Employers

Prime Minster Keir Starmer wants to “unleash AI” - promising to drive adoption and deploy AI widely across the public sector, from reducing admin for teachers to assessment and diagnosis in the NHS.

What will this mean in practice for employers? In this article we’ll explore the AI Opportunities Action Plan and what this means for the skills agenda in the UK.

What’s included in the AI Opportunities Action plan?

The plan was commissioned by the Government and developed by tech entrepreneur Matt Clifford. It includes 50 recommendations, with the goal of boosting economic growth and improving people's everyday lives by supercharging AI adoption.

Improving living standards and transforming public services are called out as key ambitions of the plan – which is made up of three pillars:

1. Lay the foundations to enable AI: including the development of ‘AI Growth Zones’, the creation of a National Data Library (unlocking data sets to enable training of new models), as well as training, retaining and attracting talent.
2. Change lives by embracing AI: pushing public sector adoption by piloting and scaling AI products and services, while encouraging the same in the private sector.
3. Secure our future with homegrown AI: ensuring the UK has national champions through a new unit, UK Sovereign AI.

“In the coming years, there is barely an aspect of our society that will remain untouched by this force of change,” said Prime Minster Keir Starmer, in part of the government’s response to the recommendations, which sets out how the government will take forward the 50 recommendations included in the plan.

In practice, it’s hoped AI will enable public sector workers to spend less time on admin and more on delivering services. The opportunity for public benefit is huge: ranging from automated pothole inspection to faster cancer diagnosis.

The Prime Minister has now tasked his cabinet to make AI adoption a top priority for their Departments.

The Multiverse take: AI tools are only as powerful as the humans that wield them

The focus on skills and talent in the action plan is welcome – AI adoption must start with skills.

Here’s what Euan Blair, Founder and CEO of Multiverse, said following the announcement:

“Being first to mass adoption is just as important as being first to innovation. We may have missed the first-mover advantage on LLMs and data centres, but it’s encouraging to see the UK Government recognises its other unique opportunity: to be first to implement AI at scale.

“None of the AI action plan can happen without a substantial investment in skills, since AI tools are only as powerful as the humans who wield them.”

Multiverse engages regularly with policymakers and will offer advice as the recommendations are taken forward.

What will happen next?

The plan sets out how the country can train, attract and retain the next generation of AI scientists and founders in a set of recommendations. These include:

• Accurately assessing the size of the national skills gap, including AI and digital skills, by Spring 2025
• Setting out how the UK will attract highly skilled AI workers from abroad in the Industrial Strategy, by Summer 2025
• Ensuring the lifelong skills programme is ready for AI by assessing the importance of digital and AI skills in the curriculum, by Autumn 2025
• Establishing headhunting capability to bring top AI talent to the UK, by Autumn 2025
• Increasing the diversity of the talent pool with a focus on improving gender balance, starting at school level, by Autumn 2026
• Expanding education pathways into AI, by Autumn 2026
• Launching an undergraduate and master’s AI scholarship programme, by Autumn 2026
• Expanding the Turing AI Fellowships offer, by Autumn 2026
• Increasing the number of AI graduates and teach industry-relevant skills, such as AI-related degrees in Higher Education, by Autumn 2027

What should employers do on AI skills?

Considering 51% of workers have received less than five hours of AI training – according to our ROI of AI report, there is a clear opportunity for employers to upskill their workforce on AI.

Equipping people with the skills and confidence to use AI will spur further growth - as workers start to see how the technology can reshape their day-to-day.

For leaders wanting to bring AI into their company, understanding skills gaps is the first port of call.

Prime Minster Keir Starmer wants to “unleash AI” - promising to drive adoption and deploy AI widely across the public sector, from reducing admin for teachers to assessment and diagnosis in the NHS.

What will this mean in practice for employers? In this article we’ll explore the AI Opportunities Action Plan and what this means for the skills agenda in the UK.

What’s included in the AI Opportunities Action plan?

The plan was commissioned by the Government and developed by tech entrepreneur Matt Clifford. It includes 50 recommendations, with the goal of boosting economic growth and improving people's everyday lives by supercharging AI adoption.

Improving living standards and transforming public services are called out as key ambitions of the plan – which is made up of three pillars:

1. Lay the foundations to enable AI: including the development of ‘AI Growth Zones’, the creation of a National Data Library (unlocking data sets to enable training of new models), as well as training, retaining and attracting talent.
2. Change lives by embracing AI: pushing public sector adoption by piloting and scaling AI products and services, while encouraging the same in the private sector.
3. Secure our future with homegrown AI: ensuring the UK has national champions through a new unit, UK Sovereign AI.

“In the coming years, there is barely an aspect of our society that will remain untouched by this force of change,” said Prime Minster Keir Starmer, in part of the government’s response to the recommendations, which sets out how the government will take forward the 50 recommendations included in the plan.

In practice, it’s hoped AI will enable public sector workers to spend less time on admin and more on delivering services. The opportunity for public benefit is huge: ranging from automated pothole inspection to faster cancer diagnosis.

The Prime Minister has now tasked his cabinet to make AI adoption a top priority for their Departments.

