The Multiverse blog

Morgan Sindall Infrastructure today announces that it will build out its data academy with Multiverse, following the success of its graduating apprenticeship cohort.

In a move that will continue to boost business potential through data-driven decision making, the upskilling initiative marks Morgan Sindall Infrastructure’s continued commitment to investing in its people. The forthcoming launch will bring the total number of employees who have enrolled on digital and data programmes with Multiverse to 80.

Impact across the business has been substantial since the inception of the academy. The most recent cohort saw a 14% increase in individual efficiency during the programme – equating to more than 7,200 hours saved per year. Apprentices have used improved data skills to drive tangible business outcomes, from mitigating weather-related project delays, to reducing workplace safety incidents, to increasing visibility of potential quality issues. Following their completion of the programme, 86% of the graduating cohort achieved a distinction.

One apprentice who drove significant impact on programme is James Macdonald, who developed skills in Python, SQL and PowerBI. He said: “I’ve already benefited from learning how to conduct hypothesis tests and build regression models in Python. I’ve been able to apply this in my work on our carbon calculation tool, to predict the carbon footprint of forthcoming projects, which will be really valuable. And elsewhere I’ve already seen value in data skills reducing time spent gathering information for reporting dashboards.”

Multiverse’s 2024 Skills Intelligence Report revealed that in construction, 29% of employees’ time working with data is spent unproductively. Morgan Sindall Infrastructure’s continued commitment to upskilling, however, will enable its teams to automate processes and tasks, leading to a significant boost in efficiency and output.

Sarah Reid, Managing Director - Water & Highways at Morgan Sindall Infrastructure said: “The Digital and Data Academy is part of our ongoing commitment to developing and protecting our people, ensuring they have the right tools and knowledge to thrive in a fast-paced industry. Having the skills to harness data effectively will not only improve efficiency across the business but also create opportunities for personal and professional growth.”

Multiverse combines work and learning to unlock economic opportunity for everyone. It works with more than 1,500 organisations to close critical skill gaps in the workforce in AI, data and technology.

Gary Eimerman, Chief Learning officer at Multiverse said: “Data is transforming the future of the construction industry. By taking a forward-thinking approach to close the data skills gap, Morgan Sindall Infrastructure will deliver measurable benefits for both its business and its people.”

Whether you’re merely considering an engineering career or looking to understand how your current salary stacks up, this blog will guide you through everything you need to know — from basic Software Engineer salary expectations, to job outlook, skills and training.

Software Engineer Salary in the UK

According to Glassdoor, the average Software Engineer salary in the UK is around £46,000. The figure is above the median gross pay for all full-time employees of £37,430 (ONS).

Many factors influence a Software Engineer’s salary, including experience level, skills, location and role. And the data reported varies by sources. But here’s what you may earn as a Software Engineer in the UK:

1. Lower-end salary: £42,000
2. Average base salary: £52,500
3. 90th percentile salary: £160,000

Data sources: Indeed, Talent and Levels.fyi.

Highest-Paying Cities for Software Engineers in the UK

Where you live in the UK impacts how much you earn as a Software Engineer. In 2025, London companies pay Software Engineers around £58,000 on average before bonuses and other incentives, while Liverpool companies pay around £40,000, according to Glassdoor salary data. The eight highest-paying cities for Software Engineers in the UK are:

1. London
2. Cambridge
3. Bristol
4. Manchester
5. Leeds
6. Liverpool
7. Nottingham
8. Birmingham

Here’s that data broken down by city, updated for 2025.

Software Engineer Salary by Job Title

Software engineering is a broad industry with many well-paying roles to choose from. Here are some common software engineering job titles and what you can earn in each. (Unless otherwise noted, all salary data is from Talent.)

Front-End Developer

Front-End Developers focus on building the front-end elements of websites or applications that people interact with and see. They learn programming languages like HTML, CSS and JavaScript.

Front End Developers also fix code errors and debug applications. As a Front-End Developer, you must understand user design and experience principles.

Front End Developer salaries in the UK, according to Glassdoor salary data:

1. Low-level salary: £32,000

2. Average base salary: £41,000

3. High-paying salary: £53,000+

Web Developer

Web Developers are similar to Front-End Developers, but they focus solely on websites. As a Web Developer, you’ll either build websites from scratch or manage existing websites. You may also be responsible for improving website loading speed, technical search engine optimisation (SEO), and other performance indicators.

Web Developer salaries in the UK:

1. Low-level salary: £26,000

2. Median salary: £33,000

3. Top-paying salary: £43,000+

Back-End Developer

Back-End Developers work on the back-end; i.e., all the elements that make an application run but users don’t see. As a Back-End Developer, you’ll likely use programming languages like Python, PHP and Ruby. Back-End Developers also work closely with Front-End and Web Developers to unite server-side (back-end) and front-end efforts.

Back-End Developer salaries in the UK:

1. Low-level salary: £53,000

2. Median base salary: £64,000

3. Top-paying salary: £78,000+

Full-Stack Developer

Full-Stack Developers work on front and back-end development. They tend to be generalists but have a few years of experience in both areas. Because their skills are so versatile, there’s a high demand for Full-Stack Developers.

Full-Stack Developer salaries in the UK:

1. Low-level salary: £41,000

2. Median base salary: £53,000

3. Top-paying salary: £70,000+

Cyber Security Engineer

Cyber Security Engineers focus on protecting a company’s networks, systems and data. They identify any potential security threats and create solutions to secure them. As a Cyber Security Engineer, you’ll be responsible for data security. Example tasks include installing firewalls, testing systems for vulnerabilities and analysing risk.

Cyber Security Engineer salaries in the UK:

1. Low-level salary: £34,000

2. Median base salary: £45,000

3. Top-paying salary: £61,000+

Data Engineer

Data Engineers combine data analytics with software engineering. As a Data Engineer, you’re responsible for designing and creating data systems. More specifically, your work will help companies collect, store and understand large amounts of raw data. You’ll also work to make data more accessible to other team members like Data Scientists and Business Analysts who interpret the data you provide.

Data Engineer salaries in the UK:

1. Low-level salary: £38,000

2. Median base salary: £48,000

3. Top-paying salary: £61,000

What Does a Software Engineer Do?

As a Software Engineer, you can develop software, websites or other applications. Software engineering is a broad discipline and can lead to many different career paths. Here are some of the basic skills and responsibilities to help you understand what you’ll do as a Software Engineer.

Software Engineer responsibilities:

Analyse complex information

Software Engineers dissect intricate data and systems to understand their functionalities and limitations. This level of analysis is important for identifying potential issues, optimising performance, and developing new features.

Translate client and user needs into practical business solutions

Bridging the gap between user expectations and technological capabilities, Software Engineers convert client requirements and user feedback into actionable development plans.

Write, test and rewrite code to improve existing programmes

Continuous improvement is a cornerstone of software development. Engineers rigorously write and test code to enhance its functionality, efficiency, and security. This process can involve tasks like debugging, refactoring, and sometimes overhauling large sections of code.

