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If all those responsibilities make you feel overwhelmed, you’re not alone. Nine out of ten UK employees say they’ve experienced high or extreme pressure in the last year.

You probably can’t cut your to-do list in half — at least, not if you want to advance your career. But you can get more done with less stress with these time management tips.

Spotting the signs of poor time management

Sometimes, it’s hard to recognise that your time management skills are lacking. Sure, you might be crossing things off your list and meeting most of your deadlines. But that doesn’t necessarily mean you’re being productive — or finding a healthy work-life balance.

You probably need help managing your time if:

• You’re always scrambling to hit deadlines at the last minute.
• Most days, you add more to your agenda than you finish.
• Important tasks at home keep getting overlooked, until you realise you haven’t vacuumed in weeks.
• You constantly stay late at the office or spend half your Saturday working to get everything done.
• Sometimes, you don’t even know what to start working on.
• You rarely have time to upskill, so you feel like you’re falling behind your colleagues.

Look out for sneaky time wasters, too. You might spend hours reading emails or trudging to boring meetings. Or maybe you’re multitasking all the time, like typing out a memo while you nod along in a Zoom meeting. These nonproductive activities may make it seem like you’re doing a lot, but they’re actually making you less efficient.

8 ways to take back control of your schedule

You can’t pick and choose all your tasks, but you can control how you handle them. Get ahead with these simple time management strategies.

1. Set clear goals and priorities

Creating goals is the foundation of effective time management. They help you prioritise tasks and say no to things that won’t help you move your career forward.

Use the SMART framework to set professional and personal goals that are:

• Specific - Imagine what you want to achieve in detail.
• Measurable - Make sure you can easily track your progress.
• Achievable - Pick something you can realistically accomplish with your current resources.
• Relevant - Your objectives should fit your long-term career plan.
• Time-based - Set deadlines and milestones.

Let’s say you’re a Software Developer with a mile-long debugging backlog. You could aim to clear out half of it in two months by fixing at least five bug reports a week.

Once you’ve got your big goals, don’t just stick them at the top of your to-do list. That’s too intimidating. Instead, break them down into bite-sized tasks that you can chip away at. Want to clear out hundreds of unread emails in your inbox? Schedule two blocks a day to answer and delete messages, and set time limits so you don’t get lost reading months-old emails.

As you work on your goals, the Eisenhower Matrix can help you spot high-priority activities. It sorts tasks into four quadrants:

For example, reading random e-newsletters goes in the “delete it” category, so use the unsubscribe button liberally. A time-sensitive client email, on the other hand, needs an immediate response.

2. Build a realistic weekly schedule

A digital calendar can significantly improve time management at work and in your personal life. Choose a mobile-friendly platform like Google Calendar so it’s always accessible.

Before you open your calendar, write down all your upcoming tasks and sort them by priority, like this:

• High: Client presentation on Friday, report due Monday morning
• Moderate: Daily standup meetings, study sessions for learning data analysis skills, walking the dog every evening
• Low: Checking email, putting away laundry

Estimate how much time you’ll need for each item, and be honest with yourself. If that detailed report usually takes four hours, don’t say you can do it in three, or you’ll just feel stressed.

Use this list to start mapping out your weekly schedule. Create colour-coded time blocks for similar tasks — like meetings and presentation prep time — so you can see what’s coming up at a glance. Schedule at least a few hours a week for deep focus, too, so you can work on creative tasks without interruption.

And don’t forget to pencil in plenty of breaks and buffer time. Sure, you could rush to six back-to-back meetings, but you’ll probably feel too drained to work on anything else. Regular breaks reduce stress and improve your overall well-being.

3. Tackle challenging tasks first

You’ve got limited energy, no matter how much coffee you chug. Start your day by “eating the frog” — a.k.a starting with the hardest or most urgent tasks on your list. This helps you build momentum and frees up time later for routine tasks.

The Pomodoro technique can help you maintain focus for those big tasks. It breaks work into 25-minute intervals, followed by five-minute breaks. After you complete four pomodoros, take a longer 30-minute break. This strategy lets you accomplish tasks faster without burning out.

Whenever possible, avoid packing your schedule with too many difficult tasks. Quality over quantity is key to protecting your mental health and productivity.

4. Minimise distractions

While most time management tips focus on doing more, you should also cut down on tasks that sap your attention. Social media and texting are two of the biggest culprits. Turn off notifications during focus time and use app blockers to stop yourself from scrolling on TikTok for “just five minutes.”

A tidy workspace can also help you concentrate. Get rid of clutter, such as random bobbleheads and knick-knacks, and create quiet zones free from unnecessary tech.

