Employers

We didn't set out to be a skills provider who always solved the same problems for the same people: we optimise for adoption, adapt to what customers really care about, and ultimately help build the workforce of the future. 

It’s approaching ten years that we’ve been delivering apprenticeships and the most important lesson we’ve learned is that you have to solve for customer value if you want to effect long-lasting change in the skills system. You might have killer content and incredible coaching expertise, but if your programme doesn’t solve a problem or address a pressing skills need it won’t deliver enduring impact.

And sometimes that means anticipating what’s next, not just what’s currently working. That’s exactly what we did with AI – developing training programmes to meet emerging customer needs, before the apprenticeship infrastructure was set up for it. This has now changed with the rollout of a new AI apprenticeship standard, but we’re now three and a half years on from the launch of ChatGPT.

At the same time, when you start to innovate at scale, scrutiny from the existing systems isn’t merely inevitable; it’s necessary. Regulation is there for a reason – it’s not a critic to be ignored, it’s a guardrail to keep standards high.

What the Ofsted inspection found

We set out to build a new system for technical training that works for everyone – from an 18-year-old retail worker on the shop floor to a 68-year-old NHS clinician. To date, we’ve supported tens of thousands of learners and more than a thousand employers across all sectors of the economy. 

Our report demonstrates that there is plenty to be proud of, and these are all things that our customers will recognise:

The achievements of our learners, who “gain substantial new knowledge and skills in artificial intelligence, use of data and business analysis and management, having in many instances started their apprenticeship with little or no prior experience.”

Our inclusive learner experience, “where apprentices, including those who are disadvantaged, feel welcomed and well supported.”

The quality of our expert coaches and instructors, who are “skilled, effective teachers.” 

And the tangible benefits we bring to our apprentices and their employers: learners gain promotions or increased responsibility by the time they complete, improve productivity, and “produce complex, meaningful projects that they successfully apply in the workplace”.

However, the report also flagged areas where we need to pay more attention. 

In the tech world, there’s a habit of attacking regulators when their feedback is constructive. We won’t be doing that. We’re using this as an opportunity to sharpen our delivery.

What we're changing following the Ofsted report

We’re taking the feedback on board - here’s a few specific examples of how:

1. Hiring more coaches and equipping them better, with workloads already back down: Our coaches and expert instructors are the backbone of what we do. We’ve invested in better internal tools to strip away admin so they can focus on the coaching conversations that matter. And we’ve already hired more than 40 new coaches and instructors in the last quarter alone, so we can continue to grow and support our learner base.
2. Career guidance for every learner: We’ve always believed in lifelong learning, but we didn’t prioritise offering specific careers advice for everyone, given some of our learners  are twenty or thirty years into their career. We’re now making sure our platform offers career support for every learner, regardless of their seniority.
3. Improving our offering for line managers: a crucial predictor of learner success is how much their managers engage with the projects and work they’re doing with us. Going forward we’re improving the manager experience on our platform and redesigning the progress review process.

Looking ahead: skills, AI, and the UK's productivity challenge

Relevant skills are the most important asset we can give workers today – just as they’re the most important lever we have to solve the UK’s productivity challenge. To make that a reality, we have to be willing to hold ourselves to the highest possible standards.

We will invite Ofsted back for a re-inspection before the end of the year, and we already know we have the best people ready to deliver that change.

In the meantime, we remain focused on the goal: ensuring that in the AI era, no one is left behind.

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Qualification Achievement Rate (QAR) is a metric used by the Department for Education to measure the proportion of learners who successfully complete an education or training programme. In the government’s own framing, it gives “one measure” of a provider’s performance.

Multiverse’s current apprenticeship completion rate is 52.6% — below where we want it to be, and below its peak in recent years. It will improve by June, when the current academic year ends, and more improvements will follow as we invest in other changes required to get QAR back to where it belongs.

But we also believe, as the regulators do, that QAR is one measure of training quality. It is not the complete picture. There are specific reasons why ours sits where it does, and those reasons are tied to our mission to build a world where tech skills unlock people’s potential and output.

Three reasons for Multiverse’s current QAR

We’re an AI training pioneer

Multiverse invested early in making AI skills training available, before the apprenticeship system had created a designated AI standard. This created a structural mismatch between what employers needed and what the regulated framework prescribed — an almost inevitable consequence of a disruptive technology moving faster than the system designed to support it. 

We acted fast and first because our view is that when AI can already automate tasks someone has spent twenty years mastering, the responsible response as a technology training provider is to make training available to them now, not after they’ve been made redundant. But innovating ahead of the regulatory system has a cost, and some of that cost shows up in our QAR.

