The Multiverse blog

It's clear that fully adopting and utilising AI workflows can help professionals of all stripes gain an edge in their careers. Those already boasting AI expertise pursue careers in a broad range of sectors. For example, the marketing sector uses generative AI to create personalised content and automate tasks. This versatile technology also has numerous applications in e-commerce, finance, manufacturing, and other industries.

You can help fill this growing demand for AI-related roles by developing essential skills. Below, we’ll explore the top AI career paths, must-have skills, and strategies to build on your AI skillset.

Why AI Skills are in high demand

The demand for AI has exploded in the last decade, and this trend shows no signs of slowing down. Bloomberg Intelligence predicts that the generative AI market will grow from $40 billion in 2022 to $1.3 trillion in 2032. This rapid expansion will create new job opportunities and transform industries worldwide.

Several factors have contributed to the high demand for AI careers. Many businesses use AI to drive innovation and increase productivity. For example, this technology allows organisations to hyper-personalise customer experiences and develop new products. Businesses can also use AI to automate repetitive tasks like data entry and credit scoring. Organisations need skilled AI professionals to leverage these capabilities and create innovative solutions.

The exponential growth of big data has also fueled the need for AI skills. Between 2020 and 2025, the amount of data generated and consumed globally is expected to triple from 64 zettabytes to 180 zettabytes. Businesses can use AI algorithms to process, analyse, and gain insights from this big data.

Top AI jobs for 2025

Spend a few minutes browsing job boards, and you’ll find many artificial intelligence careers. However, some positions have more lucrative salaries and better growth prospects than others. Here are the top AI jobs for 2025.

Machine Learning Engineer

A Machine Learning (ML) Engineer creates and implements self-learning AI models and systems. They design algorithms–or sets of instructions–that allow machines to interpret and learn from data in a human-like manner.

ML Engineers often work in healthcare, finance, tech, and other industries that depend on data to make decisions.

Salary data

• Low range - £42K
• Average base salary - £57K
• High range - £76K

Source: Glassdoor

Data Scientist

A Data Scientist collects, analyses, and visualises raw data to gain novel insights and inform decision-making. They also use ML algorithms and statistical models to classify data, uncover hidden trends, and predict future outcomes.

The US Bureau of Labor Statistics (BLS) predicts that the demand for Data Scientists will grow by 35% between 2022 and 2032—and growth is also expected to be high for these professionals in the UK. Data Scientists often work in e-commerce, healthcare, insurance, and telecommunications, among other industries.

Salary data

• Low range - £39K
• Average base salary - £49K
• High range - £63K

Source: Glassdoor

Robotics Engineer

A Robotics Engineer designs, codes, builds, and maintains robotic systems. They develop algorithms that allow robots to perform complex tasks autonomously or semi-autonomously. For example, Robotics Engineers program robots to interact with humans and navigate the ocean floor.

Robotics Engineers play vital roles in the agriculture, automotive, healthcare, and manufacturing sectors.

Salary data

• Low range - £32K
• Average base salary - £39K
• High range - £49K

Source: Glassdoor

Software Engineer

A Software Engineer develops, tests, and updates software applications. They can use AI to automate repetitive tasks, write code, and troubleshoot bugs.

According to the BLS, the demand for software development will increase by 26% from 2022 to 2032 in the US. Across the pond, there are ample reasons to believe the UK will continue to be a top destination for Software Engineers in Europe. Many industries hire Software Engineers, including business, finance, healthcare, retail, and tech.

Salary data

• Low range - £38K
• Average base salary - £50K
• High range - £65K

Source: Glassdoor

Business Intelligence Developer

A Business Intelligence Developer uses data analytics and software to collect, interpret, and visualise business data. AI-powered software can help them analyse data and design business interfaces more efficiently.

Many Business Intelligence Developers work for consulting firms, government agencies, financial institutions, and large corporations.

Salary data

• Low range - £30K
• Average base salary - £37K
• High range - £45K

Source: Glassdoor

Essential skills for AI jobs

Employers expect candidates to have a broad range of technical and soft skills for AI jobs. Here are the essential abilities you’ll need to succeed in these roles.

Machine learning

AI professionals use data and algorithms to develop and train ML models that learn and improve without human input. ML requires a strong understanding of probability and statistics. You’ll use these mathematical concepts to analyse data, design predictive models, and assess their performance. You can also use ML libraries like PyTorch and TensorFlow to create and deploy models.

Natural language processing

Natural language processing (NLP) is a subfield of AI that uses ML algorithms to understand and respond to complex human language. AI professionals use many techniques to develop NLP models like ChatGPT. For example, sentiment analysis involves assessing text or speech for emotional tone. Topic modelling is another method used to identify themes in data.

AI specialists can streamline the development of NLP models with spaCY, NLTK, TextBlob, and other libraries and frameworks.

Proficiency in programming languages

Every AI career path requires knowledge of programming languages. Python’s simple syntax and vast libraries make it the most popular choice for data analysis and ML. Other useful languages for AI professionals include:

• Java - to design complex algorithms
• JavaScript - to develop web-based ML applications
• R - for data processing and visualisation

You don’t need to master all these languages, especially for entry-level AI careers. Instead, you should research the requirements for careers you're interested in to determine which programming languages to learn.

Problem-solving

AI experts work with complex and cutting-edge technologies, so it’s normal to encounter obstacles during projects. For instance, your algorithm may make wildly inaccurate predictions, or you might struggle to find high-quality data. Strong problem-solving skills will help you troubleshoot issues and develop creative solutions.

Collaboration

AI professionals often work on complex projects that require expertise in multiple disciplines. For example, they may work with Data Scientists, Project Managers, and Product Developers. Strong collaboration skills will enable you to tackle these projects in cross-functional teams. Practice sharing your knowledge with people from different backgrounds and resolving conflicts.

Communication

AI and ML are complex topics that involve advanced technical concepts and specialised jargon. Strong communication skills will allow you to explain these ideas to stakeholders from various backgrounds. For instance, you may need to present your findings to non-technical clients and Project Managers. You can prepare for these situations by practising simplifying complex ideas and translating jargon into plain language.

AI ethics

Many challenging ethical dilemmas surround AI, leading to widespread concerns about this technology.

You can help assuage these fears by following ethical AI practices. Always obtain consent before using data, and practice transparency by documenting the methodologies and sources used.

How to acquire AI skills

Here are four possible avenues to develop the necessary skills for AI jobs.

University degrees

Some people obtain a degree in computer science, data science, mathematics, or statistics. This path allows you to gain relevant skills through a structured curriculum. But, you may not have the opportunity to develop AI-specific projects and experience. A university degree also requires a significant investment of money and time.

AI boot camps

A boot camp is an intensive program that focuses on work-ready skills. Participants also gain hands-on experience with AI projects. Some boot camps have low placement rates and high price tags, so research programs carefully before enrolling.

Self-study resources

Many websites offer free online classes, tutorials, and other resources. Aspiring AI professionals can use these materials to learn about data science, ML, programming languages, and other key concepts. Self-studying lets you learn at your own pace, but the lack of structured guidance can lead to knowledge gaps.

Apprenticeships

An apprenticeship allows you to acquire hands-on experience and follow a structured curriculum designed by AI experts. Apprentices also earn a competitive salary and build a professional network in their chosen industry.

Looking to get started or grow your skillset in AI? Explore programmes ranging from our AI and Machine Learning Fellowship to AI Strategy and Leadership with Multiverse.

Building a portfolio for AI jobs

You don’t need a university degree to pursue a career in AI, but you’ll need to show potential employers you have the right skills. Developing an online portfolio is the most effective way to showcase your abilities.

Start by developing hands-on projects that use a diverse array of AI skills. Here are a few project ideas:

• AI chatbot
• Image classification model
• Music generation model
• NLP-powered virtual assistant
• Predictive analytics model to forecast the weather or the outcome of sporting events
• Sentiment analysis tool to evaluate social media posts

These projects allow you to apply theoretical concepts to real-world scenarios. You can also gain practical experience by using real, free datasets to develop and train your AI models. Potential sources for datasets include Kaggle, GitHub, Data.gov, and the r/datasets subreddit.

As you create projects, assemble them into an online portfolio. Some tech professionals build a website from scratch to house their projects. You can also use a portfolio hosting website like Carrd, Notion, and Webflow.

Provide context with detailed descriptions, screenshots, and other supporting materials for each project. Link this portfolio in your application materials so potential employers can assess your skills.

The future of AI Jobs

AI is reshaping the future of work across industries. Microsoft released a report in 2025 showing high levels of AI applicability to roles associated with finding information, for example — implying large degrees of AI-related workforce reductions could arrive in the future.

This prediction may sound scary, but many companies won’t eliminate these jobs completely. Instead, AI will likely enhance existing roles and allow workers to focus on complex tasks that require human minds.