5. Use the right tools

Many professionals use time management tools to stay organised. Here are just a few options:

• Toggl Track to record your time and see where it’s going
• Digital calendar apps to schedule tasks and set reminders
• Forest to encourage you to put your phone away and focus
• Todoist to track and manage tasks

Experiment with a few programmes to see what works best, but don’t feel like they’re mandatory. If old-fashioned sticky notes and written calendars boost your productivity, stick with that.

6. Organise notes and tasks in one place

Nothing wastes time like searching for a scrap of paper on a messy desk — or worse, trying to remember something that your boss told you three weeks ago.

Save time by jotting everything down in a digital note-taking app like Notion or Google Keep. Keeping everything together decreases stress by making sure you always have clear, up-to-date information.

7. Avoid procrastination with small wins

Nothing kills motivation like slogging through an ocean of tasks. Give yourself something to celebrate by setting micro-goals. A major white paper might take days to complete, but treat yourself to a fancy coffee after drafting each section. Little victories will help you stay motivated and recognise your progress.

Don’t let perfectionism paralyse you, either. Just start, and tell yourself you can always fix it later.

The two-minute rule also stops little tasks from piling up. If something takes less than a couple of minutes, do it right away, even when you don’t feel like it.

8. Review and adjust your system

Mastering time management won’t happen overnight, especially if you’re a chronic procrastinator or easily distracted.

Start small by choosing just two or three of these practical strategies to increase productivity. At the end of each week, spend 10 minutes reviewing your progress and analysing how you’re spending time.

Not making progress? Try new time management apps or switch up how you prioritise tasks. You could even use AI to automate basic tasks like sending appointment reminders to clients.

Above all, flexibility is key. Your personal life may get busy, or you might decide to focus more on upskilling. Adapting your time management system will keep everything moving smoothly.

Apply these skills in your Multiverse journey

Time management skills can benefit all professionals, but they’re especially critical for apprentices. Techniques like time blocking and the Pomodoro method will help you balance work, training, and projects.

Multiverse’s free Project Management and Transformative Leadership apprenticeships allow you to apply and build these valuable skills. You’ll learn how to delegate tasks and identify areas for improvement in your organisation. These hands-on programmes also help you future-proof your career with in-demand AI and leadership skills.

Complete our quick application to learn more about how a Multiverse apprenticeship can improve your time management.

Project management itself is all about bringing people and resources together to get complex tasks done efficiently. Today, businesses are using AI to simplify everything from setting budgets to troubleshooting equipment shortages. Learning how to work with these tools can help you lead projects more successfully and open up new career pathways as employers look for tech-savvy Project Managers.

What is project management?

Project management focuses on planning tasks and leading teams to reach shared goals. It requires strong communication skills, problem-solving abilities, and other soft skills.

Even relatively simple projects often involve many steps, including:

• Creating a realistic project budget and timeline
• Managing resources, such as construction equipment or financial software
• Delegating tasks to specific team members based on their strengths and availability
• Assessing risks and planning how to avoid them
• Documenting project progress at every step

Project Managers handle these nitty-gritty details so their teams can focus on more specialised tasks. For example, a tech firm might bring in a dedicated Project Manager to plan a mobile app project, while Software Developers concentrate on the actual programming. This division of labour keeps projects moving forward smoothly, without distractions or too many people making decisions.

Organisations in all industries rely on Project Managers to plan and oversee initiatives. In the UK, these professionals contribute an estimated £186.8 billion to the economy. They help companies make strategic decisions — such as how much to invest in a marketing campaign — and use resources efficiently.

The five phases of the project management process

Project management professionals work on a wide range of initiatives, even within the same industry. One person might manage the construction of a multi-million-pound hospital, while another oversees software development for medical professionals.

While these undertakings can have very different scopes, they typically follow the same project life cycle. Here are the five stages:

1. Initiation

You probably wouldn’t backpack across Europe without a map and a budget — that’s a fast-track to disaster, or at least a stressful trip. Managing projects requires the same kind of thoughtful pre-planning.

During this phase, professionals set project objectives and map out the big-picture steps to achieve them. They also evaluate the project’s feasibility. For instance, a client may want to revamp their entire onboarding process but only have the budget for a new handbook. Figuring out these limitations early helps prevent disappointment and overspending later on.

Initiation also involves:

• Weighing the potential project’s pros and cons
• Identifying project team members
• Defining the project scope
• Establishing the project deliverables or outcomes

Project Managers often organise all this information in a project charter. This document helps stakeholders understand exactly what’s involved in the undertaking and the estimated project costs. That way, they can make an educated decision about whether to move forward — or go back to the drawing board.

1. Planning

In the planning phase, Project Managers develop a detailed roadmap for the initiative. This outline should include:

• A scope statement that defines exactly what the project will involve (and what it won’t)
• A step-by-step plan for completing the project
• A realistic timeline with milestones and deadlines
• A detailed budget that factors in every cost, from labour to office supplies
• A breakdown of which project team members will handle each task
• A communication plan, such as weekly meetings or email updates
• A risk management plan that addresses potential hazards (supply chain shortages, cyber attacks, etc.)

