence matters

A certificate helps, but it is your projects, case studies and real outcomes that show how you apply your skills in real situations.

The Employer Project

Career Accelerators include a real-world capstone project with a company, where you work on an actual business problem. This gives you hands-on experience you can confidently discuss in interviews.

Here is everything you need to know about the Employer Project.

Learners walk away with portfolio-ready outputs that demonstrate their skills. For example, learners on the King’s UX & UI Product Design Career Accelerator build product design portfolios they can share with employers.

Step 6: Get career support that is personalised to you

Changing careers feels much more manageable when you have structured support guiding you at every step.

Career coach and success manager

A Career Coach works with you to shape your direction and build confidence as you make the transition. They help you clarify your goals, position your past experience in a way that makes sense for your new field, refine your CV and LinkedIn profile, and prepare you for interviews so you can present your story clearly.

Alongside that, a Success Manager helps you stay consistent and organised throughout the programme. They support you in managing your time, keeping up with the workload, and maintaining momentum, so you keep moving forward even when life gets busy.

Career Milestone Guarantee

Programmes include a Career Milestone Guarantee, which extends coaching support if you have not reached your goal within a set period, subject to terms.

To understand the full journey, you can read how it works.

Step 7: Network, apply, and land your new role

The final step is turning your new skills into a job offer.

Reposition your CV and LinkedIn

Focus on skills, projects, and outcomes rather than past job titles.

Leverage community

Joining a network of peers and graduates can open doors and provide ongoing support.

Stand out as a career changer

Use your Employer Project as a key talking point. Show how your past experience adds value in your new field.

For more practical advice, read this guide on how to find a job with no experience.

You can also explore the real-world results of our LSE learners to see what is possible.

Real stories: Career changers who made it happen

From teacher to data scientist

A former teacher combined analytical thinking with new data skills and moved into a data role within three months.

From business owner to product manager

A fashion entrepreneur repositioned her experience with the help of a Career Coach and secured a product role.

From data engineer to an AI-focused role

 

With a background in data engineering, one learner used the programme to build skills in AI and machine learning, apply them to real-world problems, and step into a more advanced, AI-focused role with greater impact and a higher salary.

You can meet more people getting results and see how others have made the transition.

Your career change starts here

Here is a quick recap of the process:

  1. Get clear on your goals
  2. Identify your transferable skills
  3. Research high-demand roles
  4. Choose the right learning path
  5. Build proof through projects
  6. Get personalised career support
  7. Apply strategically and network

Ready to take the first step? Browse Career Accelerator programmes and find the right path for you.

If you are interested in marketing, explore the LSE Digital Marketing Career Accelerator.

If data science appeals to you, take a look at the Data Science Career Accelerator.

Frequently asked questions

Can I change careers with no experience?

Yes. Many career changers build on transferable skills and develop new ones through structured online learning and real projects.

How long does an online career change take?

With a structured approach, many learners achieve outcomes within six to twelve months. Career Accelerators typically run for six months, with strong outcomes reported within six months of completion.

What is the best online programme for career changers?

Look for a programme that combines practical skills, real projects, career coaching, and a recognised credential.

What careers can I switch to online?

Common paths include data analytics, UX design, product management, digital marketing, data science, cybersecurity, and AI leadership.

The 5 Highest-Paying Digital Jobs Right Now

Posted on by Tayla Withers

We recentlywrote about the most in-demand tech jobs – covering demand, salaries, and job satisfaction. Now we look at the highest-paying digital jobs for 2026 to give a clear view of the tech job market. We also highlight where the biggest career opportunities may lie, whether you are based in the UK or the US.

To identify the highest-paying digital jobs, we scoured job boards and industry salary reports and explored the forces behind the persistent tech talent shortage and why certain skills command such strong compensation. Throughout our research, five roles consistently rose to the top as AI-driven roles have moved firmly into the #1 position.

The new era of tech talent

Overall, IT salary growth has moderated, but certain specialisations are still seeing pay accelerate well ahead of the wider market. According to Motion Recruitment’s 2026 Tech Salary Guide, roles tied to AI, cloud infrastructure, cybersecurity, and data remain the highest-compensated in the industry, with companies competing fiercely for a limited pool of qualified candidates in both the UK and the US.