AI will also open new career opportunities for many employees. 36% of employers are already making a strong effort to reskill workers affected by generative AI. This percentage will likely grow as emerging technologies like multimodal AI and small language models create new roles.

Developing AI skills now can help you future-proof your career and gain a competitive advantage in this shifting landscape. As you gain experience, you may qualify for more advanced–and often more lucrative–roles in AI.

Level up your AI skills with Multiverse

There’s never been a better time to grow your career with artificial intelligence skills.

Prepare for opportunities in this rapidly growing field by developing or expanding AI skills. The top AI jobs in 2025 require excellent technical and interpersonal abilities. You’ll need a strong foundation in programming languages, machine learning, and natural language processing. Many careers also require soft skills like communication and collaboration.

Multiverse’s free apprenticeships will help you develop the necessary AI skills and gain hands-on experience.

Fill out our fast apprenticeship application to start your journey.

While the two titles are often used interchangeably, we’ve assembled this blog to help you explore the distinctions and similarities between Software Developers and Software Engineers that can manifest in certain organizational contexts.

Below, we’ll breakdown:

• The differences between a Software Developer vs. Software Engineer
• Key responsibilities, skills, and tools
• Salary and job outlook
• And what you need to know to grow in these respective career paths

Software Engineer vs. Software Developer: Defining the roles

Software Developers generally build software applications or systems based on designs created by engineers. Compared to engineers, they might focus on one part of the software development lifecycle (SDLC) rather than the entire SDLC. The SDLC includes the major stages and tasks that take a software application or system from start (planning) to finish (deployment and maintenance).

Even for those who'd argue in favor of a clear distinction between the two, there are crossovers between the roles of Software Engineers and Software Developers. Many professionals with a ‘Software Developer’ title will design, create, test and maintain software applications or systems. Those tasks combined are closer to the traditional role of a Software Engineer.

On the other hand, Software Engineers typically design, build, test and deploy full-stack applications. So, rather than developing a specific software or computer system, these professionals typically have a broader focus on the entire SDLC. Tasks outside the development stage often include project management and continuous improvement. That’s as opposed to software development in isolation.

Software Engineers might also engage in more complex problem-solving and system architecture tasks than Software Developers. In either case, both developers and engineers aim to help create software that meets specific requirements and addresses user needs.

Key responsibilities and projects

Although Software Developers and Software Engineers may work on similar project types, the scale and scope of their work usually differ. A Software Developer may plan the structure and functionality of software applications based on client or user requirements. Meanwhile, a Software Engineer designs the overall architecture. The latter includes defining the structure of components, their interactions, and how data flows through the system.

Another key difference in scope and complexity is creating custom-built tools for the project. Software Engineers may need to build tools to develop a software product, whereas Software Developers usually use pre-built tools.

There may also be differences when it comes to writing code. Both Software Developers and Engineers write code to execute software solutions. But Software Developers typically use programming languages to implement the designed software. In contrast, Software Engineers will need to focus on not just code quality but also maintainability and scalability across the project. Software Engineers might also work on a system's more complex or critical components.

The level of team collaboration can be different, too. The nature of a developer’s work means they tend to work more with ‘things’ (i.e. systems and applications) than people. That doesn’t mean there isn’t any team collaboration, but they will generally work more independently than engineers.

On the other hand, an engineer’s work generally requires more collaboration. So they might work with developers, other engineers and people from different teams. A Software Engineer might also collaborate with people who use the software to help them improve it.

Educational paths and qualifications

You can become a Software Developer through a variety of educational pathways. One option is to earn a relevant A Level or a Level 3 Certificate in a subject like computing. Some developers also attend university, although a university degree isn’t always necessary for software roles. Other Software Developers are self-taught through personal projects or coding boot camps. 

Like developers, Software Engineers can come from a range of educational backgrounds. Some might have a Bachelor’s Degree in Mathematics, Computer Science or Engineering. But as with developers, a university degree isn’t necessary, or even the best route, to becoming a Software Engineer.

Many engineers start out as Software Developers and upskill or are self-taught and transition into engineering through a software engineering apprenticeship. Through their apprenticeship, they gain hands-on work experience and an accredited certification. In the case of a Software Engineering Level 4 apprenticeship standard, it’s the equivalent of the first year of an undergraduate degree.

Skills and tools comparison

Software Developers and Software Engineers both need technical skills to do their jobs. They’ll both know different programming languages, such as JavaScript, SQL, Python, and CSS. But a Software Engineer will typically have a more in-depth understanding of the same programming languages and access to a broader range of languages.

Software Developers and engineers both need to understand algorithms and data structures. That said, a Software Engineer must also design, implement and optimise algorithms to solve specific problems efficiently. A Software Developer, on the other hand, will usually work with pre-existing algorithms to complete tasks.

When it comes to soft skills, both professionals need communication skills. However, because Software Engineers collaborate with people outside of their teams, they will need to communicate technical concepts to non-technical people. They will also both need problem-solving skills.

There are a range of software development methodologies that both developers and engineers use, including:

• Agile development methodology: Software is developed in iterations to reduce risk.
• DevOps deployment methodology: This isn’t just a methodology. It creates organisational change to encourage collaboration between the departments involved in the development lifecycle.
• Waterfall development method: A linear model you complete in sequential phases. Each phase has a fixed goal.
• Rapid application development (RAD): The development process is condensed so that investment costs are lower and production is quicker without reducing quality.

There’s also a crossover between the tools commonly used by both developers and engineers. Both professionals use different tools for bug tracking, development, code review and version control. GitHub, Jira, Codenvy and Adobe Dreamweaver CC are common examples of tools that both developers and engineers use.

Career progression and opportunities

There are a few different career progression routes for Software Developers. A linear pathway might involve progressing from Junior to Senior Software Developer and then Software Development Manager. But you can also choose to progress into a different specialism, such as web development, front-end development, or back-end development.

Like developers, Software Engineers might follow a junior-to-senior linear progression route. They might also progress further from Software Engineering Manager to Head of Department. Similarly, engineers can progress to a specialism. That could be a specialist role like Cyber Security Engineer, Data Engineer, or Systems Engineer.

There’s also potential to crossover between software development and engineering roles. For Software Developers hoping to transition into engineers, that will likely mean upskilling. That’s because there are usually extra responsibilities (like specifications, architecture and project management) that you might not have done as a developer.

Salary and job outlook

According to Glassdoor, the average Software Engineer salary in the UK is around £50,000, with a general range between £38,000 to £65,000.

A quick search for “Software Developer” jobs on LinkedIn shows around 15,000 UK job openings advertised on the platform. A search for “Software Engineer” brings up a similar volume of job vacancies.

Data from ItJobsWatch finds around 2,800 permanent UK jobs requiring software engineering skills through the first half of 2025, which is down from over 7,000 during the same period in 2024.

As there will be some crossover in the skill demands between engineering and development, those jobs might be relevant to both professions. In general, there’s a higher demand for Software Engineers and developers who understand Agile software development and programming languages like Java and Python.

Case study: a real-world example

Last summer, we shared stories from our industry-leading team members who transitioned from big tech to Multiverse. Our Director of Engineering, Joe Freeman, started with us after working at industry giants like Amazon and Deliveroo. Freeman has excelled in both software development and software engineering-specific roles. We asked him about his day-to-day work as Director of Engineering.

“Being a builder at Multiverse means solving problems in a product centric way, to help scale our mission. Our problem spaces straddle multiple user types — our apprentices, our enterprise customers and our coaches. We solve problems that consider the interactions between these actors which makes for exciting systems thinking and opportunity for experimentation.”

Joe adds, “We are solving unique problems in a relatively new industry and we want to become the best version of Multiverse - we don’t want to replicate how other companies work. Being able to not only challenge the status quo, but change it, is an important part of being successful here and there are no areas out of bounds. Everything from our process, our development frameworks, tools and technologies are open to challenge and improvement — if it’s not working as well as it could, you can change it!”

Joe explains that Multiverse and the engineering team are relatively early in “our journey building.” That means the technology team often has the opportunity to work on solving novel problems. Even though this can be a challenging role because Multiverse sits within a newer industry with novel problems to solve, the challenge is worth it.

According to Joe, the work is “both exciting and impactful.” To top it off, “When we are successful we make a real difference for our apprentices and provide real impact for our customers.”

Choosing the right path for you

Software Developers and Software Engineers are similar but not always the same. Whether a developer or an engineer, the scope of the role itself will largely depend on the employer. In smaller organisations, the roles are pretty interchangeable. But in larger ones, you’re typically either a Software Developer or a Software Engineer, with the distinct roles co-existing in the same team.

Still, the skills and responsibilities of the two roles will often overlap. That said, when choosing between either specialism, it’s worth considering your interests, strengths and career goals.

Software development might be best for you if you prefer to work with ‘things’ rather than people. On the other hand, software engineering is a better option if you like to collaborate and communicate with technical and non-specialists.