Planning is one of the most time-consuming steps in the project management process, but it’s well worth it. It helps build a strong foundation for the project and prevents serious issues down the line.

For instance, you might realise that a project requires a custom piece of equipment that takes months to order. By spotting this early, you can adjust your schedule and avoid frustrating delays.

Planning also prevents the all-too-common problem of scope creep. Clients often ask for more deliverables, or overachieving team members may take on extra tasks without thinking twice. With a strong plan, you can set boundaries and deliver (only) what you promised.

1. Execution

Once you’ve finished your plan, you’re ready to put your project team to work. This is the core part of the initiative, where everyone comes together to start creating the deliverables.

Every complex project involves a healthy amount of delegation. Consider your project team members’ strengths and interests when assigning tasks. An aspiring leader, for instance, might be eager to plan client meetings. Meanwhile, a Business Analyst may focus on gathering and analysing financial data.

As a Project Manager, you should communicate frequently with all your stakeholders. This might involve a daily standup with the project team, regular status updates, and quarterly reports. By keeping everyone in the loop, you’ll reduce confusion and costly errors.

Tracking is key, too. Obviously, you don’t want to micromanage your project team — that’s bad for morale. But checking their progress and setting smaller milestones will help ensure that everyone stays on track. That way, you can offer support as needed.

4. Monitoring and control

Controlling a project may sound harsh, but you’re not turning into Big Brother. This phase simply involves tracking a project’s progress and addressing any roadblocks as a team.

Start by setting key performance indicators (KPIs) to measure project success. If you’re managing a social media campaign, you might track these metrics:

• Engagement rate (comments, likes, etc.)
• Follower count
• Number of impressions

On the other hand, a software development project might focus on cycle time and code quality.

This kind of project monitoring will help you understand your performance and adjust your plan if necessary. For example, consistently poor code quality might mean that it’s time to bring in a more experienced Software Developer.

You should also closely monitor the budget throughout your project. An extra resource here, a little overtime there — these costs can add up quickly. Track all expenses carefully to keep your spending in check.

And don’t forget about the timeline. Even the most experienced Project Managers can’t avoid every delay, such as a natural disaster or a flu outbreak in the office. Be flexible and ready to shuffle around resources or deadlines to keep making progress.

5. Closure

The project isn’t over when you finish your last deliverable. You still need to hand everything over to the client and reflect on what you learned.

Share your project documentation with your client and other stakeholders. This paperwork helps them understand how to manage it moving forward. You may also need to provide hands-on training to set them up for success. Nursing staff, for instance, might need a workshop to learn how to use a new healthcare database.

Evaluate the project's success, too. Here are a few questions to consider:

• Overall, which parts of this project went well?
• How could I improve future projects?
• What obstacles did the team face, and how did we overcome them?
• Were there any unnecessary steps or resources?

Schedule a debriefing meeting to discuss these topics with your team and talk about your insights. This step will help you celebrate a successful completion and make your next project plan even better.

Types of project management

There’s no one-size-fits-all project management methodology. It depends on your goals, the industry you’re in, and your team’s strengths. Here are four popular frameworks.

Waterfall

When a river flows down a waterfall, it moves in one direction. Sure, the water might splash up a bit when it hits the bottom, but it never turns around and flies back to the top.

The Waterfall methodology works the same way. The project moves through each phase — from initiation to closure — one step at a time, without ever reversing or repeating phases.

This one-way approach requires a lot of upfront planning to get everything right the first time. But when done well, Waterfall can significantly boost efficiency and productivity. Plus, team members may feel more satisfied when they’re not constantly redoing their work.

Of course, it’s not easy to change a waterfall’s direction. This sequential method works best for simple and predictable projects that don’t require much flexibility.

Agile

Agile project management uses an iterative approach to help teams constantly improve their work. Instead of waiting for feedback at the end, they work on tasks in small bursts, get input, and make adjustments as needed.

Software Developers created the Agile method to keep up with their clients' rapidly changing demands. It’s a much more flexible approach than the waterfall method, allowing teams to make changes on the fly.

Consider Agile project management when you need to adapt quickly. It’s a great fit for marketing campaigns, product development, and other collaborative initiatives with lots of moving parts.

Lean

Lean project management is a subset of Agile that focuses on conserving resources and improving efficiency. It follows the “just-in-time” principle by delivering only the work that’s needed, when it’s needed. Teams also focus on project tasks that have the most impact instead of getting bogged down in minor details.

Manufacturers originally developed the Lean methodology, but it’s also popular in construction and healthcare. Use this approach when you want to save money without sacrificing value.

Hybrid

Sometimes, no project management framework meets all your needs. The hybrid approach lets you combine principles from different methods to fit your specific project.

