Before entering the product design world, new designers often face a daunting question: How do I prove what I can do?
In an industry that prizes process as much as outcomes, a UX/UI design portfolio isn’t just a formality — it’s a clear demonstration of your skills, your unique process, and your ability to deliver real design impact.
For learners in the King’s UX & UI Product Design Career Accelerator, that proof comes not from hypothetical exercises but from real-world projects, expert feedback, and a deepened sense of self-belief.
The programme at a glance
The King’s UX & UI Product Design Career Accelerator is a six-month, part-time, fully online programme created by King’s College London in partnership with FourthRev. It’s designed for people ready to build a career in UX and UI product design, whether they’re making a pivot, building on creative or technical skills, or looking for a structured path into the industry. Learners receive:
✅ Practical experience through hands-on case studies and a six-week Employer Project
✅ Early and ongoing portfolio development starting in Course 1
✅ Training in industry-standard tools like Figma, applied in design scenarios
✅ Career coaching, personalised feedback and peer support throughout
✅ A UX design certificate from a globally respected university
This Career Accelerator prepares you to do the work, speak to your process and stand out for your impact, and learners from a wide range of backgrounds are proof.
From blank page to portfolio powerhouse
When Rajdeep Deo, a mechanical engineer, joined the programme, she was planning a significant career pivot. At first, UX might not have seemed like the obvious next step, but for her, it quickly clicked.
“It made sense to me, even if it wasn’t a typical transition that others had made. Once I started, I thought, ‘This actually makes perfect sense.’ It has everything I enjoyed about engineering, just in a way that feels more creative and exciting for me. It really suits my background.”— Rajdeep, King’s Career Accelerator Alumna
Over the six-month programme, Rajdeep applied her structured problem-solving mindset to multiple design challenges — including building three end-to-end UX case studies with real industry companies: a voice interface concept, a health-tracking app, and a tourism site redesign. These weren’t theoretical tasks; each one mirrored actual business scenarios, designed to help learners demonstrate breadth and depth across key UX principles.
“The course encouraged us to apply what we learned to real projects and case studies — not just made-up exercises. I’ve worked on a tourism site, a health app, and now a voice interface challenge. It’s helped me show a good range.”
“The way the course is structured encourages you to go beyond just reading theoretical material — you’re constantly applying what you learn to practical projects and real case studies in a meaningful way. I think that’s going to help me stand out to employers as I start looking for jobs.”
These projects formed the backbone of Raj’s UX/UI design portfolio, which she created during the Career Accelerator. The depth and range of work, combined with the clarity of her storytelling, became something she could take pride in.
“It’s crazy when I think back to when I started six months ago. If you’d shown me my portfolio now and told me how much I’d learn by the end, I don’t think I’d believe it was mine, just because of the amount of work involved and the depth we’ve gone into. It’s been really amazing. I feel like I’ve come a long way in just six months.”— Rajdeep, King’s Career Accelerator Alumna
Feedback that sharpens, structure that guides
From the beginning, portfolio development is an integrated part of the learning journey — not an afterthought. Learners receive feedback from Course Facilitators, attend dedicated portfolio sessions, and take part in live reviews that help them refine their narrative and showcase their process clearly.
“We had a lot of portfolio classes throughout the course, which were really valuable. My approach was always to tell a simple, clear, and concise story behind each project — not just what I did, but why I did it, and making that obvious to anyone reading it.” – Rajdeep, King’s Career Accelerator Alumna
In UX hiring today, that storytelling ability is a powerful differentiator. According to Indeed’s design hiring team, the UX portfolio examples that stand out aren’t just polished — they clearly explain the thinking behind the work. They show process, not just outcomes.
Portfolio-building in the programme isn’t limited to individual assignments. Some of the most powerful and job-relevant work comes from collaboration, through group projects that mirror what it’s like to design within a product team. These experiences don’t just sharpen communication and teamwork skills — they often lead to standout case studies learners feature in their portfolios.
The final six-week Employer Project in the King’s UX & UI Product Design Career Accelerator is a prime example. Set by an actual industry company, it’s designed to simulate the realities of UX and UI design work: cross-functional teams, evolving priorities, tight deadlines, and client-facing briefs. Rather than an academic exercise, it becomes a six-week glimpse into the day-to-day working environment learners are preparing to enter.
It was during this project that Emma, a design assistant looking to step into a more strategic, user-focused role, discovered how much she could grow — both as a collaborator and as a designer. After completing the programme, she landed a UX Designer position at EasyJet, and the Employer Project became a turning point, shaping one of her most impactful portfolio pieces.
“I’m not someone to really enjoy group projects so much, but because I was in such a good team and everyone had different strengths, it allowed me to observe what other people are doing that worked, and what I should be taking on myself. It was a massive learning curve for me, and it really changed my perspective on group projects. It was very nice to be in that environment.”
That experience helped Emma connect theory to practice and produce project work she would later feature in her portfolio. It also made the leap into her new UX role feel natural.
“Now that I have a job in UX, it really made me think that what I learned in the course was very relevant to what I’m doing now. The industry project helps build that connection between what you’re learning and real-life situations.”— Emma, King’s Career Accelerator Alumna
Emma’s experience reflects a wider truth: when learning mirrors professional UX design challenges, the shift from classroom to career becomes not just achievable, but seamless.
Product design is grounded in practice — in tools, team platforms, and design systems. That’s why, from day one, Career Accelerator learners work with industry platforms like Figma and Notion, applying them to professional-level design problems.
But learning tools are only the starting point. What sets this online UX training apart is how learners apply those tools — for real-life users, under real constraints, and with tangible results.
Klaudia Skiba, who entered the programme at the beginning of her UX journey, landed her first Product Designer role at a healthcare software company while still completing the Career Accelerator — a connection made through her Course Facilitator. Her Employer Project became a standout portfolio piece: her team redesigned an NHS app, using actual user reviews to define the problem and propose validated solutions.
“You have to provide research pieces that show it’s viable and that it’s essential that it’s improved. For example, I was working on an NHS app used in the UK. I used the bad reviews from the Apple Store and showed the exact pain points that users had. Then I went through usability testing. We also presented it, so it’s essentially a whole other case study for your portfolio.”
Experiences like this, identifying user problems, conducting UX research, iterating based on insights, are what make a portfolio relevant, respected and ready for hiring conversations.
While tools and projects lay a strong foundation, the support learners receive often makes the biggest difference in building confidence.
Every learner in the King’s UX & UI Product Design Career Accelerator is paired with a Career Coach and a Success Manager who support them throughout their learning journey. Whether building a study schedule, refining a CV, preparing for an interview, or getting feedback on a portfolio case study, the support is consistent and expert.
“The Facilitators have been amazing…It’s really nice to be able to chat to industry professionals and get their take on it. I felt really supported and guided whenever I was working on coursework or assignments. If I had any questions about jobs or portfolios, it was great to have someone knowledgeable there to turn to.”— Rajdeep, King’s Career Accelerator Alumna
Making the shift: From learning to doing
Learners are drawn to this programme because it offers more than skills — it offers the chance to think, create and deliver like a UX designer from Course 1. Each activity and assignment is designed to build confidence through practical, hands-on experience.
Rizal Azlan, who has a psychology background, used what he learned to take on freelance UX projects while still in the programme, demonstrating just how immediately applicable the content is.
“The type of learning was incredibly practical for me. I was doing freelance projects for UX design, and I found that to be a really good way to embed everything that I have learned straight into a real-life application.” — Rizal, King’s Career Accelerator Alumnus
The Employer Project, where Rizal’s team worked on a hospital booking app, helped him experience what many online UX bootcamps miss — true collaboration, with real-world stakes.
“We were working with Creative Navy on a hospital booking app. I felt like that really stood out compared to a lot of my previous learnings, where it’s all very theory-based — you don’t really get hands-on to work on a project, especially with other people, until you’re in the working world, until you actually have to apply it. By that time, it’s kind of too late because they expect you to have those skills already.”
This is what makes the difference: learning that feels like doing, and doing that leads to jobs. Or, in Rizal’s case, to launching 1+1 Studios, the gaming company he co-founded after completing the Career Accelerator — applying his UX skills to create something of his own.
Across this case study, learners have mentioned companies like the NHS, Creative Navy, and real tourism and healthtech brands — and that’s by design. The King’s UX & UI Product Design Career Accelerator connects learners to the standards, tools and expectations of real companies, through case studies and projects grounded in professional practice.
At the centre of it all is King’s College London — a name that adds immediate weight to their skills and learning, recognised by employers around the world.
“The truth is, I’ve got the confidence now, and I wasn’t half as confident after four years of university, and it’s because I worked with experts.” — Klaudia, King’s Career Accelerator Alumna
“Being associated with King’s has given me a real sense of credibility and confidence in my skills. I already feel very connected to the industry, which is something I don’t think I would’ve gotten from another course. The combination of industry experience, course content, and being backed by the university — all of that has really helped set me up for the field.” — Rajdeep, King’s Career Accelerator Alumna
The combination of company-led challenges, expert feedback and university backing offers clear proof of your readiness for the field. It shows you’ve solved real employer problems and worked to high industry and academic expectations. In job conversations, this elevates your credibility, helping you stand out as someone already thinking, working, and presenting like a professional designer.
The design decisions you can show — and stand behind in interviews
One of the most valuable outcomes of the programme is learning how to showcase your UX work in a way that stands up to real hiring conversations. Through live reviews, expert feedback, and repeated practice, learners develop the clarity and confidence to present their portfolio and connect with employers in ways that resonate.
Learners like Rajdeep, Emma, Klaudia and Rizal have all shown that career progress isn’t just about what you design — it’s about how you share your thinking, demonstrate your process, and speak to the value of your work.
“The truth is that the industry is quite competitive, and having your portfolio stand out and attract well is important. The transition [into my new role] was so seamless. I could not be happier.” — Klaudia, King’s Career Accelerator Alumna
“I feel confident job hunting now and talking about what I’ve done — because I’m really proud of it. I didn’t feel that way when I was job hunting in mechanical engineering. I think that says a lot about what I’ve learned and the process I’ve gone through during these six months. It’s been a big shift in how I think about working in the industry — and I have the course to thank for that.”— Rajdeep, King’s Career Accelerator Alumna
By the end of the six months, learners graduate with a portfolio of work developed through industry-aligned briefs, shaped by feedback and strengthened by self-reflection. But more than that, they know how to showcase it — how to explain their choices, respond to feedback, and own their story.
As digital marketing continues evolving, marketers are navigating more complexity and opportunities than ever before. For those looking to upskill in digital marketing, insights from an expert-led webinar hosted by the London School of Economics and Political Science (LSE) and FourthRev reveal not just why these changes matter but also how marketers can take clear, practical steps to build confidence with AI and analytics.
In this blog, we unpack key takeaways from the event, highlight tangible advice from marketing leaders, and explore how the LSE Digital Marketing Strategy & Analytics Career Accelerator helps professionals translate AI curiosity into practical capability.
Why marketers must master AI & analytics — now
The urgency is clear. Data-driven marketers are six times more likely to exceed their business goals. Yet, many marketers still feel behind, unsure how to upskill effectively or overwhelmed by rapid change.
As LSE Enrolment Advisor, Shahana Islam, noted during the webinar:
“AI and analytics are already separating the marketers who are growing from those who are getting left behind.”
The real challenge isn’t deciding if AI belongs in marketing — it’s learning how to use it with purpose and confidence. For those wondering how to learn AI for marketing, the key lies in structured, applied learning that builds momentum and clarity.
What’s changing: Why AI and data matter
AI isn’t theoretical, it’s already reshaping the marketer’s role. Today’s most effective teams use AI and analytics to:
Deliver personalisation at scale: Tailor messages in real time with customer data.
Anticipate behaviour: Use predictive analytics to know what customers want before they act.
Create content faster: Generate copy, visuals, and campaigns at scale using GenAI tools.
Optimise ad spend: Automate targeting and bidding for better ROI.
Sian Heaphy, a senior digital marketing director, joined the panel as an expert and shared this insight on the state of AI:
“We’ve gone past the ‘this is a cool tool’ phase. Marketers are now actively thinking about how to embed AI into workflows to augment content production, automate reporting, and simulate campaign scenarios.”
Claire du Preez, the Brand Director at FourthRev and founder of the AI community Maiden, added a powerful reminder that AI doesn’t replace marketers— it amplifies them — but only if they know how to direct it.
“AI isn’t a magic wand or a crazy threat. It’s a new kind of team member — incredibly fast but not very smart on its own. You need to combine human judgment with AI efficiency.”