The World Economic Forum’s Future of Jobs Report 2025 makes the outlook clear: AI and information processing will affect 86% of businesses by 2030. Demand for AI and machine learning specialists, big data professionals, and cybersecurity experts is growing faster than educational institutions can produce qualified graduates. That gap between supply and demand is what keeps these salaries high.

Hiring trends shaping tech careers in 2026

Two major hiring shifts are defining the 2026 landscape. First, according to the WEF, AI skills are increasingly acting as an equaliser in hiring – candidates who can demonstrate practical AI competency see significantly better outcomes, regardless of their background or degree status. Second, employers on both sides of the Atlantic are prioritising demonstrable, hyper-specialised skills over academic qualifications alone, creating real upward mobility for people who invest in the right training.

Soft skills remain critical. The WEF report highlights that employers anticipate 39% of core skills will change by 2030. The most in-demand combination in 2026 is technical depth alongside analytical thinking, adaptability, and the ability to communicate complex ideas to non-technical stakeholders.

Highest-paying tech jobs in 2026

All the jobs below offer significantly above-average salaries and strong projected growth. UK salary figures are sourced from Indeed (February 2026), with supporting data from IT Jobs Watch and Morgan McKinley’s UK Salary Guide. US figures are drawn from Motion Recruitment’s 2026 Tech Salary Guide and the US Bureau of Labor Statistics, converted to GBP at an approximate 2026 rate of $1.27/£1. Salaries vary by location, experience, company size, and industry.

1. AI / Machine Learning Engineer

Why it pays well: The World Economic Forum identifies AI and machine learning specialists as among the fastest-growing roles globally, with job growth projected at over 35% through 2030. Digital Waffle’s 2026 UK AI Salary Guide projects the average salary for AI roles in the UK at around £81,000, with growth of approximately 3% annually as demand continues to outpace talent supply. In the US, AI/ML engineers are among the highest-compensated tech professionals, with pay growth running at nearly three times the tech-wide average, according to Motion Recruitment.

What they do: AI/ML engineers design and build the systems that allow machines to learn from data, improve over time, and generate predictions or automate decisions at scale. They bridge the gap between data science research and production-ready software, deploying and maintaining models across cloud and on-premise environments.

Most common skills/tools: Python, TensorFlow, PyTorch, large language models (LLMs), MLOps, REST APIs, cloud platforms (AWS/Azure/GCP), deep learning, and natural language processing.

Soft skills: Analytical thinking, intellectual curiosity, clear communication with non-technical stakeholders, and continuous learning agility.

Qualifications/experience: A degree in computer science, mathematics or a related field is often expected, though a strong portfolio can also demonstrate capability.. The LSE AI Leadership Career Accelerator and Cambridge PACE Data Science Career Accelerator provide the technical foundations and strategic AI context increasingly sought by employers.

What they earn:

  • UK: Entry ~£47,000 | Average £68,000–£82,000 | Senior £105,000–£166,000+
  • US (converted to GBP): Entry ~£102,000 | Average £139,000 | Senior £172,000+

Interested in building practical AI skills? The LSE AI Leadership Career Accelerator equips you with both the technical foundations and strategic thinking needed to lead in the AI era. 

2. Data Scientist

Why it pays well: The US Bureau of Labor Statistics projects data scientist roles will grow by 34% between 2024 and 2034 – making it one of the fastest-growing occupations in the US economy. In the UK, the government’s UK Digital Strategy identifies data science as a core pillar of the country’s economic future, with significant public and private investment driving demand. According to TechRepublic’s 2026 analysis, data roles consistently rank among the highest-paid in tech due to their direct commercial impact.

What they do: A data scientist extracts, analyses, and interprets large volumes of data using statistical modelling, machine learning, and AI tools. The most valued data scientists go beyond analysis – they translate insights into strategic business decisions and communicate findings clearly to non-technical audiences.

Most common skills/tools: Python, R, SQL, machine learning, Tableau, Power BI, predictive modelling, statistical analysis, data visualisation, and cloud data platforms.