This flexible strategy is an excellent option for more complex projects. For example, a hospital might blend Agile’s iterative approach with Lean’s cost-saving measures to create a new waiting room system.

Project team management

Because every industry needs Project Managers, upskilling in this area can prepare you for new roles and responsibilities. Here are a few essential skills to develop:

• Communication: The best Project Managers can clearly explain their expectations and goals to their teams. They also make complex information accessible for clients and stakeholders.
• Leadership: Strong managers can rally their teams behind shared goals and help them perform at their best.
• Problem-solving: Every project involves unexpected challenges, so the ability to stay calm and troubleshoot is key.
• Risk management: Project Managers should know how to assess risks and take steps to prevent them.

Industry-specific knowledge is essential, too. A Project Manager for a website may not need to know every detail of Python, but they should understand enough to help troubleshoot bugs.

Leaders should also follow effective project management practices, including:

• Clearly define each team member’s role from the beginning.
• Celebrate small wins — such as completing a tricky feature — to boost morale and build positive momentum.
• Encourage team members to share ideas and feedback freely.
• Use collaboration tools like Trello (for task management) and Slack (for communication).
• Step in early to resolve conflicts and help members compromise.

Tools and technologies in project management

Many professionals rely on traditional project management tools. Here are a few favorites:

• Asana: A task management platform that lets teams assign responsibilities and track progress together.
• Gantt charts: Visual diagrams that use horizontal bars to represent the duration and deadline for each task.
• Microsoft Project: A project management software that allows users to create project plans and schedules.

While these resources are still popular, artificial intelligence tools can help Project Managers work even more efficiently. For example, Notion AI can generate project plans and other content, while Monday.com uses AI to automatically delegate tasks and monitor progress.

How AI is transforming project management

Artificial intelligence isn’t just another tech fad. It can help you manage change and lead projects more effectively, especially when you’re juggling dozens of tasks. Here are a few ways this technology can support project management:

• Automating project scheduling and resource management: Tools like Motion use AI to prioritise tasks and create accurate schedules. They can also help you schedule team members at the right time.
• Intelligent risk forecasting: Predictive models use historical project data, economic trends, and other information to anticipate potential risks.
• Natural language summarisation of meetings: Turn your meeting recordings into summaries and to-do lists with notetaking tools, such as Fireflies and Otter.ai.
• Predictive analytics for budgets and timelines: AI can help you set realistic budgets and deadlines, reducing unexpected surprises.
• AI assistants for real-time status reports: Tools like ClickUp use data analytics to measure progress and generate status reports. They can help you quickly spot bottlenecks or underperforming employees.
• Interpret code: Use AI coding tools like Denigma to quickly understand programming languages — no more racking your brain to remember JavaScript functions.

The role of the project management office (PMO)

A project management office is a team that sets quality standards and policies for projects. It helps Project Managers maintain consistency, even when working on drastically different initiatives. For example, a PMO may require construction and HR projects to follow the same core practices.

AI dashboards allow PMOs to track every project in a centralised place. This makes it easier to spot scope creep or teams that aren’t following company policies, so PMOs can take action quickly.

Career paths in project management

In 2024, Indeed ranked Project Manager as the top job in the UK. These professionals are in demand in many industries, including:

• Construction
• Information technology
• Business transformation
• Healthcare
• Finance

Many job titles fall under the umbrella of project management. For example, Operation Delivery Leads earn an average base pay of £60,000 and manage projects across different teams. Meanwhile, a Programme Manager focuses on big-picture strategizing for multiple projects, with an average salary of £61,000.

Training paths in project management

The Project Management Institute offers numerous certifications and training programmes that teach essential skills. One popular option is the Project Management Professional (PMP) certification, which demonstrates expertise in different project management techniques.

A Multiverse apprenticeship is another excellent way to prepare for a project management career. It teaches the latest project management methods and software, including Jira and AI tools. You’ll also gain hands-on experience by planning and executing real projects in your current role.

By the end of the 15-month apprenticeship, you’ll have a portfolio that showcases your skills and mastery of different project management types. The best part? Multiverse programmes are completely free for apprentices.

Master AI-powered project management with Multiverse

Successful project management isn’t just about checking off to-do lists and meeting deadlines. It’s an art that helps teams thrive and businesses meet their strategic goals.

Learn how to lead change with Multiverse’s Project Management apprenticeship. This free programme teaches essential project management approaches that you can use to guide initiatives from start to finish. Plus, our AI modules allow you to learn prompt engineering, data analytics, and other in-demand skills.

Ready to kickstart your project management journey? Fill out our quick application today.

Learning how to use Microsoft Copilot effectively can help you stay competitive in a rapidly evolving digital workplace. With more companies embedding AI into their workflows, mastering Copilot’s features can streamline your work and save time. Users say it helps them complete routine tasks up to 29% faster.