The launch of a designated AI apprenticeship standard has addressed this directly. And when the regulatory framework catches up with businesses' needs, so too will our QAR.

We prioritised inclusive enrolment

The single biggest driver of completion rates, whether it’s apprenticeships or degrees, is who you allow to start. On our AI programmes, we made a deliberate choice to optimise for access and tolerated lower completion rates as a result. We thought it more important to give as many people as possible a shot at AI readiness than to filter for those most likely to complete. That’s a decision we stand by, even if we’ve subsequently taken steps to create a more even balance.

The success of our more inclusive approach is reflected in a 50/50 gender split across our largest AI programmes, at a time when external research shows women are significantly less likely than men to engage with AI. Across our entire portfolio, 40% of learners did not previously hold a degree, and  26% of our learners have a contextual flag, meaning they meet one or more markers of socio-economic disadvantage, such as being care-experienced. 

Our impact-first programme design

Multiverse apprenticeship programmes are designed so learners start solving real problems at work from day one, not after 18 months of theory. Many learners deploy new skills during their programme, earn the promotion or pay rise they came for, and then leave before completion. QAR registers that as a dropout but we see it differently.

Our data shows that 45% of learners receive a promotion during or within 12 months of their programme, and 60% secure a pay rise. These are strong proxy measures for employers receiving the outcomes they commissioned. If someone achieves what they set out to achieve, for themselves and their business, and then moves on, we don’t consider that a failure.

What the data really shows

The countless individual, impactful learner outcomes that sit alongside our QAR tell the full story of the value Multiverse delivers.

In a typical example, a Royal Free London NHS Foundation Trust administrator digitised an entire patient journey during his apprenticeship — doubling his department's daily caseload from 30 to 60 patients, reducing non-discharged patients by 93%, and cutting waiting times from over 30 minutes to 10. He was promoted to data coordinator as a result. That is a meaningful measure of success, and the kind of return we routinely track for our customers.

Additionally, across our customer base, 98.4% of learners who complete their course achieve a pass or above. Net Revenue Retention stands at above 100%, meaning employers consistently invest more with Multiverse after their first programme. Last year, Multiverse accounted for more than half of total growth in apprenticeship starts across the entire system. This is the side of the story that completion rates don’t capture.

Working with the system, while sticking by our convictions

As one of the largest apprenticeship providers in the country, Multiverse takes its responsibilities to the wider system seriously. Wherever regulators identify areas for improvement, we will act on them. Not as reluctant concessions, but because that’s how a serious provider engages with a regulatory framework that it’s committed to improving alongside. 

We know what it takes to have high completion rates and we know what we need to do to get back there. Elsewhere, our completion rates are higher: our degree-level programme completes at c.70%, and our software engineering programme sits well above national average at 90%.The changes underway to our apprenticeship programmes in enrolment criteria, programme design and how we support learners through to the finish line are already moving the dial and will be reflected in our numbers following the end of this academic year.

What will not change, however, is our conviction that programme completion is a means to an end, but not the end in itself. Our mission is to create a workforce equipped to win in the AI era. Everything we build, measure and invest in is pointed towards that.

Read our latest Impact Report to discover how Multiverse is transforming the way people learn and work, and helping thousands of workers unleash AI's potential. Or get in touch to learn more about how Multiverse can support your workforce.

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AI is here. But who benefits?

The answer is supposed to be “everyone.”

The evidence of the past 18 months tells a different story — one of accelerating divergence between the workers and organisations that can productively use AI, and those that cannot. At large, AI can replace the workers who use it, or it can amplify them. The same software, with the same access, can lead to vastly different outcomes.

The gap has a name. It is the adoption layer.

The three layers

There are three layers to the AI stack today, each with a different make up, and different winners.

The first is the foundation layer of frontier labs that capture the headlines, the geopolitics, and the unprecedented revenue growth.

The second is the application layer: products built on top of models to solve specific problems like customer service agents, legal research tools, design assistants, and AI tutors. The winners here are those who can identify and scale the most high-value use cases for AI to augment or replace the existing ways of doing things.

The third is the adoption layer: the human, organisational, and institutional capability to actually use any of this in a way that changes real world outcomes.

For too long the adoption layer has been treated as an afterthought, but it’s by far the most exciting and urgent challenge facing technology today.

The productivity gap between the most and least AI-fluent workers using the same tools is already 6x by some measures, and growing. For corporates, it is the difference between AI as a cost centre and AI as a productivity lever.