The Multiverse take: AI tools are only as powerful as the humans that wield them

The focus on skills and talent in the action plan is welcome – AI adoption must start with skills.

Here’s what Euan Blair, Founder and CEO of Multiverse, said following the announcement:

“Being first to mass adoption is just as important as being first to innovation. We may have missed the first-mover advantage on LLMs and data centres, but it’s encouraging to see the UK Government recognises its other unique opportunity: to be first to implement AI at scale.

“None of the AI action plan can happen without a substantial investment in skills, since AI tools are only as powerful as the humans who wield them.”

Multiverse engages regularly with policymakers and will offer advice as the recommendations are taken forward.

What will happen next?

The plan sets out how the country can train, attract and retain the next generation of AI scientists and founders in a set of recommendations. These include:

• Accurately assessing the size of the national skills gap, including AI and digital skills, by Spring 2025
• Setting out how the UK will attract highly skilled AI workers from abroad in the Industrial Strategy, by Summer 2025
• Ensuring the lifelong skills programme is ready for AI by assessing the importance of digital and AI skills in the curriculum, by Autumn 2025
• Establishing headhunting capability to bring top AI talent to the UK, by Autumn 2025
• Increasing the diversity of the talent pool with a focus on improving gender balance, starting at school level, by Autumn 2026
• Expanding education pathways into AI, by Autumn 2026
• Launching an undergraduate and master’s AI scholarship programme, by Autumn 2026
• Expanding the Turing AI Fellowships offer, by Autumn 2026
• Increasing the number of AI graduates and teach industry-relevant skills, such as AI-related degrees in Higher Education, by Autumn 2027

What should employers do on AI skills?

Considering 51% of workers have received less than five hours of AI training – according to our ROI of AI report, there is a clear opportunity for employers to upskill their workforce on AI.

Equipping people with the skills and confidence to use AI will spur further growth - as workers start to see how the technology can reshape their day-to-day.

For leaders wanting to bring AI into their company, understanding skills gaps is the first port of call.

By definition: workforce planning aligns people with current and future business needs, through analysing, forecasting, and mapping workforce supply and demand.

The purpose of workforce planning is to identify current or emerging skills gaps, prepare for future talent needs, and help manage under or over-resourcing.

In this article, we’ll explore everything you need to know about workforce planning, and how to use it to build a future-proof business.

Benefits of workforce planning

There are many benefits to strategic workforce planning, including:

• Aligning your people strategy with your business goals: Ensuring the right people – with the right skills – are in place to support the company’s goals and strategy.
• Preparing for the future: By anticipating workforce challenges and trends, adapting to changes in the market, and developing succession planning.
• Maintaining cost and resource efficiencies: By investing strategically in hiring and training to ensure each team and department has the right level of resource.
• Making data-driven decisions: Using data to make informed choices on talent and training programmes.

The importance of workforce planning today

Businesses will fall behind if they don’t have the right skills in place. Workforce planning is a means to address this challenge – let’s explore the trends reinforcing its importance.

Many leaders are looking at how to future-proof their workforce – with workforce planning one way to identify and tackle AI skills gaps. More than two thirds of leaders (69%) feel their organisation will need different skills to stay competitive by 2030, according to Multiverse. Taking this a step further, a study from IBM found that 40% of workers will have to reskill in the next three years due to AI.

Workforce planning can also be used to address productivity blockers, by mapping out skills gaps and creating a plan to close them. In a Multiverse study, we found an estimated 25 days of productivity are lost from each employee due to data skills gaps, every year.

High employee churn and lower engagement are additional challenges: one study found 90% of UK employees are disengaged from their job. Skill mismatches are one cause – meaning some employees feel their abilities are underutilised or don't align with their current roles.

In response, workforce planning can improve happiness and engagement. It places employees on tasks and projects that are a better fit for their skillset and desired development pathway – and are therefore more fulfilling.

How workforce planning can close skills gaps

The AI era is fuelling a surge in new roles – AI job postings are growing 3.6 times faster than all jobs in the UK, according to PWC’s 2024 AI Jobs Barometer. It puts AI skills high in demand, creating a tight labour market.

For leaders looking to build the right AI skills within their workforce, upskilling existing employees talent may provide a faster, more cost-efficient method than external hiring.

And the appetite from employees is there: 83% of workers think developing their AI skills will help them to drive more value for their employer in the next 12 months.

Workforce planning process – how to do it

The workforce planning process generally follows these steps in a continuous feedback loop:

Workforce planning in a 7-step cycle:

Workforce planning tools and techniques

Now let’s explore how to approach tools and techniques for workforce planning.

The ‘five Rs’ framework is commonly used to align a businesses’ workforce to its goals: ensuring the organisation has the Right People, Right Skills, and Right Roles, at the Right Time, and the Right Cost.

Building on that foundation, real-time monitoring tools can be used to assess productivity, attendance, and engagement levels of employees. Companies can use skills intelligence tools to quickly assess workforce skills and capabilities (like AI), and use this data to inform Learning and Development strategies.

For example, HR teams may decide to build in-demand skills within its existing workforce while flexibly using contract workers for short-term needs – using a model that balances cost, quality, and agility.