Soft skills: Storytelling with data, critical thinking, problem-solving, curiosity, commercial awareness, and adaptability.

Qualifications/experience: A degree or certification in data science, statistics, computer science, or a related field. The Cambridge PACE Data Science Career Accelerator and LSE Data Analytics Career Accelerator are designed to build exactly this skill set, combining technical training with real-world business applications.

What they earn (Glassdoor, February 2026):

  • UK: Entry ~£34,000–£41,000 | Average £54,000 (£50,00 -61,000 in London) | Senior £77,000–£100,000+
  • US (converted to GBP): Entry ~£88,000 | Average £125,000 | Senior £166,000+

The Cambridge PACE Data Science Career Accelerator gives you the technical and commercial skills to turn data into business impact – including a real-world industry challenge set by a leading organisation.

3. Cybersecurity Engineer / Security Architect

Why it pays well: Cybersecurity is one of the most structurally undersupplied fields in tech on both sides of the Atlantic. The UK government’s Cybersecurity Sectoral Analysis 2025 reports that the UK sector added 6,600 jobs in a single year and now contributes £13.2 billion to the UK economy – yet the skills shortage remains severe. 

According to IT Jobs Watch, the median UK salary for a Security Architect reached £85,000 in the six months to December 2025, while Morgan McKinley places Cyber Security Architects in London at £95,000–£105,000. In the US, security architects average well over $200,000 (approximately £158,000) at senior levels, according to Glassdoor.

What they do: Cybersecurity engineers and architects design, implement, and oversee systems to protect an organisation’s networks, data, and applications from threats. At the architect level, this includes strategic planning of an entire security posture, compliance frameworks, and incident response capability.

Most common skills/tools: Cloud security (AWS/Azure/GCP), zero trust architecture, SIEM tools, ethical hacking, penetration testing, CISSP/CISM certifications, identity and access management, and DevSecOps.

Soft skills: Risk-based thinking, attention to detail, communicating complex risk to leadership, ethical judgement, and composure under pressure.

Qualifications/experience: A background in IT, computer science or information security is typically required, with industry certifications highly valued.The Cambridge PACE Cybersecurity Strategy & Leadership Accelerator is designed for professionals looking to move into senior cybersecurity roles, blending technical strategy with leadership skills.

What they earn (Glassdoor & IT Jobs Watch, February 2026):

  • UK: Entry ~£40,000–£50,000 | Average £50,000–£85,000 (architect level) | Senior £100,000–£140,000+
  • US (converted to GBP): Entry ~£104,000 | Average £131,000–£179,000 | Senior £228,000+

Ready to build strategic cybersecurity expertise? The Cambridge PACE Cybersecurity Strategy & Leadership Accelerator equips you with the skills to lead security programmes at the highest level. 

4. Cloud Solutions Architect

Why it pays well: Cloud infrastructure is no longer a differentiator for businesses – it is a baseline requirement. As organisations migrate more critical workloads to multi-cloud environments and demand higher performance and security standards, the need for experienced architects continues to grow. 

Glassdoor UK data from February 2026 places the average Cloud Solutions Architect salary at over £90,000, rising to nearly £129,000 at senior level. Senior professionals at major cloud providers like Microsoft frequently earn significantly more when stock and bonuses are included.

What they do: A cloud solutions architect oversees an organisation’s cloud computing strategy and infrastructure – designing, planning, migrating, securing, and optimising systems across platforms such as AWS, Azure, and Google Cloud. They also advise on cost management and ensure cloud environments align with business and compliance requirements.

Most common skills/tools: Terraform, AWS, Microsoft Azure, Google Cloud Platform, Kubernetes, CI/CD pipelines, infrastructure-as-code, cloud security, and multi-cloud architecture.

Soft skills: Strategic thinking, stakeholder management, adaptability, problem-solving, and the ability to translate technical complexity into business language.

Qualifications/experience: A background in computer science or information technology is typically required, with cloud certifications such as AWS Solutions Architect, Azure Architect or Google Cloud Professional highly valued by employers.