At a societal level, particularly in the polarised, low-growth democracies of the West, successful adoption is the difference between technology that lifts workers, elevating their roles, and technology that simply happens to them. Without broad distribution, the benefits accumulate to those already best placed.

This is the part of the AI stack that has been most neglected. It is also the layer where Britain and Europe have particular institutional advantages: highly educated workforces, strong absorptive capacity, mature systems of employer-led training, and public services that can act as natural laboratories for applied AI. The bones of an adoption infrastructure exist. The question is whether we grasp the opportunity in front of us.

The deployment problem

If you doubt that adoption is the binding constraint, the clearest evidence is what the AI labs themselves are now doing.

In the past few weeks, two leading frontier labs have raised multi-billion dollar professional services ventures, backed by some of Wall Street’s largest investors. Job ads for Forward Deployed Engineers — essentially a hybrid between consultants and software engineers — are through the roof.

It is a striking concession. For years, the pitch was that AI would automate away the cost of professional services. The labs are now standing up the largest professional services build of the decade not to replace consultants, but to do the work themselves.

The reason isn’t mysterious. Most organisations are not the clean-edged enterprise of the demo video. When it turns out most of your customer interactions fall into the category of edge cases, getting AI to deliver inside that reality requires more human labour, not less.

These services arms might reach the very largest enterprises, the Fortune 500, and the portfolio companies of the biggest private equity funds. But it is not a workforce-scale answer across a continent as diverse and complex as Europe, where 5.5 million UK businesses and roughly 25 million EU SMEs sit far below the labs’ line of sight. And even at the largest enterprises in the world: deployment does not equal adoption.

So what will actually work?

Some people have claimed that the key to AI adoption is simply trial and error, something that anyone can do on their own. This myth of self-teaching is easy to debunk. The issue is not so much using the tools as it is finding the right thing to apply them to; that requires support. And the idea that generic AI courses or YouTube videos explaining the technology will provide any meaningful gains to the adoption of the technology for workers doesn’t stand up to scrutiny. After all, trying to deliver outcomes with AI in the workplace often exposes the profound gap between what the technology can do in theory, and what is deemed permissible on the job by legal, IT, and HR departments in practice.

Multiverse has three principles for teaching; well-supported by the underlying learning science and our experience in delivering on the job learning to tens of thousands of workers, across nearly every sector and industry.

The first is that AI fluency is not primarily a technical skill. Looking at the difference between AI ‘power users’ and the rest, our learning scientists found that the determining factors are largely human: analytical reasoning to break a problem down, creativity to push beyond the obvious prompt, systems thinking to anticipate how the model will respond, scepticism and detail-orientation to catch its mistakes. Prompt-engineering is one of the smallest things that separates the most effective users from the least.

The second principle is that adults learn complex skills through application. Real work, in real contexts, with feedback from experts who have done the work themselves. They learn by trying, failing on small things, having someone more experienced explain why, and trying again. There is no shortcut around this loop. There is no ‘AI in 30 days’ course, no matter how slickly produced, that can compress it.

The third principle is that context really matters; and context is probably the defining word of the AI-era thus far. What a financial analyst at a global bank, a nurse working for an NHS Trust, or an operations manager at a fast growing startup can do with AI in their roles differs wildly. So you can’t sheep dip your way to progress here. You need a detailed understanding of an organisation’s goals and constraints, a clear understanding of function, team, and job specific priorities, and a thorough grasp of individual worker capability and experience. The context gap is one of the key reasons why even as expertise becomes commoditised by AI, humans still have a vital role to play in delivering work.

These three principles point to the same operational answer: learning embedded inside the job, guided by experts, with the learning coming from the weeks a worker spends tackling a real problem in their own organisation, developing the necessary skills in the process, and building organisational capability that otherwise wouldn’t exist.

This is, in essence, the apprenticeship model — older than AI, older than computing, and one of the things Britain, Germany, and the European continent, our core markets, have more institutional memory of than almost any other part of the world.

The output isn’t a certificate. It’s a measurable change in how a team operates.

Where we go from here

Most of this is obvious. Nearly every business leader is frustrated that their organisation isn’t adopting AI faster, and they’re well aware that the solution will never come from top down mandates.

It comes from the analyst who notices that three hours of every Tuesday is spent reconciling the same two reports. From the case officer who realises the bottleneck in their service is one repetitive judgement call.

