Employers

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.

It’s beyond doubt there’s huge potential for AI to deliver results and economic value for businesses, from greater productivity to improved customer experience.

But to build a truly AI-native business – where AI is baked into the DNA of your business, and delivering maximum ROI – multiple elements must work in harmony, or momentum can easily stall.

We recently spoke to 2,000 tech leaders and employees – to get a realistic understanding of AI maturity today and what businesses can do to improve. And it’s clear that barriers to AI adoption are preventing the technology from delivering on its promise.

But what are they? Here’s three common roadblocks and how your business can start to overcome them:

1. Overestimating AI maturity

We found that four in five leaders say implementing AI has led to an increase in revenue generation, while 97% say the benefits have met or exceeded their expectations. Overall, 57% believe they are ahead of the competition in AI maturity.

Today, optimism in AI for businesses is understandably high. However, there are signs this may be an overestimation of progress. And optimism could be masking the realities of what it takes to fully implement and benefit from the technology.

As AI continues to evolve, establishing best practice is an ongoing challenge that’s creating risks and potential missed opportunities for the future.

Strength in areas such as data governance and security are vital hallmarks of AI excellence – and necessary requirements to reach AI maturity. But they are being overlooked by many.

Only a small proportion of leaders report they have established key hallmarks of best practice —for example, just 28% strongly agree they have provided guardrails and governance structures to limit AI risk. And less than half (43%) strongly agree they have ensured responsible use of AI in business practices.

This gap between leaders’ expectations and reality suggests that businesses are struggling to objectively assess their own progress with AI – and identify the further steps needed for full implementation.

Using a more objective framework to benchmark progress, categorise the stages of AI maturity, and create a roadmap for next steps can help businesses to plan more holistically – and realistically. We’ve included 3 actions for leaders in our ROI of AI report to get you started.

2. Difficulty securing investment and demonstrating ROI

Our research found tech leaders are positive about AI delivering financial gains in the long term – in fact, 85% expect to see an increase in revenue generation in 3-5 years.

But if businesses are unable to prove the value of AI today, and if employees lack the skills to access its full potential, then it will become increasingly difficult to unlock further investment – and AI progress will stagnate.

Of all the AI adoption barriers cited by leaders, 63% say the biggest blocker to further investment is the inability to fully use existing AI technology. Paired with more than half (58%) reporting resistance from employees to use AI and a lack of ability to demonstrate or predict tangible results (57%), it’s clear we’re at a standstill.

Value driven by AI needs to be tracked diligently and communicated within businesses. Only then can roadblocks, like resistance from employees or workforce skills, be tackled head-on. Take a look at our recommendations for employers in our ROI of AI report to find out more.

3. Employees still lack access to formal AI training

True AI maturity depends on people as much as technology, and our data shows a lack of workforce skills is slowing AI adoption progress.

Businesses need to build workforce expertise, fast, to combat struggles with implementation and get the most from AI. But training opportunities remain in short supply.

We found that most employees (51%) have received fewer than 5 hours of training on AI, with 25% opting to self-fund training. And many have gained skills by playing with ChatGPT (61%) or learning on the job (59%).

Of the employees we spoke to, 56% of workers that describe their AI skills as ‘expert’ have not received any formal training from their employer.

This gap in formal training may mean workers struggle to assess whether their actions are aligned to company policies or broader best practice – in turn, creating potential risks for the business.

Currently, workers are largely fending for themselves which has a number of ramifications for employees and businesses alike. For the worker it can be difficult to understand their personal skills gaps and learn efficiently with limited timeframes. For the business, informal AI usage from employees increases risk of misuse, and limits ability to measure ROI from new tools.

Learn more about your AI maturity

Assessing AI maturity helps businesses get the most out of emerging tech. From prioritising investment to identifying skills gaps, understanding where your business is on the AI maturity scale is the key to access future growth.

To learn more about AI maturity and next steps, check out our full ROI of AI report.

The challenge

On average, construction employees spend 29% of their time working with data unproductively, according to the Multiverse Skills Intelligence Report. It’s a growing challenge, particularly in a space like construction – where daily workflows are so closely tied to complex, business-critical datasets.