AI in action: Real-world case studies from leading brands
The LSE webinar spotlighted real examples of AI creating a measurable impact:
Zalando used GenAI to cut campaign image production time from 6–8 weeks to 3–4 days, reducing costs by 90%.
Starbucks leveraged Deep Brew AI for personalised marketing, driving a 15% sales increase and higher average order values.
JPMorgan deployed AI tools to deliver customised investment insights, which contributed to a $1.5 billion cost savings and a20% increase in sales.
As Claire du Preez put it:
“Speed without insight is just faster mistakes. Insight without speed is strategy that never ships. The real value is in combining the two — that’s what AI makes possible.”
The message is clear: knowing how to apply AI in digital campaigns can unlock not only efficiencies, but real strategic advantage.
The skills smart marketers are prioritising in 2025
The event panel and wider research point to five critical capabilities marketers are focused on:
AI-powered content creation – Create performance-driven content at scale
Marketing analytics – Make data-informed decisions with tools like GA4
Predictive testing – Apply machine learning to optimise campaigns
Performance marketing automation – Maximise ROI with smart ad tools
AI insight generation – Turn raw data into strategic decisions
These aren’t just skills. They’re markers of high-performing marketing professionals.
“It’s easy to find someone who can run a Meta campaign. What’s rare is a marketer who can spot patterns in GA4 and turn them into strategy. The gap isn’t access to tools, it’s the thinking layer.”
– Claire du Preez, Brand Director at FourthRev and AI educator
“Great operators don’t just talk about AI. They tie it to business outcomes, unblock bottlenecks early and design faster feedback loops.”
– Sian Heaphy, Senior Marketing Director at Bright
How LSE teaches these skills
The capabilities explored in this blog are exactly the practical skills you’ll build through the LSE Digital Marketing Strategy & Analytics Career Accelerator. The six-month programme is built around real-world application, giving you hands-on experience using data tools, AI strategies and analytics to solve real marketing challenges and drive measurable results.
Core competencies you’ll build through the programme:
Strategic data analysis and visualisation: Use data tools to uncover and communicate actionable insights, including data storytelling in marketing.
Predictive analytics and segmentation: Anticipate behaviour and personalise campaigns using predictive models.
AI-driven marketing and automation: Apply AI tools to content creation, performance optimisation, and audience targeting.
Ethical and responsible AI use: Understand ethical use of AI in marketing, with an emphasis on transparency, bias mitigation, and compliance.
Real-world application and feedback: Test, iterate, and get feedback via a six-week Employer Project set by a real industry company.
Drawing from her experience helping marketers build practical confidence with new tools, Claire du Preez offered this insight:
“Marketers don’t just need knowledge, they need the muscle memory of applying it. That’s why I’m afan of hands-on learning like this programme. You’re solving real problems, getting feedback, building confidence.”
Sian Heaphy reinforced the value of real-world readiness: skills you’ve learned and proven in action.
“Speed to execution matters. If someone can show they’ve applied these tools in a real context, that’s gold to an employer.”
Next steps: Your career transformation pathway
The structured nature of the LSE Digital Marketing Strategy & Analytics Career Accelerator makes career transformation achievable and sustainable:
Months 1-3: Gain foundational skills in data analysis, visualisation, and AI fundamentals.
Months 4-6: Implement predictive analytics and AI-driven strategies in practical, employer-led projects.
Months 6+: Leverage career coaching to position yourself effectively for promotions, new roles, or industry pivots.
Final reflection: Becoming indispensable
Mastering AI and analytics isn’t simply about keeping up. It’s about becoming an indispensable marketing professional, equipped and confident for the industry’s future. The LSE Digital Marketing Strategy & Analytics Career Accelerator offers the essential skills and the structured, credible learning environment that drives genuine career advancement in a fast-moving field.
Download the programme brochure to discover how the LSE Career Accelerator can fast-track your career progression in digital marketing.
Record levels of investment show that business leaders recognise AI’s game-changing potential. Yet the gulf between promise and impact remains stubbornly wide:
74% of companies still struggle to turn AI into measurable value.
42% of firms abandoned most AI initiatives in 2025, up from 17% last year.
By contrast, Deloitte’s year-end State of Generative AIpulse shows the opposite end of the spectrum: 74% of the most advanced Gen-AI initiatives are already meeting or exceeding ROI targets, while only one in five projects overall achieves that level of success — evidence of a widening leader–laggard gap.
Real-world lessons from LSE and Deloitte
The insights shared in this blog are drawn from a recent live event hosted by the London School of Economics and Political Science (LSE) and FourthRev, which brought together LSE faculty experts and an industry leader from Deloitte to explore what it really takes to translate AI strategy into business impact.
The session also introduced the new LSE AI Leadership Accelerator, a programme purpose-built for senior professionals and consultants who need to lead or advise on AI transformation. Developed in collaboration with experts from Deloitte’s Office of Gen AI, as well as Heads of AI at Inchcape Digital, Thredd and others, the Accelerator focuses not on technical training, but on strategic leadership, organisational change, and responsible implementation — the very themes explored during the webinar panel.
The industry quotes that follow are taken directly from this event. They illustrate, in the speakers’ own words, the real barriers organisations are facing and the leadership mindsets required to overcome them. You can watch the full recording of the session below:
The surprising truth: It’s rarely a technical problem
A growing body of research confirms what many executives feel intuitively: technology isn’t the main barrier.
According to BCG’s 2024 AI Radar, around 70% of adoption challenges stem from people and process issues, not technical limitations like algorithms or infrastructure.
Dr Dorottya Sallai, an Associate Professor (Education) of Management at the LSE Department of Management, calls AI adoption a “cultural transition.” As Dr. Sallai explained during the webinar:
“With every digital transformation, it all depends on the people. You can introduce a new system, but if people don’t use it, or they revert to the old systems, it’s not going to work. With generative AI and AI more generally, it’s more complex because it is not like traditional technology; it will impact a lot of different aspects of the organisation — the decision-making processes. The biggest issues are the psychological dimension, the cultural dimension, and the leadership dimension.”
Dr. Dorottya Sallai, Associate Professor (Education) of Management, LSE Department of Management
Put simply, strategic misalignment, cultural resistance, and leadership inertia — not model accuracy — explain most tech failures.
In many organisations, technical teams build capable models but struggle to communicate business value. Business teams, meanwhile, face internal friction: employees worry about job security, and executives grapple with scepticism rooted in legacy systems and past failures.
Perhaps most telling is this finding: “The biggest barrier to scaling AI is not employees — who are ready — but leaders, who are not steering fast enough.” Their research shows that leaders are twice as likely to blame employee resistance as to acknowledge their own gaps in guiding transformation.
Seven leadership practices that drive AI success
Based on comprehensive research across multiple industries and feedback from organisations actively implementing AI today, seven leadership practices consistently emerge as critical factors in successful AI transformation. These practices address the most common challenges we hear from executives: lack of strategic vision, organisational resistance, and difficulty demonstrating tangible value.
1. Foster trust and transparency
Trust is the foundation of successful AI adoption. When employees don’t understand AI’s purpose or implementation, resistance naturally follows. Harvard Business Review puts it succinctly: “Employees won’t trust AI if they don’t trust their leaders.”
Effective leaders:
Communicate openly about AI initiatives and their intended impact
Acknowledge uncertainties and address concerns proactively
Involve employees in AI experiments and decision-making
Demonstrate how AI decisions are made and can be verified
Establish formal ‘responsible AI’ processes
The impact is measurable: According to IBM research, organisations that implement formal responsible AI frameworks report significantly higher workforce adoption and engagement.
What’s more, employees back what they understand. Deloitte’s C-suite ethics survey shows 88% of companies now communicate openly about how they use AI, and 52 % involve the board when drafting AI-ethics policy.
In short, when leaders communicate clearly and implement responsible AI principles, they create the trust and clarity teams need to adopt AI confidently.
2. Lead with a clear vision and business case
Researchers warn that “AI strategies without a compelling ‘why’ rarely survive first contact with reality.” The best leaders link every AI use case to a strategic outcome and publish an enterprise AI roadmap.
Deloitte finds C-suite alignment is a top-three predictor of scaling success. AI initiatives succeed when they’re connected to strategic business outcomes rather than implemented as technology experiments. Leaders must articulate a compelling ‘why’ behind AI adoption.
Successful organisations:
Connect AI directly to business strategy and priorities
Develop compelling narratives about AI’s purpose
Establish clear ROI metrics and milestones
Focus on solving real business problems rather than deploying technology for its own sake
IBM emphasises that the most successful AI adopters ground their initiatives in clearly defined business strategies, supported by structured roadmaps that align AI efforts with enterprise goals — a sharp contrast to the ad hoc approaches seen in less mature organisations.
Strategic programme managers emphasise that developing validated narratives around AI adoption — supported by concrete business cases — is essential for earning stakeholder buy-in, particularly in organisations grappling with legacy systems or recovering from previous failed technology initiatives.
3. Establish strong governance and ethics
Ethics is now a revenue issue.Deloitte reports that 55% of C-level executives say robust AI guidelines are very important to growth, and 49% already have formal policies in place (another 37% are nearly ready). When the CEO or board takes direct oversight, there’s 3.6× boost in bottom-line impact.
As AI becomes more embedded in critical business processes, governance becomes increasingly important. Leaders must establish appropriate guardrails while enabling innovation.
Effective governance includes:
Clear ‘guardrails’ for appropriate AI use cases
Multi-disciplinary oversight spanning technical and business perspectives
Responsible AI principles addressing bias, privacy and transparency
Proactive compliance management
Executive-level accountability
This level of executive oversight has become a consistent marker of maturity. Companies that embed governance at the highest levels tend to achieve stronger alignment, greater impact, and more sustainable results.
IKEA provides an instructive example, having established a multidisciplinary AI governance team, comprising technologists, legal experts, policy professionals, and designers, that ensures AI initiatives align with business priorities and uphold responsible AI principles.
4. Invest in people and skills
AI fluency must extend across the enterprise. Nearly two-thirds of organisations prefer up-skilling existing employees over external hiring for new AI roles; almost half are already reskilling staff for Gen-AI. LSE’s “human-centred” guidance stresses training in critical thinking, change management, and prompt engineering to turn fear into curiosity.
AI fluency is rapidly becoming as important as digital literacy. Organisations must build capabilities across all levels, not just among technical specialists.
Leading organisations:
Develop organisation-wide data literacy programmes
Provide role-based AI capability training
Establish AI academies and learning paths
Build communities of practice to share knowledge
Train employees in “prompt engineering” for generative AI
BCG research indicates that organizations with a strategic focus on AI —allocating substantial resources and upskilling their workforce — achieve significantly higher ROI on their AI investments compared to their peers.
Technical leaders increasingly highlight the importance of soft skills alongside technical fluency — including the ability to frame AI solutions in terms of business value, communicate outcomes to stakeholders, and align innovations with strategic goals. As AI becomes more integrated into decision-making, this blend of business and technical acumen is proving essential for driving adoption and delivering real impact.
5. Encourage experimentation and learn from failure
Innovation demands controlled testing environments. Deloitte’s Gen-AI survey shows 76% of leaders will give AI projects at least 12 months to resolve ROI or adoption challenges before shrinking budgets, signalling patience for iterative learning.
Innovation requires experimentation, and experiments sometimes fail. How organisations handle failure often determines their long-term success with AI.
Successful approaches include:
Creating safe spaces for AI experimentation
Destigmatising failure as an essential part of the learning process
Applying agile methodologies to AI projects
Systematically reviewing lessons learned across the organisation
Celebrating both successes and valuable failures
BCG’s research identified a counterintuitive insight: Organisations that acknowledge and ‘celebrate failures’ in AI pilots correlate with higher long-term value creation, likely because they learn faster and iterate more effectively.
The hidden cost of hesitation
While experimentation is essential, many organisations remain cautious — particularly those navigating economic pressure or still carrying the weight of past transformation failures.
George Johnston from Deloitte and offered a candid reflection on this dilemma during the LSE webinar:
“You don’t experiment for nothing… there is a cost to these things. You’ll ask, is now the time to be spending money on something that may not work? Or shall we hold on to that for the time being, understand what others are doing? We don’t necessarily need to be the first mover.”
George Johnston, AI Technology, Media and Telecoms (TMT) EMEA leader, Partner at Deloitte
Pausing may feel prudent, but in fast-moving fields like AI, the bigger risk is falling behind while others build capability, confidence, and momentum.
6. Show visible leadership and align from the top
Culture follows example. High-achieving companies are three times more likely to trust AI insights over ‘gut feel,‘ yet they also invest heavily in change-management and training to channel that confidence productively. Cross-functional steering committees, and leaders who personally use AI tools, signal that transformation is non-negotiable.