What is Microsoft Copilot?

In 2023, Microsoft replaced its non-AI virtual assistant Cortana with Copilot. This new tool, the company announced, “uses AI to turn your words into a powerful productivity tool,” helping users “work smarter and faster.”

Like ChatGPT, Microsoft Copilot is powered by large language models (LLMs) — including OpenAI’s GPT-4o and Microsoft’s Prometheus framework — that interpret and respond to user inputs.. For example, you could ask Copilot to help you brainstorm content ideas — “suggest 20 Instagram posts to announce a new product” — or draft a memo.

Copilot uses a freemium structure, allowing users to access basic features at no cost. The free version is a good choice if you only want to use the Copilot app, which functions much like ChatGPT. It can generate a limited number of images, search the web, and answer questions.

For the full experience, you’ll need to upgrade to Microsoft Copilot Pro. This paid plan costs £19 per month and integrates Copilot agents with Microsoft 365 apps. It also gives you early access to the latest AI features, including multilingual speech recognition and sentiment analysis tools.

How Microsoft Copilot works

Microsoft 365 Copilot may seem like an enigma, especially if you’re not a tech professional. But this platform is relatively straightforward.

The software was built on two large language models:

• OpenAI’s ChatGPT-4o: This omni-channel model can produce audio, images, and text. This multimodal capability, OpenAI explains, “enables the model to engage in more natural and intuitive interactions with users.”
• Microsoft Prometheus: It combines GPT with Bing’s search index, allowing it to draw on real-time data and cite sources.

Copilot stands out from other AI tools because it combines these LLMs with the user’s own proprietary data. It does this through Microsoft Graph, an application programming interface (API). This platform collects data from all your Microsoft 365 apps, including Calendar, Outlook, and Teams.

When you interact with Copilot, it draws on this information to create tailored responses. For example, it could summarise emails or a dense white paper that would take hours to read. Or it might suggest a meeting agenda based on your messages in Teams.

This AI assistant also integrates directly with other Microsoft products, expanding their capabilities. These embedded Copilot features are so intuitive that you may not even realise that you’re using AI to improve your work.

Key features and use cases

Microsoft Copilot is an incredibly versatile AI tool with applications in practically every industry. Here are a few ways you can use this software to boost productivity.

Word

Even relatively short documents often take hours to write and revise. Copilot can speed up this process by generating a first draft based on a prompt or an existing document. For example, you might input, “Write a blog post about the benefits of drinking tea. Use the information in /teanotes as your reference.”

You can also use Copilot to summarise key points from meeting notes or complex documents. Rather than slogging through a 40-page transcript, you’ll get the gist in seconds.

Excel

Microsoft Excel has been a foundational data analytics tool for decades. But the Multiverse Skills Intelligence Report 2024 found that 57% of employees have no Excel skills or only basic knowledge.

Copilot can help upskillers analyse data sets in Excel and spot trends, such as best-selling products. It can also suggest formulas based on conversational prompts. Instead of racking your brain for the VLOOKUP function, for instance, you can just ask Copilot to “find Kelly Smith’s phone number.”

PowerPoint

Copilot’s generative AI software lets you turn simple outlines into full-fledged slide decks. That means you don’t have to spend hours obsessively rearranging slide layouts or fine-tuning headings.

Plus, you can instantly add your company’s branding or even translate the whole presentation to another language. It all adds up to significant time savings, especially if you’re not a graphic designer.

Outlook

UK office workers spend over 11 billion hours a year on email, scheduling, and other repetitive tasks. Lighten your to-do list by asking Copilot to draft emails and summarise your colleagues’ messages. It can also help you schedule meetings, focus time, and other events.

Teams

Microsoft Teams users receive an average of 153 messages per day. While that constant communication helps keep everyone in the loop, it can also be incredibly distracting. You may just be getting in the zone when you hear that signature “ping.”

With Copilot, you can quickly summarise your chats and conversations instead of reading every message. It can also suggest action items — “email Brad to reschedule the webinar” — and transcribe meetings. That way, you can focus on more important tasks outside of Microsoft Teams.

Copilot Chat and Pages

Copilot Chat is a free AI chatbot that works across all Microsoft apps. Its search-like interface lets you look up information on the internet without needing to open a separate browser.

It also integrates with Copilot Pages, an interactive and collaborative canvas. For example, you could ask the AI assistant to list nearby competitors, then create a page to share with your coworkers. These Copilot features simplify collaboration by keeping everything in one centralised workspace.

GitHub Copilot

Programmers can use GitHub Copilot to generate code suggestions, helping them build applications much faster. This AI coding tool also supports users by catching and fixing mistakes, drastically reducing debugging time.

A GitHub experiment found that developers who used Copilot finished a JavaScript web server 55% faster than those who didn’t use the tool. Additionally, 96% of surveyed developers reported that Copilot helps them complete repetitive tasks faster.