If you’d like to niche down into one area or discipline, development likely offers more specialist career pathways. Meanwhile, engineering could be for you if you’re interested in following a linear career pathway that leads to people or project management.

The future of software roles

69% of UK and US business leaders agree that the emergence of Artificial Intelligence (AI) will create more demand for AI skills in the workforce. Further, the U.K.’s Department for Education finds that 10-30% of jobs could realistically be automated with AI.

It makes sense then that AI, machine learning and automation will likely impact the roles of Software Developers and engineers in the future. That could mean using AI to automate coding tasks or to gather large amounts of data. Meanwhile, machine learning is pivotal in fraud prevention for software professionals specialising in cyber security.

Aside from AI and machine learning skills, a Red Hat survey found that 69% of IT managers need staff with cybersecurity skills. Further in-demand skills for software professionals include cloud computing (68%) and full-stack development (63%).

Resources for further exploration

If you’re interested in exploring careers in software development or engineering further, consider the Multiverse blog. In addition to general career information, you’ll also find articles on specific skills highly relevant to software roles, such as the differences between Java and JavaScript.

Online publications like TechRepublic and techUK.org are a great starting point for staying ahead of emerging tech trends that could impact software roles. You might also consider exploring professional organisations in the UK. The Institution of Analysts and Programmers (IAP) and the Business Application Software Developers Association (BASDA) are two examples.

If you want to take short online courses, these can help you develop entry-level software development and engineering skills. You can then add these to the skills and tools section of your CV.

Upskill your career in software

Ask one software professional, and they'll tell you there’s little-to-no difference between Software Developers and Software Engineers. Ask another, and they’ll tell you there will always be a crossover, but they are distinct roles. Long story short? The biggest determinant of the differences and similarities in either software role is typically your employer.

Even if you apply for roles in a smaller organisation where the positions are similar, if not the same, it’s still worth knowing the distinctions and overlaps. This understanding will give you a clearer idea of which pathway best aligns with your skills, personal attributes and career aspirations.

To learn the skills needed to advance in your software career, complete our fast and straightforward application to learn more about paid learning opportunities provided by your current employer.

Even as employees spend more time talking to ChatGPT or collaborating with software, human-to-human interactions remain essential. In fact, 85% of UK and US employers say skills like communication are just as important—or even more important—than they were five years ago. Strengthening your interpersonal skills can help you advance your career, step into leadership roles, and handle greater responsibilities with confidence.

Why do interpersonal skills matter in the workplace?

Strong interpersonal skills help workers fit in with their teams and collaborate effectively. Without them, people may struggle to work together or even complete basic tasks. For example, 44% of employers report that hiring people with poor soft skills has led to communication challenges, and 41% have seen reduced team productivity.

Here are four more reasons to develop these extremely valuable skills:

• Stand out to potential employers: Over two out of three (67%) UK employers prioritise soft skills over education when hiring new employees. By mastering the top interpersonal skills, you can make a strong impression in job interviews and increase your chances of landing a role.
• Grow your career: 87% of workers believe good interpersonal skills are essential for career advancement. Upskilling in these areas can help you lead teams and take on greater responsibilities.
• Stronger relationships: Many careers involve frequent interactions with customers and colleagues. Excellent interpersonal skills make it easier to connect with others and build meaningful connections. For example, when you can truly empathise with a client’s problem, you can create better solutions.
• Future-proof your career: While technical skills are often industry-dependent, interpersonal abilities are incredibly transferable. They can help you pivot into new roles in your current sector or change fields. A Registered Nurse, for instance, could use their emotional intelligence and communication skills to transition into a project management role.

Key interpersonal skills to develop

Strengthening your people skills is an excellent way to upskill or reskill. These abilities are truly timeless and apply across many industries, which means they have a high return on investment. Get started by focusing on these important interpersonal skills.

Communication

You might assume that you’re already a solid communicator. After all, you talk to your colleagues and write emails all the time, right? But effective communication takes effort and practise.

Strong communicators excel at public speaking and writing. They can clearly explain complex concepts while keeping their audience engaged. For example, you might use storytelling techniques to walk your clients through a product’s features and demonstrate how it can solve their supply chain challenges.

Good communication is also nonverbal. Eye contact, gestures, and other cues will help you build trust and share your ideas.

Active listening

Every interaction is a two-way street, so active listening is absolutely essential. This skill involves interpreting body language and genuinely trying to understand the other person’s perspective. A client may claim they love your design, for instance, but their stiff shoulders show they’re actually annoyed. By tuning into these subtle cues, you can establish positive rapport and understand other people’s true feelings.

Teamwork and collaboration

Many careers involve working in teams with people from diverse backgrounds and fields. For example, a Data Scientist may collaborate with marketing and sales teams to analyse customer behaviour and predict the next trending product.

You can become a more effective collaborator by learning how to delegate tasks based on each team member’s strengths. You should also actively work to create an inclusive team culture where everyone feels comfortable pitching ideas and giving feedback.

Empathy and emotional intelligence

Between 2016 and 2030, the demand for emotional skills is expected to grow by 22% in Europe. These abilities allow you to understand and handle your emotions and the feelings of others. They might seem innate, but they’re just as learnable as prompt engineering or programming a mobile app. For instance, you might use breathing exercises to control your frustration or offer support to a stressed-out coworker.

Conflict resolution

Workplace conflict is incredibly common, with a quarter of UK employees experiencing it in the past year. These conflicts can involve everything from heated arguments to outright discrimination.

Mastering conflict resolution can help you to defuse these situations — ideally, before relationships sour. By actively listening to others and looking for mutually agreeable solutions, you can maintain harmony in the workplace.

Adaptability and positive attitude

Even the most experienced professionals can face unexpected challenges. Economic downturns, disrupted supply chains, PR crises — anything could derail your project. Having a flexible mindset allows you to respond to these obstacles quickly and with minimal stress.

A positive attitude matters, too. When you’re upbeat and forward-looking, it’s easier to stay focused and keep your team motivated.

Leadership and interpersonal skills

Effective leadership goes hand-in-hand with strong people skills. After all, you can’t lead a team unless you can inspire and communicate with them.

Emotional intelligence is one of the most critical leadership traits. Research shows that “emotionally competent leaders perform better and are more successful.” That’s because they can recognize and manage both their team’s emotions and their own, especially during stressful situations.

You can improve your leadership skills by acting with clarity and empathy. During a crisis, for instance, your team will respond faster if you provide easy-to-understand directions.

You should also try to understand your employees’ perspectives instead of making assumptions. A worker who frequently misses deadlines might need more training, while someone who’s chronically late may be going through a personal crisis. By learning how to practise active listening, you can get to the root of these issues and start working on a solution.

Developing interpersonal skills

There’s no one-size-fits-all approach to gaining good interpersonal skills. It depends on your strengths, goals, and personality.

Start by reflecting on your existing abilities. You can conduct a formal skills inventory or just write in a journal about your strengths and challenges. Ask yourself these questions:

• What social skills do I use in my current role?
• How would I rate my personal and professional skills (teamwork, public speaking, etc.)?
• What types of social situations do I thrive in? And where do I struggle?
• What are some experiences where stronger interpersonal skills would have benefited me?
• Where are my biggest areas of improvement?
• If I want to transition into a new role, what interpersonal skills would I need?

Once you’ve reflected on these topics, choose two or three skills to focus on. That way, you don’t overwhelm yourself.

Role-playing activities

Role-playing exercises are an easy way to upskill. Consider asking your peers or a mentor to simulate different scenarios with you. Here are a few examples:

• Practise giving presentations and answering questions from the audience to beef up your communication skills.
• Improve your ability to resolve conflicts by pretending to mediate a tricky dispute between colleagues.
• Simulate a conversation with a frustrated customer to sharpen your active listening and negotiation skills.

These activities may seem a bit intimidating, especially if you’re an introvert or nervous about conflict. But these low-stakes exercises will help you gain confidence and get meaningful feedback from colleagues.

Training programmes

For more in-depth upskilling, consider a formal soft skills training programme. Multiverse’s apprenticeships can help you learn how to incorporate interpersonal skills in real work environments.

For example, the Transformative Leadership programme empowers professionals to lead change in their workplaces. It uses a combination of hands-on learning and structured modules to teach artificial intelligence and leadership skills. Similarly, the Project Management program can help you learn collaboration, communication, and other valuable skills.

Multiverse’s programmes are completely free for apprentices. You’ll continue working in your current role while completing real projects and receiving dedicated study time. It’s an incredibly effective way to grow your soft skills and gain new knowledge on the job.

Everyday practice

Look for opportunities in your daily routine to develop common interpersonal skills. This could be as simple as asking questions during stand-up meetings — “Can you explain why we’re using this software?” — and listening more closely to your colleagues. Over time, these actions can lead to noticeable improvements in your communication skills.