There are many examples of how adoption is working in practice already. A service and selling coach at a national retailer built a ‘Distillation Bot’ that compresses hours of supplier training video into bite-sized micro-learning, cutting prep time by 87.5% and lifting partner quiz scores by 25%. An NHS consultant in paediatric intensive care designed an automated shift-safety alerting system that flags senior staffing gaps fourteen days in advance, swapping costly last-minute agency cover for planned cover. An aerospace ops officer built a stock-tracking dashboard for Aircraft-on-Ground events that, beyond eight hours saved a month, surfaces compliance risks worth up to $1m a year in penalties. A placements officer at a university built a structured job-title report for students choosing between placements, cutting analysis time from twenty minutes to three.

This is a quiet revolution happening right now. All of these individuals built things that matter to their organisations because someone trusted them to learn, and gave them the tools to apply that learning on the job. All of those individuals are frontline workers.

Multiply that by every council, every hospital, every factory, every back office, every team. That is the value of the adoption layer. It is also the surest way to tip the scales in favour of human agency, and away from the real risk of many workers being automated out of existence.

The civilisational bet

Because beneath all this is the biggest question: whether AI ultimately replaces the workers who use it, or whether it amplifies them.

Too often the default running through most of the AI conversation, particularly from big tech, is the former. Multiverse is one of the very few companies in this market whose explicit mission is the latter.

So far, the story of AI has been one of humans training machines to make machines smarter. We are working towards the inverse: using a blend of machines and our coaches to train humans to make humanity smarter, and to keep human judgement, creativity, and agency in the loop, even as the technology itself becomes more powerful.

Which outcome we get depends almost entirely on what we choose to invest in, and what we choose to prioritise.

This is, ultimately, why we built Multiverse the way we did. The next decade of AI will not just be won by those who built the underlying technology, but by those who built the workforce capability, and the institutions, to benefit from it.

Behind every outcome Multiverse delivers — the productivity gains, the promotions, the millions in cost savings — there are the coaches; educators with deep subject-matter expertise and the skills to translate that expertise into measurable change for learners.

Multiverse coaches are not a supporting element of our model. They’re its engine, and a real difference maker when it comes to the quality of our apprenticeships. That’s why our learners report a coach satisfaction rating of 97%.

A coaching model built for every stage of the journey

Throughout their learning journey, Multiverse apprentices are not supported by a single generalist tutor. They’re accompanied by a team of specialists, each dedicated to maximising their progress through different phases of their programme. This way, they’re not reliant on a single generalist mastering the distinct skills required to onboard, teach content, and drive on-the-job application simultaneously.

Launch Coaches set learners up for a flying start, getting them ready — and excited — for what lies ahead. Technical Instructors help learners acquire new skills and bring their deep industry experience to each module. Cohort Coaches work with learners throughout the core of their programme, combining group sessions with individual support to help them apply new skills directly to their role from day one. And Success Coaches guide learners through their end-point assessment, helping them demonstrate everything they’ve gained throughout their programme.

Multiverse’s coach-to-learner ratio has been designed to maximise support at every step of the journey, and represents a structural commitment to the quality of human-led coaching at the heart of every programme.

Learners can also book 1:1 support on demand directly through the learning platform, as often as they want to. So support is always there when it’s needed, not just when it’s scheduled. Additional Learning Needs are taken care of from the start, or, if identified by the coach later during the programme, professionally assessed and addressed. 

Industry practitioners as well as expert educators

Multiverse coaches are industry practitioners as well as expert educators. They join the team after spending several years working in industry, bringing that expertise into their coaching sessions.

Coaches have an average of five years of industry experience. More than 70 hold advanced degrees, including PhDs and Master’s qualifications. There’s even the odd astrophysicist and former Air Force Lieutenant among them. And, to stay current, coaches complete an average of 60 hours of technical upskilling per year themselves.

"As I have practical experience in industry, I know what it takes to manage projects, solve problems and use data in real-world cases. This means I am able to take an applied learning approach and elevate it with anecdotal examples. I can share potential outcomes, common pitfalls and key tools to use based on the needs of different apprentices and partners, and make sure the theory taught is always applicable to the apprentice's role."

— Adam Harris, Data Fellowship Coach and former civil engineer

The Compass Framework: how Multiverse recruits and develops great coaches

Multiverse hires, trains and evaluates all coaches against our unique Compass Framework, built around four competencies:

Industry expertise: Coaches have the expertise to deliver effective, enjoyable and relevant learning.

Data-driven: Coaches plan every session using data on learner progress and client needs.

Connector: Coaches build trust and create the psychological safety that empowers learners to take risks, ask questions and grow.

Guide: Coaches help learners identify high-impact projects, ask the right questions to help push thinking forward, and provide constructive, actionable feedback — typically within 24 hours.