To store, use, and analyse data more effectively, construction companies are taking steps to build their data maturity.

Laing O’Rourke is one example. Since 2021, the team has worked with Multiverse to improve data skills and create data champions across the organisation. At Big Data LDN 2024, we heard how learners have created new efficiencies through data upskilling programmes.

Our panel included Pedro Rente Lourenço, Group Head of Data and Analytics at Laing O’Rourke, in conversation with Louisa Dunwiddie, Enterprise Account Director at Multiverse. We discussed how Laing O’Rourke has enhanced workforce capability and powered a data revolution within its business, laying the foundation for a valuable data strategy.

The upskilling opportunity for construction transformation

The construction industry deals with complex data, from geospatial and survey information to cost estimations and financials. As the scale of projects and data estate grew at Laing O’Rourke, decision-makers realised that data skills were needed outside of a ring-fenced IT department. If not, critical skills gaps could slow productivity.

As Pedro told us, “Data governance cannot be confined to a data team – it needs to spread out across our projects, because every project is almost like its own business.” Multiple teams across Laing O’Rourke stood to benefit from data upskilling programmes.

The Data Academy at Laing O’Rourke

Laing O’Rourke partnered with Multiverse in 2021 to establish their Data Academy. They used the Apprenticeship Levy to fund employee upskilling in data and AI, at no extra commercial cost.

Initially, 87 employees enrolled in courses to improve data skills across the company – transforming how they handle and gain insights from data.

Today, Laing O’Rourke has had nearly 300 members of staff enrol on the programme, driving transformation within the firm.

The results: The impact of the Data Academy

Pedro reflects that initially, Laing O’Rourke simply wanted to see whether the programme would “stick”.

They saw fast success, and now, staff from teams across engineering, quantity surveying, HR and more have learned how to use data more productively.

“It’s created more and more demand, because when staff see the value, they see there is a clear return on investment.”

The Data Academy has shifted Laing O’Rourke’s operating model – bringing data capabilities out of the IT team and closer to other employees who use it every day. It has two main advantages:

1. Measurable efficiencies for dashboard product owners

Employees with newfound data skills have driven new levels of productivity for Laing O’Rourke. Pedro told us how data-literate teams can now generate dashboards, develop systems for automation, and reduce silos across the organisation.

The programme has also helped staff make sense of data and explore new opportunities with technologies like AI. Pedro highlighted how they can “look beyond chatbots” to applications such as risk management and data-driven sustainability initiatives.

But while anecdotal evidence tells a compelling story, Laing O’Rourke’s transformation journey needs to be informed by data. Tools to measure the success of change help the team validate the value of upskilling through a standardised return on investment analysis.

“We are continuously analysing ROI in a standardised way, so when people are going through the cohort and developing new solutions, we can see how much it cost and how much time it saved them,” Pedro explained.

The figures are then validated with line and functional managers to support the business case for upskilling. Ultimately, Laing O’Rourke has found that if more people in the business have data skills, more value can be unlocked.

2. Organisational culture change

To drive the success for the programme, Laing O’Rourke selected members of staff who worked with a lot of data to participate. These were the stakeholders who could influence the most change and increase wider data literacy across the organisation.

They found that once staff were aware of the importance of data quality, they would try to design new ways of working that led to process change. “That’s where we really see increased capabilities,” Pedro reflected, “when staff ask “why is this important? what can I actually do with this?”

This mindset change spread down from leaders, and out through individual branches of the business.

“There’s a butterfly effect when you’re reducing silos and breaking down barriers. When employees can save themselves five hours a week, they can enable their team to each save five hours a week. That’s where we see the culture change and the real transformation.”

Data skills are critical for modern organisations

Since recognising the lack of data maturity in the business, the team at Laing O’Rourke has successfully developed data capabilities and driven business growth.

Embedding data skills throughout the workforce is critical for staying competitive as industries accelerate their transformation efforts.

As Pedro put it, “you can bring as many great technologies in as you want, but if you don’t bring people’s knowledge up and give them skills to work with that data, you’re not going to get the benefit.”

Watch the full session with Pedro Rente Lourenço and Louisa Dunwiddie at Big Data LDN 2024.

