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

Why data cleaning matters

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

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

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

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

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

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

Common data issues

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

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• Outdated information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

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

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

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

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

Step-by-step data cleaning workflow

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

Data auditing

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

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

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

Cleaning

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

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

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

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

Validation

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

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

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

Documentation and versioning

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

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

Techniques for cleaning data

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

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

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

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

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

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

Handling missing data

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

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

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

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

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

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

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

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

Tools of the trade

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

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

Automating and scaling data cleaning

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

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

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

Preparing data for modelling

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

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

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

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

Final tips for beginners

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

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

Learn how to turn messy data into sparkling insights with Multiverse

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

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

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

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

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

What’s the difference between Java and JavaScript?

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

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

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

5 key differences between Java and JavaScript

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

Applications

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

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

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

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

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

Complexity

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

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

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

Syntax

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

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

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

Learning curve

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

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

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

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

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

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

Popularity

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

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

What is Java?

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

Java’s key features include:

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

Disadvantages of Java include:

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

What is JavaScript?

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

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

Key features of JavaScript include:

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

Disadvantages of JavaScript include:

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

Which is better, Java or JavaScript?

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

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

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

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

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

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

Learning Java or JavaScript in 2025

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

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

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

Make this year the year you uplevel your coding skills

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

Complete our short apprenticeship application today to get started.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Job 1: Data Analyst

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

Data Analysts rely on some of the following key skills:

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

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

Job 2: Software Engineer

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

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

Essential skills for a Software Developer include:

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

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

Job 3: Digital Marketing Specialist

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

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

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

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

Job 4: AI and Machine Learning Expert

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

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

AI and ML Experts need expertise in these areas:

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

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

Job 5: Cybersecurity Analyst

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

Cybersecurity jobs require these skills:

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

Job 6: UX/UI Designer

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

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

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

Job 7: Cloud Solutions Architect

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

Here are a few basic requirements for Cloud Solutions Architects:

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

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

Job 8: Blockchain Developer

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

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

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

Job 9: Internet of Things (IoT) Engineer

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

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

IoT engineering jobs require many skills, including:

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

Job 10: Sustainability Officer

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

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

Key skills for Sustainability Officers include:

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

Step into the future with Multiverse’s apprenticeships

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

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

Complete our quick application today for current professionals and the Multiverse team will get in touch to discuss the next steps.

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

What is Skills England’s role in assessing skills?

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

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

Introducing the Skills for Growth and Opportunity Report

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

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

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

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

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

And in two additional ‘critical’ sectors:

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

Skills England report: Three takeaways from Multiverse

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

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

Three themes particularly caught our attention:

1) The digital and AI revolution is transforming workforce skills

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

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

2) Reskilling is essential to combat the widening digital gap

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

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

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

3) Flexible apprenticeships and training options are key

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

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

The path forward

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

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

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

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

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

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

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

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

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

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

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

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

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

The role of digital skills in the NHS

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

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

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

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

Improving data visibility at Barts Health NHS Trust

Apprentice role: Data Analyst, Transfer of Care Hub

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

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

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

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

Minimising waiting times at Royal Free London NHS Foundation Trust

Apprentice role: Administrator, Adult Assessment Unit

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

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

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

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

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

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

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

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

Improving theatre audits at Medway NHS Foundation Trust

Apprentice role: Band 5 Endoscopy Nurse

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

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

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

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

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

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

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

Upskilling initiatives empower the NHS

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

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

Article originally featured in HSJ Online

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.

You’ve probably already used AI tools like ChatGPT to help craft the perfect email subject line or come up with a snappy Instagram caption. AI coding works in a similar way. It uses large language models to support or even automate software development. But instead of generating sentences or images, it produces lines of code.

AI coding tools make programming faster and more accessible for everyone — not just expert developers. All you need is the right mindset and some foundational knowledge. Here’s how you can use this fascinating technology to build smart applications with less code.

Understanding AI coding: What is it (and isn’t)

An AI coding tool refers to any platform that assists users with the software development process. Take Snyk Code, for instance. It scans code to detect security vulnerabilities and automatically fixes them. But it can only edit existing code, not generate new code from scratch.