What they earn:

  • UK: Entry ~£55,000–£71,000 | Average £75,000–£90,000 | Senior £129,000–£154,000+
  • US (converted to GBP): Entry ~£87,000 | Average £102,000 | Senior £157,000+

5. Product Manager

Why it pays well: Product-led growth remains the dominant strategy for tech companies scaling in 2026. According to Product School, demand for skilled product managers has skyrocketed as businesses invest in building better digital experiences to acquire, activate, and retain customers. The role sits at the intersection of business strategy, design, engineering, and data – making truly excellent PMs rare and well-paid. 

Glassdoor UK data shows senior product managers in the UK averaging £86,000 nationally and £91,000 in London, with top earners at major tech companies reaching well over £140,000.

What they do: Product managers own the strategy, roadmap, and delivery of a product or feature set. They work across design, engineering, data, and business teams to prioritise what gets built, ensure it meets user and business needs, and measure success through analytics and growth metrics.

Most common skills/tools: Product analytics (Amplitude, Mixpanel, Google Analytics), roadmapping tools (Productboard, Aha!), Figma, Jira, A/B testing, Agile/Scrum methodologies, and user research.

Soft skills: Communication, leadership, stakeholder management, prioritisation under ambiguity, commercial acumen, and cross-functional collaboration.

Qualifications/experience: A marketing, computer science, or software engineering background. The King’s Product Management Career Accelerator is designed to give you end-to-end product management skills, from discovery through to delivery and growth, including mastery of industry tools like Miro alongside the leadership and stakeholder skills top PMs need.

What they earn (Glassdoor, February 2026):

  • UK: Entry ~£35,000–£48,000 | Average £63,000 (£69,000 in London) | Senior £78,000–£115,000+
  • US (converted to GBP): Entry ~£91,000 | Average £118,000–£138,000 | Senior £158,000+

The King’s Product Management Career Accelerator teaches you to manage the product lifecycle from end-to-end, using industry-standard frameworks and tools, while building the leadership and stakeholder management skills that set top PMs apart.

Keep these high-paying jobs on your radar

These roles sit slightly further down the demand rankings but offer excellent earning potential in both the UK and the US. 

If you are drawn to high-earning roles that combine creativity and strategy, often within tech companies, consider:

  • UX/UI Designer – UK average £45,000–£60,000; US average ~£60,000–£75,000
  • Digital Marketer – UK average £35,000–£50,000; US average ~£47,000–£57,000

Get a top-paying job with the right skills

The biggest lesson from the 2026 job market is that skills beat credentials – but only when those skills are genuinely current, demonstrable, and paired with the business acumen to apply them. 

Everyone’s version of success looks different. For Gåbriela, it involved getting a bigger salary so that she could better support her son as a single mother. The King’s Product Management Career Accelerator helped her do just that. Read her story here.

Whether you’re starting out, changing jobs, or need to upskill in the latest digital technologies, a Career Accelerator programme is designed to give you relevant and lasting skills in some of the most in-demand digital economy jobs. The university certificate and practical industry experience proves to recruiters and leaders what you’re capable of, and can help you get the salary and responsibility that you’re after.

Sources: All salaries are annual, based on most-likely pay without benefits, and sourced from Glassdoor—other inputs from Indeed, Dice.com Salary Report and TechTarget.

10 In-demand Tech Careers for 2026

Posted on by FourthRev Team

Tech Nation reports the UK tech sector reached a combined market valuation of $1.2 trillion in 2025, cementing its place as the number one tech ecosystem in Europe.

But the roles powering that growth are shifting fast: AI is now woven into everyday workflows, and employers increasingly expect role-relevant AI literacy alongside core technical skills. 

In this guide to the top 10 most in-demand tech careers for 2026, we’ve analysed data and pulled together the roles that consistently show up across hiring and skills-gap insights, plus the skills that make candidates stand out. Salary ranges are UK-wide and will vary by location, industry and seniority.