George noted in the event:
“When I think about some of the experimentation that’s not worked so well… probably the most common factor that we’ve seen is that there has not been sufficient senior sponsorship, and/or there’s a limited path to that scaling. That tends to be where things fail —not understanding the end-to-end process of how you are going to transform, and having the senior buy-in.”
George Johnston, AI Technology, Media and Telecoms (TMT) EMEA leader, Partner at Deloitte
When leaders demonstrate personal commitment to AI adoption, it signals importance to the entire organisation. Alignment at the top is particularly crucial given AI’s cross-functional nature.
Effective leadership alignment includes:
Ensuring C-suite consensus on AI strategy and priorities
Establishing cross-functional steering committees
Creating dedicated transformation offices when appropriate
Leaders personally using and championing AI tools
Regular board-level engagement on AI progress
IBM found that in the most successful AI organisations, the C-suite and IT leaders work in lockstep — a sharp contrast to companies where AI remains siloed in technical teams.
7. Prioritise ethical leadership & responsible AI
Beyond compliance, ethics is a brand differentiator. According to Deloitte, board-level involvement in AI-ethics policy is becoming standard practice, with 52% of boards always engaged. Organisations with mature ethical frameworks are also 2.5× more likely to earn customer trust. LSE experts add that open dialogue about risks creates the psychological safety needed for rapid, responsible experimentation. As Dr. Dorottya Sallai put it:
“My advice would be — equip yourself with the knowledge. If you have the knowledge and you understand what’s happening, you will be able to take leadership. I think the biggest challenge for leaders today is to understand what’s going on.”
Dr. Dorottya Sallai, Associate Professor (Education) of Management, LSE Department of Management
As AI systems become more powerful and influential, ethical leadership has emerged as a critical practice for sustainable success. Organisations leading in this area recognise that responsible AI isn’t just about risk management — it’s about competitive advantage and long-term viability.
Effective ethical leadership includes:
Defining and enforcing clear ethical boundaries for AI use
Ensuring diverse perspectives in AI development and governance
Proactively addressing potential biases in data and algorithms
Creating transparent processes for addressing ethical concerns
Aligning AI initiatives with organisational values and societal expectations
Ethical leadership in AI isn’t just about doing the right thing — it’s increasingly essential for driving adoption, earning customer trust, and unlocking long-term value. Deloitte finds that organisations with clear AI governance structures are more likely to see real business impact, while the World Economic Forum warns that consumers are paying closer attention to how companies design and deploy AI.
As AI scales, so do the consequences of getting ethics wrong — making proactive, transparent leadership non-negotiable.
The business impact of effective AI leadership
Combine these practices and the numbers tell a compelling story: effective AI leadership isn’t a soft skill — it’s a measurable differentiator. Organisations that lead in this space grow revenue 1.5× faster, achieve higher ROI, and stay ahead of disruption.
The gap between leaders and laggards continues to widen, creating urgency for organisations to address leadership capabilities around AI implementation. As Dr. Sallai highlighted, leadership today demands not just strategy — but the willingness to stay ahead of fast-moving change.
Developing AI leadership capabilities: LSE AI Leadership Accelerator
Most AI leadership programmes still focus on coding or data science. The LSE AI Leadership Accelerator is different: it equips executives to apply these seven practices, turn strategic intent into business value, and join the elite 20% of transformations that succeed. Participants leave with:
Practical tools to implement the seven leadership practices in their organisations
A board-ready AI business case and implementation roadmap
Toolkits for responsible AI governance and culture change
Direct feedback from Deloitte and LSE faculty on live projects
A peer network of leaders closing the AI value gap
Transforming organisational approaches to AI requires intentional development of leadership capabilities. Many professionals are now aiming to break through the next leadership ceiling — shifting from tactical roles into strategic influence by mastering AI implementation.
The LSE AI Leadership Accelerator helps leaders close the implementation gap by focusing on real-world business cases, responsible governance, and the human of AI transformation — exactly where most initiatives fall short.
As the research clearly demonstrates, the gap between AI potential and business value is primarily a leadership challenge, not a technical one. Organisations that develop strong AI leadership capabilities position themselves to capture the substantial value that AI offers while avoiding the pitfalls that have derailed so many initiatives.
Ready to bridge the gap between AI potential and real business impact?
To learn more about the LSE AI Leadership Accelerator and how it can help your organisation bridge the AI implementation gap, download the programme brochure.
When Sheldon Lee Kemper joined the Data Science with Machine Learning & AI Career Accelerator, he wasn’t looking to change direction – he was ready to take the next step. With a strong background in data engineering, Sheldon recognised that the future of his field would be shaped by AI and machine learning. He needed a programme that could bridge that gap – providing advanced, applied training in AI while maintaining a strong foundation in practical data science.
The Career Accelerator delivered. Towards the end of the programme, Sheldon was headhunted by Capgemini for a new role as a Data Engineer – one that came with a salary increase and a more AI-driven focus. His story is a powerful example of how experienced professionals are using the programme to deepen their expertise, expand their impact, and lead in an evolving data landscape.
In his own words, here’s Sheldon’s journey – from structured pipelines to intelligent automation, and everything in between.
A few years ago, I wouldn’t have predicted that I’d be diving deep into AI and machine learning. My background was in data engineering, and I was comfortable working with big data pipelines, ETL processes, and cloud computing. But as I watched the industry evolve, it became clear – AI wasn’t just an add-on; it was transforming how data was processed, interpreted, and used for decision-making.
That realisation led me to upskill. At first, I explored machine learning through self-study, online courses, and small projects. But I knew that to truly integrate AI with data engineering, I needed a structured, hands-on programme. That’s what drew me to the Data Science with Machine Learning & AI Career Accelerator from the University of Cambridge Professional and Continuing Education (PACE) – a seven-month immersive experience focused on real-world applications.
I wasn’t looking to transition into data science entirely – I wanted to create a new role that blends AI with data engineering, bridging the gap between intelligent automation and scalable data infrastructure.
Getting into the deep end: A crash course in AI and ML
The first few months of the programme were intense. We covered everything from machine learning fundamentals to neural networks, NLP, and time series forecasting. Even with my technical background, I quickly realised that building AI solutions required a different mindset than traditional data engineering.
Some of the key areas I focused on included:
Neural networks and deep learning – Manually working through forward and backward propagation to understand how models learn.
Optimisation and regularisation – Implementing dropout, L2 regularisation, and hyperparameter tuning to improve model performance.
Natural language processing (NLP) – Exploring sentiment analysis, topic modelling, and transformer models like BERT and T5.
Time series forecasting – Comparing ARIMA, exponential smoothing, and machine learning approaches to predict trends.
One of the most challenging yet rewarding aspects was transitioning from structured, relational data processing to working with messy, unstructured data – especially in NLP.
The Employer Project: Bringing AI into financial risk analysis
Midway through the programme, we were introduced to our Employer Project with the Bank of England. The challenge was to extract insights from quarterly earnings call transcripts from Global Systemically Important Banks (G-SIBs). These transcripts contained valuable but unstructured financial data, making it difficult for analysts to efficiently interpret key insights.
Our goal was to develop an AI-powered NLP pipeline that could automate financial analysis. The team built:
Sentiment analysis (FinBERT) – To determine the tone of statements made in earnings calls.
Topic modelling (BERTopic, LDA) – To extract key themes and discussions from the transcripts.
Abstractive summarisation (T5, BART) – To generate concise summaries for analysts.
Retrieval-augmented generation (RAG) chatbot – To allow users to query transcripts and retrieve relevant insights.
I focused on developing the RAG chatbot, ensuring that it could retrieve, summarise, and interpret financial insights in real time.
The challenges we faced (and how we solved them)
The messy data problem
Financial transcripts are long, inconsistent, and full of irrelevant content. Speaker labels weren’t standardised, and important insights were buried in corporate language.
Solution: We built a robust preprocessing pipeline to clean and structure the data before feeding it into our models.
Siloed workstreams led to disjointed outputs
Initially, team members worked on separate models independently, which caused issues when trying to integrate everything.
Solution: We introduced clear dependencies between components, aligned outputs, and used regular checkpoints to ensure all models worked together seamlessly.
Summarisation wasn’t quite working
Our summarisation models were producing generic responses because the input formatting was inconsistent.
Solution: We refined our data inputs and prompting techniques, improving the ROUGE scores and ensuring more meaningful summaries.
Adapting when models were incomplete
Some models weren’t ready on time, which could have delayed the pipeline’s progress.
Solution: We found alternative data sources and models to continue testing while waiting for finalised components.
The moment it all came together
The real turning point came when the team shifted from working in silos to working as a unit. Instead of just focusing on our own components, we started thinking about how they interacted.
One of the best moments was getting the retrieval chatbot to work smoothly – it was incredibly rewarding to query a transcript and instantly receive a meaningful, summarised financial insight. It was no longer just an academic exercise; it was something that could have real-world applications.
Lessons learned: More than just AI
This experience taught me far more than just technical skills.
AI isn’t just about accuracy, it’s about usability. Our models needed to work in a way that made sense for financial analysts, not just data scientists.
Collaboration is everything. The best AI models are useless if they don’t integrate well with the full system. Aligning workflows was critical.
Being adaptable is a necessity. AI projects never go 100% as planned, and the ability to pivot and refine is what makes them successful.
AI and data engineering are converging. The future isn’t just about building better AI models, it’s about integrating them into scalable, automated data pipelines.
Full circle: From early interest to real-world AI implementation
Thinking back to my early journey into data science, I started by exploring theory and small-scale projects, but this programme helped me apply AI in a business-critical environment. The Cambridge PACE Career Accelerator provided me with the technical expertise, leadership experience, and problem-solving skills to bridge the gap between data engineering and AI-driven decision-making.
For anyone considering this programme, my advice is simple: if you want to go beyond theory and work on AI that actually solves problems, this is the right place.
The experience has given me the confidence and capability to integrate AI into real-world data workflows, and I know that this is just the beginning of the next phase in my career.
Sheldon’s story is just one example of how the Data Science with Machine Learning & AI Career Accelerator is equipping learners to solve real business problems, lead cross-functional projects, and step confidently into the next chapter of their careers. Whether you’re a career changer, an early career professional or, like Sheldon, an experienced practitioner aiming to expand your impact – the programme provides the technical depth, industry exposure and career coaching to help you get there.
Download the brochure to learn more about how this programme can accelerate your career in data science.
On 1 July 2025, the Institute of Continuing Education became the University of Cambridge Professional and Continuing Education (PACE). As part of this transition, you may still encounter some materials or communications bearing the previous ICE branding. Please be assured that while the name and identity are evolving, the learning experience and programme outcomes of the Career Accelerator remain unchanged — with Cambridge academics continuing to deliver world-class education.
Making a career switch can feel daunting, especially without formal experience in a new industry. However, with structured learning, real-world application and expert career support, a successful transition is entirely possible.
At a recent Ask-Me-Anything (AMA) session, she shared her journey, insights and key takeaways for anyone considering a career transformation through the programme. You can watch Sofia’s full Career Accelerator review below:
Why Sofia chose the LSE Digital Marketing Career Accelerator
Before enrolling, Sofia knew she needed a structured, data-focused and industry-relevant pathway to build credibility and skills in digital marketing. She had explored self-paced online courses but found them too fragmented to provide a clear career roadmap.
“Honestly, it turned out to be one of my best decisions. I gained a ton of knowledge about digital marketing, which not only helped me understand the field better but also improved my skills in my day-to-day job. While doing an apprenticeship with my previous company’s marketing team, I noticed that everything I learned in the course was directly applicable to my daily projects. In just six months, my growth was exponential.”
A structured, job-ready learning experience
Unlike traditional academic degrees, the LSE Career Accelerator integrates real-world projects, industry case studies and AI-driven marketing strategies, making it directly applicable to today’s job market.
Rather than spending years in a degree programme, Sofia gained job-ready skills in just six months, balancing learning with full-time work.
“Because I also did my Master’s degree in Edinburgh I can understand the difference. The Career Accelerator is like a very fast-paced micro Master’s. You get a really good idea of what’s happening in that specific sector, also from a professional aspect, not just the academic perspective.”
Mastering marketing data: Turning insights into strategy
Data is at the heart of modern digital marketing, and the LSE Digital Marketing Career Accelerator ensures learners develop the analytical mindset needed to interpret and leverage it effectively. From understanding key performance metrics to applying analysis techniques, the programme equips students with the skills to translate data into actionable marketing strategies.