Find and fix bugs

Identifying and resolving software bugs is another Software Engineer responsibility. Engineers use a variety of debugging tools and techniques to diagnose problems, ensuring applications run smoothly and efficiently.

Collaborate with other programmers, Technical Writers, clients and colleagues across departments

Engineers work alongside programmers, Technical Writers, and other stakeholders, sharing knowledge and insights to guide the software’s development, documentation, and deployment.

Source new technologies that solve business problems

Software Engineers research and integrate emerging technologies that can offer competitive advantages. This can involve evaluating new tools, languages, and frameworks, that can improve product offerings and drive innovation.

Keep up with training needs and best practices

The tech landscape is ever-evolving. So continuous learning is essential for Software Engineers. They must stay informed of the latest industry trends, best practices, and technological advancements.

To become a Software Engineer, you should be interested in developing these skills in your career:

1. Technical skills: You’ve built a software application in JavaScript, for example.

2. Coding skills: You know different programming languages like JavaScript, SQL and CSS.

3. Commercial mindset: You understand the Software Development Life Cycle and how to meet business needs.

4. Communication: You communicate technical concepts to non-technical people.

5. Problem-solving: You ‘troubleshoot’ tech problems and fix bugs.

6. Analysis: You analyse technical information while understanding user and client requirements.

7. Commitment to training: You want to become a master in your field by continuously learning and improving.

Aside from demonstrating relevant skills (or a commitment to learning them), employers may require specific qualifications:

• At least a grade of 4/C GCSE (or equivalent) in Maths and English
• A Levels in relevant subjects like Computing
• A Software Engineering Level 4 apprenticeship standard or the willingness to work towards one

Are Software Engineers in Demand?

It’s unknown exactly how many Software Engineers currently work in the UK, but the total is likely comparable to other leaders in Europe, such as Germany. The problem for employers? The demand for Software Engineers doesn’t equal the supply. Add to that the fact that nearly 20% of engineers in the UK are likely to retire by 2026, and it’s clear that the role is in demand.

A quick search for “Software Engineer” jobs on LinkedIn also highlights the demand, with more 18,000 UK job openings on the platform as of February 2025.

Typical Software Engineer career progression

Freshly minted programming professionals often start their careers as Junior Software Developers. As they gain experience and specialisations, many progress into Data Engineering or other domains or become mid and senior-level SWEs.

To make the leap to mid-senior level, Software Engineers often require training and skill development in areas ranging from cyber security to AI. They also need to understand how to innovate, increase productivity, and connect the impact of specific projects to larger organizational goals.

If you’re looking to take the next step in your career as a Software Engineer, Multiverse’s Advanced Software Engineering programme could be right for you. Our programme focuses on applied impact and measured learnings, helping teams unlock enhanced productivity. The best part? You upskill on the job, meaning you don’t have to pause your career while learning — and employers cover the costs of the programme once they partner with Multiverse.

If you want to take the next step in your engineering career, create a profile with us today in just minutes. Our team can then double-check your eligibility and discuss apprenticeship options.

Nearly three in four (72%) businesses are using AI, which is up from 50% in previous years, according to McKinsey.

Here are 10 key benefits of AI in the workplace – and four ways you can unlock them within your business:

10 benefits of AI in the workplace

1. New career opportunities: The World Economic Forum (WEF) predicts that by 2027 we’ll see 2.6 million jobs added by AI.
2. Revenue increases: Four in five leaders say implementing AI has led to an increase in revenue generation (Multiverse ROI of AI report). About 75% of the value that generative AI (GenAI) could deliver falls across four areas: customer operations, marketing and sales, software engineering and R&D (McKinsey).
3. A productivity boost: Businesses using AI to sort vast amounts of customer and market data are seeing as much as an 80% reduction in data processing time – supporting a 40% improvement in speed to market for new products and services (Accenture).
4. Increased GDP: PwC and McKinsey report that global GDP will be 14% higher in 2030 as a direct result of AI – the equivalent of a staggering $15.7 trillion of new value.
5. Creating efficiencies: More than 80% of workers surveyed – who use generative AI (GenAI) daily – expect it to make their time at work more efficient in the next 12 months (PwC).
6. Business value: 83% of workers think AI skills will help them to drive more value for their employer in the next 12 months (ROI of AI report).
7. Greater employee satisfaction: Deloitte reports that, on average, more than half of Millennials (55%) and Gen Z (58%) employees believe GenAI will free up time and improve work/life balance. And half (49%) of all daily GenAI users expect the technology to lead to higher salaries (PwC).
8. Increased demand for AI specialists: The WEF expects to see a 40% jump in the number of AI and machine learning specialists. And a 30%-35% rise in demand for roles such as data analysts and scientists or big data specialists.
9. Heightened competitive advantage: 68% of senior leaders in financial services and telcos believe that competitive advantage in their industry will depend on who can make the best use of AI (Experian).
10. Transforming industries: According to cross-industry research from Accenture, 90% of C-level executives expect GenAI to revolutionise their industry and customer interactions. However, separate insight from the company found that the banking (54%) and insurance (48%) industries hold the highest potential for AI impact through automation.

4 ways to unlock the benefits of AI in your workforce

1. Create a future-ready AI skills strategy

Tech investments need to be combined with an AI-enabled workforce to get the most from the technology. But there are several barriers holding businesses back from reaching AI maturity – and technical skills are a big one.

In fact, our ROI of AI: Unlocking AI maturity through workforce skills report found that leaders currently name AI as their most significant skill gap (45%).

That’s because AI and data literacy is an ongoing challenge in the workplace. Half of workers have received less than five hours of AI training. And employees struggle with the basic data sksills needed to achieve the full benefits of AI, such as making data more efficient (53%) or analysing data to make informed data-driven decisions (46%).

Fixing these skills gaps starts with a targeted upskilling strategy. One which equips your teams the most needed AI skills for your business.

These skills may be different across sectors, job titles, roles and functions, and your crafting an effective AI skills strategy needs to first begin with identifying your business opportunities to generate Return On Investment (ROI) from AI.

2. Define measurable goals

Measurement should sit at the heart of your strategy.

Setting a benchmark for measuring success with AI also helps to ensure all training ladders up to your business’s wider picture. Do your customer service executives need training in how to automate manual processes? If one of your goals is to improve the speed of customer responses, then the answer could be yes.

However, measurement is only as successful as the strength of the strategy and goals set in the first place. Only then can results truly be measured to anticipate hurdles and uncover opportunities.

3. Give teams the tools and training they need to succeed

Once you’ve got a solid skills strategy in place, implementing tools and training is the next step.

When we think of tools, it’s easy to go straight to technology. But, when it comes to unlocking the benefits of AI in your workforce, providing safe guardrails to innovate is vital. That means creating clear policies, guidelines or even Centres of Excellence with best practice examples.

Today, just 45% of employees have received formal AI training provided by their employer. So, it’s likely workers will struggle to assess whether their actions are aligned with the company without policies or broader best practice – creating potential risks for the business.