Get started with strategic workforce planning

As the era of AI continues to heat up, so will the pressure to find and plan to have the right talent in place. Strategic workforce planning can help you spot what’s needed for success today, while being ready for the challenges of tomorrow.

HR and L&D teams can use a skills inventory as a tool to identify skill gaps, simplify talent management, and support strategic workforce planning.

Crucially, a skills inventory reveals to leaders the specific capabilities that need to be developed to hit company goals.

In this article, we’ll explore everything you need to know about a skills inventory, how to build one, and an example of how one company used a skills analysis to improve efficiency on data tasks.

What is the purpose of a skills inventory?

There are three main reasons for developing a skills inventory:

1. Identifying skills gaps which could be holding you back from growth. A skills inventory helps to quantify the scale of the challenge and the action you need to take.
2. Informing your learning and development strategy by pinpointing the areas that need the most focus. It also supports the creation of tailored employee development plans that support the overall business goals.
3. Creating new opportunities for employees: As well as identifying the skills that employees need to build, a skills inventory can also uncover hidden or underused talent in your workforce – allowing you to open up new upwards or sideways progression pathways for individuals.

What is the difference between a skills inventory and a skills matrix?

Both of these tools can be used to address skills gaps and support your workforce transformation strategy. However, they have key differences:

• A skills inventory is a dataset of all employee skills across a whole organisation.
• A skills matrix focuses on specific skills relevant to individual roles or departments, and includes the scoring captured in the evaluation process.

How to create a skills inventory in 5 steps

Now we understand what a skills inventory is for, let’s look at how to build one.

1) Establish the business goals you want to achieve

Start by setting out the overarching goals and how you hope skills can help you achieve them. One way to do this is by writing a problem statement that explains the challenge you want to overcome. The statement should factor in:

• What is the problem blocking you from?
• What value would solving the problem unlock?
• How will that challenge change if unresolved in the next 12 to 18 months
• How can skills help you reach the goal, or goals, that you want to achieve?

2) Identify the skills needed to hit your goals

To answer your problem statement, next consider what capabilities your workforce will need to achieve your goal outcome.

Group these into skill sets and organise into broader categories, such as soft skills, technical skills, and leadership skills. Qualifications and certifications can also be factored into a skills inventory.

Find out more on the differences between soft skills gaps and technical skills gaps in our guide: ‘What is a skills gap?

3) Conduct an employee skills assessment

Decide on the best way to test or assess the skills of your workforce. Different methods include employee self-assessment (either manually, or using a dedicated skills intelligence tool), skills tests, and performance reviews.

Next, rate proficiency for each skill using a uniform scale (such as 1 to 5) to establish a consistent benchmark.

If you choose to interview one-to-one or if employees are self-evaluating, here are some questions to include:

• What skills do you have that the company could use more effectively?
• How do you rate your proficiency on [skill] out of five?
• Where do you want to focus your personal development in the next six months?

4) Map the results in a skills matrix

The evaluation results can be captured in a skills matrix at an employee, team, or department level.

The tool gives managers and leaders a view of proficiency levels of the desired skills, clearly showing strengths and skills gaps.

Discover our guide: What is a skills matrix?

5) Regularly update your inventory

With skills gaps identified, update your inventory data and treat it like a live document – for example, making updates when an employee training programme has finished and there’s new self-assessment data from employees to input. This is an opportunity to measure the programme’s impact, calculate ROI on employee training and inform your next steps.

Another check-in point is when there are changes and updates to the business strategy. Always think: how will this impact the roadmap of skills you need? Make sure any new requirements are captured in your skills inventory.

Skills inventory example: EDF Energy

Let’s look at how EDF Energy was able to identify their workforce skills gaps and create a skills inventory, with support from Multiverse.

A Multiverse skills analysis assessed EDF employees who use data daily – and it found they were spending 19.9 hours a week on data tasks.

The analysis mapped employee skill levels in areas like predictive modelling, engineering data, producing visualisations, managing spreadsheets and analysing data.

The exercise revealed 50% in potential time savings which could be achieved through data upskilling.

To capitalise on these efficiency gains, EDF launched a Multiverse Data Academy programme, enrolling staff on apprenticeships to build data skills.

Use a skills inventory to close skills gaps

A regularly updated skills inventory supports strategic workforce planning.

By following these steps, leaders can get the right people working on the right projects, faster.

Specific skill gaps can be targeted through learning and development programmes. And leaders can identify the skills needed to stay competitive and ahead of the curve.

Unblocking digital transformation can improve operations in public services – and ultimately better serve the taxpayer. So what can leaders do to drive their digital transformation approach forward?

We recently held a virtual roundtable with representatives from local authorities and the NHS to discuss methods for unlocking success in digital transformation.

We heard from Sarah Wilkins, CDIO for North London NHS Foundation Trust, Tony Clements, CEO at Ealing Council, and Euan Blair, Founder & CEO at Multiverse, on how data and AI strategies can help overcome digital challenges and deliver a seamless service to local communities.

If you’re short on time, these are our five takeaways from the session:

1. Collaboration creates better citizen outcomes

Collaboration is critical for NHS Trusts and local government. It helps continue service between health and social care, resulting in the best patient outcomes.