By contrast, AI coding involves co-writing code with artificial intelligence software. This human-machine collaboration can take many forms.

Some applications, like GitHub Copilot, offer relevant suggestions as you code, helping you program much faster. This type of software requires some knowledge of programming languages. For example, if you can only write very basic HTML, GitHub Copilot won’t spontaneously generate thousands of lines of Python code. It needs your code as the foundation.

On the other end of the spectrum, low-code and no-code platforms allow users to build apps with minimal technical expertise. These tools are a great option for people who want custom programs without hiring a professional.

No matter which tool you choose, AI coding is an inherently collaborative process. Artificial intelligence can’t fully replace developers; it can only augment them.

As LeetCode founder Winston Tang explains, “[S]software engineering goes beyond mere coding. It involves creativity, problem-solving, and innovation — qualities AI cannot fully replicate.”

In other words, an AI code completion tool can help you do some of the heavy lifting, but the programming process still needs that all-powerful human touch.

Why AI coding matters for upskillers

At first, AI code generation may not seem very relevant if you’re not a professional Software Developer. After all, you probably don’t spend much time writing JavaScript for fun or coding mobile apps.

But AI coding can be incredibly useful. It lowers the barrier to entry for careers that may only require occasional coding knowledge.

For instance, a Product Manager might need to troubleshoot software issues without writing the code themselves. An AI code explanation tool can help them understand what they’re looking at — no coding bootcamp necessary.

AI coding also has many benefits for career changers and upskillers. It can help you write better code than you’re capable of producing on your own, allowing you to take on more complex projects. Instead of spending years preparing for a junior developer role, you might be able to create a strong portfolio in just a few months.

Other practical applications for AI code generation tools include:

• Analysing other developers’ source code to learn how their applications work
• Experimenting with more advanced features
• Rapidly prototyping
• Automating repetitive tasks, such formatting text and generating documentation

How to start AI coding (even with minimal experience)

You don’t need a computer science degree to begin coding, but you will need to understand some basic concepts.

AI-assisted platforms like ChatGPT and Microsoft Copilot can help you learn the fundamentals of programming languages. They use natural language to explain technical concepts and can personalise their responses to match your skill level. For example, you might input, “Can you explain what a variable is in JavaScript?” Or you could request a custom study plan for total novices.

AI tools can also teach you how to read and understand other people’s code. Use software like Denigma to analyse Python code snippets, or ask ChatGPT to break down what they mean like you’re a kindergartener. This strategy can help you test your knowledge and learn more about the inner workings of real applications.

Debugging is another invaluable coding skill. Ask ChatGPT to generate buggy code, and see how long it takes you to come up with a solution. When you get stuck, ask for a hint or two until you can figure out a fix. This game might occasionally feel frustrating, but it’s an excellent way to sharpen your critical thinking and problem-solving skills.

Of course, you’ll need to put your new coding abilities to the test. Sandboxes and notebooks like Google Colab and Replit are the perfect place to experiment in private. These platforms both have free versions, so you can play with code as much as you’d like without worrying about your budget.

As you immerse yourself in all these great resources, be sure to build small and build often. You might create a simple weather app one week, then an internal chatbot the next. As you become more familiar with the development process, your confidence and skills will grow quickly.

Key AI coding tools to know (and how to use them)

With so many AI platforms available, you may feel like a video game lover at a Nintendo sale — excited and ready to splurge. But you don’t need to learn everything at once. Set yourself up for success by picking just two or three applications to build your AI skills, then branch out from there.

Take the first step by trying out some of these tools:

• GitHub Copilot: This popular AI powered code completion tool offers tailored suggestions in real-time. It also automatically reviews your code and gives feedback to help you improve.
• Replit AI: With its no-code development tools, this platform is perfect for beginners. Simply describe your ideal app or website to the Replit Agent, and it will automatically turn your vision into a reality. If you prefer a more hands-on approach, Replit’s Ghostwriter tool will complete your code and even convert it to different programming languages.
• Windsurf (formerly Codeium) / Tabnine: Use these tools to write clean and consistent code quickly. They’ll edit, test, and iterate your project until it’s ready to launch.
• AskCodi: This user-friendly AI assistant is perfect for generating code and documentation. It also provides smart code explanations so you can truly understand the ins and outs of programming.
• Flowise: This low-code platform’s drag-and-drop interface makes it easy to build custom AI agents and chatbots.