These competencies all work to reinforce each other to guarantee a high level of apprenticeship coaching quality. Industry expertise forms the foundation, but it is the data-driven preparation, the human connection and thoughtful guidance that turn expertise into learner outcomes.

50 million reasons great coaching matters

In just one story illustrating the impact of Multiverse coaches, a new customer enrolled 50 apprentices onto data-focussed programmes, and tasked them to develop the skills to help their teams with a major cost-saving initiative.

When poor data quality emerged as a recurring issue for the company, one of their coaches guided the group toward problem solving: how could apprentices apply their data cleaning skills? What approaches would work in their specific context? Which stakeholders needed to be engaged to make changes stick?

The outcomes were transformative. Cost assessments that previously took weeks became near-instant. A spend management system developed by one apprentice was adopted across every category lead in the organisation. Overall, more than £50 million in savings were identified.

Future ready coaching, today

Our coaching model is tried and tested — it works. But that doesn’t mean we’re not continuously improving our approach.

Atlas, Multiverse’s AI coach, works alongside the human coaching team to supercharge their impact. Recent improvements mean Atlas now resolves 88.3% of routine inbound support queries, such as scheduling questions and platform navigation. To date, it has answered more than 1.5 million questions and earned a 99% helpfulness score from users. 

The proportion of coach time spent on routine support has dropped from 41% to just 18% as a result, meaning they have more time for the high-impact work that AI can’t replicate: mentorship, pastoral care and deep developmental coaching.

We’ve also hired 40 new coaches and instructors in the last quarter alone, while making our recruiting pipeline stronger and more predictable than ever.

This investment in coaching capacity and advanced tooling is a reflection of what we’ve always believed: that the quality of the coach-learner relationship is what determines the quality of the outcomes at the end of it.

Read our latest Impact Report to discover how Multiverse is transforming the way people learn and work, and helping thousands of workers unleash AI's potential. Or get in touch to learn more about how Multiverse can support your workforce.

The world of work is undergoing the most profound transformation in living memory. AI has supercharged the relentless advance of tech, software and systems, and has fundamentally changed what employers need from their teams. It has also supercharged how quickly both employers and employees need to evolve.

From Claude to CoPilot, the tools reshaping organisations are proliferating faster than traditional education and skills programmes can keep up with. Knowledge transfer is no longer the primary goal to aim for, because learning without impact no longer cuts it.

What today's employers need is people who can apply new data and AI skills to real challenges and drive measurable results. The way apprenticeship ROI is measured must take this into account.

That's why Multiverse's results-based training model focuses on more than whether learners finish the programme. That still matters, but we also emphasise the value they have created thanks to their training, for themselves and for their employers. And we celebrate their success against this more complete, deeper definition of achievement.

The modern era of upskilling demands modern measures of success

UK productivity has been stagnant for a decade. And yet, not only does the technology to reverse this trend already exist in the shape of AI-driven automation, data analytics and intelligent workflows, it is also now within reach of organisations of every size and in every sector.

But for these technologies to deliver the productivity gains the economy so badly needs, we need to build a critical mass of people with the right skills.

Training providers must teach more than theory and deliver more than simply qualifications. They must equip learners to apply new capabilities directly to their work — and quickly; from day one, not after 18 months of learning — and they must measure whether those capabilities are making an impact.

This is the standard of quality Multiverse holds itself to, and we're building the human and technological infrastructure to deliver this at scale.

Measuring what matters most

AI and modern data systems make it possible to connect learning directly to concrete business results. Multiverse's measurement approach embraces this new level of transparency, allowing the organisations we partner with to assess the ROI of apprenticeships against an accurate before-and-after picture of meaningful business outcomes.

The success of this approach is reflected in the results Multiverse tracks across its customer base:

• $2bn+ in confirmed ROI delivered to customers to date
• Approximately one working month per year saved per learner through productivity gains
• 45% of learners receive a promotion during or within 12 months of their programme
• 100%+ net revenue retention — customers consistently invest more with Multiverse after their first programme
• 98.4% pass rate among programme completers
• 97% learner satisfaction with coaching

What that impact looks like in practice

Behind each of these data points are individuals from our all-time cohort of 30,000+ learners who have used their new skills to solve real and urgent problems for their companies.

Across our customer base of more than 1,500 customers — including over a quarter of the FTSE 100, half of Russell Group universities, more than 100 NHS trusts and 50+ local councils — the pattern is consistent: learners acquire new skills, apply them to current workplace challenges and deliver results their employer can tangibly measure. Examples include:

Retail

A product manager at a national food and clothing retailer built an automated pipeline to report on available merchandise space, and turned a two-hour manual process into a 60-second cycle. The time saved allows for decisions to be made daily, based on actionable insights, instead of occasionally and based on guesswork.