Tony already has ideas about how to bridge the data divide using AI. He said: “We're in a trial cohort at the moment of designing a generative AI tool that takes social workers’ notes and relational data, turning them into formats that work for clinicians.”

He hopes to remove technology boundaries between the NHS and councils, creating a robust and personalised approach to care.

Sarah told us how the Trust supports collaboration by participating in schemes like the London Care Record. The system shares patient data across organisations to support integrated care.

Gradually, greater collaboration helps to bridge professional, cultural and informational gaps between public services – all to provide better outcomes for local communities.

2. Data is most effective when strategically integrated

Public sector bodies have the opportunity to better integrate data into workflows – helping drive greater productivity and improve decision-making.

Sarah explained: “We’re drowning in data, but we weren’t always using it intelligently, to generate the insights that we need.” To develop the skills to use data more strategically, the Trust partnered with Multiverse to start a Digital Academy, funded by the Apprenticeship Levy.

Today, clinical teams can use their caseload management tool, MaST, to identify patients at risk of going into crisis. It helps them allocate resources accordingly and reduce hospital admissions.

Ealing Council is also taking action to improve its digital transformation approach by enrolling 45 employees in a data academy, with the hope of expanding the partnership further.

Tony says the area is ripe for collaboration: “There's a real opportunity to combine NHS and wider local authority data so that we can move away from some of those lag indicators and move towards earlier predictions.” For example, the team has started using predictive analytics to predict which residents might enter into adult social care soon.

3. AI can accelerate productivity and performance

Teams are also integrating AI into workflows to boost productivity in the public sector.

At Ealing Council, teams use a tool called Magic Notes to minimise the administrative workload associated with adult social work. AI also helps to identify the need for practical interventions - if a final assessment doesn’t match the evidence, the system flags discrepancies for Ealing’s social workers to review.

Tony told us: “The more we can get our social workers doing what they do best, which is working in people’s homes on the things that matter to them, the better.”

Despite some initial scepticism, teams now understand that AI can have significant benefits for their own workloads. Sarah told us: “It's about removing that admin burden from clinicians and freeing them up to work to the top of their licence – and do the things that only they can do as clinicians.

“It helps them focus their attention on the caseload that’s most in need, and give those patients better outcomes.”

4. Upskilling is a critical tool for building data literacy

Sarah and Tony both highlighted the critical role of upskilling to build data literacy. Training enables learners to better interpret data and apply it to decision-making around service design.

Alongside formalised learning, Tony stressed the importance of a distributed model for the training and adoption of new data and AI tools. He explained: “I'm not a chief exec that's going to come in with a centralised transformation programme designed in the back room by a bunch of consultants. Our approach is to put tools directly into people's hands and to establish those use cases through practice.”

He explained: “We’ve been experimenting with Microsoft Copilot, with 300 of our staff initially given licences, to use in the ways that they feel best work for their area of business. We’re bringing that together in what we've called our centre of excellence – where staff learn from each other how they’re using the new tool, and then spread that knowledge and skills laterally, as much as top down.”

Euan agreed with the two-pronged approach, accounting for both tech and skills: “If we want to benefit from the huge potential technology offers, we have to train and skill the incumbent workforce. Tech tooling can create huge amounts of value, but only if it's implemented and people understand it.”

5. Buy-in is needed from top to bottom of the org chart

The panel spoke about the importance of building a future-ready workforce and a culture of continuous learning. But to build that foundation, you need buy-in at all seniority levels.

Sarah stressed the importance of bringing end-users along for the journey, demonstrating the value of digital skills and systems. She said: “It really is about empowering all levels of the organisation. It can't just be top down, or you meet resistance.”

Executive sponsorship for change initiatives is essential. As Euan put it: “You need clear sponsorship from the business at the leadership level across technology and data. If it ends up being an HR and L&D effort only, then it often ends up not fully delivering everything it can.”

For Tony, it’s about change leadership and managing a gradual process: “You can't always get enough buy-in. So sometimes, you've just got to take a moment, be conscious that you are probably pushing the organisation a bit further than it's ready for, and then be prepared to pick up the pieces.”

Digital transformation in public services isn’t just about investing in new technology – people and skills are critical to success. To learn how to boost productivity, deliver better outcomes for communities, and create new opportunities through data and AI skills, speak to our specialist team.

Appetite for AI in business is high, and 98% of leaders believe the benefits have met or exceeded their expectations. But understanding of AI – and the skills to use it – remains inconsistent in the workforce.

Generative AI throws another curveball into the mix. Employees are already actively experimenting with ChatGPT, without necessarily understanding best practice.

In this complex picture, what’s the best way to equip employees with AI knowledge that will deliver value for the business – and manage the risks?

At our recent panel, Bridging the AI gap: From classic challenges to generative opportunities, leaders looked to answer just that.

David Reed, Chief Knowledge Officer at DataIQ, chaired the session featuring PA Consulting’s Global Head of AI, Alwin Magimay, Capita’s Director of Operational Excellence, Edward Boyns and Multiverse’s Head of AI, Anna Wang.

Build AI skills in the business with formal learning pathways

AI tools are becoming more commonplace in every organisation. 81% of tech leaders plan to increase AI investment in the next three years and beyond.