It’s about ensuring policies are being adhered to, with people not only accountable for how they’re using AI, but also proud of it. That means fostering a positive culture around AI in the workplace, with the integration of technologies into operations, processes, and employee interactions.

4. Enable new career opportunities for employees through AI skills

Businesses need to build expertise in AI, fast, but formal AI training opportunities remain in short supply.

Our data shows that most workers learn AI skills informally by experimenting with ChatGPT (61%) or learning on the job (59%). And half (51%) have received fewer than five hours of training on AI.

This presents challenges for both the worker and the business, from struggling to assess knowledge gaps to unlocking efficient processes.

According to our ROI of AI report, businesses are aware of the gaps and leaders are looking to invest in data upskilling in 2025. Half of the organisations that have identified skills gaps as a key barrier to full implementation of AI plan on upskilling employees through long-term external AI training programmes (56%) and ad-hoc/short-term external AI training programmes (50%).

There’s a clear opportunity for businesses to upskill employees in AI – unleashing productivity benefits, opening up new career pathways, and delivering measurable impact.

King’s College London is upskilling 75 of its staff through a new Digital and Data Academy in partnership with Multiverse. The initiative will support the University’s ongoing digital transformation, which includes the review and automation of time-intensive tasks.

The Digital and Data Academy will enhance skills in key areas such as professional services technology and academia, further strengthening the organisation’s ability to make data-informed decisions to improve service provision to both students and staff. Once accredited, the cohort will have increased capacity to focus on the most impactful and strategic elements of their roles.

Training is funded by the apprenticeship levy and delivered by Multiverse, a tech company that specialises in high-quality training through applied learning. Multiverse has trained more than 20,000 apprentices at over 1,500 organisations in data, AI and digital skills since 2016.

The 75-strong cohort will undertake Multiverse’s Level 4 Data Fellowship for professionals looking to establish or develop core data skills. At the end of the 13-month programme, King’s College London employees will have strengthened skills to support accurate, data-informed decision making while confidently using visualisation tools like Power BI.

According to Multiverse’s Skills Intelligence Report, the education sector is impacted by a lack of data skills, with 38% of employees’ time working with data spent unproductively, compared to the average of 30% across 18 other sectors. King’s College London’s new Digital and Data Academy will seek to proactively address this ahead of current sector concerns.

Kirti Swift, Deputy Director - Organisational Development at King's College London said: “Through this academy, we are strengthening a culture of data literacy and digital capability across the university. This will enable smarter decisions, deeper insights, and more efficient ways of working - freeing us to focus on what truly matters: enhancing the experience and outcomes for our students and staff.”

Isabelle Leung, Senior Research Grants Administrator at Kings College London, who is enrolled in the apprenticeship, said: “My team leverages data from across multiple systems daily, identifying and resolving problems or discrepancies. This training has already allowed us to streamline processes, improve accuracy and drive efficiencies, reducing the turnaround time of our monthly reviews from one week to just two days.”

Multiverse combines work and learning to unlock economic opportunity for everyone. It works with more than 1,500 organisations to close critical skill gaps in the workforce in AI, data and tech, through a new kind of apprenticeship.

Gary Eimerman, Chief Learning Officer at Multiverse said: “King’s College London is a global epicentre of exceptional research and innovation, led by the finest professionals. It has identified an opportunity to strengthen its operations through an enhanced understanding and utilisation of data – our Data Fellowship program is designed to help individuals on their team for exactly this.”

As the name suggests, DevOps combines development and operations, allowing modern businesses to create and iterate software more efficiently. This hybrid approach helps companies deliver high-quality products faster — and, ideally, beat their competitors to the punch.

What is DevOps?

DevOps is an engineering workflow methodology that brings together development and operations teams for seamless collaboration. These employees work closely together to design, build, and refine software applications.

The traditional software development model mostly separated operations and development teams. Software Developers would create an application, then hand it over to the operations team for deployment and management. If any issues occurred, the operations team would send it back for the Software Developers to correct — a process that could take days or even weeks.

DevOps breaks down these silos by encouraging collaboration and shortening feedback loops. Instead of working independently, all team members review applications at every stage. That way, they can catch issues early and make necessary improvements immediately.

While this approach might sound revolutionary, its core ideas have been around for over two decades. In 2001, a group of tech professionals published the Agile Manifesto, introducing a new methodology for software development. The Manifesto emphasised the importance of frequent feedback, laying the foundation for DevOps.

In 2009, John Allspaw and Paul Hammond presented a paper at the Velocity Conference titled “10+ Deploys Per Day: Dev and Ops Cooperation at Flickr.” Inspired by this paper and Agile practices, Patrick Debois organised the first DevOps Days in Belgium, officially starting a new movement.

Key principles of DevOps

The DevOps methodology is built on several guiding principles that businesses can adapt to their specific projects.

Most obviously, this model revolves around collaboration between cross-functional teams. Professionals work closely throughout the entire development process, sharing expertise and resources. This teamwork can empower them to create more innovative and effective products than they could have separately.

DevOps also encourages teams to automate processes to improve efficiency. For example, professionals might automate testing after every code change to double-check that the application still functions correctly. Many DevOps teams also use Infrastructure as Code (IaC) to automatically configure and manage infrastructure. Automating these routine tasks can save significant time and reduce the risk of costly mistakes.

Building on Agile development principles, continuous feedback and improvement are also core DevOps values. Tools like Datadog and Prometheus help teams monitor performance and identify opportunities for improvement. For example, if you notice glitches recorded in the system logs, you can quickly review the code to find the root cause. DevOps teams also gather feedback from colleagues and users regularly, promoting a culture of transparency and continuous improvement.

Benefits of DevOps

DevOps offers several significant advantages, contributing to its widespread popularity.

Rapid deployment

DevOps speeds up software delivery by streamlining the development process. When projects move smoothly through every stage, time-to-market decreases, reducing project costs. Faster releases also improve customer satisfaction and retention.

Improved software quality

Traditionally, development teams gathered feedback only near the end of the process — often when it was too late to make significant changes. By contrast, DevOps enables continuous feedback throughout the development lifecycle, allowing teams to address issues as they appear. The result? A more polished product, perfectly tailored to the end user.

Improved security

Treating security like an afterthought can leave applications vulnerable to cyber threats and data breaches. Plus, there’s always the risk that last-minute security practices will negatively affect the software’s performance.

Businesses can avoid these issues by adopting DevSecOps. This model works like regular DevOps, but it also integrates the security team into the entire application lifecycle. By designing systems with security in mind from the very beginning, companies can increase compliance and mitigate vulnerabilities.

Understanding the DevOps lifecycle

The DevOps lifecycle consists of five distinct phases:

Plan

The two teams collaborate to create a detailed project plan. This process involves prioritising tasks, setting deadlines, and developing a realistic budget. The collaborators also define their roles and delegate tasks, reducing the risk of future conflict or miscommunication.