Practise managing your emotions, too. Consider writing about how you feel to increase your self-awareness. You can also use deep breathing and meditation to regulate your feelings.

Interpersonal skills and career growth

Don’t underestimate the positive impact that interpersonal skills can have on your everyday work life. When you build relationships with other professionals and clients, collaborations become rewarding and fruitful instead of a chore. And while navigating workplace dynamics may never be effortless, it’s certainly easier when you have useful skills like verbal communication. As a result, you may feel less stressed and more satisfied with your job.

These abilities can also make you a stronger job candidate. For example, strong communication abilities will help you write persuasive cover letters. Plus, it’s easier to highlight interpersonal skills during an interview when you’re a confident speaker.

Additionally, interpersonal skills can help you expand your professional network. When you can maintain good relationships and show genuine interest in others, you’ll naturally attract collaborators and mentors. Over time, these connections could lead to new career opportunities.

Add new skills to your toolkit with Multiverse

Why is learning interpersonal skills important? It’s not just about charming potential employers or getting along with your colleagues (though those are certainly perks). These abilities can help you thrive in social settings and improve your performance. Plus, they empower you to become a better leader and collaborator.

Ready to level up your skills? Explore Multiverse’s free Transformative Leadership programme. This apprenticeship will help you develop the necessary interpersonal skills to lead change in your organisation. You’ll also learn how to spearhead high-performing projects and teams.

Take the next step on your upskilling journey by filling out our quick application.

Data cleaning means finding and fixing those errors so your dataset is accurate and reliable. It’s one of the very first steps in the AI and ML pipeline, and without it, algorithms can produce misleading results or even misinterpret the information entirely. This guide walks you through the essential tools and approaches for cleaning data — no experience required.

Why data cleaning matters

Data cleansing can sometimes seem like overkill, especially for vast datasets. Sure, you may have a few incorrect values, but does it really matter?

The answer is yes. Even a small amount of dirty data can have a huge impact on machine learning models. For example, algorithms may learn incorrect patterns from unclean data, leading to inaccurate predictions or outputs. Even worse, errors can unintentionally introduce biases into the model.

When businesses give poor-quality data to ML models, they’re more likely to make bad — or at least misinformed — decisions.

Take AI scheduling tools, for instance. Researchers studied 99 million shifts for retail employees that had been planned using popular AI software. They discovered that managers had manually corrected 84% of the shifts, and about 10% of these adjustments were caused (directly or indirectly) by faulty input data.

As doctoral student Caleb Kwon explains, “if you put in garbage, the AI tool — no matter how sophisticated it is or how complex it is or how much data you feed it — will produce something that’s suboptimal.” For the retailers, the faulty schedules hurt productivity and left stores understaffed.

Maintaining data integrity can prevent costly mistakes and help businesses make more informed decisions. It also ensures that companies are analysing truly reliable data — instead of getting led astray by insights that seem trustworthy but aren’t.

Common data issues

Data Analysts can encounter many types of problems when working with raw data, including:

• Missing values, such as customer phone numbers without all the digits
• Outdated information
• Duplicate data
• Inconsistent data, like “Street” vs. “St.” for addresses
• Inaccurate data, such as product numbers entered incorrectly

Many data errors are syntactic, meaning they contain simple formatting mistakes. For example, you might accidentally misspell a product name while manually updating your inventory.

By contrast, semantic errors look correct but don’t make sense logically. A medical chart, for instance, might say a patient has a heart rate of 600 beats per minute instead of 60. These errors can be harder to spot without close scrutiny.

Structural errors affect the layout of the entire dataset, not just a handful of data points. They can involve anything from mislabelled rows (like “Addresses” instead of “Application numbers”) to unnecessary line breaks.

It’s normal for humans to make mistakes, no matter how diligent they are. You may forget to update an appointment time or mishear a vendor’s name. But machines aren’t infallible, either. For instance, sensors can malfunction and give faulty readings, or Microsoft Excel could glitch and delete data. With so many potential errors, data cleansing is absolutely essential.

Step-by-step data cleaning workflow

When you hear the phrase “data cleaning,” you might picture yourself poring over thousands of data points with bleary eyes. But today, you can mostly automate it using the right methods and tools.

Data auditing

You can’t start cleaning and organising your closet until you know what’s inside. The same goes for your datasets. Without a proper audit, you’re just blindly guessing about what needs to be fixed.

An audit involves profiling your dataset and identifying all the problems. Sometimes, you can do this manually, especially if you only have a few data points. For example, anyone can spot missing data in an Excel spreadsheet — just look for the blank cells.

For larger datasets, you’ll need automated tools to get a handle on your information. One popular platform for this is OpenRefine, which lets you filter and sort data. It also flags errors, such as null values and duplicate records. Using data auditing tools like this helps you quickly understand how clean (or dirty) your information actually is.

Cleaning

Data cleaning focuses on standardising data and correcting any errors. This step usually involves these tasks:

• Correcting spelling and grammar errors
• Removing duplicate information
• Rearranging columns and rows
• Fixing inconsistent or incorrect data

Data cleaning tools can significantly speed up this process. Many professionals use pandas, a Python library, to tidy data. Other options include Alteryx One and SQL.

Always back up your data before and during cleaning. That way, you won’t have to stress if you accidentally delete the wrong information or realise that “Smyth” was the correct spelling of that customer’s name after all.

Validation

While data cleaning software can be incredibly useful, it doesn’t always catch every mistake. Take the time to double-check your dataset and output quality for anomalies or errors. That way, you can feel confident that you’re using genuinely clean data.

The best validation techniques vary depending on the type of data you’re handling. Here are a few options:

• Presence check: Verifies that every field has a valid value.
• Range check: Sort numerical data from low to high to make sure it falls within an acceptable range. A temperature of 758°F wouldn’t make sense for climate data, so it’s safe to assume it’s a mistake.
• Format validation: Check that all your data follows a standard format, such as military or standard time.
• Uniqueness check: Some data points — such as credit card numbers — should never repeat in a dataset.

Documentation and versioning

Always document each step in your data cleaning process. This could be as simple as noting which rows you removed or how you fixed an anomaly. By keeping transparent records, you’ll help your team understand your decisions and troubleshoot any problems.

Creating multiple versions is another best practice. Save different copies of your data at each stage, and clearly label them. (“Instagram Captions – Pre-Cleaning,” “Instagram Captions – Minus Duplicates,” and so on.) That way, you can easily go back to an earlier version to recover lost data or start over with a new approach.

Techniques for cleaning data

You don’t have to memorise advanced maths formulas to clean data, but you should understand some basic techniques.

String cleaning focuses on improving the quality of textual data. It often involves removing extra whitespaces, deleting random punctuation, and fixing capitalisation errors. This process is key for preparing data for natural language processing and textual analysis.

Many datasets also contain “noise,” which is random outliers or irrelevant data points. Like static on a radio, they make it hard to make sense of information. Data visualisations, such as heat maps and scatter plots, can help you identify these anomalies. Depending on the context, you may decide to remove or correct them.

Normalising formats is another key technique. You may discover that some rows with currency include the pound sign and some just have a number. Or some days might start with the year while others end with it. ETL (Extract, Transform, Load) tools can automatically standardise these formats.

In some situations, you may also need to convert categorical data into numerical codes. This process involves giving each variable a distinct number. For example, a clothing retailer might label small shirts as 1, medium shirts as 2, and so on. That can make it easier for machine learning models to interpret your data and make predictions.

Not sure which techniques to use? Consider the data analysis methods you plan to apply. For example, you’ll need clean textual data to analyse the sentiment of your customers’ reviews. On the other hand, normalised dates are essential for time regression analysis.

Handling missing data

It’s normal to have incomplete data, especially when you’re gathering information from several sources. But these missing values can throw off your data analysis, so you can’t just ignore them.

This might seem a bit paradoxical, but start by looking for patterns in what’s not there. There are three kinds of missing data:

• Missing completely at random (MCAR): There’s no rhyme or reason to this incomplete data. For example, a glitchy form may not collect data from 20 out of 1,000 survey respondents, with no obvious pattern.
• Missing at random (MAR): The missing data is related to other variables in the dataset, but not to the value itself. A Business Data Analyst, for instance, may notice that first-time shoppers are 25% less likely to leave a review than repeat customers. That’s not necessarily because they’re less satisfied, but because they have a shorter purchase history.
• Missing not at random (MNAR): The reason for the incomplete data is directly related to what’s missing. Poor test-takers, for instance, may skip a question about their grades out of embarrassment.

Once you understand the reason for the missing data, you can take steps to fix it. You might just remove the incomplete rows, especially if it’s MCAR. Or you may need to rethink your entire data collection method to get more accurate information.