But people will be crucial for AI to deliver on its promise – and to date, the majority of workers (51%) have received fewer than five hours’ training on AI.

For both Capita and PA Consulting, building capabilities in the workforce by training existing employees is crucial. As Ed explained, AI is a central part of the business transformation strategy underway at Capita.

“We need to educate our own people on how to use AI and extract value from it, because we need to practise what we preach to sell it to our clients in a compelling way,” Ed explained.

“We now have a cohort of 95 apprentices, working on a Multiverse programme. It’s about teaching our organisation how to get the most value from AI, for us and our clients.”

Find out how Capita launched a new AI training Academy in partnership with Multiverse, delivering AI apprenticeships for more than 100 colleagues

Upskilling is part of an equitable value exchange with employees for PA Consulting, according to Alwin. “We aim to give as much as we receive to our staff. We keep things interesting with a 70:20:10 ratio of time spent on consulting, research and personal development respectively, including learning AI skills.”

With an AI-enabled workforce, businesses can realise the value of their investments – and support employee satisfaction and retention.

Nine in 10 employees are keen to improve their data skills, and 83% say AI skills will help them to drive more value for their employer in the next 12 months.

Teach employees when to use AI for the best ROI

Generative AI has introduced a new dynamic for businesses – by significantly lowering the AI barrier to entry.

“The biggest innovation of ChatGPT is that it put this technology into the hands of everyone,” Anna explained. “Any person can now tell a computer what to do and what not to do. That breakthrough really levels the playing field.”

There are parallels with past technologies. In the 1900s, you had to be a mechanic to drive a car, Alwin highlighted. Now, you can just focus on the driving – you don’t need to understand what’s under the bonnet.

In the same way, employees at all AI skill levels have direct access to the tools and are keen to use them. Most workers have gained their AI skills by playing with ChatGPT (61%) or learning on the job (59%).

But context is everything. According to David, “It’s not just understanding how to use the tools and technology. It’s also when it’s appropriate and when it isn’t.”

Alwin explained, “The tasks we do at work tend to fit into four quadrants: bespoke, curator, repeatable, predictable. AI is best applied to tasks that are repeatable and predictable – the low-hanging fruit.”

To leverage AI to its full potential, workers need to understand this cost/benefit analysis – to see where the technology will add value, and where it won’t.

As Ed noted, “Be really clear on what your costs are from an AI perspective. Don't start with AI being a solution that you really like, and you're going to try and find a problem to fix with it.”

Managing AI risks: key questions to ask

Many businesses lack clear guardrails over how AI is used. Only 28% of leaders are confident they have established best practice in providing governance structures to limit AI risk.

“The main challenge of using generative AI is data custody,” explained Anna. “The data input shouldn’t be confidential. Consider the malicious applications of the data, the potential for impersonation and vulnerability to cyber-attacks. There are a lot of risks.”

Ensuring a system is fair, transparent, responsible and accurate depends on knowing the questions to consider.

“For each application, you have to explain what AI tools are involved and how you’re going to use it,” explained Ed. “Are you going to store the data? Are you going to let the data be used to teach the model?”

Employees need to understand the impact of how AI is used, but many lack foundational data skills. 57% have no or basic Excel skills – and as David noted, this means businesses may be “trying to run with AI before they can walk.”

Nine in 10 employees want to improve their data skills – and this could unlock greater productivity, as well as increasing their understanding of the implications of different AI uses.

Ensure AI upskilling is consequential, with a clear business ROI

AI projects must deliver meaningful and measurable results for businesses. Likewise, upskilling programmes have to be tailored to benefit the organisation, and not only the individual.

As Anna explained, “The vision of Multiverse is to make learning more scalable, personal and consequential for every business.”

Ed has worked closely with the AI apprentices at Capita, helping to shape the project proposals they are developing in the programme. “We can make sure that they will drive value, and they are looking at problems that are significant to the organisation, that are scalable, and can drive value for Capita.”

AI is in turn helping to reimagine the learning process at Multiverse. Atlas, an always-on copilot, has also been supporting learners on Multiverse apprenticeships since early 2024.

With personalised learning pathways, upskilling can deliver for both individual students and the business as a whole.

Read more examples of AI upskilling in practice

Our panel was clear that AI holds great promise for businesses. But people need the skills and understanding to use the technology to its full potential, and proactively mitigate risk.

Training programmes that are directly tied to business benefits will not only support individuals, but deliver better outcomes for the whole business– as Capita and PA Consulting show.

To learn how upskilling can deliver meaningful business results for you, watch the full panel and explore our AI courses for business.

Last week at Multiverse, we were thrilled to host the Minister for Skills, Baroness Jacqui Smith, to discuss the future of apprenticeships and the role of AI. Baroness Smith met learners and alumni to hear their experiences of apprenticeships and what can be done to improve this crucial pathway even further.

“It’s been great to come to Multiverse and to meet some of the apprentices, who spoke to me about the opportunities that they found through apprenticeships,” said Baroness Smith.

Enhancing apprenticeships with technology

With the speed and scale at which skills gaps are emerging in the UK, it’s important to consider how technology can help to shape and deliver effective apprenticeship programmes. Baroness Smith joined CEO Euan Blair for a demonstration of how AI can enhance learning and improve outcomes.