For those without prior exposure to data analysis, the structured learning approach gradually builds your skills as you progress through the programme. Sofia found this aspect particularly transformative:
“I didn’t have any exposure to data before, especially, for example, in regression analysis, but it gets you in the mindset to understand how to read data…Now it’s really beneficial, because in my new role, they asked me to read data, so it’s really important.”
This real-world focus on data-driven decision-making prepares learners to confidently navigate performance marketing, customer insights and campaign optimisation – essential skills for any modern marketer.
Tip for prospective learners: Look for a programme that is industry-recognised, hands-on with data, and strategically structured – so you gain not just knowledge but real employability.
How networking helped Sofia make her career move
One of the most valuable aspects of the programme for Sofia was the strong industry network and professional connections she developed.
Industry-connected faculty and peer learning
Sofia worked alongside experienced professionals from diverse industries, providing a network that extended beyond her immediate job search.
She engaged with top marketing professionals and guest speakers, gaining insights into emerging trends and best practices.
Through collaborative group projects, discussion forums and informal meet-ups, she strengthened her industry network.
“You meet many people from the very beginning. You meet your peers and many of them are already in the marketing sector. They’re very successful professionals who just wanted to get more knowledge and go to managerial positions. You collaborate with them a lot and you get to know each other on a personal level.”
Applying new skills in the real world
One of the biggest benefits of the LSE Digital Marketing Career Accelerator is its practical, hands-on learning approach.
“This was the only programme that offered real projects. By working with an actual company, it provides a better understanding of the real market and how to apply my knowledge in practical scenarios, ultimately increasing opportunities for employment after the programme.”
Transforming Learning into Action: The LSE Digital Marketing Employer Project
Learn more about the final six-week live business project set by a leading digital marketing agency in this blog post.
Real-world business cases and industry projects
Sofia worked on projects that mirrored the challenges faced by marketing professionals, including:
Developing a full-funnel digital marketing strategy for a real brand
Using SEMrush and Google Analytics to track campaign performance
Executing A/B testing strategies to optimise engagement rates
“In all the courses that we have, either you have to create the plan for a fictional company or a real company. So, no matter what, you have to get into the mindset of a real digital marketing professional to be able to apply your knowledge and complete assessments because that’s how you’re going to get assessed as well. So they want to see if you actually thought about how this is going to apply in a real-time scenario.”
By the time she finished the programme, she had a portfolio of real work to showcase to employers, demonstrating not just knowledge but execution.
“When the job came in August, they asked me to do a mini-campaign, including email, social media and SEO. I immediately knew what to do with a strategy, and trust me, I would never have been able to do it before.”
Balancing work, life and study – Sofia’s approach
Managing a rigorous programme alongside work requires a structured approach to time management.
Sofia’s top productivity strategies:
Create a study routine that works for you
Sofia recommends creating a setup that keeps you motivated. “I highly recommend finding a study environment that really inspires you,” she says. For her, that meant working in cafés and libraries. She also made it a priority to attend live sessions whenever possible for maximum engagement—though recordings are available if you miss them.
Use programme support
The structured curriculum and check-ins from your dedicated Student Success Manager will help keep you on track and accountable. The Course Facilitators are available in live sessions to guide you through learning challenges and any complex topics.
Break tasks into milestones
Sofia reassures, “If you’re very organised, it can happen without any [issues].” She created dedicated study sessions for herself during the week and kept the weekend open.
Key takeaway: With the right approach, you can integrate learning into a busy schedule without burnout.
Fast-tracking career growth with coaching
Beyond just learning technical marketing skills, Sofia credits the programme’s career coaching for helping her secure a new role.
How career coaching helped Sofia make the transition
1:1 personalised career guidance – refining her CV, LinkedIn profile and personal brand
Mock interview training – helping her confidently pitch her transferable skills
Strategic career direction – coaching on how to transition into digital marketing roles effectively
Shortly after completing the programme, Sofia transitioned into a Brand Executive role, applying her new expertise in performance marketing, SEO and AI-driven advertising.
“My career coach – and think all of them – have very good extensive knowledge in terms of how to build your career. So, even now that I got the role that I wanted, I had my call with my Career Coach and she gave me some advice and tips that you won’t be able to think on your own, especially if you’re new to the field. The career coach was amazing, honestly, my coach helped me a lot throughout the whole journey.”
Next steps: Ready to launch your digital marketing career?
Sofia’s journey proves that with the right structure, expert guidance, and hands-on experience, a career switch into digital marketing is within reach. From gaining job-ready skills and building a powerful network to receiving personalised career coaching, the LSE Digital Marketing Strategy & Analytics Career Accelerator provides the tools you need to break into the industry and progress in your current career with confidence.
Did you know that 87% of marketers are already using or experimenting with AI? AI-powered tools are transforming everything from personalisation to predictive analytics, making it essential for marketers to stay ahead. In this blog, we break down key insights from our webinar, “The Future of Digital Marketing: Using AI and Analytics to Stay Ahead.”
From redefining strategies and enhancing personalisation to optimising campaigns, AI and analytics are revolutionising the digital marketing landscape. Our recent webinar brought together industry experts Simon Bullmore and Claire du Preez from the LSE Digital Marketing Strategy & Analytics Career Accelerator to discuss the most pressing topics: how AI is transforming marketing, how professionals can harness its potential, and what ethical considerations must be addressed. You can watch the full event below:
Key takeaways from the webinar
The marketing industry is evolving rapidly and staying competitive requires adapting to AI-driven changes. Whether you’re looking to future-proof your career, refine your digital marketing strategy, or understand how AI and analytics can enhance decision-making, these key takeaways will provide valuable insights from our expert panel.
1. Striking the right balance: AI vs. human decision-making
A recurring theme in our discussion was how marketers should decide where to draw the line between AI automation and human oversight. The consensus? AI should be leveraged to handle repetitive, data-heavy tasks, such as audience segmentation, A/B testing, and predictive analytics; while human intuition and creativity remain irreplaceable in strategic decision-making and brand storytelling.
2. Ethical considerations in AI-driven marketing
AI can be a powerful tool, but with great power comes great responsibility. Our panellists emphasised the importance of AI ethics, data hygiene, and responsible AI use. Companies must establish clear policies to ensure that AI-driven marketing efforts remain transparent, unbiased, and aligned with brand values.
3. Career growth in AI-infused digital marketing
AI is not replacing marketers; it’s empowering those who know how to use it. Roles requiring AI proficiency have grown by 237%, and marketers with AI and analytics skills earn up to 45% more in the UK. For professionals seeking to future-proof their careers, mastering AI-driven tools and analytics is no longer optional, it’s essential.
4. The role of AI in hyper-personalisation
Personalisation at scale has become a reality thanks to AI. From real-time customer interactions to predictive behavioural analysis, brands are leveraging AI to create highly customised experiences. AI-powered analytics enable marketers to segment audiences dynamically, ensuring that messaging is tailored to individual preferences and behaviours.
Your questions answered
During the webinar, we received numerous insightful questions from the audience. While we addressed many in the session, we promised to follow up with responses to the remaining questions. Below, we have included every question asked, along with our expert responses.
List of questions Use the list below to navigate to the question and answer you’re most interested in.
How do you decide where the line is in terms of how much you use AI and when to take over manually?
A crucial perspective – the ethics of AI in marketing: Where to start with procedures? What matters more – organisational-level policies and procedures or micro-level marketing processes?
How do you make sure that the AI tools you’re using are delivering an output that is also reliable?
What are the best AI tools for analytics?
Data-driven decision-making is not new, marketers have had to do this for decades – what’s different now?
In scaling digital marketing services (using AI tools) to multiple geographies and considering different regulatory requirements towards use of AI, are there existing strategies that can address that or is it still something that is in development?
Which specific reports have you used AI for?
How do you see the role of an AI assistant, often discussed in the context of ChatGPT? Shouldn’t we emphasise more in marketing that this is an assistive technology for marketers rather than a superpower for marketers?
How can AI improve email marketing segmentation?
How does LSE’s Digital Marketing Strategy & Analytics Career Accelerator address AI learning?
What career support does the programme offer?
Do you have practical examples of where AI has been able to take away tedious, repetitive tasks? What exactly were these tasks?
How should data collection policies and data hygiene practices precede the implementation and use of AI? After all, marketing often deals with a large volume of unstructured and messy data.
How can I strategically revamp my skills as a home learner, given the vast array of marketing tools – some of which are enterprise-grade? How should I position my portfolio to appeal to recruiters?
How fast is court practice developing with respect to intellectual property/rights on AI-generated content, specifically used in marketing/advertisement campaigns?
In yesterday’s AI Masterclass, Claire spoke about giving her AI assistants ‘performance reviews’. How would you go about doing that? Going back to the data and double-checking their work?
Can you provide a practical example of using AI for personalisation?
AI is a powerful accelerator, but relying on it to “fix everything” without understanding the mechanics can create long-term problems. How do we balance the incredible productivity of AI with the need for solid foundational knowledge?
How about tools like HubSpot or SalesForce? Get the data, as it is not included in the list, what is your perspective on this?
Does the Career Accelerator detail using these AI tools?
ChatGPT 4.5 has been characterised as a somewhat strange “animal” in the sense that it isn’t quite clear what its added value is. The one thing I have heard rather consistently is that it seems to mimic these human, emotional, authentic “moments” we just heard about.
It’s all so overwhelming! How does one move on from casual use to starting to use it effectively?
What are the top AI tools for email marketing, especially regarding analytics? Do Mailchimp and Klaviyo provide sufficient AI capabilities, or are there better alternatives? Which tools excel in segmentation?
1. How do you decide where the line is in terms of how much you use AI and when to take over manually?
This comes down to understanding both the capabilities and limitations of AI – what is it good for, and what is it not? The best thing to do is to typically treat AI as an aid or intern, and not an all-encompassing solution. A good rule is to let AI handle high-volume, data-heavy or repetitive tasks, while you oversee strategy, creative judgment, critical thinking and final approvals. For instance, AI can draft initial copy, but you need to refine that copy as the AI likely will miss out on nuance, context and brand alignment that your human experience brings to the table. An AI tool can do analysis, but you need to verify that analysis, and interpret it to provide valuable insights that once again take into account your domain expertise, context and nuance. You should look to augment human work with AI rather than replacing it – AI can amplify your human capabilities.
A 2024 survey found that 48% of marketing leaders say AI most significantly improves customer interactions, yet they still rely on human creativity to craft authentic messaging.
In practice, many teams adopt a “human-in-the-loop” approach: AI generates options or insights, and marketers curate or modify them. This balance maximises efficiency while avoiding the pitfalls of unchecked AI output.
“As AI takes the reins on automation and personalisation, it’s crucial to remember that meaningful connections with customers are still forged in the crucible of human empathy and creativity. AI is not a usurper, but a co-pilot, amplifying our capabilities while leaving the wheel firmly in our hands.”
The key is to use AI where it excels (speed, scale, pattern recognition) and step in when human empathy, context, or ethical judgment is needed.
2. A crucial perspective – the ethics of AI in marketing: Where to start with procedures? What matters more – organisational-level policies and procedures or micro-level marketing processes?
Both organisation-level AI policies and on-the-ground processes are vital, but start at the top. Companies are increasingly creating responsible AI guidelines to ensure ethical use across all departments.
These high-level policies set the tone on issues like data privacy, transparency, and avoiding bias. For example, marketers should comply with emerging regulations (e.g. the EU’s AI Act or Brazil’s AI Bill) – understanding such laws helps “avoid penalties and ethical missteps”
However, policies mean little without implementation in daily workflows. It’s crucial to train marketing teams on micro-level practices: obtaining proper consent for data use, ensuring AI-generated content is reviewed for fairness and accuracy, and monitoring campaigns for unintended bias. In 2024, 24% of organisations cited data quality (an ethical and operational concern) as a top challenge, indicating a need for better data governance as a foundation.
Bottom line: Begin with a robust organisational AI ethics framework (covering data handling, disclosure of AI use, etc.), then operationalise it via checklists and marketing process training. This two-tiered approach ensures that big-picture principles translate into ethical day-to-day marketing actions.
3. How do you make sure that the AI tools you’re using are delivering an output that is also reliable?
Rigorous evaluation and oversight are essential whenever AI tools are deployed. In practice, marketers “ground” AI in verified data and double-check its work.
For example, if an AI analytics tool flags a trend, the team validates it against raw data or alternate sources. Many organisations implement human review stages – treating AI like a junior analyst whose findings need confirmation. It’s wise to set up quality metrics for your AI’s output. If using a content generator, measure accuracy and brand tone compliance; if using AI for predictions, back-test its recommendations against historical outcomes. Indeed, businesses that succeed with AI often keep humans in the loop – 80% of companies stress the importance of human oversight to guide AI and catch errors.