Common use cases for AI coding

Businesses in all industries rely on programming languages to handle complex tasks. Even if you’re not a professional Software Developer, here are a few practical ways that AI coding can assist with your work.

Web applications

There are countless reasons why someone who isn’t a Web Developer might decide to build a website. If you’re applying for jobs, a professional website is the perfect place to showcase your portfolio and resume. Or maybe you just want a small corner of the internet where you can talk freely about your hiking trips or shoe obsession.

With an AI code generation tool, you can develop your dream website in hours. Copilot, for instance, can help you build the frontend of your site with HTML and JavaScript. This tool gives you much more freedom than conventional web builders like SquareSpace and Wix, which only offer pre-made templates.

Chatbots and automation

Conversational AI chatbots have become all the rage with businesses. It’s easy to see why. They can automate many workflows, including:

• Answering basic customer questions, such as “How long does delivery take?” and “When will this shirt be back in stock?”
• Qualifying leads
• Gathering contact information
• Scheduling consultations

Use tools like GPT-4 and LangChain Templates to build intelligent chatbots with minimal coding.

Data science and analysis

Employees spend an average of 36% of their work week on data tasks, according to the Multiverse Skills Intelligence Report. Yet 86% have no Python skills, and 53% struggle to analyse data efficiently. That adds up to a lot of wasted time and potential.

Expand your skill set by using an AI code completion tool for Python scripting. For example, you can use Python to mine data from social media platforms and websites. This programming language is also helpful for building data structures and generating data visualisations.

These applications don’t take much time to develop with a code generator and can go a long way toward boosting productivity.

API integration and backend workflows

Even the most basic applications have a lot going on under the metaphorical hood. Code generation tools like Copilot can help you develop APIs to link your app to other platforms. They also assist with constructing data pipelines and other backend functions. That way, your sites can function as efficiently as possible.

Best practices for building with AI support

For a beginning coder — or even someone more seasoned — working with an AI coding assistant can feel downright thrilling. There’s something deeply satisfying about seeing neat lines of code generated based on your input.

But don’t get too carried away. While AI powered tools can support your growth, they can also stifle it if you rely on them too heavily.

Follow these tips to strike a healthy balance:

• Start with a plan: Some AI tools let you input natural language descriptions (“build a mobile app to track the time the sun sets each day”), while others require code snippets. Take the time to create pseudocode or prompt outlines to guide the development. That way, you’re sharpening your programming skills instead of letting AI do all the work.
• Don’t try to learn multiple programming languages at once: While platforms like ChatGPT can teach foundational programming concepts, true understanding takes time and practice. Focus on mastering one language before moving on to the next. Otherwise, you might find yourself struggling to tell the difference between Java and C# or forgetting what a string is.
• Resist the urge to blindly copy AI generated code: Sure, this code might be perfectly functional. But you’re not learning anything if you just copy and paste it into your application. Take the time to carefully read each line, and use a code explanation tool to interpret confusing snippets. You should also conduct thorough testing to make sure the code actually works. These steps will help you expand your knowledge and avoid overreliance on AI.
• Use version control early: Get in the habit of using GitHub repositories to track every change you make to your code. This practice makes pinpointing and fixing errors much easier, because you can simply go back to the moment when things went haywire.
• Collaborate with others: While AI can provide invaluable code assistance, it’s no replacement for human interaction. Set aside time for pair programming with your mentors or peers. They can share their personal experiences and teach you coding tactics that you’d never learn from a machine. It’s one of the best ways to upskill while building meaningful connections in your field.

Where Multiverse fits in

You already know that you can gain a healthy amount of coding knowledge simply by using the right AI tools. But for more in-depth technical expertise, consider a Multiverse apprenticeship.

Apprentices gain hands-on experience through structured coursework and real-world learning opportunities. For example, the Business Transformation Fellowship teaches you how to use AI and data analytics to drive change in your organisation. You’ll learn how to identify growth opportunities and manage cross-functional projects.