How to access Microsoft Copilot

Because Copilot is so deeply enmeshed with other Microsoft technologies, it has multiple access points, including:

• Taskbar integration: Windows 11 lets you pin Copilot to your taskbar for easy access. Some newer laptops also come with a Copilot button on the keyboard that you can tap to open the app.
• Bing chat: The search engine includes a Copilot tab in the top menu, which you can click to launch the app.
• Toolbar buttons: Microsoft 365 apps feature Copilot buttons in the ribbon menus.
• Teams and Outlook add-ins: When you launch these platforms, you’ll see the Copilot icon in the upper-right corners.
• Microsoft’s Edge browser: Open this browser to view the built-in Copilot sidebar. It can create images, give you custom daily news briefings, and more.

You can also access Copilot on your smartphone by downloading the mobile app.

Advanced Tools: Vision, Voice, & Labs

Once you’ve mastered Microsoft 365 Copilot’s basic features, it’s time to level up with more sophisticated tools. These platforms can help you future-proof your career by boosting your efficiency and helping you acquire new skills.

Copilot Vision

Microsoft has revamped the way people search with Copilot Vision. It’s exclusively available with Microsoft’s Edge browser and acts as a personalised AI companion.

The premise is simple. Copilot scans all the web pages that you browse, almost like an invisible friend looking over your shoulder. It then analyses and contextualises this information to provide insights you might not get on your own.

Say, for instance, you’re planning a business trip to Madrid and want to design the perfect itinerary. You can describe your interests to Copilot Vision: “I want to take my clients to dinner at authentic Spanish restaurants and schedule a walking tour.” As you explore websites, Vision will highlight relevant information and activities, accelerating the research process.

Copilot Voice

Sometimes, you don’t have the time (or patience) to type out prompts. With Copilot, you can use voice commands to ask for information or perform tasks. For example, you might say, “Can you add a meeting with my assistant to my calendar for noon tomorrow?”

Copilot Voice also offers multilingual interactions in over 40 languages. It’s perfect for studying for exams or practising your conversational skills before an international trip.

Copilot Labs

Microsoft is constantly experimenting with new Copilot features. Commercial customers can sign into Copilot Labs to get early access to these projects. It’s a fun way to see what’s in the works and play with more advanced tools.

One available product is Copilot Actions, which automates web tasks based on user prompts. For example, you could ask it to book a hotel or order flowers for your spouse. You can also use Copilot Podcasts to create a custom podcast, or chat with an adorable visual avatar with Copilot Appearance.

Some of these tools might not directly improve productivity, but they give you the opportunity to learn about cutting-edge AI applications. And who knows? That AI-generated podcast or a conversation with Copilot Appearance might spark new ideas.

Benefits for productivity

Like any new technology, Microsoft Copilot has a bit of a learning curve. But once you get the hang of its features, it can have a huge impact on your productivity. Here are four advantages of using this AI tool.

Save time

Every professional has a laundry list of time-consuming (and often quite tedious) tasks. Microsoft Copilot can automate many of these activities, including:

• Summarising documents, from emails to hours-long webinar transcripts
• Generating emails, articles, proposals, and other content
• Researching information
• Prioritising emails based on urgency or deadlines
• Editing content, such as reports and intricate Python code

By automating these tasks, Copilot frees up your schedule for activities that require a human touch.

Develop stronger collaborations

Using AI to improve human relationships may seem paradoxical, but it can be extremely effective. For example, you could use Copilot to write meeting summaries and track tasks. That way, you can keep your team on the same page and make sure everything gets done on time.

Streamline data analysis

According to Multiverse’s The ROI of AI report, 52% of tech leaders believe their organisation lacks essential data skills. Professionals can help fill this gap by combining Copilot with Microsoft BI to “chat” with data sets.

A Business Analyst, for instance, could prompt Copilot to find trends in sales data and generate data visualisations. These applications are much faster than building dashboards and designing charts from scratch.

Improve project management

When it comes to managing complex projects, Microsoft Copilot can be incredibly useful. Use it to draft budgets and timelines based on your clients’ needs. You can also use it to communicate updates through Microsoft Teams and Outlook.

Copilot also supports change management by enabling you to clearly communicate the benefits of changes to your team. That way, you can get employee buy-in. Or use it to build training materials to get everyone up to speed quickly. These use cases can streamline projects and reduce stress for everyone.

Master Copilot and other innovative AI tools

Microsoft Copilot is a powerful ally for any professional. With its diverse applications, it can improve many aspects of your daily routine, from simple administrative tasks to programming and project management.

Sharpen your AI skills with a free Multiverse apprenticeship. Our AI for Business Value programme teaches you how to use Copilot and other AI solutions to make an impact in your organisation. You’ll gain hands-on experience solving real business problems while studying AI ethics and business analysis fundamentals. Together, this knowledge will help you drive data-driven change. Plus, you’ll receive personalised career coaching from industry experts.