Another tactic is starting with pilot tests: run the AI on past data where you know the answers to see if it produces reliable results before trusting it with new campaigns. Finally, maintain and update your AI models and data. An outdated model can drift, so retraining on recent data and monitoring performance over time is crucial. In summary, treat AI outputs as hypotheses – verify them through audits, A/B tests, or expert review. This way you gain the efficiency of AI while safeguarding accuracy and trustworthiness.
4. What are the best AI tools for analytics?
Marketers now have many AI-driven analytics platforms to choose from. Google Analytics 4 (GA4) is a popular choice – it has built-in machine learning to deliver insights (like automatic anomaly detection and predictive metrics such as purchase probability). GA4’s AI-powered features can spot significant changes in user behaviour without manual analysis.
Another top tool is Tableau (by Salesforce), which introduced “Tableau AI” to automatically surface trends and correlations from your data. Tableau’s integration of Einstein AI means it can point out anomalies or key drivers in plain language, helping non-analysts understand their data
For those deep into multi-channel marketing, Salesforce Marketing Cloud’s Datorama (Marketing Intelligence) is highly regarded – it unifies data from various platforms and uses AI to provide optimisation recommendations and predictive insights
In terms of campaign optimisation, Albert.aiis an AI platform specifically for marketing campaigns. It analyses performance and even adjusts budget or targeting on the fly to improve ROI. Alternatively, Julius.ai is a great tool for general analysis, along with ChatGPT directly.
In summary, the “best” tool depends on your needs: GA4 and Adobe Analytics (with Adobe’s Sensei AI) excel for web/app analytics, Tableau and Power BI for flexible BI with AI assistance, and marketing-focused platforms like Albert or IBM Watson Analytics for campaign-specific optimisation. Notably, 54% of marketers are already using AI for reporting and data visualization in some formshowing that these tools are becoming mainstream in analytics workflows.
5. Data-driven decision-making is not new, marketers have had to do this for decades – what’s different now?
It’s true that using data in marketing is not new – even 20 years ago, savvy marketers relied on spreadsheets and databases for decisions. What’s changed today is the sheer volume, velocity, and variety of data, and the AI-driven automation that can harness it. We produce an astonishing amount of data now – 90% of the world’s data was generated in just the last two years. In 2024, an estimated 147 zettabytes of data will be created over the year.
This explosive growth means no human team can manually parse all relevant information. That’s where modern AI and big data tools make the difference: they process millions of data points in seconds, providing timely insights. It’s not that data-driven thinking is new, but real-time, AI-assisted decision-making is the game-changer. For example, instead of waiting weeks for a market research report, a marketer can get instant dashboard alerts when KPIs swing, and automation can even trigger responses (like adjusting an ad bid or sending a personalised offer) immediately. The timeliness and scale are key – an AI can detect a tiny shift in customer behaviour overnight and help you pivot a campaign the next day. As one marketing commentary put it, being data-driven decades ago meant looking at last quarter’s results; today it means reacting on the fly with automation and predictive analytics.
Additionally, AI enables handling unstructured data (social media sentiment, video content insights) that we simply couldn’t leverage effectively before. The takeaway: using data is table stakes (always was), but now marketers have tools to use more data faster and more proactively. Those who combine data with AI-driven speed and scale are leaping ahead. (For instance, companies using AI report automating up to 40% of marketing tasks, allowing teams to focus on strategy while machines crunch the numbers at scale.)
6. In scaling digital marketing services (using AI tools) to multiple geographies and considering different regulatory requirements towards use of AI, are there existing strategies that can address that or is it still something that is in development?
Deploying AI-powered marketing globally introduces a patchwork of evolving regulations, from data privacy to AI ethics. Right now, there isn’t a one-size strategy fully matured, but companies are developing workarounds. A smart approach is to build a compliance-by-design strategy: implement robust data governance and responsible AI practices that meet the strictest region’s requirements, and use that as a baseline everywhere.
For example, if you comply with Europe’s GDPR and the upcoming AI Act, you’ll be well-positioned for other markets. In practice, firms often maintain separate data environments per region (to respect data residency laws) and add features like consent management and AI transparency notices for regions that require it. We’re seeing strategies like AI risk assessments before launching tools in a new country, and configuration options to disable or tweak certain AI features to meet local laws. Indeed, experts advise a “policy-first approach”, establishing internal guidelines for ethical AI use and vetting any AI tools for compliance and security.
Major organisations are creating AI ethics committees to keep abreast of legal changes and update marketing practices accordingly. While there’s progress – e.g., regulators in the EU are drafting clear rules (the EU AI Act will categorise AI use cases by risk and impose obligations), and a U.S. federal court just set a precedent on AI training data copyright – much is still developing. So, existing strategies revolve around staying agile and informed: investing in legal monitoring, partnering with local compliance experts in each region, and choosing AI vendors that offer compliance support (such as data localisation or documentation for audits). In short, the industry is in a learning phase – leading multinationals are creating their own playbooks (responsible AI frameworks, strict data hygiene, human oversight requirements) to safely scale AI marketing across borders, even as laws continue to catch up. Expect these strategies to be refined as court decisions and new regulations emerge in the next 1–2 years.
7. Which specific reports have you used AI for?
As marketers, we’re increasingly able to lean on AI to automate and enhance all types of reports. For example, campaign performance reports—using an AI analytics tool to produce a weekly dashboard that highlights anomalies or key drivers across Google, Facebook, and email data or quickly pulling multi-channel data and spitting out a narrative like “Your conversion rate jumped 10% due to X campaign” without you crunching manually. Almost 55% of marketers say they use AI for reporting and visualisation now.
Another area is SEO and content reports: tools like HubSpot’s content AI or MarketMuse can audit your website and auto-generate reports on content gaps or SEO opportunities, saving hours of tedious analysis.
Personally, we’ve used AI to draft monthly marketing summaries for stakeholders – e.g., feeding raw data to ChatGPT (or BI tools with GPT integration) to get a first draft of the story (“Sales grew x% month on month, driven by [source] or [segmemt]” etc.), which we then fine-tune. In one instance, our team used AI to analyse sentiment from customer feedback and incorporate it into a quarterly report; the AI categorised thousands of comments (positive and negative themes) far faster than a human could.
Another concrete example: using Google Analytics Intelligence in GA4 to ask questions like “What caused the dip in traffic last week?” – the AI might identify a specific referrer drop and essentially write part of your analysis for you. Overall, many tedious reporting tasks (data aggregation, basic trend analysis) can be offloaded to AI, allowing us to focus on interpreting insights and planning actions. This aligns with the trend that over half of marketers are using AI to automate reporting and glean insights faster.
8. How do you see the role of an AI assistant, often discussed in the context of ChatGPT? Shouldn’t we emphasise more in marketing that this is an assistive technology for marketers rather than a superpower for marketers?
You’re right. It’s important to frame tools like ChatGPT correctly – they are assistive technologies, not magic wands that turn marketers into superheroes without effort. In other words, think of generative AI as your copilot, not an all-knowing autopilot.
Its role is to amplify your capabilities: speed up drafting, suggest ideas, and crunch data for you – while you remain the decision-maker. Many marketing leaders now stress that ChatGPT should be seen as an intern or assistant on your team. For example, it can quickly generate a first draft of an email or social post, but a human adds creative polish and ensures it aligns with the brand voice. This positioning helps set proper expectations. As one article put it, “AI is not a usurper, but a co-pilot, amplifying our capabilities while leaving the wheel firmly in our hands.”
In practice, that means we use ChatGPT to assist – to brainstorm copy variations, to summarise data, to translate technical jargon – but we don’t abdicate our judgment. Emphasising the assistive nature also encourages teams to develop AI literacy and not blindly trust outputs. The added value comes when marketers use these tools to enhance productivity (e.g., generating 5 campaign ideas in the time it used to take to draft one), yet still apply their marketing savvy to choose the best ideas. So yes, we should communicate that AI, like ChatGPT, is here to help marketers. It’s a powerful accelerator of tasks, but the marketer remains the strategist. This mindset also helps alleviate fear – the tool isn’t replacing your creativity, it’s augmenting it. By highlighting successes as “AI-assisted” rather than AI-performed, organisations can foster adoption while reinforcing that human insight remains at the core of marketing.
9. How can AI improve email marketing segmentation?
AI can analyse historical user data, engagement patterns, and predictive analytics to create dynamic audience segments, allowing brands to send more relevant, personalised messages. Platforms like Klaviyo, Mailchimp AI, and Salesforce Marketing Cloud offer AI-driven segmentation.
10. How does LSE’s Digital Marketing Strategy & Analytics Career Accelerator address AI learning?
Our programme includes an AI Learning Track, where participants master AI-powered tools, learn how to integrate AI into marketing strategies, and develop practical AI-driven projects under expert guidance, alongside the core programme content. Given the evolving nature of this technology, our facilitators work hard to bring real-world and current experience in leveraging AI in practice during live sessions that help you to apply that knowledge to your learnings.
While it does not provide deep technical training in AI development, it ensures that learners understand how AI-powered tools fit into modern marketing strategies. By the end of the programme, participants – whether experienced marketers or those newer to digital marketing – will have gained hands-on exposure to AI applications in marketing, equipping them with practical, actionable skills to leverage AI for efficiency, insights, and strategic decision-making.
11. What career support does the programme offer?
The LSE Digital Marketing Career Accelerator is designed not just to impart skills but also to bolster your career outcomes. While it’s not a traditional degree programme with on-campus recruitment fairs, it offers strong career support services. For example, you get access to one-on-one career coaching sessions (the programme provides up to a full year of 1:1 coaching sessions) and group workshops on topics like CV building, interviewing, and current market trends. Throughout the programme, there’s an assigned “success team” focused on helping you reach your personal career goals.
A distinctive feature is the Employer Project: a real-world consulting project with an industry partner. In 2024, our learners teamed up to devise a Q4 digital strategy for an e-commerce company as part of a partnership with a marketing agency.
This not only gives you hands-on experience for your portfolio but also direct exposure to potential hiring managers in the field. Many graduates cite this project and the networking opportunities it provides as key benefits. In lieu of campus recruitment, the programme leverages its industry network – the involvement of industry experts and partner companies can open doors (for instance, you might impress a partner company during the employer project).
Moreover, upon completion, you join LSE’s alumni community, which can facilitate job connections. In summary, while you won’t have a “placement day” like a university campus, you will have career coaching, practical projects with employers, and networking opportunities to support your job search. Many of our alumni note the value of these elements, highlighting the coaching aspects, alongside the employer project as instrumental in landing a role post-course
12. Do you have practical examples of where AI has been able to take away tedious, repetitive tasks? What exactly were these tasks?
AI shines at taking over the mind-numbing chores that used to eat up marketers’ time. One clear example is data cleaning and integration – previously, assembling marketing data from multiple sources or scrubbing spreadsheets for errors was a dull, lengthy process. Now, AI tools can automatically clean datasets, merge information, and even flag anomalies. This means marketers spend less time managing CSV files and more time on strategy. Another big one is customer inquiry handling: AI chatbots handle repetitive questions (“What are your store hours?”) endlessly, sparing human reps those FAQs. This has a real impact – by 2025, AI chatbots are projected to save businesses about $8 billion in support costs, precisely by automating those routine Q&A interactions.
Content generation at scale is another tedious task reduced. Need product descriptions for 500 items? An AI writing assistant can draft them in a fraction of the time it would take a person, turning what was once drudgery into a quick review task. In our team, we’ve seen AI cut down the time on weekly report generation – an AI tool now compiles the numbers and even writes a first-pass analysis, whereas an analyst used to spend half a day on it. A McKinsey study found AI could automate up to 40% of marketing tasks, many of which are exactly those repetitive activities (like scheduling social posts, sorting leads, basic image editing/cropping for creatives, etc.). Email marketing provides a relatable example: AI can automate send-time optimisation and audience segmentation. Rather than manually creating segments and sending schedules every week, the AI learns and does it continuously. Marketers at companies using AI for personalisation have reported significant time savings – one report noted teams saved ~20 hours per week on content and outreach tasks due to AI streamlining these processes
In summary, tasks like data entry, basic research, initial copywriting, routine customer comms, and simple optimisations have largely been lifted off humans and given to AI. This frees marketers to focus on creative and strategic work, while the “grunt work” gets done in the background.
13. How should data collection policies and data hygiene practices precede the implementation and use of AI? After all, marketing often deals with a large volume of unstructured and messy data.