If you choose to simply delete information, you can take one of two approaches. Listwise deletion erases all the information for a subject with one or more missing values. It’s the simplest method, but it can drastically shrink your sample size. By contrast, pairwise deletion uses all the available data, only excluding the missing values.

Data imputation is another useful option. It uses mathematical formulas to fill in gaps — basically, making an educated guess about what the unknown data could be. Here are a few imputation methods:

• Mean: Replace missing values with the average of the observed data.
• Median: Use the middle value of the data points to fill in the gaps.
• Regression: Predict missing values by analysing the relationship between values. For example, you might use age and job title to predict salary.
• K-Nearest Neighbours (KNN): Use similar data points (“neighbours”) to estimate the missing values.
• ML-based: Use complex algorithms to spot patterns in the dataset and approximate missing values.

No matter which approach you choose, avoid projecting your own biases onto the gaps. And be cautious about deleting data — the last thing you want is to end up with a tiny sample size because you cleaned it too aggressively.

Tools of the trade

You don’t need to master every data science tool, especially as a beginner. Set yourself up for success by upskilling with these essential platforms:

• Microsoft Excel: Helpful for learning basic data cleaning skills, such as removing duplicates and correcting formatting errors.
• Python libraries: Have pre-written snippets of code that you can use to automatically clean and manipulate data. Popular frameworks include NumPy (best for numerical data), pandas (for structured data), and scikit-learn (designed for machine learning tasks).
• OpenRefine: A free tool for data transformation and cleaning.
• Jupyter Notebooks: Great for documenting your data cleaning process and batch processing information.

Automating and scaling data cleaning

Once you understand the principles of data cleaning, take your skills to the next level by learning how to write simple automation scripts in Python. These functions save time by handling basic tasks like removing duplicate entries.

You can also combine these scripts into a data cleaning pipeline. Data Engineers and other professionals often build these workflows for repetitive tasks, such as string cleaning and date normalisation.

For more complex or extensive projects, consider using a data cleaning framework or platform. Tools like OpenRefine come with a learning curve, but they can help you scale your projects more efficiently than writing every script yourself — or worse, cleaning vast datasets manually.

Preparing data for modelling

Data cleaning lays the foundation for all the other preprocessing steps.

Feature engineering transforms raw data into new variables (or “features”) that help train ML models. If you have sensor data, for instance, you might create features using temperature readings to detect malfunctions. Clean data supports this process by allowing you to develop more accurate features.

After you’ve cleaned the data, you can also conduct exploratory data analysis (EDA). This step helps you start detecting patterns and anomalies in the dataset.

Of course, you should always double-check your data before using it for training or inference. Look for missing data, outliers, and other issues. You should also make sure that you’ve correctly formatted and encoded everything. By using the most reliable data available, you’ll improve your model’s performance and make smarter forecasts.

Final tips for beginners

Learning how to clean data doesn’t have to be complicated or nerve-wracking. Set yourself up for success with these best practices:

• Document every step of the data cleaning process so you can retrace your steps (especially if something goes wrong).
• Start small with simple datasets, such as sales records from the last week. Once you gain confidence, you can branch out to larger projects.
• Build reusable Python scripts for basic cleaning tasks like capitalising names.
• Use validation methods frequently to maintain data quality.

Learn how to turn messy data into sparkling insights with Multiverse

The Multiverse Skills Intelligence Report found that 41% of employees struggle to source and clean data. By learning data cleaning, you can get more precise results and take on advanced AI and ML projects.

Multiverse’s free Data & Insights for Business Decisions apprenticeship is an excellent way to deepen your data knowledge and gain new technical skills. The 13-month programme combines hands-on projects with group learning and personal coaching. You’ll learn how to use data to drive change, support machine learning projects, and influence decision-making in your organisation.

Get ahead of the competition by taking the next step on your data science journey. Fill out our quick application now.

Java and JavaScript have distinct purposes, strengths, and limitations. These factors can influence the types of projects you can create and the careers you pursue. Some tech professionals learn both Java and JavaScript, but they may be optional, depending on your professional goals.

This guide covers the key differences between Java and JavaScript to help you decide which programming language to learn.

What’s the difference between Java and JavaScript?

Many people think that moving from Java to JavaScript is like switching from driving a car to driving a lorry. Java and JavaScript, however, are as distinct from each other as a car and a sailboat. While both are a means of transportation, their skills, knowledge, and environment are entirely different.

JavaScript is an interpreted language, which means it must be executed – or activated – by a JavaScript engine. The developer writes JavaScript code and embeds it in a webpage. The code remains dormant until a user opens the webpage in a web browser like Google Chrome or Mozilla Firefox. These web browsers contain JavaScript engines that execute the JavaScript code, activating interactive features like image carousels and drop-down menus.

By contrast, Java is a compiled language requiring two steps to execute. First, a compiler translates the human-written source code into bytecode, the language of computers. The Java Virtual Machine (JVM) interprets the bytecode into a usable format for a specific operating system and executes it. This process allows developers to write Java code for any platform.

5 key differences between Java and JavaScript

The differences between Java vs. JavaScript go beyond their methods of execution. Here are five factors that set these languages apart.

Applications

The JavaScript language is typically used for web development. Developers add JavaScript code to static web pages to make the client-facing side more dynamic and interactive. For example, you can use JavaScript to add:

• Contact forms
• Buttons
• Animated text
• Pop-up windows

Frameworks like React Native and Node.js enable JavaScript Developers to create mobile apps, web servers, and browser-based video games.

Java is a platform-independent language that builds a broader range of applications, such as:

• Artificial intelligence programs
• Cross-platform desktop software
• Enterprise software
• Internet of Things applications
• Web applications

Complexity

JavaScript is a relatively lightweight scripting language that resembles human language. As an object-oriented programming language, JavaScript is designed to make web development more interactive and dynamic — allowing developers to create rich interfaces and complex web applications.

Say you’re creating a website for a doctor’s office and want to model a “patient” object. In JavaScript, your code might look like:

Java involves a more complex system that represents data as objects within classes that define how they behave. The equivalent code in Java might look like this:

Syntax

Syntax refers to the grammar rules, structure, and order of operations for programming languages. Developers use syntax to write code, just like writers use grammar and formatting conventions to create sentences.

JavaScript has a simple and relaxed syntax consisting of functions and variables. Functions are reusable building blocks of codes that perform specific tasks, while variables are containers that store data. For instance, you can use functions to add and subtract variables like price tags and weights.

Java has a more structured syntax that organises data into classes. You must declare the data type of a variable when you create it, and you can’t change this type later. Java also uses getters and setters to retrieve and modify the data stored inside classes. Java’s syntax allows developers to create more robust applications, but it’s also more challenging for beginners to learn.

Learning curve

Many factors impact how long it takes to learn coding, such as your level of programming experience. However, most people can learn JavaScript faster than Java.

Tech professionals often consider JavaScript one of the easiest programming languages to learn. Most people can pick up the basics in a few study sessions, but mastering advanced concepts takes approximately six to nine months.

JavaScript Developers should also spend a few weeks learning HTML and CSS. You can use HTML to build a website's structure or backbone, while CSS allows you to style its appearance. Together, these three languages form the building blocks of most websites.

Java is a more complex and demanding language. Coding beginners can learn foundational Java skills in around six months, but true mastery may take one to two years.

Today, both Software Engineers and non-technical professionals rely on AI-powered tools like Windsurf and Cursor to write, edit, and debug code with ease. These platforms make coding more accessible than ever — but there’s still real value in understanding the fundamentals yourself.

Learning how languages like Java and JavaScript actually work gives you the foundation to think critically about the code these tools generate, spot errors they might miss, and build more complex solutions when automation isn’t enough.

Popularity

According to Stack Overflow’s 2025 Developer Survey, JavaScript remains the most popular programming language among professional developers, with 66% reporting use of the language. Conversely, only 29.6% of developers use Java, good for 8th on the list.

But Java’s lower ranking doesn’t mean that it’s an obscure or outdated language. According to Azul's 2025 State of Java Report, 99% of surveyed enterprises use Java — including 50% using the language to build burgeoning AI functionality.  Learning this language could open career opportunities in enterprise app development, server-side programming, and many other areas.

What is Java?

Java is a platform-independent, object-oriented programming language. James Gosling invented Java in 1995 to code digital devices like televisions. Today, many industries use Java for mobile app development, enterprise software, web applications, robotics, and more.

Java’s key features include:

• Multiple threads: Java handles multiple tasks simultaneously, improving performance
• Compiled programming language: Devices must have the Java Virtual Machine to run Java programs
• Strong typing: Variables must have defined data types, reducing errors
• Platform independence: Java programs can run on all operating systems

Disadvantages of Java include:

• Time-consuming to learn
• Slower than some other programming languages, like C++
• High memory consumption
• Complex syntax

What is JavaScript?