10 in-demand tech careers

  1. Artificial Intelligence (AI) and machine learning engineers
  2. Data analysts and scientists
  3. Cybersecurity analyst
  4. Cloud engineers
  5. DevOps engineers
  6. Full-stack developers
  7. Blockchain engineers
  8. UX/UI product designers
  9. Product managers
  10. Network and systems administrators

Become the most sought-after talent

This list highlights careers across AI, data, development, cloud computing, machine learning, cybersecurity, blockchain, UX design and product management, roles that are integral to the UK’s digital economy. We also considered the availability of roles, core job functions, job satisfaction and annual earning potential.*

*Tech salaries remain competitive, with wage growth outpacing other sectors in 2024. Addison Group’s 2026 Workforce Planning Guide predicts tech salaries to jump 8-10% this year, outpacing expected inflation of 2.6%. While cities like London often offer higher average salaries, remote work continues to broaden access to opportunities across the country. 

The skills that show up across most tech job descriptions 

  • AI literacy for your role: understanding where AI helps, how to evaluate outputs and how to work responsibly with AI-enabled tools
  • Cloud fundamentals: identity and access management basics, networking foundations, and cost awareness
  • Security basics: secure-by-design habits, risk awareness and good data handling
  • Product thinking: user needs, prioritisation and measurement
  • Delivery habits: version control, documentation and clear stakeholder communication

How to choose the right tech career path

  • If you enjoy building models and working with complex data, explore AI and machine learning engineering or data science.
  • If you like finding patterns and influencing decisions, data analytics can be a strong fit.
  • If you’re motivated by protecting organisations and reducing risk, cybersecurity can offer purpose and stability.
  • If you enjoy systems, reliability and scale, cloud engineering, DevOps or network and systems administration may suit you.
  • If you like shaping what gets built and why, product management can be a strong pathway.

If you like designing experiences and improving usability, UX/UI product design can be a great fit.

Top tech roles for 2026

1. Artificial Intelligence (AI) and machine learning engineers

Demand: AI and machine learning are driving demand for engineers who can develop and improve algorithms and models. These professionals contribute to automation, forecasting and decision support.

Typical responsibilities: Building and testing models, improving performance, working with data pipelines and collaborating with product and engineering teams to deploy solutions.

Skills to build (and tools employers mention): Python, machine learning fundamentals, model evaluation, experimentation discipline and the ability to communicate trade-offs.

Job satisfaction: This role often involves high-impact problem-solving and continuous learning.

Salary: Starting salaries are around £45,000 per annum, with experienced professionals earning up to £120,000. 

If you’re exploring this path, there are two strong routes depending on your goals:

If you want to lead AI strategy and adoption (and make confident decisions about where AI creates value), the LSE AI Leadership Career Accelerator is designed for senior leaders and transformation-focused professionals. 

If you want to build hands-on technical capability in data science and machine learning, the Cambridge PACE Data Science Career Accelerator can help you develop advanced skills and apply them in a six-week live business project designed and facilitated by industry leaders (for example, the Bank of England).

What makes this programme distinctive is its focus on commercially minded data scientists who can use advanced data science, AI and machine learning skills to solve business challenges. You’ll get to practise these skills in a six-week live business project that’s designed and facilitated by industry leaders, such as the Bank of England. 

2. Data analysts and data science engineers

Demand: It is no secret that AI is changing the way companies are hiring in 2025 and beyond, but AI data scientists are being hired at a rapid rate. Data professionals analyse large data sets to uncover patterns and insights that inform decisions. Data analysts and data scientists often differ in methods and depth of modelling, but both remain in extremely high demand as organisations invest in data-driven strategy.

Typical responsibilities: Cleaning and analysing data, building dashboards, answering business questions and communicating insights to stakeholders.

Skills to build (and tools employers mention): SQL, Python, Tableau, statistics, data storytelling and stakeholder communication.

Job satisfaction: These roles can be rewarding for people who enjoy structured problem solving and measurable impact.

The growing importance of data in decision-making processes underscores the indispensable role of data scientists and analysts in today’s business landscape.

Salary: Data analyst salaries in the UK typically range from approximately £30,000 to £60,000 per year. Data scientists can earn between £45,000 and £85,000 annually. 