Clean, well-governed data is the backbone of effective AI in marketing. The saying “garbage in, garbage out” very much applies – if your customer data is messy or collected without clear policies, your AI outcomes will suffer. So, before layering AI, organisations should strengthen data collection policies: ensure you’re gathering the right data (relevant, consented) and storing it in an organised way. This involves establishing data standards (for example, define consistent formats for customer names or a single source of truth for product data) and privacy practices (make sure you have consent for the data your AI will use and that you’re compliant with laws like GDPR). Equally important is data hygiene – de-duplicating records, correcting inaccuracies, and updating old info. Many companies now run periodic “data audits” to sanitise their databases before running AI models on them. The impact of poor data practices can be huge: a 2024 marketing study noted data availability and quality remain one of the biggest barriers to successful data-driven work, with 33% of organisations saying their data is insufficient or low-quality. In complex AI projects, “data quality is [often] an impeding factor and exposes a fundamental problem” if not addressed upfront.
Practically, this means marketers might spend time labelling data or working with data engineers to structure datasets before an AI campaign. For example, if you want an AI to personalise offers, first ensure your customer purchase history data is complete and standardised (no mixed formats or missing fields). Also, implement robust data governance: set roles and responsibilities for data upkeep, and maintain documentation so everyone knows the data definitions. It might feel like extra work, but it pays off – companies that manage data will see much smoother AI deployments. In fact, data management was the top cited challenge in AI projects in 2024, and resolving that by front-loading good data practices will make your AI far more reliable. Think of it this way: before you let an AI drive insights from your marketing data, tune up the engine (the data) so it runs clean. By having strong collection policies (only collect what’s useful and with transparency to users) and hygiene routines (regular cleaning, validation, and security checks), you set your AI up for success and avoid “junk data” leading you astray.
14. How can I strategically revamp my skills as a home learner, given the vast array of marketing tools – some of which are enterprise-grade? How should I position my portfolio to appeal to recruiters?
With so many AI and analytics tools (some enterprise-only), the key is to focus on broadly accessible skills and demonstrate real projects. As an experienced professional returning after a break, you can start by learning the popular tools that have free versions. For example, get hands-on with Google Analytics 4 and its AI features (it’s free) to show you understand modern web analytics. You could also use free tiers of Mailchimp or HubSpot to practice building automated campaigns with AI-driven elements (HubSpot’s AI content assistant, for instance, is available on free plans). Since enterprise tools like Salesforce or Adobe might be out of reach at home, look for simulation platforms or case studies – many course providers or community blogs share sample datasets and scenarios you can work on to mimic using those tools.
When building your portfolio, the key is to show outcomes and processes. For instance, you might include a self-initiated project: “Predicted e-commerce sales using Python – achieved X% accuracy” or “Implemented an AI-based customer segmentation on a sample dataset using KNIME”. Emphasise your 15-year background by framing your projects around consumer psychology insights augmented by AI (“Combined my consumer behaviour knowledge with an AI clustering algorithm to segment customers more intuitively”).
Recruiters will look for evidence that you can apply AI practically, not just theoretical knowledge. That could mean including before-and-after examples (e.g., a marketing plan you optimised with an AI tool’s input). Also, consider getting certified: certifications in Google Analytics IQ, Azure AI fundamentals, or even a course certificate from LSE’s programme can lend credibility.
In terms of how a recruiter views your portfolio: they’ll appreciate seeing that despite your career break, you’ve proactively upskilled. Showcasing projects completed on your own initiative signals self-drive. It’s also good to highlight any real-world context you can – if you did a volunteer or freelance project using AI, include that. And don’t underestimate the value of explaining the why and how in your portfolio documentation. For example: “I identified that predicting customer churn could be valuable, so I trained a model on open-source data – here’s what I used and the result.” Given that 68% of business leaders struggle to find talent to manage AI solutions, your blend of psychology, analytics experience, and new AI skills can be very attractive. Just make it easy for them to see those skills in action through concrete portfolio pieces and articulate the impact or insights gained from each project.
15. How fast is court practice developing with respect to intellectual property/ rights on AI-generated content, specifically used in marketing/advertisement campaigns?
The courts and regulators are rapidly trying to catch up with AI’s impact on intellectual property. Over the past year, we’ve seen a flurry of landmark cases and decisions. In the US, it’s been clarified that purely AI-generated works cannot be copyrighted – the U.S. Copyright Office (and a federal judge in 2023) held that if there’s no human author, the content isn’t eligible for copyright protection. This means if you have an AI create an image or copy for an ad entirely on its own, you likely can’t stop others from using a similar output; there’s no traditional IP protection for that AI-only content. Meanwhile, multiple high-profile lawsuits have emerged regarding the training data used by AI models. For example, Getty Images is suing Stability AI (maker of Stable Diffusion) for allegedly using millions of Getty’s photos in training without permission. In another case, a class-action lawsuit by artists against AI image generators (like Midjourney/Stable Diffusion) is moving forward, with a judge in August 2024 upholding the artists’ copyright infringement claims – an early victory for creators concerned about their style being mimicked. Most relevant to marketing content, a recent decision in February 2025 (Thomson Reuters v. ROSS) found that using a copyrighted dataset to train an AI can infringe copyright when that AI competes in the same market.
In that case, an AI company was ruled against for training on a publisher’s written summaries. This is a precedent-setter: it suggests that if, say, an AI was trained on a news article and spits out something very similar for an ad, it might not be protected by fair use. Courts are basically establishing that feeding copyrighted content into AI doesn’t magically nullify the copyright – context matters (especially if the AI output replaces the original work’s purpose)
Additionally, regulatory bodies are starting to weigh in: the US Copyright Office released guidelines that if a human simply provides a prompt and the AI does the rest, the output isn’t a human-authored work and can’t be registered
So where does this leave marketers? Essentially in a cautious position. The “court practice” is evolving monthly. It’s moving fast in the sense that lawsuits dominated 2024 and early rulings like the ROSS case arrived, but final outcomes (especially at appellate or Supreme Court level) are still pending.
Marketers using AI-generated content need to watch these developments: ensure they have proper rights for any training data used (if they train custom models) and be transparent in usage. Also, be mindful of trademark and personality rights – using AI to generate a celebrity likeness or a brand logo variation could invite legal issues under existing laws, which are being interpreted for AI now. In summary, the legal terrain is actively being mapped. Expect more clarity in the next couple of years as these cases resolve, but for now, the best practice is to err on the side of caution: treat AI outputs as you would source content – vet it for potential IP conflicts (some brands even avoid using AI images in campaigns until there’s more certainty). Keep an eye on cases like the above, because they’re setting the rules that will directly affect how we can use AI in advertising.
16. In the AI Masterclass, Claire spoke about giving her AI assistants ‘performance reviews’. How would you go about doing that? Going back to the data and double-checking their work?
The idea of giving your AI assistants (in this instance, customGPTs) a performance review is essentially about regularly evaluating their output and impact as you would a team member’s work. You shouldn’t just set an AI tool and forget it – you need to periodically assess: Is it doing a good job? Is it improving or causing any issues? In practice, you’d gather some metrics and qualitative observations.
For example, if you’re using an AI copywriting assistant, you might track how content generated by the AI performs (engagement metrics, error rates, amount of editing needed) and compare it over time or against human-generated content. This is akin to a KPI review for the AI. If the AI is a chatbot handling customer queries, you might review transcripts each month and see if it’s satisfying customers – perhaps looking at resolution rates or customer satisfaction scores.
Going back to the data is key: just like a manager would look at an employee’s outputs, you’ll pull reports on your AI’s outputs. Did the AI’s recommendations actually boost conversion? Are there patterns of mistakes (for instance, the AI consistently messes up product names or tone in email drafts)? You’d “coach” the AI by refining prompts or updating its training data based on these findings. Literally, sit down and scrutinise the AI’s work output at regular intervals.
For us, that means scheduling a periodic review (say, monthly) where we audit a sample of AI outputs: maybe 10 email subject lines it suggested and the outcomes of those campaigns. If we find, for example, that the AI’s subject lines with a certain phrasing consistently underperform, that’s feedback to adjust either our usage or the AI’s parameters.
Another facet is checking the AI’s accuracy and compliance. Part of the “performance review” could be running fact-checks on a random subset of AI-generated content to ensure it’s not hallucinating or straying from guidelines. If issues are found, we address them – much like telling an employee where to improve. We can also give positive feedback: identify what the AI did well (“These social posts required minimal editing – great job on tone!”) and use that insight (maybe use similar prompts going forward). It’s a slightly tongue-in-cheek concept, but it underscores an important practice: continuously monitor and fine-tune your AI tools.
In essence, treat your AI assistant as a junior coworker who needs oversight and feedback. By doing so, you maintain high quality and can gradually train the AI (through better prompts or additional data) to get even better. It’s a proactive approach – rather than waiting for a major error, you catch small issues in these “reviews” and course-correct. So yes, it involves going back to the underlying data and outputs, measuring them, and adjusting accordingly, on a regular cadence.
17. Can you provide a practical example of using AI for personalisation?
Starbucks’ AI-powered personalisation is a standout example. Starbucks leverages a predictive analytics engine called Deep Brew to tailor marketing messages to each customer.
Through the Starbucks mobile app, AI analyses individual purchase history, preferences, and even local weather – then recommends products or sends offers timed to when you’re most likely to want a coffee
For instance, on a hot afternoon, the app might suggest a cold drink you’ve enjoyed before, while on a rainy morning, it might promote a warm latte. This personalisation strategy increased app engagement and sales, as customers received highly relevant suggestions
Another great example is Sephora’s Virtual Artist: an AI-driven advisor that provides personalised makeup recommendations and lets users virtually “try on” products. Sephora’s AI analyzes user preferences and feedback, delivering tailored product suggestions – contributing to a 30% increase in online sales
The common thread: AI ingests lots of customer data and outputs uniquely personalised content or offers, resulting in a better customer experience and improved business outcomes.
18. AI is a powerful accelerator, but relying on it to “fix everything” without understanding the mechanics can create long-term problems. How do we balance the incredible productivity of AI with the need for solid foundational knowledge?
It’s all about augmentation, not automation in isolation. To harness AI’s incredible productivity safely, you need a strong foundation in marketing principles and data literacy within your team. Concretely, this means training your marketers in how the AI tools work – not the deep code, but the logic. For example, if you use an AI to optimise bids, ensure the team understands concepts like how the algorithm makes decisions, what data it’s using, and its limitations. By having that knowledge, they can spot when the AI might be going astray (say, over-prioritising a wrong metric) and intervene. One approach is to establish guardrails: set policies that certain decisions must be reviewed by a human, especially in early implementation. Perhaps AI can auto-generate content, but nothing goes live without human approval. That way you get the speed (AI drafts in seconds) but maintain quality control.
Another tactic is investing in AI literacy for staff – indeed, many organisations now run workshops on understanding AI outputs. (As a side note, a webinar on “Mastering AI Literacy – What You Don’t Know WILL Hurt You” underscored that blind reliance is risky.) Supporting data: 80% of companies in a 2024 report said human-in-the-loop machine learning is vital, meaning they explicitly keep humans involved to guide AI and handle context or ethical considerations. This human oversight ensures that while AI handles the grunt work, the strategy and critical thinking remain human-driven.
We also balance short vs long term by monitoring AI decisions for unintended consequences. For example, if an AI recommends doubling down on one customer segment because it’s easiest to convert, a marketer with foundational knowledge might realise that’s a short-term win but long term could narrow our brand appeal – so they adjust the strategy. It’s similar to how pilots use autopilot but must know how to fly the plane if something goes wrong.
Practically, encourage your team to question AI outputs (“Does this recommendation make sense given what we know about our customers?”) and to use AI as a starting point. Build a culture where AI is the first draft or initial analysis, and human expertise refines and finalises. Over time, as the team’s confidence and understanding of the AI grows, they can let it handle more, but they will always have that fundamental knowledge to fall back on. Finally, mix training with experimentation – allow the team to run small tests where they use AI and measure results vs. a human-only approach. This hands-on experience cements their understanding of the mechanics. In short, to balance the two: never stop learning the “why” behind AI decisions. Use AI’s speed, but always apply human insight before acting on its output. That ensures you get the best of both worlds – efficiency with wisdom.
19. How about tools like Hubspot or SalesForce? Get the data, as it is not included in the list, what is your perspective on this?
HubSpot and Salesforce are indeed major platforms that weren’t on the earlier tool list, likely because they’re all-in-one ecosystems rather than standalone AI tools. Both have been integrating AI features extensively. HubSpot has introduced multiple AI-driven capabilities in 2023, including an AI content assistant (for writing marketing copy) and AI-powered predictive lead scoring and forecasting.