Meanwhile, the Software Engineering programme focuses on foundational development techniques and tools. You can expand your AI skills as you build full-stack applications and study various programming languages.

An apprenticeship is the best way to gain practical experience with the latest tools in your field, such as AI-assisted development environments and data visualisation software. And it’s completely free for apprentices. You’ll continue working in your current role while developing your skills during protected off-the-job time.

AI coding is the future — Start now

Say farewell to expensive coding bootcamps and endless Java tutorials. With artificial intelligence, programming is more accessible — but also more essential — than ever before.

Developers have created AI tools for virtually every coding task, from interpreting programming languages to patching potential security vulnerabilities. But these applications don’t just help you deliver a polished final product. They’re also valuable resources for gaining coding experience more quickly and efficiently.

Ready to start building? Learn how to code with AI while gaining real-world experience in one of our paid apprenticeship programmes. Complete our quick application to learn more.

You might assume that tech experts are behind this rapid adoption, but that’s not strictly true. Sure, plenty of Machine Learning Engineers and other specialists are using advanced AI tools. But people across all roles and experience levels are taking advantage of it, too. With so many beginner-friendly options, AI is truly a jack-of-all-trades kind of technology.

This guide breaks down the best AI tools for your everyday work. We’ve got something for everyone — from marketers to analysts, career changers to developers, and paid or free users.

What are AI tools? A quick primer for beginners

AI tools include any software that’s powered by machine learning or large language models (LLMs). These applications use complex algorithms — or snippets of code — to process data and perform tasks.

While artificial intelligence is still relatively new, developers have already created many types of tools, including:

• Automation software
• Chatbots
• Code assistants
• Image recognition software
• Task management tools
• Text and image generators

Contrary to popular belief, most AI software doesn’t require any technical skills. All you need is a little curiosity and the willingness to experiment.

Must-have AI tools for everyday workflows

Popular AI tools like ChatGPT and Microsoft Copilot have quickly become household names, even for non-techies. But these applications only scratch the surface of everything that’s available.

Here are seven types of AI tools to consider adding to your work routine. Some are ground-breaking AI models, while others are familiar software with new AI powered features. Together, they can help you jumpstart your creativity and reach new levels of productivity.

1. Writing and content creation

Even the most creative people can sometimes feel bogged down by the writing process. Brainstorming, drafting, editing, then drafting again — it’s a lot of work, to put it mildly. Content creation tools can help you speed up these steps and improve the overall quality.

For a free AI writing tool, you can’t go wrong with the classic ChatGPT. It’s perfect for brainstorming ideas for virtually any type of content. For instance, you could ask it to generate social media captions with prompts like, “Suggest 10 pun-filled captions for a photo of a golden retriever eating an ice cone.” Or request content ideas for how-to videos and email newsletters.

ChatGPT can also generate long form content, such as:

• Case studies
• Ebooks
• Entire blog posts
• White papers
• YouTube scripts

Meanwhile, Jasper.ai is designed for marketing teams. This AI writing assistant learns your brand voice and generates marketing copy that matches it flawlessly. With over 90 built-in marketing apps, it can create product descriptions, blog posts, and more.

AI tools assist with the editing process, too. Grammarly and Wordtune correct grammar mistakes and help maintain a consistent tone. Both applications integrate with Google Docs, so you don’t even need to switch to a new platform to check for mistakes.

Most content creation tools have free versions with basic features. But it’s often worth investing in paid plans for custom, high quality outputs.

1. Visuals and creative assets

Most businesses need a virtually endless supply of creative assets. For example, if you plan to publish two social media posts a week, you’ll need over 100 images or videos a year. Producing all this content with traditional tools can be incredibly time-consuming. That’s why many companies are turning to AI for assistance.

Canva’s Magic Design is one of the best AI image generators for beginners. All you need to do is describe what you want to see with text, and voila — you’ve got dozens of matching images. This platform is the ideal tool for creating social graphics and stylish slide decks for presentations.

DALL-E and Midjourney are slightly more advanced image generation tools. Like Canva, they create visuals based on written prompts. Their outputs can vary drastically based on how you phrase your request, so take the time to experiment with different inputs.

And don’t overlook video creation platforms like Lumen5 and InVideo. They let you generate short films based on text — no video editing skills or cameras required.