If you’re looking to fast-track your path to a data analyst career, the LSE Data Analytics Career Accelerator can help you build practical capability in tools like SQL, Python and Tableau, alongside career support.

Read this blog post to see what career results look like for LSE Career Accelerator learners (and potentially you, too). 

If you’re looking for practical guidance on how to move into a data career (from those who’ve already done it) watch the webinar below:

3. Cybersecurity analyst

Demand: Cyber-attacks remain increasingly common, and many organisations need people who can help identify risk, respond to incidents and improve security practices.

Typical responsibilities: Monitoring for threats, supporting incident response, assessing vulnerabilities and helping teams improve security processes.

Skills to build (and tools employers mention): security fundamentals, risk thinking, incident response basics, clear documentation and calm decision-making.

Job satisfaction: Many analysts value the sense of purpose and the stability that comes with protecting organisations.

Salary: In the UK, cybersecurity analysts earn an average annual salary of £45,000, with entry-level positions starting around £40,000 per year and experienced professionals earning up to £62,500. 

4. Cloud engineers

Demand: Cloud adoption continues to expand, and organisations need engineers who can build, maintain and optimise cloud infrastructure. 

Typical responsibilities: Designing and maintaining cloud environments, improving reliability, supporting deployments and managing access and cost.

Skills to build (and tools employers mention): cloud fundamentals, IAM basics, networking foundations, reliability thinking and cost awareness.

Job satisfaction: Cloud engineering can suit people who enjoy systems thinking and continuous improvement.

Salary: In the UK, cloud engineers earn an average salary of £54,286 per year, with entry-level positions starting around £30,000 and experienced professionals earning up to £144,552 annually. 

The growing reliance on cloud technologies highlights the essential role of cloud engineers in modern business operations.

5. DevOps

Demand: DevOps can include administrators, engineers, consultants and system engineers as well as platform specifc software specialists. Those in DevOps help teams deliver software more reliably by improving workflows between development and operations. The demand for DevOps professionals is experiencing significant, sustained growth, driven by the fact that roughly 74% to 80% of organisations worldwide have adopted DevOps practices.

Typical responsibilities: Supporting CI/CD, improving deployment processes, monitoring reliability and collaborating across teams.

Skills to build (and tools employers mention): automation mindset, scripting basics, reliability practices and strong communication.

Job satisfaction: Many DevOps engineers enjoy the blend of technical work and cross-team collaboration.

Salary: The median salaries for DevOps system engineers in the UK typically begin at £55,000 per annum, with experienced lead professionals earning from £85,000 to £120,000. 

6. Full-stack developers

Demand: Full-stack developers remain valuable because they can contribute across front-end and back-end development.

Typical responsibilities: Building features end-to-end, integrating APIs, improving performance and working with designers and product teams.

Skills to build (and tools employers mention): front-end fundamentals, back-end basics, APIs, testing habits and strong debugging.

Job satisfaction: This role can suit people who enjoy variety and building tangible products.

Salary: Salaries range from an average base salary of £55,00 to £87,000 annually, depending on experience and location. 

7. Blockchain engineers

Demand: Blockchain work remains specialised. Where organisations adopt decentralised systems, they need engineers who can build secure, reliable implementations. 

Typical responsibilities: Designing and implementing decentralised components, focusing on security, performance and reliability.

Skills to build (and tools employers mention): secure engineering habits, careful testing, systems thinking and an ability to communicate risk.

Job satisfaction: Many engineers value the complexity of the problems and the opportunity to work on emerging systems.

Salary: Entry-level positions start at £65,000 per annum, with experienced professionals earning up to £90,000. 

8. UX/UI Product Designers

Demand: As organisations prioritise customer experience, UX and UI designers play a key role in creating usable, accessible products. According to the US Bureau of Labor Statistics, the UI/UX job market is growing with an expected growth rate of 4.7%, and the employment rate is expected to rise by 7% for digital designers in 2024-2034, which is deemed much faster than average.

Typical responsibilities: Researching user needs, designing flows and interfaces, prototyping and iterating with product and engineering.

Skills to build (and tools employers mention): user research, interaction design, prototyping, accessibility awareness and portfolio quality.