Under the hood, HubSpot’s AI features work by leveraging the data already in your CRM – web interactions, email engagement, and sales activities. HubSpot centralizes customer data (from form fills, site tracking, etc.), and its AI can then analyse that to do things like suggest the best time to send an email or which leads are likely to convert. It’s essentially pulling data from within the HubSpot CRM and using machine learning models on it. Salesforce, via its Einstein AI, similarly ingests data from across the Salesforce ecosystem – your email opens, ad clicks, sales history, and more (plus external data integrations) – to provide insights and automation. For example, Einstein can automatically segment audiences or recommend content by learning from your past campaign data. Salesforce’s Marketing Cloud (and their acquired tool Datorama) unifies data from ads, websites, CRM, etc., and uses AI to find patterns and optimization opportunities
Why weren’t these in some lists? Often when people list “AI marketing tools,” they highlight specialised tools or newer entrants. HubSpot and Salesforce might not have been mentioned if the context was AI analytics tools specifically – but make no mistake, they do have strong AI capabilities. Data-wise, these platforms get their data through integration and native tracking: HubSpot has tracking code on websites and forms capturing info, and it merges that with email engagement data in its database. Salesforce can intake data from its CRM entries, e-commerce plugins, social listening integrations, etc., funnelled into its CDP (Customer Data Platform) and Einstein. Both are walled gardens to a degree – they primarily work with data within their system, though you can import external data too. My perspective: If you’re already on HubSpot or Salesforce, their built-in AI can be very powerful and convenient (no need to export data to another AI tool). For instance, HubSpot’s AI can draft you an email using your CRM context in one click.
Salesforce’s Einstein might highlight that a certain customer segment is at risk of churn by looking at all their touchpoints. The key is that these aren’t “out-of-the-box” AI point solutions – they’re part of larger platforms. So, many “top tools” lists focus on independent AI tools that anyone can adopt. But in reality, many companies will get a lot of AI functionality from HubSpot or Salesforce if they’re users. They were effectively absent from the earlier list because those lists often concentrate on newer, standalone AI offerings, whereas HubSpot and Salesforce are comprehensive suites that include AI among many features. In sum, HubSpot and Salesforce get their data from the integrated customer interactions within their systems (plus any you connect), and they absolutely use AI on that data – even if they weren’t name-checked, they are heavyweight solutions for AI-driven marketing analytics and automation. As a marketer, leveraging the AI inside these platforms can be low-hanging fruit since it operates on data you’re already collecting, albeit typically at an enterprise price point.
20. Does the Career Accelerator detail using these AI tools?
Yes – the LSE Digital Marketing Career Accelerator is structured to provide hands-on exposure to modern marketing tools, including AI tools. The curriculum emphasises the practical application of digital marketing tactics. In our dedicated AI and Digital Marketing Learning track, you’re introduced to specific generative AI and analytics tools as part of your learning.
For example, during the course you might learn to use tools like Google Analytics 4 or a social media analytics tool with AI insights when covering the analytics module. In the content creation sections, you could experiment with an AI copywriting tool (perhaps using ChatGPT or a tool like Jasper) to understand how to generate and refine marketing copy.
The presence of industry experts on the instruction team also means you’ll get exposure to the tools they use day-to-day. For example, one expert, Jill Quick, could demonstrate using Google Data Studio/Looker Studio with AI integrations for reporting. The programme doesn’t just speak about tools in abstract; it incorporates them.
21. ChatGPT 4.5 has been characterised as a somewhat strange “animal” in the sense that it isn’t quite clear what its added value is. The one thing I have heard rather consistently is that it seems to mimic these human, emotional, authentic “moments” we just heard about it.
GPT-4.5 (the intermediate update OpenAI released) has been noted for its higher “emotional intelligence” in responses and more human-like tone. In other words, its answers feel warmer, more intuitive, and better at mimicking those authentic human “moments” of understanding. OpenAI themselves highlighted that conversations with GPT-4.5 “feel more emotionally nuanced” and attuned to the user’s intent.
For marketers, this added value translates to AI that can engage customers in a more relatable way. Think of customer service chats or brand social media interactions – an AI that can sense if a user is frustrated and respond with appropriate empathy (rather than a stilted generic reply) is a big step up. GPT-4.5 is basically making AI a better conversationalist. Its role, then, can be as a more effective creative and communicative assistant. For instance, for content teams, GPT-4.5 picks up subtle cues in a creative brief much better.
If you imply you want a playful tone in an ad copy, GPT-4.5 is more likely to deliver that without explicit instructions, compared to earlier models. It also maintains context over long dialogues with fewer reminders, which helps in brainstorming sessions that mimic a human creative partner. So I see GPT-4.5 being used as an “emotionally aware copywriter” or chatbot that can produce marketing content carrying a semblance of human touch. Marketers have reported that GPT-4.5 gives responses that align more closely with how a human teammate might respond in a brainstorming meeting – including encouragement or understanding of the approach of an idea.
The consistent feedback that it mimics authentic moments suggests, for example, if a user says “I’m not sure about this product because I feel X,” GPT-4.5 might address that feeling (“I understand how you feel…”) in a way older models wouldn’t. From a strategic view, this can enhance customer experience: imagine AI-driven email campaigns that adjust tone per segment – upbeat for engaged customers, reassuring for hesitant ones. GPT-4.5’s nuance could help power that. However, we should remember it’s still a tool – it’s not genuinely feeling emotions; it’s pattern-matching emotional language. Its role should complement the marketing team by handling personalized touches at scale, but oversight is needed to ensure those “emotional” outputs remain on-brand and appropriate. In summary, GPT-4.5’s key added value is more human-like interaction quality. Its role is likely to be as an even more capable assistant in content creation and customer engagement, perhaps serving as the backbone for more natural AI chatbots, or helping marketers craft messages that resonate emotionally with target audiences. It’s an evolution making AI a bit less robot, a bit more human in flavor – and used wisely, that can strengthen how brands communicate authentically at scale.
22. It’s all so overwhelming! How does one move on from casual use to starting to use it effectively?
Feeling overwhelmed by AI’s possibilities is common – the remedy is to start structured and small. To move from dabbling to real productive use, begin by identifying a specific marketing task or pain point that AI could help with. For example, instead of trying to use AI for “everything,” pick one area: say you often spend too much time A/B testing email subject lines. That could be a focus – you might start using an AI tool to generate and test subject line variations, which is a contained use case. By narrowing the scope, you can learn deeply and see results, which builds confidence.
Next, invest time in learning the tool or technique properly (watch a tutorial or take a mini-course on that particular application). Often, people remain casual users because they haven’t fully learned the features; a bit of front-loaded learning can unlock a lot of value. Set a mini-project or goal: for instance, “This month, I will use an AI scheduling assistant to automate our social media calendar and measure if it frees up 5 hours/week for me.” Having a concrete goal and metric helps you evaluate effectiveness. Many marketers find it helpful to integrate AI into their routine by scheduling it – literally put on your calendar time to use AI for a task, so it becomes a habit. Also, document your experiences.
As you transition to serious use, keep notes on what works or fails. This will turn into your personal best-practice guide and reduce the trial-and-error feeling. Another tip: leverage community knowledge. Join a marketing AI forum or LinkedIn group; seeing how peers apply AI can spark ideas and provide proven workflows. It helps to know you’re not alone and to pick up use cases that have worked for others. A statistic to encourage you: 90% of workers using AI report it saves them time, so the effort to integrate it effectively does pay off in efficiency.
The trick is to be intentional – don’t try to use AI for its own sake, tie it to a strategy. For example, if your strategy is improving personalisation, maybe start using an AI-driven segmentation tool. As you see positive results (like better engagement from those segments), it will motivate further adoption. Finally, treat moving to effective use as an iterative journey. Start small, evaluate, and then scale up to more areas. Perhaps begin with content generation, then add analytics, then chatbots, etc., rather than all at once. Each success builds momentum. In essence: focus on one use case at a time, align it with a clear goal, measure the impact, and gradually expand. This way, AI becomes a regular part of your toolkit, and the sense of overwhelm gives way to a sense of opportunity.
23. What are the top AI tools for email marketing, especially regarding analytics? Do Mailchimp and Klaviyo provide sufficient AI capabilities, or are there better alternatives? Which tools excel in segmentation?
Email marketing has gotten an AI boost in both content and analytics. For analytics and segmentation, Klaviyo stands out as a platform with strong AI capabilities. Klaviyo offers predictive analytics for e-commerce (it can predict customer lifetime value or the next purchase date) and automatically creates segments based on customer behaviour and likelihood to buy. In fact, Klaviyo has built-in AI-powered segmentation features that Mailchimp lacks – for example, it can dynamically group customers (e.g., “high spenders likely to churn”) and update those segments in real time. Mailchimp, on the other hand, is known for user-friendliness and does have some smart features (like send-time optimization and content suggestions), but it doesn’t yet have the same depth of predictive segmentation as Klaviyo.
If you’re considering alternatives: HubSpot is a great option if you need an all-in-one with AI – HubSpot’s email marketing tool integrates an AI content writer and can leverage CRM data for personalization (e.g., its AI will auto-tailor email text based on CRM fields, and even record campaign performance automatically)
For pure-play AI enhancements, tools like Phrasee (now rebranded as Jacquard) specialise in AI-generated copy for emails. Jacquard uses a proprietary ML engine trained on tons of marketing language data, allowing it to generate and optimize subject lines and messaging that resonate with different audience segments.
It’s enterprise-level (used by big brands for millions of emails) and known to outperform generic A/B tests by finding the right language for each segment. Another tool in segmentation is Optimove – it’s a CRM marketing platform that uses AI to constantly re-segment audiences based on multi-channel behaviour and orchestrate tailored campaigns. Optimove can be heavy-duty, but it excels at calculating customer micro-segments and determining the next best action for each.
If we focus on analytics, Litmus (while primarily an email testing platform) has introduced some AI-driven insights for subject lines and even spam prediction. There’s also Mailmodo, which is newer. It’s more about interactive email, but they’ve been adding AI features (like an AI subject line assistant).
For whether Mailchimp and Klaviyo are “sufficient”: If you’re a small to mid-sized business, Klaviyo’s AI segmentation and reporting is likely more than sufficient – it’s quite advanced and tailored for commerce. Mailchimp is beefing up its AI slowly (they added a creative assistant for design and some smarter targeting), but for now, power users often supplement Mailchimp with external AI tools. For example, one might use Phrasee integrated with Mailchimp to get better subject lines, since Mailchimp’s built-in suggestions are basic.
Segmentino and Zeta Global’s platforms excel in segmentation, but those are more enterprise tools. Also, keep an eye on Salesforce Marketing Cloud—its Einstein AI can analyse email engagement and automatically segment audiences by likelihood to engage and even choose optimal send times and frequencies for each subscriber.
In summary: Klaviyo is top-tier for AI-driven email segmentation and analytics for ecommerce. Mailchimp is catching up but might need help from specialised tools for advanced AI functionality. HubSpot is strong if you want AI within a broader marketing suite. Specialised tools like Jacquard (Phrasee) excel at the content optimisation side of email (subject lines, copy) using AI, often driving higher open rates through language that best fits each segment
For segmentation excellence, Klaviyo and Optimove are great; for AI content and testing, look at Phrasee/Jacquard or even Seventh Sense (which focuses on send-time optimisation via AI). The best choice depends on your scale and budget, but even with Mailchimp or Klaviyo alone, you can leverage AI – Klaviyo’s auto-segmentation and Mailchimp’s smart send features are built-in conveniences that use AI under the hood.
But it’s changing every day…Keep an eye on your favourite tools’ product roadmap and updates regularly.
Why learning AI and analytics is crucial for marketers
With 87% of marketers already using or experimenting with AI, those who fail to adapt risk being left behind. AI is revolutionising marketing strategies, from data-driven decision-making to hyper-personalized customer experiences. Professionals equipped with AI expertise are securing AI marketing jobs, excelling in data-driven marketing careers, and positioning themselves at the forefront of the future of digital marketing.
Whether you’re a career advancer, career changer, or career starter, our programme is designed to provide in-demand skills, hands-on learning, and personalised career support to help you thrive in the AI-driven marketing landscape.
When Zarif Shafiei was searching for the best data science courses available, he knew he wanted more than just theory. Like many aspiring data scientists, he wanted a programme that offered hands-on training – something that would prepare him for the challenges of working in the industry. After exploring various options, one feature of the Data Science With Machine Learning & AI Career Accelerator was unique: the Employer Project with the Bank of England.
“What stood out for this Career Accelerator over other programmes I looked at was the Employer Project. Being able to experience what it’s like working in an industry and being teamed up with an employer. I knew that I’d be getting taught skills that employers would really value, and a company like the Bank of England is really big.“
Zarif’s words reflect the reality that many learners face today. Theoretical knowledge alone is no longer enough. With the data job market becoming increasingly competitive, employers value practical experience through real datascience projectsthat focus onsolving business challenges.