Build

This phase involves the nitty-gritty tasks of building an application, such as writing code and designing product features. Developers typically take the lead here, but the operations team is still closely involved throughout the process.

Integrate

Individual team members typically often work on code independently, especially in remote work environments. Continuous integration allows them to frequently merge their code into a central repository. That way, every member can instantly see any changes made, reducing the risk of duplicate code.

This process is also essential for quality assurance, as it enables teams to continually check each other’s work. Catching errors earlier makes them easier and less expensive to fix.

Deliver

DevOps doesn’t stop after a business officially releases an application. Dev and ops teams typically continue to refine the software after the launch, updating features and making improvements. With continuous deployment, they can automatically push new features and other changes to users, allowing them to quickly respond to customer feedback.

Operate

Once an application is live, the DevOps team must also monitor and maintain software performance. Many issues can arise in the blink of an eye, from cyberattacks to unexpected issues. By automatically tracking performance metrics and setting up alerts for potential issues, the team can intervene quickly and resolve incidents.

Core DevOps practices

If you want to contribute to development and operations teams, you’ll need to understand a few essential practices:

• Continuous integration (CI): All code changes made by multiple developers get merged into a single codebase for consistency.
• Continuous delivery (CD): New code is automatically deployed after leaving the production pipeline, allowing users to see updates immediately.
• Infrastructure as Code (IaC): This model deploys the same infrastructure — such as networks and virtual machines — every time, enabling DevOps teams to test applications in consistent development environments.
• Monitoring and logging: Tools automatically track an application’s performance and log interactions, making it easier to identify bugs or anomalies.

There are plenty of resources available to help you learn these practices. For example, Coursera and Udemy offer free online courses on CI and CD. You can also watch tutorials on YouTube and other video platforms.

DevOps tools: The key tech and tools used in DevOps

Tech professionals rely on a wide range of tools to streamline and automate DevOps workflows. While mastering every technology isn’t necessary, especially at the beginning of your career, you should at least be familiar with key tools you might encounter on the job.

Jenkins is an open-source automation software that enables continuous integration and continuous delivery. DevOps professionals use this tool to build and test applications. The platform features hundreds of plugins for easy integration with other services, such as Amazon Web Services and Zoho.

Kubernetes and Docker often go hand-in-hand to develop and deploy software. Docker allows users to build and operate container applications that don’t require their own infrastructure. Developers can then use Kubernetes to manage and scale these containers. These applications save time, allowing DevOps teams to focus on designing unique software instead of creating everything from scratch.

For monitoring and analytics, many DevOps teams turn to two free tools: Prometheus and Grafana. Prometheus is a powerful open-source monitoring tool that continuously tracks your application’s performance. It offers real-time alerting, so you can instantly get notified of any problems.

Meanwhile, Grafana is an open-source analytics tool. It integrates with Prometheus and many other data sources, allowing you to compile all your performance data in one place. You can also transform this information into accessible data visualisations, making it easy to spot trends. For example, if you notice that your software tends to log errors at a specific time, you can investigate more closely to determine the reason.

How to get started with DevOps

As more companies adopt DevOps, it’s become one of the highest-paying tech jobs. According to Glassdoor, DevOps Engineers in the UK earn an average salary of £49K. It’s also a top career choice for introverts who balance a mixture of independent work and energetic collaboration.

Of course, you don’t need to become a DevOps Engineer to embrace this methodology. No matter your role, you can help promote a DevOps culture in your organisation by following these steps.

Start small with pilot projects

Convincing colleagues and managers to try DevOps practices can feel like an uphill battle. If your development and operations teams are firmly siloed, they might resist your invitation to collaborate. Plus, many people get attached to their familiar software delivery process and feel reluctant to overhaul it.

Luckily, you don’t need to jump into the deep end with DevOps right away. Instead, start gradually with low-stakes pilot projects. For example, you might practice monitoring an older project with Prometheus or encourage your team to give each other feedback using a tool like GitHub. These initiatives will help you introduce the DevOps process without the pressure of strict deadlines or major risks.

As your team gains confidence, you can slowly begin integrating more DevOps best practices into your projects. This could involve setting up a continuous integration tool like Jenkins or GitLab or adding continuous delivery for minor features. This gradual approach will allow your team to see the benefits of DevOps for themselves without overwhelming them.

Get stakeholder buy-in

DevOps isn’t the responsibility of one individual or team; it’s an organisational philosophy requiring universal support to implement. Many people, however, are wary about new tech frameworks. Change can be scary, especially for people who are used to working a different way.

Earning stakeholder buy-in is key to DevOps success. Start by gaining the support of leadership by educating them about the benefits of DevOps, such as improved collaboration and faster time-to-market. Consider using case studies and statistics to make your appeal more compelling. You could also develop a pilot project so the stakeholders can see the results of DevOps for themselves.

Once you’ve secured leadership support, it’s time to get the development and operations teams to buy in. This might be trickier, but don’t feel discouraged. Emphasise how DevOps can streamline their workloads by automating repetitive tasks and prioritising continuous feedback. Additionally, be sure to tout the benefits of increased collaboration and efficiency, which can reduce stress and boost productivity.

Focus on continuous improvement

At its core, DevOps is all about striving for continuous improvement. The best DevOps teams are dedicated to perfecting their products, returning to them as often as needed to keep everything working flawlessly.

At first, this process might sound exhausting or frustrating — after all, you don’t want to get stuck in an endless cycle of adjustments and updates. However, prioritising continuous improvement will help you provide the best user experience and maintain customer satisfaction. It can also save time in the long-run by allowing you to develop a strong foundation for your applications. That way, you’re less likely to encounter significant issues like bugs or security vulnerabilities when you deploy the finished product.

Master DevOps principles with Multiverse

DevOps has surged in popularity as organisations search for new ways to increase efficiency and gain a competitive edge. In 2024, approximately 10% of permanent tech job postings in the UK required DevOps skills, underscoring its growing importance.

A Multiverse tech apprenticeship can help you gain hands-on experience with DevOps methodologies and tools. Our 15-month Software Engineering programme teaches you how to develop full-stack applications in agile environments. Our structured modules cover a wide array of topics, from automation to CI/CD. Our knowledgeable coaches can also help you chart a career progression framework in tech to grow on your existing career path and interests.

Take the next step on your DevOps journey by completing our quick application today.

A Data Engineer builds data pipelines to collect and process information from multiple sources. These data streaming systems allow businesses to perform in-depth analyses and answer complex questions.

As more businesses embrace data-driven decision-making, the demand for Data Engineers will continue to grow. That means there are plenty of promising opportunities for people looking to upskill core data-based capabilities. In this guide, we’ll explore the role of Data Engineers, their key responsibilities, salaries, and more.

What is a Data Engineer?

A Data Engineer is responsible for building, managing, and fine-tuning an organisation’s data infrastructure. They create data pipelines that gather raw information and transform it into a usable format for data analysis.

As the amount of data grows exponentially, businesses rely on Data Engineers to sift through the noise and collect the most valuable information. Without these professionals, organisations would struggle to make sense of a vast sea of raw data in different formats.