1. Productivity and admin

Every career involves repetitive or just plain tedious tasks. For instance, you might respond to the same customer questions over and over. (“How long will it take my product to ship?”) An AI assistant can automate this busy work, so you can focus on more meaningful activities.

Here are some of the best AI productivity tools:

• Notion AI is a great tool for summarising meetings and planning tasks.
• Otter and Fireflies automatically transcribe audio files and recap meetings
• Superhuman and Sanebox use AI to help you manage your inbox and draft emails.

These handy applications can significantly shrink your to-do list, reducing stress. Plus, they help prevent errors, like forgetting to respond to your boss’s urgent email or overlooking an action item after a standup meeting.

1. Project and task management

No matter your career stage or role, chances are you work on complex projects with a lot of moving parts. Use these AI powered project management tools to stay organised and meet deadlines:

• Trello’s AI features include intelligent task suggestions and decision-making tools.
• ClickUp is one of the best AI tools for task management and reporting.
• Asana AI automates tasks, generates status updates, and suggests next steps.
• Slack AI improves team collaboration by summarising conversations and answering questions about projects.

1. Coding and technical work (for beginners, too)

AI software can also assist with technical tasks. The best part? Most of these tools don’t require in-depth coding knowledge.

The majority (91%) of Web Developers now use AI tools to generate code. One of the most popular applications is GitHub Copilot, which analyses your code and offers suggestions to improve it. Essentially, it’s a co-writer that helps you program faster. It also detects and fixes bugs, allowing you to create more accurate code.

Similarly, Replit’s Ghostwriter automatically completes your code and explains how other developers’ applications work. This AI assistant is especially helpful for junior developers who want to learn by doing.

AskCodi is another AI coding assistant that automates small tasks. For example, you can use this application to generate code snippets and write documentation.

1. Data and analysis

The average professional spends around 14 hours a week completing data tasks — yet over four of those hours are spent working inefficiently, according to the Multiverse Skills Intelligence Report.

The good news is that there are plenty of user-friendly AI tools that can help you analyse data more efficiently, including:

• ChatGPT can generate Excel formulae to manage complex spreadsheets.
• MonkeyLearn is a no-code platform for text processing. It lets you analyse sentiment in emails, social media posts, and other qualitative datasets.
• Power BI and Tableau offer AI powered features like forecasting and intelligent data visualisations.

In the UK, the demand for data skills has increased by 158% since 2013. These AI tools can help you future-proof your career and take on more advanced, data-driven responsibilities.

1. Career and personal growth

UK businesses in all industries are facing a critical digital skills gap. Artificial intelligence can help you develop new abilities and attract the attention of potential employers.

Expand your skill set with Multiverse’s AI Skills Jumpstart course. This module covers fundamental AI skills like prompt engineering and data modelling. It also teaches you how to ethically use this technology in your daily workflows.

When you’re ready for a new role, optimise your job materials with LinkedIn’s free AI resume builder. Or use Rezi to write cover letters and resumes. Just be sure to add your own voice to the final versions. Employers can sniff out purely AI generated content immediately, and they might overlook your application if they think you didn’t put in any effort.

How to choose the right AI tools for your role

With so many options, it’s natural to feel a little lost. Overwhelmed, even. But you don’t need to try every tool at once.

Start by researching how other professionals in your field are using artificial intelligence. You’ll find plenty of industry-specific conversations about this technology on LinkedIn, online forums, and even podcasts. Surveys can also provide valuable insights about what works — and what doesn’t.

In the marketing industry, for instance, UK professionals already use AI content generators to create 36% of their social media content. Meanwhile, Data Analysts typically rely on AI tools to automate tasks like cleaning and processing datasets.

Once you’ve got a sense of how AI tools work in your sector, you can begin assembling your toolkit. Choose platforms that offer a free tier or detailed demos so you can make sure they meet your needs before committing to a paid plan.