Job satisfaction: Many designers enjoy the creative, human-centred nature of the work and the ability to influence product outcomes.

Salary: Salaries for UX/UI designers start at £36,000 per annum, with experienced professionals earning up to £71,000. 

For those entering or pivoting into product design, portfolio development and real-world experience can be key. The King’s UX & UI Product Design Career Accelerator supports you in building both through applied learning and a six-week business project set by top UX/UI companies like Creative Navy.

Siobhán was already working as a UX designer when she realised she needed a broader understanding of UX processes to advance her career. Within two months of starting the King’s Career Accelerator, she was promoted to a lead role. Read her story.

9. Product Managers

Demand: Product managers help teams build the right thing at the right time by aligning user needs with business goals.

Typical responsibilities: Discovery, prioritisation, roadmap planning, stakeholder alignment and supporting delivery.

Skills to build (and tools employers mention): structured thinking, communication, prioritisation, measurement and comfort with ambiguity.

Job satisfaction: Many product managers value the strategic involvement and cross-functional collaboration.

Salary: Starting salaries are around £45,000 per annum. There are many titles within product managament and its important to apply for the right type of product management job. The median Lead product manager salary in the UK is £77,000 per year, with experienced professionals in the Digital product management space earning up to £80,000. If you’re looking to build product capability with practical application, the King’s Product Management Career Accelerator offers a structured pathway with applied learning and industry exposure.

10. Network and Systems Administrators

Demand: Over 80% of UK organisations now rely on a mix of cloud and on-premise systems, sustaining strong demand for infrastructure and reliability specialists. Site reliability engineers are especially valued for ensuring uptime, stability and performance in increasingly digital workplaces.

Typical responsibilities: Maintaining systems, troubleshooting issues, supporting users and improving reliability.

Skills to build (and tools employers mention): infrastructure fundamentals, troubleshooting, documentation and a calm approach under pressure.

Job satisfaction: Moderate to high. The stability and critical importance of this role make it a secure and rewarding career choice.

Salary: On average, network administrators in the UK earn around £34,000, while network systems administrators can earn upwards of £52,000. Site reliability engineers have a median salary of about £74,000.

Accelerate your tech career

As the tech sector continues to grow, the demand for skilled professionals remains high. However, the digital skills gap persists.

The UK is already experiencing a growing data skills crisis, with 63% of companies investing in AI technology claiming they have a shortage in the skills required for implementation. This issue will grow as more companies are already set to further invest in AI technology within the next two years.

Our research shows that without access to the right quality of talent, businesses are unlikely to achieve their key strategic deliverables. We found that 54% struggle to find good candidates for entry-level digital economy roles, and 53% said they’d pay whatever it takes to acquire the digital skills they need.

To address this, we’ve partnered with leading universities and top tech companies to deliver Career Accelerators, online programmes designed to equip you with in-demand skills, hands-on experience and career support.

These programmes help bridge the gap between industry needs and talent, supporting learners into high-growth roles across data, AI, product management and UX/UI product design. Learn more about Career Accelerators and start your journey into the tech industry today.

Frequently asked questions

Which tech career is most in demand in the UK in 2025?

Demand varies by sector, but roles across AI, data, cloud and cybersecurity continue to feature strongly in hiring and skills-gap reporting.

What’s the best tech career for career changers?

Many career changers start in data analytics, UX/UI product design or entry-level cloud and security pathways, depending on their strengths and the kind of work they enjoy.

Do I need a degree to work in tech in the UK?

Not always. Many roles value demonstrable skills, projects and practical experience. Requirements vary by employer and role.

What’s the difference between a data analyst and a data scientist?

Data analysts typically focus on answering business questions through analysis and reporting, while data scientists often work more deeply with modelling and advanced statistical or machine learning methods.

Is AI replacing tech jobs?

AI is changing how work gets done. In many roles, it can improve productivity and shift which tasks matter most, which is why building role-relevant AI literacy is increasingly useful.

Which tech careers pay the most in the UK?

Compensation depends on seniority and location, but roles in AI/ML, cloud and specialised engineering areas can command higher salaries.