Banking

A project manager built a GenAI workflow to automatically generate and distribute new bank account numbers, compressing processing time from 25 hours per 1,000 accounts to under 15 minutes, while virtually eliminating manual errors.

Healthcare

A service manager on the long-term conditions team used Copilot to analyse patient feedback following a continence product change, producing reports that enabled direct cost savings to be negotiated with the supplier.

Aerospace

An operations officer developed a compliance tracking dashboard for critical aircraft parts, saving eight hours a month and identifying potential compliance risks worth up to $1m a year in avoided penalties.

We're investing in traditional metrics — and always thinking about what comes next

In this era of rapid technological change, the ability to apply learning quickly and effectively is more important than ever. Metrics like course completion do matter, but they only measure whether a learner gets to a finish line. Taken alone, they offer an incomplete picture of the value a training programme delivers along the way.

That's why we are continually investing in improving our performance against traditional metrics, while also pushing the frontiers of what outcome-focused training can achieve.

The standard we hold ourselves to is straightforward: are our learners better equipped to drive value in their organisations because of their time working with Multiverse? And the evidence shows, consistently, that they are.

Read our latest Impact Report to discover how Multiverse is transforming the way people learn and work, and helping thousands of workers unleash AI's potential. Or get in touch to learn more about how Multiverse can support your workforce.

Instead of a level playing field, we’re seeing a widening AI adoption gap that threatens to leave a huge portion of the workforce in the digital dust.

The reality? Most companies are treating AI upskilling like a generic software update. But as the numbers show, a one-size-fits-all approach is a recipe for stagnation.

The Seniority Divide: Who’s Actually Using AI?

The most striking trend in our latest analysis is the massive disparity in daily AI usage based on job level. While 52% of mid-level workers collaborate with AI daily, only 21% of junior employees do. This 30-percentage-point gap presents a pipeline problem: If the next generation of talent isn't learning to work alongside AI today, we’re creating a future skills bottleneck.

The research also found a significant divide between those steering the ship and those doing the specialised, day-to-day tasks. Of middle managers, 48% are using AI regularly, while only 20% of individual contributors do the same. When nearly half of managers are leveraging AI but only a fifth of their direct reports are, the productivity gain of AI stays trapped at the top, rather than fueling the engine of the entire team.

While senior staff and middle management have leaned into the tech, junior employees and individual contributors are being left to figure it out on their own.

The Perception Gap: A 17-Point Reality Check

It’s not just that usage is uneven—it’s that leaders often don’t realise how uneven it is. There is a significant disconnect between what’s happening in the C-suite and what’s happening on the ground: 59% of leaders believe their teams are regularly engaging with AI, but in reality, only 42% of employees are doing so.

This 17-percentage-point perception gap suggests that while leaders are sold on the vision of an AI-powered workforce, they haven't yet provided the practical, job-specific pathways to get there.

The Solution: Training for the Real World

The bottom line is clear: One-size-fits-all upskilling benefits no one. You can't give a data analyst and a marketing coordinator the same prompt guide and expect magic to happen.

That’s why we’ve expanded our portfolio of AI programmes to make sure we can teach anyone, in any job, how to use AI to be more effective at work. To date, we’ve trained 1,220 unique job titles. Today, we’re taking that a step further by introducing:

• AI Solutions Builder: Designed to develop agentic AI experts who design custom tools to boost productivity.
• AI Transformation Architect: To train experts to orchestrate and scale automation across the entire enterprise.

Whether you're looking to empower a junior contributor or train a specialist to overhaul your entire workflow, the training must be as specific as the role itself.

Ready to Close the Gap?

If you’re ready to move past the AI hype and start seeing real-world ROI across your entire organisation, we’re here to help. Learn more about our full offering here.

While installing a new tool is easy, achieving true AI transformation is hard.

At Multiverse, we have committed to making this change a full team effort. As a result, we increased our revenue per employee by 37% last year. Here’s how we approached it:

1. Communicate a clear vision and expectations

Successfully driving AI adoption begins with clear communication about what the transformation means, and what expectations come along with it.

Vision

For us, transformation meant integrating AI into the core of how the business operates and how our employees work. Our vision for transformation is built on three pillars:

• Building the foundations: Unlocking our data safely and responsibly so everyone has access to it and knows how to use it to inform business decisions.
• Enabling our people: Upskilling and instilling an AI-first culture, giving our team the resources and support they need to feel confident using AI in their day-to-day.
• Delivering the change: Transforming each department to deliver outcomes with AI, based on their individual workflows and needs.