Here are a few AI starter packs for different careers:

Marketers:

• A large language model (ChatGPT, Jasper) to assist with the content creation process and prevent writer’s block
• Ocoya for social media management and AI generated content
• Midjourney for speedy image generation
• SEO.AI for keyword research and AI writing assistance
• Invideo for video generation

Analysts:

• Tableau for sophisticated data analysis and accessible AI models
• KNIME for predictive analytics and data modelling
• Canva Magic Design to present findings to business leaders and stakeholders

Software Developers:

• GitHub Copilot for fast and accurate coding
• ClickUp to manage Agile workflows
• Figstack to explain code and automatically translate it to another programming language

But don’t get too caught up in other people’s recommendations. Ultimately, AI tools should help you do your job, not someone else’s. In other words, don’t spend time learning Midjourney if a Graphic Designer handles most of your organisation’s creative assets.

Building real-world experience with AI tools

There are plenty of free tutorials and other resources for artificial intelligence platforms. However, reading about this technology can only take you so far. To truly understand its potential and limitations, you must actually apply it in your work routine.

A Multiverse apprenticeship lets you practise using AI tools in real business settings. You’ll learn how to integrate software like ChatGPT and project management AI in your existing role. This might involve automating some of your workflows (goodbye, repetitive data entry) or using an AI writing assistant to generate ideas.

Apprentices also build their skills with industry-specific tools. For instance, the 13-month Data Fellowship teaches you how to use PowerBI and Tableau for advanced data analytics. By contrast, the AI for Business Value apprenticeship focuses on data modelling and productivity tools.

Multiverse apprenticeships are fully paid for by employers and include protected time for on-the-job learning. They’re an excellent way to upskill without interrupting your career.

Learning how to use AI tools benefits your organisation, too. According to the ROI of AI report, approximately half of tech leaders say their businesses lack critical skills, such as data analytics and the ability to implement AI projects. By expanding your personal skill set, you can help close that gap and drive change within your organisation.

Best practices for integrating AI models and tools into your work

Experimenting with new technology is always thrilling, but don’t rush into adopting AI tools. Follow these guidelines to make sure you’re using it responsibly and effectively:

• Double-check all AI outputs: Like humans, AI can make silly or even dangerous mistakes. For example, AI generated content sometimes contains misleading medical advice. Always review and improve outputs to protect your clients and reputation.
• Use thoughtful prompts: Content creation tools are only as effective as the inputs you provide. Always give the software the necessary context and request a specific tone. You can also explain your goals — “I want to persuade my target audience of 30-something pet owners to buy my leashes” — to help the AI generate the most relevant content.
• Set ethical boundaries: According to Forbes, one in three Brits (37%) feel concerned about the ethical implications of artificial intelligence. You can soothe these anxieties by prioritizing consent and transparency. Always ask customers for permission before inputting their data into AI tools, and clearly explain how you plan to use this technology. You should also carefully review AI generated content for any signs of bias, such as only creating images of one race or gender.
• Encourage consistency: AI tools work best when you treat them as part of your routine, not a novelty. For example, you might summarise meeting notes every Friday with Otter or generate social media posts twice a week.

Multiverse and the future of work

It’s no secret that artificial intelligence is accelerating and transforming the way we work. The IPPR predicts that this technology may disrupt up to 8 million jobs in the UK over the next few years.

The right training can help you adapt to these changes and stay competitive. While it’s possible to learn many AI tools on your own, Multiverse’s structured apprenticeships are the best way to gain hands-on experience. Our structured curricula teach you how to apply artificial intelligence in business and technical environments. You’ll also receive one-on-one mentorship and career coaching from industry experts to help you achieve your goals.

Our AI Jumpstart module is the fastest way to gain foundational knowledge and skills. You’ll learn how to use this technology to generate ideas, solve problems, and more. For more in-depth training, consider our Data Fellowship or Business Transformation Fellowship.

Start small, experiment often, and upskill along the way

Developers may have originally created artificial intelligence, but it’s no longer their exclusive domain. Anyone can benefit from this technology. AI image generators, productivity tools, video editing software — there’s truly something for everyone.

Join the tech revolution by experimenting with two or three free AI tools from our list. As you gain confidence, you can gradually expand your repertoire and try even more sophisticated software.

Or maybe you’re ready to deepen your knowledge and learn more about the theory behind this technology. A Multiverse apprenticeship could be exactly what you’re looking for. Explore our free AI programmes, or fill out our quick application today.