That’s why the Career Accelerator from the University of Cambridge Professional and Continuing Education (PACE) offers a unique employer-led data science project as part of its curriculum. With the Career Accelerator model, you’ll get to experience the best of university education, built in collaboration with tech industry leaders, and guided by Employer Partners.
This project not only bridges the gap between theory and practice but also gives learners the data science portfolio and confidence they need to stand out in their job search.
A message from the Bank of England
James Benford is the Bank of England’s Chief Data Officer and is part of the expert team that is overseeing the programme’s Employer Project.
You can get a glimpse into some of the work that past learners have done in the video below.
How the Employer Project works
The six-week Employer Project is at the very end of the seven-month Career Accelerator. By this point, you’ll have acquired a strong understanding of core data science techniques, machine learning and advanced skills in natural language processing (NLP) and time series analysis.
The capstone project brings all of this learning into focus. Working within a team and supported by mentors from both academia and industry, you’ll dive deep into the data, apply your skills, and craft innovative solutions to the business challenge at hand. At the end of the project, you’ll present your findings back to the business and receive feedback, simulating a real-world consultancy experience.
This process not only enhances your technical skills but also allows you to build confidence in your ability to communicate complex data insights – a crucial skill for every data scientist. As learner Zarif highlights, the real-world application of these skills, coupled with the guidance of senior professionals, is what makes this programme so valuable.
Boost your credibility and employability with hands-on experience
Our research shows that learners are increasingly looking for programmes that combine theory with practice. In fact, many aspiring data scientists feel their biggest challenge is applying the technical skills they’ve learned in real-world settings.
The Employer Project offers a true-to-life experience, allowing you to understand what it’s like to be part of a data science team working on high-stakes issues. You also get the opportunity to work with renowned and established businesses, which adds a layer of credibility to your experience, enhancing the value of your portfolio and significantly boosting your employability. Zarif speaks to this directly:
“There are loads of courses that you can do and bootcamps on software engineering and programming, but this was the only one that I saw that actually did an Employer Project. I felt that was the key differentiator. Because when you start applying for roles, a lot of companies ask, ‘What experiences have you had that relate to this role?’, and you immediately have the Employer Project you can talk about.”
If you’d like to hear more about Zarif’s Career Accelerator experience outside of just the Employer Project, watch the full Q&A below:
Build a portfolio that employers want to see
One of the most important outcomes of the Career Accelerator and the Employer Project is the portfolio that each learner builds. For those transitioning into data science or looking to advance their careers, having real-world projects to showcase is critical.
During the programme, you’ll work on 20+ industry-relevant projects set by companies such as PureGym, Study Group and Nielsen BookData. The hands-on data science training you get in the courses, coupled with the final six-week Employer Project, ensure you walk away with an impressive portfolio of work.
A practical portfolio, and a Certificate of Completion from Cambridge PACE, will signal to employers that you’re ready to contribute from day one. Whether applying for a first data science role or aiming to advance in your current career, you’ll have a powerful proof point that you can refer to during interviews as well as salary and promotion negotiations.
Ready to experience real-world learning?
For Zarif and his peers, the Employer Project was more than just a learning opportunity – it was a way to prove their skills in a setting that mirrors the environments they will soon enter as professional data scientists.
The Data Science With Machine Learning & AI Career Accelerator is designed for learners who want more than just a boot camp. If you’re looking to gain hands-on experience, work with leading industry experts, and build a portfolio that will set you apart in the job market, this is the programme for you.
Download the brochure here to learn more about how the Employer Project can accelerate your data science career.
On 1 July 2025, the Institute of Continuing Education became the University of Cambridge Professional and Continuing Education (PACE). As part of this transition, you may still encounter some materials or communications bearing the previous ICE branding. Please be assured that while the name and identity are evolving, the learning experience and programme outcomes of the Career Accelerator remain unchanged — with Cambridge academics continuing to deliver world-class education.
Artificial intelligence (AI) is changing the data landscape. But the field of data analytics isn’t just weathering a quick trend – AI is poised to drive significant economic growth in the years to come. AI – which is fundamentally driven by data – is projected to increase UK GDP by up to 22% by 2030.
This statistic tells us something crucial: developing your data skills isn’t just about job security, it’s about positioning yourself at the forefront of an economic and job market revolution.
We discuss these topics in the webinar: “From Spreadsheets to AI: How to Start & Grow a Career in Data”. Brought to you by the creators of the LSE Data Analytics Career Accelerator, this event featured a panel of industry and recruitment experts who shared their insights on:
The evolving data career landscape
The skills you need to go from spreadsheets to advanced analytics
The value of specialised data education
We’ve summarised the key learnings and insights from the webinar below.
There’s an ongoing demand for data analysts
The tech industry has been in flux for the last few years; both small and large companies have experienced significant downturns and layoffs. As a result, many tech roles have seen a significant decrease in job postings and demand. But roles like data scientists, data analysts and data engineers have decreased by only about 15% since the end of 2022. To put this into perspective, let’s consider what it means:
Stability: While we’ve seen a slight dip, it’s crucial to note that this 15% decrease is relatively small compared to the broader tech industry, which has experienced more significant downturns and layoffs.
Quick recovery: The article from Datalore notes that these numbers have remained quite stable since the beginning of 2023, suggesting that the data job market quickly found its footing after the initial adjustment.
Ongoing demand: This resilience underscores the continued importance of data skills across various industries. Companies recognise that data-driven decision-making is not a luxury, but a necessity for remaining competitive.
The data skills most needed
We asked our one panellist Elodie Hudson, who is the Director of Development at software company AssessTech and an LSE Data Analytics Career Accelerator graduate, what skills she’s looking for in candidates:
“We are very specifically looking for an advanced SQL data analyst. We’re also looking for a data engineer, so someone who can build pipelines to create a data lake or data warehouse for us. There are loads of different sorts of jobs in data, and for anyone transitioning into that, it’s good to understand what that landscape looks like and what might appeal to you, and what you want to do.”
Elodie Hudson, Director of Development at AssessTech and Career Accelerator graduate
We see this need for programming and database skills reflected in the research of the most important skills needed in data roles:
Programming skills: 86% of data scientists reported Python as their main programming language for current projects. This highlights the importance of Python skills in the data science field.
Database skills: SQL remains crucial, appearing in up to 60% of all data job posts alongside Python. This underscores the continued importance of database management skills in data roles.
AI and machine learning: With the rapid growth in AI engineer roles, skills in areas like deep learning, natural language processing and computer vision are becoming increasingly valuable. Data analysts are also expected to understand a few AI-powered tools to enhance their analytical capabilities.
Data visualisation: Tools like Tableau and Power BI are essential for communicating insights effectively.
Cloud computing: Familiarity with cloud platforms like AWS, Azure or Google Cloud is increasingly important as more data operations move to the cloud.
Soft skills: According to Hays, communication and self-motivation are the two most in-demand soft skills among employers right now. These skills are crucial for data professionals who need to explain complex insights to non-technical stakeholders.
Another panellist, Barabra Forbes, who is the Analytics Manager at Birdie, confirmed that communication skills are what sets candidates apart:
“What I really value are people who can go into a conversation with somebody who’s not a data-specific person. So they’re talking to a product manager, or they’re talking to an account manager, and they can come out of that conversation and know exactly what data will be useful for that other person to do their job better.”
Barabra Forbes, Analytics Manager at Birdie
AI is creating more data roles
According to the World Economic Forum, AI could result in the creation of more jobs. These range from newer specialist jobs, such as prompt engineer, to roles in higher demand – for example, electrical engineers and those who work with data.
But, the nature of data work is also changing.
Routine data tasks are increasingly being automated.
There’s a growing emphasis on higher-level analysis and strategic decision-making.
The low-code market, which is closely tied to AI and data analysis, is forecast to grow at an annual average rate of 22.9% from 2023 to 2030. Low-code platforms offer pre-built components for creating workflows and applications without manual coding, accessible to both business users and IT developers. This suggests an increasing demand for professionals who can work with low-code and no-code platforms to democratize data analysis.
Ollie Gower, the Vice President of Product at FINN, confirmed the low-code boom during the webinar:
“We focus a lot on no code and low code solutions. So we actually made a role called a business automation manager, and their specific job is to come up with no code or local solutions. If you haven’t yet, go and try it, you can literally integrate it with another platform in about three minutes and then have those insights gathered in in no time. [For instance] you’ve got loads of unstructured data, and you want to see how many times customers are complaining about the price. That goes from hours or week-long tasks to a few minutes.”
Ollie Gower, Vice President of Product at FINN
The range of data roles is becoming increasingly diverse, which shows that the field of data analytics offers multiple pathways for career growth and specialisation. Whether you’re interested in the technical aspects of managing data infrastructure, the analytical side of deriving insights, or the cutting-edge world of AI and machine learning, there are opportunities available.
The most important thing to focus on when starting or advancing your data career
For career starters, hiring manager Cecilia Silvi recommended proof of your continuous learning. Employers want to see your learning experience, particularly training in key skills such as SQL and data visualisation.
For career advancers, the ability to communicate data insights to both leaders and junior data analysts is what Cecilia looks for when hiring.
We also had Joel Hawkins, a graduate of the LSE Career Accelerator join the panel, and he listed what he thought were the most important factors in securing his new role as a data analyst:
Soft skills
The willingness to learn
Portfolio of work
A foundation of technical skills
Your skills pathway to data analytics
The main focus of the webinar was revealing and understanding the key skills our expert panel of data leaders and recruiters were looking for in candidates.
The LSE Data Analytics Career Accelerator teaches you both the technical and soft skills that were identified by the webinar panel and our research. Over six months, you’ll:
Develop core data-handling capabilities across databases and tools, and use SQL and Excel to identify insights through data analysis.
Build technical coding skills in high-demand data programming languages Python and R.
Hone your communication skills, including data visualisation, to ensure your analysis and insights support actionable business decisions.
Understand advanced analytics solutions to help you achieve business impact, and do dedicated AI and data training in a series of in-depth Masterclasses.
All of these skills are applied to practical projects which later form a portfolio of work. For graduate Joel, that practical application is what helped him achieve his promotion:
“I was able to see that the theory I was learning actually applied to my job. That practical element is what allowed me to really progress here, and ultimately receive the promotion.”
Joel Hawkins, Business Data Analyst at Wellesley and Career Accelerator graduate
Elodie’s journey was slightly different. She had no previous coding experience and joined the programme with the goal of achieving a complete career change from teaching to data. She hit this goal within a few months of starting the LSE Career Accelerator, which was followed by two promotions, first as the Lead Data Scientist and then as the Director of Development (all within the space of 11 months). You can read more about her story here.
“The course just gave me so much more than I bargained for. The technical skills were fantastic, it was all new to me, but it was the career coaching that really helped me navigate that leap.”
Elodie Hudson, from teacher to data scientist
Are you ready to fast-track your career with LSE?
The LSE Data Analytics Career Accelerator equips you for a career, not just a task. You’ll build the technical competencies, soft skills and business knowledge that employers look for. You’ll also receive one-on-one carer coaching, to help you make that change or land that promotion, and get ongoing results in your career.
After Jenna bought a toilet seat from Amazon, she was bombarded by emails and Instagram ads selling toilet seats. It turns out, she wasn’t alone.
This is an example of marketing automation gone wrong. When it comes to AI-driven customer service, segmentation and analytics, context is everything.
You can watch the full one-hour webinar hosted by Jenna, who is an industry expert on the LSE Digital Marketing Strategy & Analytics Career Accelerator. During the session, Jenna goes in-depth into the opportunities and challenges of using AI as a digital marketer, with multiple real-world use cases and ChatGPT demos.
We’ve summarised a few key points from the webinar to help you use AI strategically as a digital marketer:
Jenna’s top ChatGPT digital marketing tips:
Be clear and directional when using ChatGPT for content creation. Use the AIDA formula to guide your prompts.
Use ChatGPT for a customer market scenario analysis. Or, ask ChatGPT to analyse your own internal marketing efforts and identify gaps.
Like any new technology or tool, it takes practice. The pay-offs – like automation, increased efficiency, content creation and pulling data insights – make it one of the best learning endeavours to commit your time to.
You’ll step beyond traditional digital marketing skills training to build expertise in data-driven strategies, performance and team management, growth marketing and emerging technologies such as AI.
Alongside the core curriculum, you’ll get access to a series of expert-led Masterclasses that teach you industry-relevant AI skills that’ll keep you relevant in the age of AI.