“The world moves quickly, particularly in the area of technology, and we’ve got to make sure that apprenticeships are keeping up,” the Skills Minister reflected. “It’s been really great to see how it’s possible using technology here to personalise apprenticeships for both employers and for the learners, the apprentices, so they’re getting what they need, including support along the way.”

Apprenticeships for every age and every stage

Baroness Smith joined a roundtable to hear the experiences of apprentices, including their thoughts on how the format can evolve — particularly quickly enough to keep up with changes in technical skills.

One point of discussion was how to make apprenticeships suitable for every age and every stage — appealing to both experienced professionals and school leavers. The applied learning offered by apprenticeships delivers significant value for people starting their careers, but equally provides invaluable training for those further along their career journey, looking to reskill.

Some of the group used their apprenticeships to return to the workplace after a career break. Nadine, a Senior Analyst at Citi, applied to Citi's Reactivate Your Career programme and completed a Level 3 Data Technician apprenticeship with Multiverse after an 11-year career break to care full-time for a daughter with special needs. Through the programme, she’s upskilled in data analysis and technology, preparing her to succeed in the next phase of her career.

Others commented on the value of continuous learning: “I found it really heartening — and such a boost to my confidence — that you can still learn, whatever your age,” shared Shubhada Paranjape, Product Engineer at John Lewis Head Office.

Apprenticeships are designed to meet the needs of employers and individuals, which can vary significantly. The group explored ways to make programmes even more accessible by tailoring them to each individual and job role, such as through modular apprenticeships and shorter courses.

With the government exploring ways to introduce greater flexibility in apprenticeships through the Growth and Skills Levy, there may soon be more ways to shape personalised courses that deliver for employers and the wider economy.

The transformative power of apprenticeships

Apprenticeships in AI, data and tech skills can transform careers — and the roundtable attendees shared many positive experiences. As well as upskilling for their roles, several alumni have now established a habit of continuous learning, to futureproof their careers as technologies evolve.

The individual coaching provided during each apprenticeship was highlighted for providing support and advocacy. “My Multiverse coach really helped me with developing soft skills and confidence — it was so powerful to have a safe space to be honest and just say 'I'm struggling with this concept'", reflected Ryner Gold, Level 4 Software Engineering Alumni.

Given the benefits they had gained, the apprentices spoke to Baroness Smith about ways to increase the appetite for apprenticeships among learners and employers. Everyone agreed on the importance of challenging more traditional views around the relative merits of university and apprenticeships — and giving equal kudos to each route.

Engaging teachers and parents at the school stage, and employees and organisations at the professional stage, will help to improve the reputation of apprenticeships.

One former apprentice, Tasnem Chawdhry (Level 3 Data Technician Alumni) is using the confidence she gained during her course to speak with school leavers directly. "As an introvert, I could never have imagined myself stepping out of my comfort zone to connect with others,” she said. “Now, as a Multiverse ambassador, I visit my own school as well as other schools and colleges to inspire and encourage people to pursue apprenticeships.”

Solving skills gaps, fast-tracking careers

Apprenticeships are a powerful means of solving skills gaps for organisations and fast-tracking career growth for employees of all ages and stages. We’re grateful to Baroness Smith for joining us to discuss the future of apprenticeships and how the UK can continue to benefit from this valuable training.

Explore the proposed reforms to the Apprenticeship Levy and what the changes could mean for your business.

As many as 25 days a year are lost to data skills gaps according to Multiverse research.

A skills matrix can help map these gaps and give leaders direction on what action to take.

In this article, we’ll explore what is a skills matrix, how to use one, and best practices for creating your own.

What is a skills matrix?

A skills matrix maps employees’ skills onto a grid with each person rated for proficiency, helping leaders understand how well different teams or a whole workforce can perform specific tasks.

The exercise helps identify skills gaps and growth opportunities for current employees while acting as a roadmap for a wider skills strategy.

A skills matrix is different from a competencies matrix. A competency matrix looks broadly at attitudes, knowledge, and behaviours, whereas a skills matrix focuses purely on skills.

What is a skills matrix used for?

An estimated 11% of the working week is lost to data skills gaps, according to the Multiverse Skills Intelligence Report. But by mapping the gaps using a skills matrix business leaders can:

• Create well-rounded teams with complementary skills – putting the right people in the right place.
• Track progress in skill acquisition over time, with success measured against business goals.
• Support the case for employee training programmes and upskilling.

How to use a skills matrix

A skills matrix is based on data, helping you make better decisions about placing the right people on the right projects.

If information is lacking – for instance when a company has no central record of the skills available – it’s harder to make the best choices.

So, alongside a skills matrix, the best practice is to build a skills inventory – which digitally documents all the capabilities in a company.

Using this foundation, a skills matrix can assess the strengths and weaknesses of individuals, teams or departments – scored against company goals.

By comparing available skills, it helps you make informed decisions about employee training, resource allocation and hiring.

And because it’s grounded in data, you reduce the need to make skilling decisions based on guesswork or industry trends.