Continue your upskilling journey by completing our quick application today.

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.

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.

Needless to say, things have changed almost overnight. Between 2023 and 2024, the percentage of organisations using AI leapt from 55% to 78%. This rapid adoption isn’t surprising when you consider the technology’s impressive versatility. From crunching huge datasets to managing projects, AI can assist with (almost) any operation.

But incorporating AI into your daily workflows isn’t as simple as downloading Microsoft Copilot onto every computer or sharing tutorials about AI image generation. Companies that adopt this technology can face many obstacles, from tight budgets to employee resistance. Effective change management is key to navigating these transformations successfully and getting your whole team on board.

This article covers essential strategies and resources for change management. By mastering this skill, you can help your organisation adopt AI and prepare for whatever comes next.

Understanding change management

The Association for Project Management defines change management as “the overarching approach taken in an organisation to move from the current to a future desirable state using a coordinated and structured approach in collaboration with stakeholders.”

In other words, change management helps individuals and organisations transition from one point to another as smoothly as possible. For example, a business might develop a change initiative to shift from barely dabbling in AI to fully embedding it in every part of its operations.

To outsiders, change management may seem a little over-the-top. Even unnecessary. After all, companies change things all the time — do you really need a special plan for it?

Absolutely, especially when you’re introducing new technologies or processes. Here are a few reasons why it pays to manage organisational change proactively:

• Improve communication: Even the most laid-back employees can feel stressed if you suddenly switch to a new system or tool. With a change management plan, you can keep everyone in the loop about the transition and help them understand their roles.
• Get employee buy-in: Employees often balk at change, especially if they believe it will create more work for them. Some workers may also fear that AI will eventually replace them, leading to anxiety or resentment. Change managers can help soothe these concerns by explaining the benefits of the new technology upfront. For example, your HR team’s resistance to change may evaporate when you demonstrate how AI can automate their scheduling tasks.
• Provide training: While some AI tools are intuitive, they all have a learning curve. Content generators, for instance, require careful prompting to get high-quality outputs. By planning ahead, you can help employees upskill and get comfortable with the software before it becomes part of their daily routines.
• Reduce disruptions: Launching a new tool without a plan is a surefire way to cause chaos and confusion. A structured approach enables you to introduce the transition gradually and troubleshoot any issues that occur along the way.

The impact of AI on organisational change

Some organisational changes barely register for most employees. For example, your IT team may be the only people who notice when your payroll system gets a software patch. But that’s not the case for adopting artificial intelligence.

This technology is almost always a catalyst for much larger transformations. That’s because it disrupts existing workflows and helps people step outside their traditional roles. Suddenly, a marketer with no data science training can analyse a ten-thousand-line spreadsheet with AI. And instead of spending hours sifting through client emails, a Sales Representative can automate replies.

While these changes can be empowering, they may also raise new challenges. For instance, employees who lack technical skills, such as prompt engineering, might not know how to use AI effectively. Workers may also need to learn new behavioural norms, such as checking AI outputs for bias and misinformation.

The solution? Investing in change management. Organisations that dare to reinvent their workflows and roles are 1.5 times more likely to meet their goals than those that stick to the status quo. AI can also help businesses reach new levels of efficiency and productivity.

Change management models in the context of AI

You don’t need to reinvent the wheel to manage change effectively. Here are several existing models that you can adapt for AI-driven transformation.

Lewin’s change management model

The psychologist Kurt Lewin developed one of the most popular change management frameworks. It includes three phases:

• Unfreeze: The organisation recognises that it needs to transform and let go of the status quo. During this stage, change managers challenge existing beliefs and persuade key stakeholders to accept the coming transition.
• Change: Leaders begin applying changes and upending outdated systems. They focus on overcoming resistance and helping team members adapt to the new world order.
• Refreeze: Change managers establish new policies to ensure that the transformation takes root.

Although Lewin invented this model in the mid-twentieth century, it’s still incredibly relevant today. Project Managers can “unfreeze” their organisations by researching the benefits of AI and pitching the transformation to the leadership team.

During the change phase, they can implement strategies like offering training sessions or piloting AI in one or two departments. And, after the successful implementation, AI usage policies could help cement the shift.

ADKAR model

In the 1990s, Jeff Hiatt created the ADKAR model to help businesses effectively manage change. It focuses on “guiding individuals through a particular change and addressing any roadblocks or barrier points along the way.”

This framework has five stages:

• Awareness: The individual understands the underlying reasons for the transition and the potential consequences of not evolving.
• Desire: They want to see the change implemented successfully and feel inspired to actively participate in it.
• Knowledge: They gain the knowledge and skills needed to support the transition.
• Ability: The individual has the capability to apply what they’ve learnt.
• Reinforcement: They commit to the change for the long term and alter their behaviour accordingly.

