The Multiverse blog

Super-intelligent humanoid robots aren’t roaming the streets (at least, not yet). AI technology, however, is transforming industries and reimagining the way businesses operate. For workers, it’s also an inflection point — one that’s sparking a reevaluation of the skills needed to achieve staying power in a challenging job market. By 2027, an estimated 42% of companies surveyed by the World Economic Forum will prioritise training workers in AI and big data skills.

As AI becomes more integral to everyday operations, businesses need skilled workers to develop, train, and apply this technology. 81% of tech leaders plan to increase their investments in AI over the next three years.

This poses a massive opportunity for forward-thinking professionals to take charge of their career trajectory by learning high-value AI skills. According to a Multiverse report, 56% of surveyed workers at AI-integrated organisations plan to negotiate higher pay in the next 12 months.

Below, we’ll take a deep dive into the crucial AI skills and tools needed to thrive in the AI-enabled job market — both today and tomorrow. We'll also share practical tips and resources for expanding knowledge in these areas.

Understanding the LLM revolution

AI adoption has skyrocketed as organisations race to stay ahead of the competition. According to consulting firm McKinsey & Company, the percentage of businesses using AI tools jumped from 55% in 2023 to 72% in 2024. With 50% of organisations already using AI for two or more business functions, it’s clear that this isn’t just a momentary trend; it’s a seismic shift in how we work.

The versatility of AI tools has significantly contributed to their surging popularity. Many businesses rely on this technology to automate repetitive or time-consuming workflows. In the healthcare industry, for instance, professionals are using AI to automate document classification, patient indexing, and other data entry tasks. Meanwhile, marketing and sales teams are turning to AI to hyper-personalise content and automatically send follow-up emails to prospects.

Beyond automation, businesses across industries are employing AI to analyse data and make more strategic decisions. For example, Schneider Electric’s Sustainability Business uses AI-assisted forecasting tools to predict extreme weather events. As Schneider Electric Sustainability President Steve Wilhite explains, “These forecasts, partnered with human-expertise, will support everything from energy efficiency and optimisation to emissions reduction to grid resiliency.”

Despite the massive gains, even the most AI-savvy businesses have struggled to unlock the technology’s full potential. Only 27% of business leaders consider their organisations “AI Adept,” which means they’ve embedded AI across their operations to improve strategic decision-making.

“All organisations should strive to be AI native — fully embedding and realising the ROI advantages of AI — in the years ahead,” explains Anna Wang, Head of AI at Multiverse. “However, because of the newness of the technology and the pace of change, many organisations are struggling to get a clear view of their own progress.”

For workers and organisations, understanding the tools and technologies leading the AI revolution — and how to leverage them to drive demonstrable business value — is essential. Below, we’ll highlight three key technologies gaining traction in workplaces in the UK and beyond.

ChatGPT

In November 2022, OpenAI launched ChatGPT, an AI-powered conversational model that quickly became a household name. By August 2024, the company estimated that an astonishing 200 million people were using the tool weekly.

ChatGPT is a large language model (LLM) trained on vast amounts of data. Developers create LLMs using neural networks that contain interconnected nodes and layers. These structures learn how to process and transmit data, just like neurons in the human brain.

ChatGPT’s neural networks use natural language processing to understand and respond to human language. The LLM breaks down text into patterns and smaller components, analysing it for meaning and context. It uses this information to generate relevant responses that closely mimic human writing or speech.

The conversational nature of ChatGPT makes it incredibly accessible, contributing to its widespread popularity. According to the ROI of AI report, 61% of workers have picked up new AI skills by experimenting with ChatGPT.

Text-To-Image Generators

Generative AI tools like MidJourney and DALL·E 3 use advanced machine learning techniques to create images from text prompts. Unlike ChatGPT, which is powered by LLMs designed for text generation, these platforms rely on diffusion models or other image-generation architectures. Diffusion models work by adding “noise” (random pixels) to data, such as images, and then gradually removing noise through multiple iterations to generate new images based on the text prompt.

Businesses can use image-to-text generators like Midjourney to generate personalised images in a fraction of the time it takes to create traditional art. This technology also helps professionals brainstorm new content ideas, such as film posters and social media graphics.

London-based ad agency 10 Days is one company that has embraced text-to-image generators. Their creative team uses these tools to design visually complex brand characters, logos, packaging, and picture books.

GitHub Copilot

GitHub Copilot is an AI-powered programming assistant built on an LLM. It allows users to input code snippets and generates suggestions to complete them. The software also answers coding-related questions, detects bugs, translates code into different programming languages, and more.

According to Stack Overflow’s 2024 Developer Survey, 44.2% of professional developers use GitHub Copilot for programming tasks. This tool lets professionals write code more quickly and accurately, significantly improving efficiency. A GitHub study found that developers who used Copilot completed coding tasks 55% faster than those who didn’t use this tool.