Expectations

We communicated expectations by:

• Establishing clear guidelines: Making AI a core part of the organisational performance framework. Our new operating principle, "AI to deliver outcomes," embeds the expectation that AI is an engine for performance at every level and is now a core part of our performance framework.
• Fostering an AI-first mindset: Employees should be encouraged to think, "Can AI do this?" when assigned a task, promoting the use of AI for increased day-to-day productivity.

2. Engage every level of the organisation

While having the full backing and role modeling from the highest leadership is crucial, true ownership must be felt across the board.

An every-level approach to engagement is vital to ensure everyone is part of the change. One effective way to achieve this is by creating different roles within the organisation, outside of the technical team, to foster widespread participation:

• Amplifiers: These are all employees who are supported and enabled to utilise AI in their day-to-day work.
• Builders: A subset of people who sit within departments, go through certification, and gain the skills to build AI solutions for their respective department responsibly.

This structure ensures both roles are recognised as important, with Amplifiers feeding into Builders and Builders supporting Amplifiers, distributing engagement across the company.

3. Support continual learning and skills development

AI adoption requires continuous learning. At Multiverse, we’ve committed to all of our employees that they’ll become the most AI-enabled version of their profession, and we provide the upskilling opportunities to get them there. Support for skills development can be integrated into an organisation in several ways:

• Tailored training: Providing customised enablement for different roles to ensure relevant learning opportunities for everyone.
• Ongoing support: Establishing a helpline for when employees get stuck. At Multiverse, our “AI Genius Bar” is available throughout the week for those seeking a bit of extra guidance.

4. Balance financial and cultural metrics for success

Measuring success must be holistic, tracking both hard metrics and qualitative ones. While impacting company financials is important, it’s also crucial to ensure employees are being taken along the journey. We break our metrics down into:

• Revenue per employee: Focuses on how AI drives top-line delivery, not just operational efficiencies or time saved.
• Employee sentiment: Tracks cultural change by using pulse surveys to see if confidence is increasing as new arrangements are rolled out.

Ultimately, achieving AI transformation is a journey, not a destination, and it requires a holistic strategy that integrates technology with people and culture.

This significant extension allows Multiverse to further its model, enabling more high-impact, debt-free degree pathways.

Multiverse is the UK's only independent training provider with degree-awarding powers, a distinction that confirms the quality of education it provides its apprentices. In the National Student Survey commissioned by the OfS, it has held the highest satisfaction rating within the Digital & Technology Solutions Professional standard for the past two years, currently sitting at 85.6%.

This expansion immediately enables the launch of Multiverse’s newest degree apprenticeship programme, AI Product Engineering (AIPE), which is uniquely designed for the future of the industry, amplifying software engineers' capabilities through AI so they are able to deliver more value, faster. The AIPE programme is focused on training engineers to design end-to-end systems and effectively integrate AI across the Software Development Lifecycle. It teaches engineers how to use AI as a co-engineer to amplify their productivity and deliver better commercial outcomes.

The AIPE programme will be quickly followed by additional degree-level programmes within the Chartered Manager and Project Manager standards.

"This recognition from the OfS reinforces our approach. We are proud to be the UK's only independent provider with this power, and its extension means we can rapidly scale up the creation of vital, high-growth degree pathways," said Euan Blair, Founder & CEO at Multiverse. "By expanding our degree-awarding scope, we can deliver more high-quality, job-relevant qualifications that produce the talent the UK economy desperately needs."

The value of this model is best described by those currently benefiting from it. Louise Gardner, a Multiverse degree apprentice, spoke about the programme’s impact: "Doing a degree apprenticeship has helped me advance my career - after graduating, I was able to step into a secondment in the global corporate responsibility team. But more than that, it provided me with critical insights and skills, which I’ve leveraged to add tangible value to my team by improving how we use data to inform decisions.”

This expansion allows Multiverse to deliver more impactful, degree-level apprenticeships that directly address the skills needs of the UK economy, preparing a new generation of talent for the future of work.

Last year, our commitment to empowering our team’s AI use resulted in a 37% growth in revenue per employee. We believe the most powerful AI tool is not a new piece of software, but a workforce that has been taught how to effectively use it.

Our transformation shows that true AI adoption requires more than just access to technology—it demands an entire behaviour change.

Beyond the tool: It's a mindset shift

Many leaders believe their teams just need to learn a new tool to achieve meaningful results, but true transformation demands more. It involves reimagining entire workflows and understanding where the AI tool fits—or where it completely replaces existing processes. Furthermore, it requires focusing on the essential human skills, like critical thinking, to filter results and identify inaccuracies.