For example, a retailer might want to understand why clients prefer certain products. The business could analyse many types of data, from customer reviews to purchase history and returns. A Data Engineer speeds up this process by collecting the relevant data points, organising them, and delivering them to Data Analysts for further evaluation.

Data engineering is the foundation of data science, which includes several similar but distinct career paths. Data Engineers focus on developing data pipelines to extract and organise information. This processed data is then analysed by Data Analysts to answer questions and detect trends. Some businesses also employ Data Scientists, who perform more complex statistical analyses and develop predictive models. In the above scenario, a Data Scientist could use historical customer data to predict future buying behavior.

Day in the life of a Data Engineer: Core responsibilities

A Data Engineer’s exact responsibilities can vary by role, but here are a few common tasks:

• Build data pipelines to collect and process information from various sources
• Maintain data systems to keep information flowing smoothly
• Evaluate the quality and reliability of collected data
• Store information in accessible data warehouses or data lakes for easy retrieval
• Collaborate with Data Scientists, Analysts, and other team members

Data Engineers use various tools and techniques to accomplish these fundamental tasks. For example, extract, transform, and load (ETL) processes allow them to combine both structured and unstructured data into a centralised location. Many professionals also rely on cloud infrastructure platforms — such as Amazon S3 and Google Cloud Storage — to store vast datasets remotely.

Key skills for Data Engineers

If you’re interested in a career in data engineering, you’ll need these essential technical skills:

• Structured Query Language: Used to build and communicate with relational databases
• Programming languages: Python and Scala are often used to construct pipelines, process data, and automate tasks
• Apache Spark: A powerful computing framework used to process enormous datasets quickly
• Mathematics: Discrete mathematics is used to build algorithms and handle data structures, while statistics helps detect trends and anomalies
• Cloud computing: Familiarity with cloud platforms like AWS and Azure is critical for storing and managing data securely

Of course, hard skills aren’t enough to excel in this field; strong soft skills are just as critical. Many data engineering projects are highly collaborative, requiring close collaboration with people from different departments. For example, a Data Analyst might ask you to aggregate information for business intelligence reports. Effective communication helps you simplify complicated concepts and build positive relationships with all team members.

You’ll inevitably encounter challenges, especially when dealing with complex or messy data, so excellent problem-solving skills are a must. Being adaptable will also enable you to learn new techniques and master the latest technologies.

Certifications can help you gain new skills and demonstrate your expertise to potential employers. Consider pursuing industry-recognised credentials, such as AWS Certified Data Analytics and Google Professional Data Engineer.

The importance of data engineering in modern businesses

In 2025, it’s predicted that users will generate and consume a staggering 182 zettabytes of data. Traditional data processing and analytics methods simply can’t keep up with this exponentially growing flood of information.

That’s where Data Engineers come in. They develop infrastructure to collect the right information and make the data usable for analysis. This process allows leaders to make strategic business decisions based on accurate and relevant data, rather than a random jumble of information.

Many industries rely heavily on data engineering for high-stakes decision-making. For example, finance institutions use fraud detection systems to automatically identify and block suspicious transactions. Similarly, data engineering empowers healthcare organisations to analyse patient data and improve the quality of care. Without this field, businesses would struggle to analyse information and address emerging issues effectively.

Tools and technologies used by Data Engineers

Even the most experienced Data Engineers can’t manually process millions of data points. Instead, they rely on sophisticated tools to automate and streamline data workflows.

Apache Hadoop is one of the most critical technologies, using parallel processing to handle big data efficiently. Many professionals also turn to Snowflake for building batch data pipelines quickly, while Apache Airflow allows users to design and automate workflows. These technologies can save significant time and reduce the risk of errors.

How to become a Data Engineer

There’s no one-size-fits-all approach to becoming a Data Engineer. You can use many strategies to gain the necessary knowledge and skills, giving you the freedom to shape your professional journey.

It all starts with developing foundational programming and database skills. Most Data Engineers rely on SQL to manage databases, while Python is commonly used for routine tasks like data storage and processing. Online courses or coding bootcamps can help you learn these languages quickly.

Beyond these basic skills, a Multiverse apprenticeship is one of the best ways to expand your data engineering knowledge. Our three-year Advanced Data Fellowship teaches you how to analyse information and design innovative data solutions. It covers advanced topics like data governance and predictive analytics. You’ll also gain hands-on experience by applying your newfound skills in your current role.

Once you’ve gained the necessary skills, start looking for entry-level roles like Business Data Analyst or Junior Data Engineer. These positions will allow you to refine your skills further and may lead to more advanced positions as you gain experience.

For example, Daniel Beach began his career in a completely unrelated field — agriculture. In 2013, he decided to pivot to data engineering and taught himself SQL. With this self-taught knowledge, he landed a Data Analyst position at a bank. From there, he quickly climbed the career ladder, moving from Senior Data Analyst to Business Intelligence Engineer, then to Data Engineer, and finally to his current role as Senior Data Engineer.

Career progression for Data Engineers

Data engineering is a vast field with ample opportunities for advancement and specialisation. Here’s one possible career trajectory, with salary data from Glassdoor.

Data Analyst

Average UK salary: £41K

A Data Analyst uses statistical methods and software to gather and study data. They search for meaningful trends in datasets and communicate their insights to stakeholders. For instance, they might create data visualizations or dashboards to present their findings in accessible formats.

Junior Data Engineer

Average UK salary: £31K

A Junior Data Engineer typically works under the guidance of more experienced data professionals. Their responsibilities include fundamental tasks like collecting, cleaning, and storing data. They may also assist with building and optimizing data pipelines and troubleshooting issues.

Data Engineer

Average UK salary: £48k

A Data Engineer handles more advanced tasks, such as designing sophisticated data pipelines and solving business problems autonomously. This mid-level role typically requires two to five years of experience in data engineering.

Big Data Engineer

Average UK salary: £46k

A Big Data Engineer specialises in developing and managing pipelines for big data. These systems must be scalable and capable of handling vast, ever-increasing amounts of information without experiencing performance issues. Big Data Engineers often collaborate with Machine Learning Engineers and other professionals who rely on big data for analysis and decision-making.

Lead Data Engineer

Average UK salary: £72K

As the name suggests, a Lead Data Engineer oversees a team of engineers. They plan complex data architecture, delegate tasks, and manage large-scale projects. They’re also responsible for mentoring junior employees.

Advance your data engineering career with Multiverse

Once a niche field, data engineering has become an integral part of modern business operations. It’s the foundation of advanced analytics and data management, helping businesses get the most out of their data.

Take the next step on your upskilling journey with a Multiverse apprenticeship. Our Data Fellowship programmes teach fundamental technical skills, such as data visualization and machine learning. You’ll strengthen your data engineering knowledge as you complete our online curriculum and collaborate with peers. Plus, you’ll receive personalised career coaching from industry experts. All without having to put your career on hold.

Ready to take the leap? Get started today by completing our quick application.