Find out more about Multiverse’s data programmes to upskill your staff, achieve greater data literacy and build toward business transformation goals.

Half of business owners believe gaps in key tech and data areas will negatively affect business performance over the next decade, across metrics like profit and customer satisfaction.

But policymakers have plans for change. Labour’s new body Skills England is currently being established to drive economic growth, widen career opportunities, and meet future workforce skills needs.

Here’s what employers need to know.

What is Skills England?

Skills England was one of Labour’s key skills manifesto pledges. It is a new Government agency, sponsored by the Department for Education that aims to unify the skills landscape, assess the UK’s skills gaps and transform the system. It will bring together stakeholders across government and beyond, including businesses, training providers, unions, Combined Authorities and regional organisations to collaborate and inform the design of apprenticeships and other training.

In June 2025, Skills England replaced the Institute for Apprenticeships and Technical Education (IfATE), which was the non-departmental public body that oversaw the UK’s skills system. Skills England will take over IfATE’s responsibilities, and it also has an expanded remit including helping inform policy development.

Skills England is chaired by Former Cisco UK & Ireland CEO and Chairman Phil Smith CBE. The board and members, are responsible for shaping its strategic direction and have been appointed from across the skills system.

Skills England and Apprenticeship Levy reforms

One key responsibility Skills England will hold is to create and maintain a list of training courses eligible for funding through the new Growth and Skills Levy, which is set to replace the Apprenticeship Levy. You can learn more about the Levy reforms in our guide for employers.

What will Skills England do?

Skills England has already started assessing the state of skills in the UK, which will inform future policy on apprenticeships and technical qualifications for businesses. Its first report sets out the challenges limiting growth across three pillars:

• Local-level disparities and immobility
• Mismatched skills
• Future megatrends

One of the main ‘mismatches’ the report flags is between employer needs and digital skills. It calls out that less than half (41%) of the UK’s adult workforce are able to perform all 20 tasks deemed essential digital skills for work. These include everyday workplace skills such as communicating using digital platforms and accessing tax information digitally.

While skills shortages aren’t limited to digital roles, they represent a large gap. According to a Government Employer Skills Survey, vacancies are more likely to be due to skills shortages for digital roles (81%) than across all occupations (63%).

Skills England will use these findings to inform changes to the existing skills system. It plans to bring together different partners to match skills supply to demand and build a more coherent approach to training.

A simpler, more effective system is welcomed – providing businesses with access to essential resources for skill development and filling sector-specific skills gaps.

The Multiverse take

It’s exciting to see the UK Government focus on addressing digital skills gaps in the workforce. We’re looking to working with Skills England and seeing how it will drive positive change.

There's no doubt it will play an integral role in bringing together employers, training providers and the many moving parts of the UK’s skills economy. Collaboration that will help build the workforce the UK needs.

To learn more about Skills England, the Levy, or other ways you can upskill your workforce, get in touch with Multiverse today.

Updated: June 2025

Leaders and workers alike are seeking opportunities to increase productivity, transform the customer experience, and unlock new product capabilities using AI tools.

But if workers aren’t also equipped with basic AI literacy to help them leverage new tech effectively and responsibly, AI may only create more headaches – rather than curing them.

Successful AI adoption is often held back by a lack of workforce skills, and leaders name AI as their most significant skills gap. Despite this, many workers still lack access to AI literacy resources, with the majority of workers (51%) having received under 5 hours of training so far.

In this article, we’ll assess how the AI literacy gap forms, and what employers can do about it.

What is AI literacy? Definition and examples

AI literacy refers to employees’ understanding of AI as a technology and how it can be applied in daily work. This includes understanding what types of AI exist, identifying use cases for AI, and knowing the basics of how to use it safely.

Examples of employee AI literacy can include:

1. Using AI tools – leveraging the likes of Microsoft Co-pilot or ChatGPT in their day-to-day role
2. Spotting use cases for AI – for example, streamlining processes or increasing speed of outputs.
3. Using AI safely and responsibly - protecting sensitive data, understanding ethical considerations, and mitigating risks.
4. Technical skills - to build or develop AI tools, or integrate AI into systems, like data analysis, data engineering, or machine learning.

Why is AI literacy important?