JavaScript is a simple scripting language that creates dynamic and interactive webpage content. Brendan Eich developed JavaScript in 1995 for Netscape. The programming language was originally called LiveScript, but the company changed the name to capitalise on Java’s popularity.

JavaScript enables frontend and backend development. On the client-facing side, you can use JavaScript code to create stylish and interactive user interfaces. On the server side, JavaScript manages tasks like processing data.

Key features of JavaScript include:

• Single thread: JavaScript executes tasks one at a time
• Interpreted language: Web browsers contain engines that execute JavaScript code
• Simple syntax: JavaScript consists of functions and variables
• Cross-browser capability: JavaScript works on all web browsers

Disadvantages of JavaScript include:

• Less security because clients can see the code
• Older web browsers may not support newer JavaScript functions
• Time-consuming to debug

Which is better, Java or JavaScript?

There’s no right or wrong answer when deciding between Java vs JavaScript. Each language has specific capabilities and limitations, making each more suitable for certain tasks.

Java is a general-purpose programming language with many applications in finance, healthcare, tech, and other booming industries. Depending on your career path, you could use Java to build and maintain:

• Android applications
• Big data engineering tools
• E-commerce platforms
• Enterprise software
• Server-side applications
• Software as a service platforms

Java can be a valuable tool in many careers, including those of an Android App Developer, Big Data Engineer, Security Engineer, and Software Engineer.

Unlike Java, JavaScript is specifically used to develop the client-facing and server sides of websites. This programming language can also be used to create browser-based video games and other web applications.

Careers often requiring JavaScript programming knowledge include Frontend Developer, Backend Developer, and Full-stack Developer.

Learning Java or JavaScript in 2025

JavaScript is a beginner-friendly programming language that can teach you how to think like a programmer. You can also use this language to pursue careers in web development. Java is a more versatile but challenging programming language. It could be an excellent choice if you want to build a wide range of applications.

No matter your path, you don’t need to enroll in a college programme to learn to code. You can use many resources to study Java and JavaScript, including:

• Bootcamp: Coding bootcamps are intensive courses that teach programming languages and other job-ready technical skills. However, many coding bootcamps have hefty price tags and low placement rates.
• Online course: Many colleges and websites offer affordable or free online coding classes. These courses can help you learn foundational skills but often don’t include career support.
• Apprenticeship: Working Software Engineers can learn additional programming languages and other in-demand technical skills via a Multiverse apprenticeship programme without having to pause their career.

Make this year the year you uplevel your coding skills

You don’t need to spend money on expensive bootcamps to learn Java and JavaScript. A Multiverse apprenticeship allows you to get paid to learn code and expand your skill set on the job. We also support your professional development with mock interviews, personal coaching, and much more.

Complete our short apprenticeship application today to get started.

We spoke to Claire Bolton from Capita, about the steps for launching a new upskilling programme and how to align apprenticeships with AI adoption.

Welcome to Apprenticeship Architects, Claire. Can you tell us about your role and the apprenticeship programme you launched with Multiverse?

I'm Claire Bolton, Head of Apprenticeships and Professional Development at Capita. I work with Multiverse and other providers to promote apprenticeship programmes for Capita across the business.

I ensure programmes are of good quality and are fit for purpose, for the individuals and the organisation. I work with senior leaders at Capita to align the apprenticeships with Capita’s strategy. I’ve been working with Multiverse for some time on the AI for Business Value apprenticeship.

You’re now on your fourth intake of employees to the apprenticeship programme. Can you walk us through how it’s evolved?

At first, it was a big bang – we had so much interest across the business. It's evolved quite significantly over the four cohorts.

Initially, we started with a ‘transformation accelerator’ group. We held in-depth stakeholder interviews to understand:

• Where they sit in the business and what they’re involved in
• Who they work with and who works for them
• How could this programme add value to them?
• What are the problem statements this programme could potentially support?

We gave that information to the coaches working on the programme.

This is how you know, while building this knowledge, you're going to make a difference. And then we can use the projects they're working on, like a full cycle, and feed it back to those senior leaders.

What’s the biggest impact you’ve seen from the Multiverse apprenticeship programme?

The biggest thing is confidence. The [AI for Business Value] apprenticeship gives everybody the opportunity to start at the same level and build confidence.

For those in client-facing roles, they can go into a client meeting, take that new knowledge, and share the journey they've been on. That will be hugely powerful in our organisation to ensure our clients have confidence in us as their trusted advisor.

Can you explain the steps you took so learners knew what to expect?

In any apprenticeship at Capita, we're keen to make sure we have the right people, on the right programme, at the right time. No one wants to start a course and then drop out.

With Multiverse, we built out a comprehensive insight session. Following the first two cohorts, we took it one step further and it grew into a taster session.

It transformed from a factual session about the course, goals, and eligibility criteria into a preview of what sessions are like and how they're delivered. That way, learners get an idea of whether it would work for them. Having that exposure before they've committed has been really successful.

How do learners on AI for Business Value actually implement their new projects

We're very lucky to have Tiina Stevens, Director of Digital at Capita, who leads our AI engagement internally. She's been really involved in the programme, hosting sessions for apprentices to provide insight into her role, highlighting how they can have an impact on the business and its direction.

Tiina has introduced our AI Catalyst Lab, [which houses] all of the identified opportunities where AI could be applied as part of a solution. Apprentices have full access to align their projects to this Lab and have the opportunity to work with Tiina and her team to see it to fruition, which is really exciting.

How are you sharing success with the rest of the business?

Seeing the projects - and the impact individuals are creating - brings it to life.

This isn't just sitting in a classroom and learning the theory. They genuinely are changing the way our organisation is performing, developing and serving our clients and their customers, because they understand AI and how it can benefit them.

We share [learner] outputs in a variety of ways. We might do a session with a senior leader who previously identified the challenge. It might be that we'll have an internal community where we'll post various case studies of the projects. It varies, but it's important to complete that circle and show the successes of individuals and the impact on the business.

In National Apprenticeship Week, we had a panel session which we live-streamed across the organisation. We had six learners on the programme at the time who were able to speak about their experience.

That was incredible. Not only did it touch so many different people, it managed to get a huge audience watching virtually. It spotlighted the individuals as well as the programme overall.

We've talked about lots of positives, were there any challenges, and how did we overcome them?

One that sticks out for me – which was probably a good challenge – is we had to be careful about who we put on the programme. There was so much excitement and so much appetite. Understandably, everyone wanted to go on it. So we had to be strategic and selective as to who we could invite to join.

We did that by ensuring we had really good relationships with senior stakeholders who understood the needs of their divisions. They could work with us to identify who’s top of the list that needs to go on this immediately, who can afford to wait until the next cohort, and so on.

Without that strategic partnership with the wider business, the leaders, and their knowledge, we wouldn't have that clarity.

What’s your top advice for HR teams at the start of this journey?

I've got two. One would be to not underestimate the power of real stories. We've four cohorts in, and our first isn't far off graduating. We've got tangible impact statements, reports, projects and outcomes that we can share with our business and with our clients. It's real, it's not theory. The more we can promote those, the better.

My second bit of advice is to work with the business, layer by layer. The real success has been understanding each area of our business and how this can help individual teams, before tailoring the nominations and the offering.

We've spoken lots about the wider impact, do you have any individual success stories you want to share?

Many learners [have created] significant impact since being on the programme, but one in particular stands out from our first cohort. They had no exposure to AI and joined full of enthusiasm and excitement, like everyone does.

There was a moment when she wasn't sure if she was going to stay on the programme. And she'll tell you that she's delighted she did, because she turned that corner, and started to see the change in her thinking and doors opening internally. She's doing some brilliant work in leading AI change in her area of the business.

It’s a lovely, good news story, about achieving something that you might not have thought you could and going through all of the challenges to get there, but actually seeing the benefits afterwards.

What's your long-term vision for the AI for Business Value programme at Capita?

I’d love to create a sense of communal learning in Capita. AI is changing all the time, and it's important our colleagues are continuously developing their learning in this space.

So even once they've completed the programme, it would be lovely to create a space for them to share their learning regularly. That might be a regular spotlight ‘lunch and learn’ where we invite apprentices to share the projects they're working on.

That would be a way to spread the knowledge, excitement, and confidence across the business as well as keep momentum. We can't stand still, because we'll get left behind.

AI's rapid popularity surge and adoption has already created entirely new career paths. For instance, AI Ethics Specialists help companies use this technology responsibly, while AI Engineers and AI Product Managers develop AI applications. And as more organisations invest in this tool, new positions will continue to emerge.

Many people worry that the widespread adoption of AI will replace human labour. Data from consulting firm McKinsey indicates the number of ads for jobs with high risk for AI disruption has dropped 38% between 2022 and 2025 compared to just 21% for those with low AI exposure. But that doesn’t mean UK workers should lose hope for gainful employment in the future. Instead, upskilling and preparing to work with emerging technologies can help you future-proof your career and stay relevant in the changing job market.