Leading a team through this shift, especially when some are reticent to adopt new tech, is a core element of culture change.

How our Learning team uses AI for impact

Our dedicated Learning team is constantly adapting, recognising that in the world of AI, continuous, real-time learning is paramount.

1. Improving the learner experience

We are focused on using AI to deliver a hyper-personalised, contextualised, and enjoyable learning experience.

• Personalisation at scale: AI allows us to move beyond one-on-one mentoring, which was previously necessary for deep customisation, by using the knowledge of LLMs to personalise and contextualise content for each learner.
• Real-time relevance: The world of AI moves fast, so we created protected learning time for our subject matter experts to stay current. We also use AI agents to scan the entire market, collect information, and bring back reports to highlight where we need to focus our attention, ensuring our content is always up-to-date.
• Innovative tools for apprentices: AI is interwoven into the apprentice experience to make learning easier. Our contextual, AI-powered coach, Atlas, helps learners understand why they are stuck and how to unblock themselves, rather than just giving the answer.
• Tools for creators: We've built internal AI tools that remove the challenge of the "blank page" for our learning designers, helping with initial creation, automation, and consistency.

2. Delivering outcome-driven learning

In the AI era, learning is not for learning’s sake; it is for enabling new individual or company capabilities.

• Measuring impact: The rise of AI and modern data systems means we can now connect learning to concrete, measurable results. We can get a "before and after snapshot" to understand the true impact that learning has on outcomes in a way that was never possible before.
• Outcome focus: This capability makes learning truly outcome-focused. We start with what we’re trying to achieve, and work backwards from there to understand what skills our learners need to develop in order to get there.

The foundation of our success is understanding that human adoption drives technology's value. By shifting our internal culture, we are not just keeping pace with AI—we are actively shaping the future of work, delivering personalised experiences for our learners that deliver true transformation.

At Multiverse, we have seen firsthand that investing in AI isn’t just about the technology—it’s about the people building it. This approach has yielded tangible results: last year, we grew our revenue per employee by 37%, a direct result of our investment in AI technology and the cultural transformation that enabled it.

So, how do you shift an engineering mindset from traditional development to AI-first innovation? Here is how we approach AI transformation within our engineering capability:

Embracing experimentation

The key to unlocking innovation lies in individual empowerment, giving team members the freedom to experiment, trusting them and their domain expertise to get things done.

To truly build with AI, teams must shift their perspective on failure. In our engineering team, we encourage engineers to try, learn, and even fail, knowing they might not succeed on the first attempt.

• Learning at pace: We view experimentation not through the lens of risk aversion, but as an instrument for learning at pace.
• Dedicated time: Innovation doesn't happen if it isn't on the calendar. We utilise "Hack Weeks" and allocate 10-20% of our engineers' time specifically for learning and experimentation.

Balancing Speed with Security

A common hurdle in AI adoption is the fear of security risks. How do you balance the need for speed with the necessity of safety?

Our approach is to create guardrails so that our builders don't have to overthink compliance while they are in the creative flow.

• Programmatic boundaries: Whether it is through role-based access control or context-setting files in coding tools, we ensure engineers know it is safe to experiment within specific environments.
• Removing friction: By making security programmatic, engineers don't stumble over administrative fears; they can roll into their creativity and embrace the technology.

Overcoming the fear of failure

With any new movement, there is a natural human fear of humiliation or failure. To foster a true builder’s mindset, leadership must prioritise psychological safety.

By democratising the technology, we remove the fear that AI is rocket science. When the team sees that failure is just part of the process, they gain the confidence to build the future.

• Leading by example: Leaders need to embrace the technology themselves so they’re able to lead from the front.
• Celebrating the losses, as well as the wins: We openly share our mistakes to show that failure is just part of the process. This builds the confidence needed to try something new.

Innovation in action: AI grading

When you combine psychological safety, clear guardrails, and a culture of experimentation, you get tangible results. A prime example of this approach in action is our AI Grading capability.

An engineer on our team identified that grading homework was a prime opportunity to leverage AI and built a solution from scratch to address it. This tool has transformed how we operate:

• Empowering coaches: It reduces a massive amount of hours for our human coaches, freeing them up for higher-order activities like spending actual face time with learners.
• Faster feedback: Learners now receive faster feedback on their work, motivating them in their drive to improve.

This is the power of a builder mindset: when engineers are empowered to experiment, they build solutions that elevate the entire learning experience.