In today’s fast-changing workplace, traditional training methods no longer suffice. As AI automates routine tasks, employees must develop higher-order skills such as critical thinking, problem-solving, and the ability to apply technical knowledge in context.

Multiverse and Skillable are joining forces to bridge the divide between learning and real-world application. This partnership integrates Skillable’s hands-on virtual labs with Multiverse’s structured coaching model, creating an immersive ecosystem where learners gain both theoretical knowledge and practical, scenario-based experience. By combining strengths, Multiverse and Skillable empower employers with a scalable solution that ensures professionals can confidently apply technical skills in real-world settings—reinforcing their shared philosophy that people learn best by doing.

Gary Eimerman, Chief Learning Officer at Multiverse, added, "Our partnership with Skillable expands our experiential learning approach by providing practice environments that mirror real-world use cases. Coupled with our coaching, Skillable's virtual labs allow us to create experiences where learners can safely practice technical skills before applying them in production environments."

Through this collaboration, apprentices benefit from structured skill-building experiences that provide immediate feedback, with the ability to reset and try again without consequences—creating low-pressure learning opportunities that build confidence. These experiences are further enriched by Multiverse's personalized coaching, which helps learners connect technical skills to broader business contexts.

Frank Gartland, Chief Product and Technology Officer at Skillable, remarked, "Our partnership with Multiverse represents an exciting opportunity to enhance their industry-leading apprenticeship model with our hands-on virtual lab technology. Together, we're creating a learning experience combining the best of human coaching with immersive scenarios that stretch skills and bolster confidence, giving learners both the guidance and practical experience needed for ongoing success in today's digital economy."

The partnership will launch with Multiverse's Applied Data Engineering (ADE) apprenticeship program, the latest addition to its portfolio of data skills products. This program exemplifies how the combined approach elevates learning outcomes: learners gain hands-on experience in data infrastructure management through Skillable's customizable labs, while Multiverse coaches provide strategic guidance on applying these skills to real business challenges.

Learners in the ADE program gain hands-on experience managing complex data infrastructure, developing advanced pipelines, securing databases, implementing rigorous data governance, and even applying machine learning to enhance data solutions—all within Skillable's safe learning environments that protect critical production systems and sensitive data. Throughout their learning journey, Multiverse's expert coaches provide targeted support, helping employees translate these technical skills to their organization's unique environments and technology stacks. This powerful combination bridges the gap between controlled practice and real-world application, ensuring apprentices can confidently implement their new capabilities using their organization's specific tools, processes, and business context.

As the workforce continues to adapt to new technologies and business challenges, Multiverse and Skillable are committed to delivering the hands-on, outcome-driven learning environments needed to thrive. With plans to expand across technical domains, including cloud computing and AI, this partnership marks the start of a new era in applied learning—where talent development is measured not by knowledge alone but by the ability to deliver results.

Headlines

• Our mean pay gap increased in 2024 to 13%, however, this remains lower than the average gender pay gap in comparable sectors, which is 16%.
• Our mean bonus gap declined slightly compared to 2023, and our median bonus gap was negative for the first time. Only 37% of reporting employers have a negative median bonus gap.
• In 2024, representation of women in our senior leadership roles declined; however, we have made a number of significant appointments since the 2024 snapshot date.
• We continue to see increased representation of women across our Tech team for the second consecutive year, with 39% of our Tech team identifying as women in 2024 (an 8% increase since 2022).
• The median gender pay gap for our Sales team was 0% in 2024. Even considering the varying effect that Sales commissions have on pay gap figures, this is something we are proud of.

The Gender Pay Gap explained

First - some technical bits. The gender pay gap is the difference between the average hourly pay of men and women within an organisation. The UK government requires businesses with 250 or more employees to report on the following metrics each year:

• Hourly pay gap - mean and median
• Bonus pay gap - mean and median
• Proportion of men and women receiving a bonus
• Proportion of men and women in different earnings quarters

The gender pay gap is distinct from unequal pay, which is illegal in the UK and has been since 1970. At Multiverse, we take active steps to ensure there is equal pay for equal work at all levels within our business, for example, regular pay audits and benchmarking. Reporting on our gender pay gap is an important moment for us to reflect on the initiatives we introduce to embed fairness and equality of opportunity in our business, and whether these are successful.

A note - HMRC specifies that Gender Pay Gap data sets should only include people who identify as a man or woman. Multiverse employees can voluntarily complete a series of census questions through our HR Information System, including their gender identity. To align with HMRC’s guidelines, we have excluded any employee who has self-identified as ‘Non-binary’ or ‘Another gender identity’. However, this approach is not in line with our internal pay audit practices or overall approach to gender identity and equality.

Key Figures

As a reminder, this report is retrospective and covers the period between 6th April 2023 - 5th April 2024, with a snapshot date of 05 April 2024.

Pay Gap

In the UK, Multiverse’s mean gender pay gap was 13%. This means that the average hourly pay of a man at Multiverse was 13% higher than the average hourly pay of a woman.

Multiverse’s median pay gap was 11.1%. The median pay gap is the difference between the salary in the middle of the range of all employees who are men, compared to the middle salary among all employees who are women. At Multiverse, the median man earned 11.1% higher than the median woman. The median is an important measure because it reduces the impact of what may be a small number of outlier values.

According to the Office for National Statistics (ONS), the 2024 UK median gender pay gap was 13.1% (source). Research also tells us that the mean gender pay gap in both the Technology and Professional Services sectors is approximately 16% (source), which are the sectors that offer the best comparisons to Multiverse.

Bonus Gap

In the reporting period, 48.8% of Multiverse’s UK employees received a bonus - 49.3% of women, and 48.3% of men.

Our mean gender bonus gap declined from 34% in 2023 to 33.2% in 2024. This means that on average across the 12 months leading up to 05 April 2024, the bonus of a man at Multiverse was 33.2% higher than the bonus of a woman.

Our median gender bonus gap declined from 11% in 2023 to -16.5% in 2024. This means the median man at Multiverse had a bonus 16.5% lower than the median woman. Our median bonus gap differs significantly from the mean gap because the median factors out some of the impact of outlier bonus amounts. We will explore what this means in greater detail later in the report.

Across all reporting employers in 2024, on average, 39% of women and 40% of men received bonuses, and in 63% of reporting employers, median bonus pay was higher for men than for women (source). We are proud of our results and the progress we are making here.

Earnings Quarters

The proportion of men in our UK business increased from 2023 to 2024, leading to a decreased proportion of women at all quarters except for lower hourly pay, meaning that overall we have more men in our highest paid roles and more women in our lowest paid roles. While we are focused on ensuring our organisation is representative of the communities we operate in, at all levels, unfortunately, over-representation of men in high-earning roles is a systemic driver of the gender pay gap; it is at these levels where the most significant pay gaps exist and they have been the slowest to narrow (source).