Below, we explore 10 jobs with growth potential in an AI-enabled future.

Job 1: Data Analyst

A Data Analyst gathers, processes, and interprets data to gain meaningful insights. Companies use these conclusions to make strategic decisions and predict future trends.

Data Analysts rely on some of the following key skills:

• Programming languages - Data Analysts often use Python and R to process and visualise data
• Data collection - Gather information from various sources, such as documents, spreadsheets, and customer surveys
• Statistical analysis - Use different statistical techniques to interpret data and uncover trends
• Communication - Share results with clients, managers, and key decision makers
• Collaboration - Work in cross-functional teams with Data Scientists, Project Managers, and other specialists

Looking to take the next step in your data career? Explore Multiverse's programmes for date professionals to learn how to make the connections between data and business insights that will help you prepare for mid and senior-level roles.

Job 2: Software Engineer

A Software Engineer develops, deploys, and tests software solutions. Depending on the nature of their role, they may participate in every step of the software development lifecycle, from design and development to maintenance.

Software engineering is a broad field with many specialisations. For example, App Developers produce mobile applications for smartphones and tablets. Web Developers design the front and back ends of web apps and websites.

Essential skills for a Software Developer include:

• Programming languages - Write the code for software programmes with JavaScript (web apps), C++ (video games), Kotlin and Swift (mobile apps), and other languages
• Front-end web development - Use HTML, CSS, and JavaScript to design stylish and accessible user interfaces
• Source control management - Track changes to the software’s code and collaborate on projects remotely
• Debugging - Diagnose and fix programming errors
• Encryption - Use algorithms and encryption techniques to protect user information

Are you a Software Engineer? Learn advanced skills and prepare for more senior-level roles with Multiverse’s Advanced Software Engineering programme.

Job 3: Digital Marketing Specialist

A Digital Marketing Specialist advertises brands, products, and services online. They use digital tools to identify and reach their target audiences. For instance, they may create social media posts and email newsletters to promote a new service.

Digital marketing has become an invaluable tool in all industries. In 2025, online marketing made up 80% of the advertising spend in the UK. This competitive landscape has led to an increased demand for skilled Digital Marketing Specialists.

Some foundational skills for Digital Marketers, depending on specialisation, include:

• Search engine optimization and Generative Engine Optimization (SEO and GEO) - Create high-quality, optimised content that ranks at the top of search results in both search engines and AI platforms
• Content creation - Develop compelling and valuable content that attracts audiences, such as blog posts, infographics, and videos
• Social media marketing - Use Facebook, Instagram, and other social media platforms to engage customers and generate leads
• Marketing automation - Build automated workflows and personalise marketing content with platforms like HubSpot and Mailchimp

Job 4: AI and Machine Learning Expert

Artificial intelligence is a burgeoning field across numerous industries, from agriculture to transportation. Companies use this technology to generate content, automate tasks, power autonomous vehicles, and more.

As more companies leverage this tool, the demand for AI and Machine Learning (ML) Experts has soared. In January 2024, 27% of tech jobs advertised in the UK were AI-focused positions. Additionally, almost 90% of business leaders expect all employees will need basic AI training in the coming years.

AI and ML Experts need expertise in these areas:

• Coding - Popular languages for programming AI applications include Python, Java, Julia, and C++
• Machine learning - Use supervised and unsupervised learning techniques to teach ML models how to detect patterns and make predictions
• Mathematics - Use calculus, statistics, and other mathematical concepts to build algorithms and models
• Natural language processing - Create applications that recognize, understand, and respond to spoken or written human language

Gain proficiency in these areas with Multiverse’s best-in-class programmes dedicated to growing AI/MLskills, including AI for Business Value, the AI and Machine Learning Fellowship, AI Powered Productivity, and more. Learn how to harness AI to drive business growth and innovation — all for free when your employer partners with us.

Job 5: Cybersecurity Analyst

The UK cybersecurity market is expected to grow 10% in 2025, reaching a total revenue of £9.42 billion, according to Statista.

Cybersecurity jobs require these skills:

• Intrusion detection - Use cybersecurity software to monitor networks and detect suspicious activity
• Incident response - Isolate compromised systems and use mitigation techniques to stop cyberattacks
• Cryptography - Develop protocols to stop cybercriminals from accessing confidential data
• Communication - Prepare security reports and educate colleagues about cybersecurity best practices

Job 6: UX/UI Designer

A User Experience/User Interface (UX/UI) Designer creates user interfaces for apps, websites, and other tech products. They increase user satisfaction by designing accessible and visually appealing interfaces.

Here are three must-have skills for a career in UX/UI design:

• Prototyping - Develop simulations or models of the final product to test with users
• Usability testing - Conduct research to see how real users interact with the product and refine your design accordingly
• Collaboration - Collaborate with Software Developers, Product Managers, and other professionals to develop products

Job 7: Cloud Solutions Architect

Cloud computing has become an integral part of the digital world. This shift has raised the demand for Cloud Solutions Architects, who design and manage cloud-based infrastructure.

Here are a few basic requirements for Cloud Solutions Architects:

• Network knowledge - Understand how cloud-based networks transmit and receive information
• Cybersecurity - Safeguard cloud infrastructure and applications with access controls, encryption, and other techniques
• Coding - Most Cloud Solutions Architects use Java, Python, and C++ to develop cloud infrastructure

Prepare for future jobs in cloud computing with Multiverse’s Software Engineering programme. You’ll learn foundational computer science concepts and become proficient in top coding languages. Our electives also let you develop expertise in cloud engineering, cybersecurity, or a related field.

Job 8: Blockchain Developer

Blockchain technology uses cryptography to verify transactions and create secure, unchangeable records. Companies use this technology to process payments and protect intellectual property.

Blockchain Developers design, build, and manage blockchain applications and platforms. This career path requires proficiency in these areas:

• Blockchain architecture - Understand the components of blockchain systems, including blocks and Distributed Ledger Technology
• Programming - Blockchain Developers typically use C++, JavaScript, and Ruby to build systems
• Cryptography - Use cryptography protocols to encrypt and decrypt information

Job 9: Internet of Things (IoT) Engineer

The Internet of Things (IoT) landscape has expanded rapidly in the last few years. These physical devices transmit information to an interconnected network of other objects and the cloud.

The growth of IoT has led to an explosion of jobs. An Internet of Things Engineer designs and develops IoT ecosystems and devices.

IoT engineering jobs require many skills, including:

• Computer-aided design - Use AutoCAD and other software to design the physical hardware components of IoT systems
• Software development - Create applications to control and monitor IoT devices
• UI/UX design - Use UI/UX design principles to develop easy-to-navigate IoT solutions

Job 10: Sustainability Officer

UK businesses will increase spending on sustainability by 260% between 2018 and 2030. As more companies invest in the environment, the demand for Sustainability Officers has grown.

Sustainability Officers develop and oversee sustainability initiatives. They also make sure businesses comply with relevant environmental regulations.

Key skills for Sustainability Officers include:

• Reporting - Document and share sustainability efforts
• Stakeholder engagement - Build partnerships with managers, employees, regulatory agencies, and other stakeholders
• Communication - Discuss the company’s sustainability practices with internal and external stakeholders

Step into the future with Multiverse’s apprenticeships

As technology develops, new professions will emerge to address the evolving needs of businesses and customers. The best jobs for the future combine cutting-edge technology with transferable skills that will help you continuously adapt.

Prepare for the ever-evolving job market with a Multiverse apprenticeship. Our programmes equip apprentices with the knowledge and skills needed to excel in the rapidly changing work landscape. You’ll receive hands-on training, one-on-one mentorship, and a structured education to help you succeed in your current and future jobs.

At Multiverse, we’ve been keenly following the establishment of Skills England, a new government agency sponsored by the Department for Education, which brings together partners from inside and outside government to drive improvements to England’s skills landscape, to power economic growth and opportunity.

What is Skills England’s role in assessing skills?

As the country’s authoritative voice on current and future skills needs, one of Skills England’s primary responsibilities is identifying skills gaps across the economy through comprehensive skills assessments.

The goal is to create a skills system ‘fit for the future’, and to ensure that the government’s skills strategy and policies are informed by a data-driven approach.

Introducing the Skills for Growth and Opportunity Report

Skills England published its Skills for Growth and Opportunity Report in June 2025, following a period of data analysis and engagement with employers and other stakeholders about the country’s growth and skills offer.

The report brings together a wealth of evidence including analysis of Government data and insights from 743 stakeholders, including employers.

It identifies the challenges faced by employers in developing skills pipelines and the critical importance of the skills system in delivering the Government’s missions and wider priorities, including its Industrial Strategy.