Skills matrix example – free template to use

Below is an example of a skills gap matrix – mapping out the desired skills with scoring for where an employee is today and the ambition for the future.

What should be included in a skills matrix?

When used as part of a skills gap analysis, an effective skills matrix will bring together several elements beyond the table of desired skills scored against a proficiency scale.

Here are five best practice points for using a skill matrix:

1. Create a skill taxonomy: Follow a structured list of skills defined across all departments, as part of a skills inventory. Standardising the language makes it easier to measure and track progress.
2. Calibrate skills matrix proficiency scales: Make sure these reflect business needs and with clear progression across different levels. This ensures consistency when used across the business.
3. Integrate with HRIS (HR information systems) and skills assessment tools: Make your skills matrix a live document, allowing for continuous monitoring with updates made in one central place – helping you track progress and prove ROI.
4. Workforce planning: Use predictive analytics to assess future skill needs with strategic workforce planning. Identify opportunities for upskilling/retraining the existing workforce and ensure efforts create immediate business impact, not just provide an employee benefit.
5. Foster a culture of continuous skill development: Encourage employees to take a stake in their progression while showing how they can make an impact on business goals.

Using a skills matrix to prevent skills gaps

When demand for AI and data skills is growing, leaders looking to the future can benefit from mapping their current skills gaps using a skills matrix.

The time to act is now: 90% of employees want to improve their data skills, according to the Multiverse Skills Intelligence Report

Skills are viewed as crucial to future business success. According to Multiverse research, more than two thirds (69%) of leaders believe their organisation will need different workforce skills to stay competitive by 2030.

It’s no surprise that businesses are upping their investment in learning as a result, with some 77% of leaders predicting their learning and development budget will increase by 2030.

Artificial intelligence (AI) is another driving force. As leaders push their organisations to adopt AI, workforce skills shortages are slowing progress – or even stopping AI initiatives in their tracks entirely. Tech leaders name skills gaps as a top blocker to AI implementation, with half of organisations planning to plug the skills gap with training as a result.

With these forces already in motion, HR leaders will face growing demand to show proof that employee training initiatives are delivering real business impact – and measurable ROI.

In this article, we will explore five simple steps for calculating the return on investment of employee training, helping you measure impact and make the case for future spending.

Understanding ROI in the context of employee training

ROI is a financial metric that measures the profitability of an investment by comparing the net gain or loss to its initial cost. A ROI calculation helps business and HR leaders evaluate the effectiveness and value of learning and development initiatives.

As well as validating the use of training time and budget, calculating ROI helps businesses to monitor the effectiveness of training programmes, and connect training with wider business goals.

Step 1: Set clear employee training objectives

Before launching any new employee training or upskilling initiative, clearly define what you want to achieve, and the metrics to measure success.

Wherever possible, align all learning programmes with strategic priorities and business outcomes. For example, an outcome could be: implementing an AI strategy, boosting productivity, or improving employee retention.

Next, assess the barriers that prevent you from achieving these aims, such as low employee satisfaction, low adoption rates for new AI tools, or high turnover rates for data specialists.

Then, agree on the metrics you will track to measure changes to these outcomes. Increasing productivity, for example, can be measured by looking at output per employee, the time saved on tasks, or quality improvements.

If addressing specific skills gaps, such as the AI example mentioned above, conducting pre- and post-training employee skill assessments can help you further benchmark and measure success.

Step 2: Work out the costs of employee training

Tallying up your costs will come from two different sources. Firstly, direct costs include the payment for any training materials, an external trainer or supplier if you used one, and any venue or equipment charges.

Secondly, indirect costs come from the time employees spend away from their daily tasks to complete the training.

Step 3: Map the tangible and intangible benefits of employee training

Here, the benefits split into two categories:

• Tangible benefits include productivity gains, employee retention improvements, and any cost savings created by employees using their new skills from the training programme.
• Examples of intangible benefits are enhanced collaboration, improved confidence, and an increased potential for innovation – which can be measured through methods like employee surveys or testimonials. Keep these in mind for reporting back (see step five).

Step 4: Run your ROI calculation

When going through this exercise, consider how to balance any short-term costs with the long-term benefits. A typical ROI formula looks like this:

ROI = (Benefits of employee training, minus costs) divided by costs x 100

For the clearest ROI figure, include the metrics which can be quantified. Usually, this is monetary: it can be measured against both the inputs of time and cost, as well as the outputs, such as increases in revenue and productivity.

Step 5: Tell a story when reporting results

Now it’s time to communicate the impact. After running the ROI calculation, don’t forget about the intangible benefits you recognised: they form a crucial part of the story to tell.

Make sure your metrics and objectives line up with the business objectives, visualise your data to make it more digestible, and include qualitative data such as employee testimonials to add colour.

Be transparent about any challenges to help inform future plans. What did you learn, and what could change next time to improve ROI?

Case study example: Jaguar Land Rover

Let's look at how Jaguar Land Rover measured the impact of its upskilling efforts. The company had three objectives:

• Equip employees with the knowledge and skills needed to identify opportunities to reduce costs and increase revenue.
• Build a data-driven culture that knows how to apply and leverage data in various scenarios – with the goal of increasing productivity and efficiency across the business.
• Increase employee satisfaction and engagement by investing in skills development.