Explaining our Data

While we are pleased our mean gender pay gap remains lower than the average for the UK Tech and Professional sectors, we acknowledge that our pay gap increased in 2024. While we are beating the average, we strive to be higher performing than the average. In pursuit of this goal, we will continue to work on identifying the causes of our pay gaps and how we can close these. This section sets out some key factors which have influenced our pay gap data this year.

Impacts on Pay

Through intentional steps, we have made progress in many of the focus areas we identified in last year’s report.

Gender Representation in Tech: According to the 2024 'Diversity in Tech' report, women make up 29% of the UK Tech industry. At Multiverse, we have made year-on-year improvements in the representation of women across our Tech team (Engineering, Product, and Data & Insight), which has increased from 31% in 2022, to 35% in 2023, and now 39% in 2024. This highlights how our transparent and consistent hiring framework is enabling us to source brilliant talent and increase our gender representation in critical areas at the same time. Roles in the Tech sector continue to attract more men than women (source) and these roles are often higher paid, so improving gender representation in our Tech team is vital.

Since the snapshot date of 05 April 2024, we have continued to make strides in this area, appointing several women into some of our most senior roles in Tech, including VP Engineering and Director of Product Design & Research. Research shows that women are underrepresented in these fields, making up 20% of Engineering talent (source) and 30% of AI roles (source). Our median Engineering team pay gap for 2024 was -3.8%; however, for Product it was 9.7%, and for Data & Insight 5%.

Sales Commissions: Commissions and other bonuses paid in the month of April are included in our calculation of an employee’s ordinary pay, as required by the UK government. At Multiverse, the only bonuses paid in April are Sales commissions. In previous years, many of our top-performing women in Sales joined our management pathway, which had an accelerating impact on our gender pay gap. We are proud that in 2024, due to several of our highest performing Sales reps being women, the median pay gap for our Sales team was 0%. It is important to note that there is an inherent variability in commission payments, because they are highly dependent on individual in-year performance, and so this picture can change year-on-year. Despite this, we hope our continued focus on bringing a diverse range of talent, including women, into our Sales teams and investing in enablement opportunities for all reps, will help us maintain our strong progress in this area.

However, some key factors have limited our progress:

Senior Representation: A contributing factor to our increased gender pay gap was lower representation of women at our upper pay quarter for 2024, which in real terms, means a decrease in women at “Director” and above levels. Representation at these levels changed from 58% in April 2023, to 42% in April 2024. This was fueled by the departure of our COO and CFO during the 2023-2024 tax year, both of whom were women. As an organisation of less than 1000 employees, we have a small overall population at senior levels and so a small number of key departures can have a significant effect. Since April 2024, we have appointed a new COO and CFO, both of whom are women. This means that our Executive team is currently gender balanced, which is something we are proud of - women occupy 43% of board positions and 35% of Leadership roles at the FTSE 350 level (source).

Lower-Level Representation: The increase in our pay gaps for 2024 is also the result of increased representation of women at our lower pay quarter. While we are always pleased to increase the diversity of our business, having more women in lower-level roles, which are associated with less pay, can particularly impact the mean pay gap. However, several of these lower-level roles held by women were in teams like IT and Finance, which are typically male-dominated, including at lower levels. Having women enter these areas of our business, combined with our aim to provide equitable access to development opportunities, provides a pathway to diversify our future leadership pipeline.

Impacts on Bonuses

Our mean bonus gap decreased in 2024; however, at 33%, it remains higher than we would like. This is closely related to the disproportionate representation of men in our most senior roles at this time, because larger bonus payments are typically granted to employees in these roles.

However, our median bonus gap significantly decreased to -16.5%. As the median figures lessen the impact of outlier figures on each end of the spectrum, this is a useful representation of the picture within Multiverse for the typical employee. The majority of Multiverse employees are on our annual bonus plan, rather than a commission plan. Since our 2023 report, we have made further progress on our approach to data-driven performance reviews, with a rigorous calibration process and structured approach to paying out bonuses based on both company and personal results. Performance ratings have a large impact on bonus payments, and so we believe this is evidence that our new approach is leading to improved outcomes for women at Multiverse.

Our Approach to Closing the Gender Pay Gap

In early 2024, we launched our Career Mobility strategy, which focuses on how we can bring Multiverse’s mission to life for our employees through an equity-first approach to People policies, processes, and practices. We have made a number of strides in our Career Mobility journey so far, which we believe over time will help us close our gender pay gap:

• Pay Philosophy - In late 2022, we started our journey towards pay transparency by creating our first organisation-wide pay philosophy, which is applied to all compensation decisions. Since our last gender pay gap report, we have appointed a dedicated Reward Lead to help us monitor pay equity more closely across the organisation and within departments. Additionally, all employees can now view the pay band for their position in our compensation tool, Pave, and where they sit in the band - a step that only 16% of UK companies have taken (source). We’re continuing to progress with this work and are moving towards even greater transparency, because we believe evidence-based compensation discussions are critical for interrupting bias.
• Equitable Performance Reviews - As discussed earlier in this report, we introduced a structured performance appraisal and calibration process in 2023. Our belief was, and still is, that this would enable us to make more accurate decisions for all employees regarding pay and progression - and we believe this is evidenced by our reduced mean and median bonus gaps. In 2024, we are evolving our approach by taking leaders through a ‘nine-box’ exercise to identify our highest-performing and highest-potential talent. This is the first step in succession planning, which we hope will enable us to take a long-term, business-area agnostic look at our organisation. From this, we will identify opportunities to strengthen and diversify pipelines for key roles, including at senior levels.
• Employee Resource Groups (ERGs) - As well as equitable policies, processes, and practices, building an inclusive culture where everyone can grow and thrive is a key focus for Multiverse. Effective ERGs are shown to increase engagement and reduce attrition for the communities they serve, which means they play a key role in helping us retain high-performing talent from underrepresented backgrounds, including women. In 2024, we launched Women@Multiverse, our fourth ERG. In the nine months since, over 100 employees have joined the ERG and members have held events ranging from ‘Irrational Ways to Influence People’ to ‘Having Challenging Conversations’. Since introducing these networks, employees have reported feeling a greater sense of belonging and community at Multiverse, which is measured by our quarterly employee engagement survey. When combined with the work we are doing to create fairer reward and progression structures, this is likely to increase retention for women across the organisation - something we will monitor across the next 12 months.
• Inclusive Leadership - We want to ensure all People Managers have the tools they need to lead high-performing teams, while supporting individuals to grow and thrive at Multiverse. In early 2024, we launched a 9-month training programme called M-Powering Managers, in partnership with an external training provider. The programme covered key leadership skills from Giving Feedback, to Coaching, and Managing Change. There was also a dedicated session on Diversity, Equity, and Inclusion, specifically focused on practical ways managers can create a sense of belonging within their teams. In 2025, we are planning to introduce a Manager Onboarding programme for all new people managers; inclusive leadership skills and interrupting bias will act as a thread underpinning all three modules.