Crucially, it highlights long-standing skills shortages across the eight Industrial Strategy Growth-Driving Sectors:

• Advanced Manufacturing
• Clean Energy Industries
• Creative Industries
• Defence
• Digital and Technologies
• Financial Services
• Life Sciences
• Professional and Business Services

And in two additional ‘critical’ sectors:

• Construction - essential for achieving the government’s wider house building aims)
• Health and Adult Social Care - facing pressures as a result of demographic shifts

Skills England report: Three takeaways from Multiverse

Skills England offers an overarching perspective on themes pervasive across these sectors.

These include the escalating demand for highly qualified workers, the prevalence of gender inequality in various priority sectors, and the importance of the wider education system, including careers advice, in addressing skills challenges.

Three themes particularly caught our attention:

1) The digital and AI revolution is transforming workforce skills

According to Skills England, the unprecedented pace of technological change is a ‘major driver of changing skills needs across sectors’ with AI in particular reshaping the future of the workforce.

For example, in the creative industries, 69% of employers say their staff need urgent retraining due to new technologies. The report demonstrates that both advanced digital skills and digital literacy are in critical demand - with basic digital skills set to become the UK’s largest skills gap by 2030.

2) Reskilling is essential to combat the widening digital gap

Employers highlighted the importance of both reskilling the existing workforce alongside upskilling new entrants, with apprenticeships identified as an important tool in enabling this, particularly in relation to the adoption of AI and data science.

Despite this technological revolution, Multiverse research has shown that more than half of workers have received fewer than five hours of training on AI, and just one third (34%) of FTSE 100 companies reference AI training in their latest annual reports.

That’s why we’ve recently launched a commitment to train 15,000 new AI apprentices over the next two years.

3) Flexible apprenticeships and training options are key

Employers see the value in apprenticeships but are calling for more flexible, responsive models such as shorter, flexible courses or ‘bolt-on’ training in AI, to meet business needs as they evolve.

Multiverse has long been calling for increased flexibility in the apprenticeship system so as to widen access to learning and ensure the apprenticeship system truly serves the immediate and evolving needs of businesses.

The path forward

The Department for Education is currently developing a Post-16 Education and Skills Strategy, which will articulate its long-term vision for skills.

While we don’t expect major changes any time soon, decisions also lie ahead regarding the future of the Growth and Skills Levy, and how much increased flexibility this might offer for employers in the future.

Skills England’s assessment is a critical first step in closing the nation’s skills gaps and designing a system that will unlock economic growth. The message is clear - investing in workforce skills is instrumental to driving productivity and economic growth.

D’Arcy joins from MongoDB, where as Head of EMEA he helped scale the business to $700M in ARR, representing over 30% of MongoDB’s global revenue. His appointment as CRO will help scale Multiverse's goal to build the AI adoption layer for the enterprise through transforming workforce skills.

D’Arcy is the latest in a series of strategic Multiverse leadership appointments in the past year, including MongoDB’s Jillian Gillespie as CFO and digital pioneer Martha Lane Fox to the Board. This latest move underpins Multiverse’s ambition to become a generational British tech success story by solving a critical problem: while companies are investing heavily in AI tools, they lack the workforce skills to unlock their true value.

D’Arcy brings extensive experience in scaling high-growth technology companies. Prior to his success at MongoDB, he spent over twelve years at BMC Software, where he led BMC UK to $500M in revenue, making it the top-performing region worldwide. His expertise will be instrumental as Multiverse builds upon its partnerships with leading global companies. Multiverse works with over a quarter of the FTSE 100, as well as 100 NHS trusts and more than 55 local councils.

Euan Blair, Founder and CEO of Multiverse, said: “Truly seizing the AI opportunity requires companies to build a bridge between tech and talent - both within Multiverse and for our customers. Bringing on a world-class leader like Donn, with his incredible track record at MongoDB, is a critical step in our goal to equip every business with the workforce of tomorrow.”

Donn D’Arcy, Chief Revenue Officer at Multiverse, said: “Enterprise AI adoption won't happen without fixing the skills gap. Multiverse is the critical partner for any company serious about making AI a reality, and its focus on developing people as the most crucial component of the tech stack is what really drew me to the organisation. The talent density, and the pathway to hyper growth, means the next chapter here is tremendously exciting.”

D’Arcy joins as Multiverse continues to ramp up business momentum. Multiverse has more than doubled its revenue in the last two years, with more than 22,000 learners now in its global community of tech, data and AI upskillers.

To accelerate this growth and help organisations capitalise on AI, the company recently announced a commitment to create 15,000 new AI apprenticeships over the next two years, directly addressing the skills bottleneck that hinders technology adoption.

Add AI into the mix and the opportunity grows even larger. But a complex ecosystem of legacy technology and workforce skills gaps makes transformation a challenge.

The NHS is investing in updated IT systems, including a £1.5 billion NHS framework to support the analogue to digital switch, but enduring change goes beyond new hardware.

To achieve tangible benefits and ROI, the NHS workforce needs the critical skills to implement digital systems and AI effectively.

The role of digital skills in the NHS

Today, all NHS employees – clinical, administrative, or otherwise – are expected to be data consumers, whether using dashboards, filing electronic patient records, or communicating using a patchwork of online systems. As a result, all roles now need a baseline level of digital literacy.

Yet, when some hear words like digital, AI and upskilling, all they hear are buzzwords.

Upskilling in critical digital, data and AI skills translates into greater productivity, which in turn, results in a better standard of patient care.

Let's explore stories of tangible, employee-led change happening at NHS trusts across the country.

Improving data visibility at Barts Health NHS Trust

Apprentice role: Data Analyst, Transfer of Care Hub

Barts Health NHS Trust launched a data and digital academy with Multiverse to help drive efficiency gains and improve patient experience.

A Data Analyst on the apprenticeship programme used her skills to build a central dashboard that monitors compliance levels and key performance indicators in the Transfer of Care Hub.

The dashboard streamlines access to critical data for the entire team, saving two hours per week in the creation of data packs and ad-hoc reports. By identifying and resolving data quality issues, the learner has also enabled more reliable reporting and the development of targeted interventions.

Today, the hub enjoys streamlined access to data that helps them understand performance and areas for improvement. New automations save each team member between 30 minutes and one hour per day, freeing their time to focus on patient care.

Minimising waiting times at Royal Free London NHS Foundation Trust

Apprentice role: Administrator, Adult Assessment Unit

The Royal Free London NHS Foundation Trust partnered with Multiverse to improve data literacy within data teams, supporting the Trust’s strategy to become a data-driven organisation.

One learner was an administrator in the Adult Assessment Unit at Barnet Hospital’s emergency department. He felt the unit could be made more efficient by migrating paper-based patient management processes to a digital Electronic Health Record (EHR).

The learner used skills acquired from his apprenticeship to map the patient journey and demonstrate how it could be improved.

He consulted with small focus groups comprising employees across the unit to iterate and ensure the new solution delivered value to every user.

Today, the digital tool enables efficient, end-to-end patient management for the unit.

For instance, patient sign-ins automatically trigger arrival notifications for the relevant clinicians, and test results are sent digitally instead of being printed and physically distributed.

Digitalisation has helped double the daily department caseload from 30 to 60 patients and reduce waiting times from over 30 minutes to 10 minutes.

For his efforts, the learner has earned a well-deserved promotion to Data Coordinator.

Improving theatre audits at Medway NHS Foundation Trust

Apprentice role: Band 5 Endoscopy Nurse

The Endoscopy Unit at Medway NHS Foundation Trust faced inefficiencies and delays in decision-making due to time-consuming, paper-based processes.

An Endoscopy Nurse, Joy Onuoha, applied the data skills she learnt on the Multiverse apprenticeship programme to develop a Power Business Intelligence (BI) dashboard that automated the theatre audit processes.

Joy integrated multiple data sources and designed interactive visualisations to track critical metrics, delivering real-time insights to inform the unit’s decision-making.

Automating the process has led to reducing delays and minimising errors, and enhanced resource allocation for clinical sisters so they can focus on what they do best – delivering high-quality care to patients.

In acknowledgement of her work to streamline data collection, Joy recently received a Chief Nursing Officer’s Award for Most Innovative Nurse.

Joy said: “This achievement wouldn’t have been possible without the skills, knowledge, and confidence I gained through the Multiverse fellowship. Learning about data analytics, visualisation, and automation has empowered me to identify clinical inefficiencies and implement data-driven solutions."

Since completing the apprenticeship, Joy has been promoted to the role of Clinical Practice Facilitator.

Upskilling initiatives empower the NHS

Across trusts, hospitals, and all other healthcare organisations, upskilling is delivering tangible value to the NHS. Employees aren’t just building digital, data and AI skills for the future, they’re applying them within their roles to deliver real benefits operationally and, importantly, the patients in their care.

Multiverse is empowering every NHS employee to unlock innovation and improve patient care through our applied learning programmes. Discover more

Article originally featured in HSJ Online