Jaguar Land Rover launched the Multiverse Data Fellowship programme to equip employees with the skills needed to become experts in data analysis, modelling and machine learning. Currently, there are 600 employees in the programme across every department.

After assessing the ROI of the employee training programme, JLR found that learners had:

• Identified new opportunities to increase efficiency
• Increased productivity
• Increased employee satisfaction

Read the full story here.

Linking ROI with the bigger picture

Calculating the ROI of employee training is not just a financial exercise – it's a powerful way of advocating for further training initiatives.

By showing the real benefits of past training, HR leaders can make a compelling case for additional investments in employee development – especially given today's urgent demand for workforce AI skills.

Get in touch about your skills transformation today.

Without it, a lack of clear vision, skills, and data literacy will hold back growth – with companies unable to turn an exponential explosion of data into a competitive advantage.

By 2030, GDP could increase by as much as 26% from AI productivity gains, according to PWC. This expansion will only come if workers have the skills to input clean data into AI models.

It means companies with a strong data culture will have the upper hand as AI adoption takes hold.

In this article, we’ll explore what a data culture is and the practical steps for building one from our data experts.

What is a data culture?

Data culture is where data is deeply integrated into all aspects of an organisation’s operations and decision-making, with every individual fluent in what data means for their role.

The ingredients of a strong data culture include:

• Data-driven decision-making: A commitment to make decisions based on data rather than intuition or guesswork.
• Widespread data literacy: Any employee, at any level, can read, understand, create, and communicate data.
• Data governance and trust: Solid data governance frameworks ensure data quality, security, and compliance – creating trust for anyone using it.
• Data accessibility: There’s transparency in how data is collected, processed, and used. There are no silos, with data readily accessible to anyone.
• Data is seen as a strategic asset: There’s a clear understanding of how data contributes to success and competitive advantage.

In a strong data culture, the average employee lives and breathes data within their day-to-day tasks. Managers use data to inform decisions. And senior leaders underpin the wider business strategy with data.

Why build a data culture? Benefits and examples

Nearly nine out of ten (88%) business transformation initiatives fail to achieve their original goal, according to Bain & Company. For many companies, this is because they lose focus on maintaining and developing their new capabilities.

A data culture overcomes this, with teams ready to take on new tools and change their ways of working. Benefits include:

• Greater productivity and operational efficiency: Employees can easily process and visualise data, saving time on every data task. Examples include using predictive analytics to optimise the supply chain or improving inventory management by accurately forecasting demand.
• No more ring-fenced data teams: With widespread data literacy, employees can self-serve insights, reducing the load on internal data specialists – who in turn gain time back to focus on more complex initiatives in the overall data strategy.
• New opportunities: When encouraged to work with a data-first mindset, employees can accelerate the speed of projects and uncover new revenue stream opportunities through advanced insights – bringing new ideas and products to market faster.

Five steps to build a strong data culture

Once you’ve identified your current state, be bold in your ambition. A strong data culture is not the destination, it’s a journey. Here’s how to bring everyone along the way:

1. Align a data culture with your business goals

Start with a clear rationale for your data culture. Assess the internal data capabilities and employee skills you would need to establish one. Set out the benefits for the business as a whole, as well as the benefits for individual functions and role types.

2. Spot skills gaps and spread data learning across all levels

Assess your training needs by identifying data skills gaps. A skills matrix is a simple framework to map out your state of play, helping you target learning opportunities for all employees at any seniority level. Building data capabilities at all levels of the org chart means everyone takes a stake in supporting culture change, rather than creating silos.

3. Help employees understand the value of data

When employees see the value data can create, more will look at how their data skills can be applied to improve their roles. When a data culture takes hold, this mindset supports data-driven decision-making. Managers and leaders will act on real insights rather than hearsay, making decisions more targeted and impactful.

4. Create space to share ideas and best practices

Cross-functional data projects and creating Centres of Excellence (COEs) can help to build good data practices across the workforce. By offering opportunities for teams to collaborate with data, knowledge sharing and data-driven efficiencies break out of silos.

5. Measure the impact of your data culture

Transparency and reporting back progress to the whole business creates a feedback loop grounded in data, showing success and keeping everyone bought in. One example is CBRE, which measured the time saved on run-rate processes and calculated the overall time and financial savings for the business.

Discover how CBRE built data skills across every level of the business to create a self-sustaining data culture.

The need for upskilling in a strong data culture

Across the workforce, data skills are in high demand and short supply. According to our Skills Intelligence Report, 25 days of productive time are lost to data skills gaps annually. More than half (57%) of workers have no – or just basic – Excel skills. Some 86% have no Python skills.

Upskilling is one way to bridge this gap: by expanding skills and knowledge to better meet the demands of evolving job roles. It’s a route that helps your existing workforce make the most of vast internal and external datasets, readying them for the rise of artificial intelligence.

When coupled with continuous learning, these training tactics can help employees make the most of data, supporting a strong data culture.

Start shaping your workplace data culture with Multiverse

Need more advice on building a thriving data culture?

Multiverse can help. Learn more about our range of employee training and data upskilling programmes.