Many employees may well have started their journey to AI literacy– most commonly, they may already be experimenting with generative AI tools like ChatGPT to increase their personal productivity.

But if teams haven’t been equipped with a strong foundation in AI skills, challenges can easily appear.

Taking AI from “toy to tool”

A lack of AI literacy may mean employees struggle to identify use cases for new tech, or select the wrong AI tool for the problem they’re trying to solve.

In this scenario, AI won’t be used to its full potential to deliver real results or solve a genuine business issue. The new technology never fully becomes a tool – it remains a toy.

This can create headaches for teams down the line. Not only can they find themselves with the wrong solution in place, but they’ll also struggle to get the desired value back from any financial or time investment.

Offering AI literacy training can help teams to think critically about how and when to leverage AI, and select the correct solution for their needs.

Mitigating risks

As well as technical skills, employees will require AI literacy training to understand and mitigate the risks associated with the technology.

Leaders and workers cite risk as their top barrier to full AI adoption – yet only a small proportion of leaders strongly agree that their organisation has established best practice in providing governance structures to limit AI risk (28%), and less than half of strongly agree their business is ensuring responsible use of AI in business practices (43%).

Training teams on AI ethics, and how to identify and manage risks around data usage, can help to prevent pitfalls early on.

How build AI literacy across the organisational structure

AI literacy is best considered across the org chart, acknowledging the different types of skills employees may need, depending on their role and seniority.

Team level

A ‘bottom-up’ approach begins with building a strong foundation of AI literacy at a team level – arming teams with the basic know-how to use AI safely and effectively in their day-to-day roles.

At this stage you can also nominate designated AI champions within your teams, responsible for spotting new opportunities for AI use cases, sharing findings, and building your AI Center of Excellence (COE).

To avoid the ‘toys, not tools’ conundrum, those AI champions take an analytical and evaluative mindset to problem-solve through the lens of AI.

Management and leadership level

Business leaders and managers are then involved as they look to place their strategic bets on AI and deliver tangible return on investment from emerging tech.

In addition to developing their individual AI skills, leaders and managers can benefit from additional training in strategic thinking and change management - to help them empower and motivate employees at all levels to adopt AI solutions and use them in a way which aligns with their goals and strategy.

Strengthen AI literacy across your workforce

If you want to unlock potential in your business using AI, it starts with a strong AI literacy foundation.

Discover how to build AI literacy in your organisation with our AI upskilling courses, and equip teams with the essential skills needed to deliver impact from AI.

HCLTech's AI academy aims to upskill its workforce in AI and generative AI (GenAI) to deliver significant business value to clients with AI solutions and boost overall productivity.

The partnership will see select HCLTech employees in the UK embark on a 13-month ‘AI for Business Value’ program, with a focus on business benefits and ethical aspects of AI projects. This initiative aligns with HCLTech’s goal of upskilling 50,000 employees in GenAI by 2025, improving productivity and enhancing client and employee satisfaction.

Upon completion of their training, employees will be better equipped to analyze their AI-integrated performance metrics, fostering a culture of insightful continuous improvement and maximizing individual and team potential.

"This strategic initiative underscores HCLTech's commitment to harnessing AI responsibly to drive business outcomes. By partnering with Multiverse, we are not only equipping our workforce with advanced AI competencies but are also amplifying our capacity for innovation and excellence in service delivery," said Ashish Kumar Gupta, Chief Growth Officer, Europe and Africa, Diversified Industries, HCLTech. "Through this collaboration, we aim to position HCLTech at the forefront of ethical AI deployment, ensuring that our clients benefit from the transformative power of AI while upholding the highest standards of integrity and productivity."

"Capturing the potential gains from AI doesn’t just rely on technology and deploying the right models, it also requires individuals equipped with the right skills to apply it in the real world. HCLTech plays a pivotal role in the global tech arena and their clients depend on its cutting-edge capabilities. By empowering teams with advanced AI skills and instilling confidence, HCLTech and its clients are set to unlock the transformative potential of ethical, precise and productivity-enhancing AI," said Euan Blair, CEO at Multiverse.

Multiverse has trained more than 16,000 apprentices in data and digital skills since 2016.