Change management professionals can win over employees in the awareness and desire phases by highlighting the advantages of AI. This could involve sharing case studies of competitors who have successfully used the technology or demonstrating how AI tools would fit their workflows. These practical examples can inspire curiosity instead of fear.

During the knowledge and ability phases, education is absolutely critical. Consider organising AI training workshops or bringing in outside experts to teach new skills. When employees feel empowered, they’re more likely to embrace change initiatives. Plus, professional development will help foster a company culture centred around continuous improvement.

Kotter’s 8-step change model

John Kotter created a more extensive model for building change capability within organisations. It has eight stages, including:

• Create a sense of urgency: Make people feel excited and passionate about the upcoming change.
• Build a guiding coalition: Assemble an A-team of change leaders who will shepherd the business through the transition.
• Form a strategic vision: Tell a convincing narrative about how the change will help the business accomplish its goals.
• Enlist a volunteer army: Bring together individuals who are eager to contribute to the change.
• Enable action by removing barriers: Develop solutions for any obstacles you encounter.
• Generate short-term wins: Celebrate achievements to build momentum and keep the team motivated.
• Sustain acceleration: Keep your foot on the metaphorical gas pedal after your early accomplishments.
• Institute change: Reinforce new behaviours and mindsets until old habits fade from memory.

Businesses often use Kotter’s framework for digital transformation. For example, your AI coalition might consist of Data Analysts, IT specialists, and communication experts. And your marketing department might happily volunteer to test a new AI tool.

Leading change in the age of AI

AI transformation projects can be highly disruptive, both mentally and operationally. You’ll need strong change management skills to integrate the technology while keeping everyone happy.

Organisational change management begins with strategic planning. This ability allows you to define a clear vision and goals that your team can rally behind. For example, your company might aim to use artificial intelligence to increase productivity by 20% and help employees learn new skills. You’ll also need to clearly explain how the transition will help reach these objectives.

Effective communication is vital, too. You can use many techniques to inform your team throughout the change management process, such as:

• Hosting one-on-one meetings with the managers and employees most impacted by the change
• Organising town halls to address the staff’s concerns and questions
• Sending out weekly updates via email or Slack

The best project management professionals also empower their teams. Encourage your employees to take ownership of organisational change initiatives by asking for their feedback and recommendations. You can also recruit early adopters to train their colleagues and troubleshoot problems. Small gestures like these can go a long way toward implementing change effectively.

An apprenticeship is the best way to gain and implement desired skills. Multiverse’s Business Transformation Fellowship teaches you how to identify opportunities for digital change in your existing organisation. You’ll also learn how to use the latest project management techniques and tools to drive transformation. These valuable skills can help you future-proof your career in the UK’s constantly evolving job market.

Throughout your apprenticeship, you’ll develop hands-on experience as you work on real projects for your current employer. The best part? The programme is completely free for apprentices, and you can continue earning your regular salary while you learn.

Case studies and real-world applications

Researching examples of successful change management can help you plan your own initiatives. Plus, case studies can help you win over stakeholders who may not be fully sold on your strategic vision.

At Marks & Spencer, for instance, AI is a significant focus in practically every department. The marketing team recently launched a new AI tool that offers personalised recommendations for wine. The company also uses an AI platform to manage its supply chain. What’s the secret to its success? Marks & Spencer rolls out changes gradually and partners with outside tech companies to help build its AI applications.

Small businesses have conquered the AI change process, too. Take Phoenixfire Design & Consulting, for instance. This UK-based marketing firm uses budget-friendly AI tools like ChatGPT to generate content ideas. Founder John Fuller notes that the company had to overcome a learning curve on its transformation journey: “We got a huge bump in efficiency once we worked out the prompt engineering.” Now, Phoenixfire drafts projects with AI and finishes them with human creators.

Along with reading case studies, you can set yourself up for success by following these best practices for organisational change:

• Have a clear and inspiring strategic direction.
• Acknowledge employees’ anxieties about AI and offer resources to help them adapt.
• Keep up with the latest techniques by joining professional organisations like the Change Management Institute.
• Monitor progress with performance metrics, such as employee productivity and engagement with AI-generated content.
• Use community-based learning to build your team’s confidence and spark curiosity. For example, you might invite an early adopter to demonstrate how they use AI to edit videos or engage clients.

Guide your organisation (and your career) into the future

Successful change management doesn’t happen by accident, especially when AI is involved. You need the right attitude and strategies to guide your organisation through a huge transformation. And, of course, the skills to manage complex projects.

Strengthen your change management skills with Multiverse’s free Business Transformation Fellowship. This apprenticeship will help you develop the agile mindset and leadership capabilities needed to spearhead organisational change efforts. You’ll also learn how to use data to drive transformation as you complete real projects.

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