Conclusion

While reporting our gender pay gap for the 2023-2024 tax year provides an important moment for us to reflect, the work to build a fairer and more equitable Multiverse for every employee does not pause between reports! We are committed to providing equitable access to opportunities internally, as well as in the wider workplace, and will continue to strive to make this a reality at Multiverse.

But while financial institutions are making significant investments in AI technology, many are still developing the workforce capabilities needed to maximise its potential. This presents a timely opportunity for organisations to gain competitive advantage through strategic upskilling.

Our recent research reveals an industry at an inflection point: enthusiastically adopting new technology while simultaneously working to develop the human skills that drive AI success.

"The future of financial services isn't written by algorithms, but by the people who understand how to make those algorithms work for humanity." Anna Wang, Head of AI, AI Advisory Board Member - Multiverse

Based on our comprehensive survey of senior leaders in UK financial institutions,* here are the critical insights defining the state of AI in financial services today:

AI adoption surges, while results lag behind

67% of financial organisations are using AI for process automation, yet only 37% report transformative business results.

Financial institutions are enthusiastically embracing AI across multiple functions:

• 67% for process automation and operational efficiency
• 64% for customer service enhancement
• 57% for strengthening cybersecurity
• 52% for risk management and fraud detection

But despite widespread implementation, the majority (47%) experience only moderate benefits, while 9% admit they aren't measuring AI's impact at all.

The workforce readiness gap

Only 46% of financial institutions are heavily investing in AI upskilling, while 11% have no formal AI training initiatives whatsoever.

Organisations face critical skills gaps in:

• Building AI features (40%)
• Identifying AI use cases (37%)
• Implementing AI projects (33%)
• Risk management (32%)
• Ethical AI practices (30%)

The competitive landscape: An industry of explorers

Only 37% of financial institutions rate their AI maturity ahead of competitors.

The research reveals most organisations remain in early maturity stages:

• 52% classify themselves as "AI Explorers"
• 55% view their AI maturity as comparable to peers

This relatively level playing field creates a significant opportunity for ambitious organisations to gain competitive advantage through strategic skills development.

The future of AI in financial services

Our research shows financial leaders expect AI to transform:

• Cybersecurity (64%)
• Customer service & engagement (55%)
• Regulatory compliance & reporting (48%)
• Process automation & operational efficiency (57%)

Yet this transformation depends entirely on workforce readiness. While 36% of leaders believe AI will transform their roles and create new opportunities, 12% fear their roles may become redundant without proper adaptation.

"The biggest risk is being left behind and seen as uncompetitive because the organisation cannot deliver the service levels that others will have developed." Senior Financial Services Leader

The path forward

Organisations that successfully bridge the AI skills gap will lead the industry through:

• Enhanced cybersecurity capabilities
• Hyper-personalised customer experiences
• Automated compliance through regulatory technology
• Operational efficiencies with AI co-pilots
• Strategic decision-making powered by predictive insights

But this future is only possible with strategic investment in people alongside technology.

Methodology

*The survey, conducted by Radish on behalf of Multiverse between February and March 2025, targeted 157 leaders within financial services organisations. An online survey was used, with all respondents based in the UK. Phone interviews with leaders within the financial services sector were also conducted.

Most of the UK’s largest listed companies are underprioritising skills development in relation to technology, according to new Multiverse analysis of a sample of FTSE 100 annual reports spanning the last 10 years.

Despite seven in ten FTSE 100 companies mentioning a strategic priority relating to technology, only 7% describe skills and training as a strategic priority in their latest annual reports. This proportion has not improved since 2013 (6%), while technology has shot up, suggesting that boardrooms are not yet recognising the sweeping impact of technology on workforce skills and people requirements.

With Goldman Sachs predicting that AI investment will rocket to $200bn this year, companies who do not act are potentially putting record levels of investment at risk.

To uncover this data, Multiverse’s data science team worked with expert data analyst David Abelman (ex-Meta, Bain & Company), to build a Large Language Model (LLM) system to analyse structured information from over 100,000 pages of publicly available annual reports. The resulting Boardroom Skills Agenda report provides empirical evidence on how people and skills are missing from the boardroom’s top priority list.

The missing skills piece

Where companies do proactively reference skills strategies, they are often not undertaking reviews of the existing skills capabilities of their workforce. Only 17% describe running skills reviews of their workforces in the latest reports, while 78% of companies reference reviewing their Board of Directors’ skills.

According to the report, companies are also not targeting skills development relating to the most consequential technologies that will shape the future of work. For example, while 97% of companies mention critical compliance and DEI training, only 34% of companies referenced Artificial Intelligence (AI) training.

These findings follow Institute for Fiscal Studies (IFS) reporting, which confirms that the average number of days of workplace training received each year has fallen over the last decade. Employer spending on training has decreased over the same period, and there has been a fall in both public and private investment in training.

Meanwhile, growth is top of the UK’s political and economic agenda, with the Government promising to break down the current barriers to equipping the workforce with the right skills to maximise new technologies.

Euan Blair, CEO of Multiverse, said:

“Annual reports are a weathervane for the issues that are capturing the boardroom’s attention. What we can see in the data is that investment in technology is skyrocketing but skills and training has stagnated. It’s telling that at the same time, so has UK productivity.

“Technology tools are only as powerful as the people who use them. Without prioritising people, companies will be left with tech strategies that are missing a key piece of the puzzle. The tech revolution will not arrive until companies connect the dots between tools and talent.”

Further headline findings from the Boardroom Skills Agenda report include:

Technology is impacting workforce skills, despite absence from boardroom top priority list

The growing impact of technology on the workforce is starting to be signalled in some reports, with discussion of “reskilling” and technical “upskilling” on the rise. Yet overall the incidence and prioritisation of technical skills initiatives is notably still low.

• The proportion of companies mentioning reskilling has nearly tripled in the last 10 years from 7% to 20%, while technical upskilling has more than doubled (23% to 51%).
• Yet in the most recent set of annual reports, only 10% of companies provided a sum for how much they were investing in training.
• When referenced, the average (median) spend was £600 per employee. A median of just 0.16% of company revenue was spent on training, and only 1% of working hours.

Apprenticeships overtake graduate schemes

The AI analysis also found that companies are delivering training via a number of different schemes, and referencing these schemes more than they were 10 years ago:

• 59% of companies referenced an apprenticeship scheme (vs 48% 10 years ago), while 48% have a graduate scheme (vs 39% 10 years ago).
• Meanwhile, 32% said they had an internship scheme (vs 19% 10 years ago).

David Abelman, Data Science Consultant, added:

“When implemented carefully, LLMs provide a fantastic way to extract quantitative information from textual documents at scale. We were able to craft a workflow to make sense of over 100,000 pages of annual reports, giving us a unique understanding of how companies discuss their people development in relation to their increasingly strategic prioritisation of technology.

“It was clear that whilst technological focus has ramped up, strategic skill development is generally lagging behind. But it’s also promising to see signals of change in the tactical implementation of learning and development initiatives. It will be fascinating to see how this plays out in the coming years as the increasing impact of AI is felt.”

Download the full report.