According to payments startup Pockyt founder Mason Lin, this achievement is only the beginning of a larger digital transformation for the startup.

“We anticipate a 500% increase in productivity in the medium to long term as we continue adapting AI and fine-tuning our software development life cycle,” Lin says.

AI and job growth: The path to a resilient career

As more businesses embrace AI, many professionals are understandably curious about how it will affect their careers. While it may take decades to understand the full impact of these advancements, one thing is certain: AI is fundamentally reshaping the workforce.

By 2030, generative AI and similar technologies may automate up to 30% of current working hours. This shift could require up to 12 million Europeans to transition into new roles — twice the pre-pandemic rate.

But it’s not all doom and gloom. AI is unlocking exciting new job opportunities across all sectors. A 2024 Gartner survey revealed that 67% of mature organisations are developing positions related to generative AI.

The UK job market already reflects the growing influence of AI. According to a PwC report, AI-related job postings have increased 3.6 times faster than other positions. The report also found that UK employers are willing to pay a 14% wage premium for workers with AI skills — a clear indication of the value of skilled human operations in workplaces increasingly fueled by AI-driven insights.

“Skilled people are crucial to realising the full value of AI,” explains Gary Eimerman, Multiverse’s Chief Learning Officer. “Without a thorough understanding of AI, businesses may be limiting the value derived from the technology in the long-term.”

The UK government has developed several initiatives to support AI skill development. For instance, the Digital Skills Council offers resources to help workers reskill and upskill for digital careers. Additionally, the Secretary of State recently appointed tech entrepreneur Matt Clifford to spearhead the AI Opportunities Action Plan. This project will outline strategies to develop AI talent in the private and public sectors.

Expanding your AI skills now will help you get ahead of the curve and navigate the coming technological disruptions. Upskilling can also prepare you for emerging AI careers, such as:

Digital Transformation Consultant

A Digital Transformation Consultant helps businesses use AI and other technologies to automate workflows and drive innovation. They assess each client’s existing tech stack and develop a strategic plan for integrating new technologies.

Salary data:

• Entry-level: £41K
• Average: £60K
• High earners: up to £74K

Source: Glassdoor

AI Compliance Officer

An AI Compliance Officer oversees their organisation to make sure all employees use AI tools and data ethically and legally. They develop policies for AI usage, educate workers about best practices, and audit AI systems.

Salary data:

• Entry-level: £27K
• Average: £33K
• High earners: up to £50K

Source: Talent.com

Automation Consultant

An Automation Consultant identifies opportunities to develop more efficient and streamlined operations. They use AI software and other tools to automate workflows, from sending appointment reminders to ordering supplies.

Salary data:

• Entry-level: £35K
• Average: £47K
• High earners: up to £63K

Source: Glassdoor

Future-proofing your career: The 5 skills you need to stay relevant in the age of AI

The rapid adoption of AI technologies has led to a nationwide talent shortage. In 2024, 81% of UK IT managers agree that there’s a critical AI skills gap, an increase of 9% from the previous year.

A lack of education and few opportunities for hands-on practice have contributed to this growing skills gap. According to Multiverse data, only 45% of employees received formal training from their employers.

“Workers are fending for themselves, either funding their own AI training or learning through trial and error,” Wang says. As a result, “it is difficult for them to self-assess their own knowledge gaps and learn most efficiently with their limited time.”

Fortunately, there are plenty of resources to help you learn AI concepts and expand your digital toolbox. Here are the most critical skills needed for success in the AI-enabled workplace.

1. Prompt engineering

Prompt engineering involves writing and refining specific inputs to get more accurate and tailored outputs from generative AI tools. According to Multiverse data, only 14% of tech leaders believe their organisation lacks this skill — a testament to the primacy of prompt engineering in the hierarchy of foundational AI skills.

Whether using ChatGPT or other generative text tools, workers can deploy numerous strategies for writing effective AI prompts. These include:

• Providing necessary context, such as your desired tone, industry, and target audience
• Using roleplay prompts, like “Imagine you’re an experienced UX Designer who wants to create user-friendly features for a mobile app”
• Giving the AI a list of “do’s” and “don’ts”
• Inputting an example and ask the AI tool to model its response after it
• Experimenting with different versions of the prompt until you get the desired output

Prompt engineering allows professionals to generate more engaging and precise content. For example, a Data Analyst could use prompt engineering to create a detailed report highlighting actionable insights based on specific findings. Meanwhile, a Software Developer could prompt an AI tool to review code output for a new feature.

Taking an online course in natural language processing can help you learn how to develop better prompts. Experimenting with free tools like OpenAI’s Playground will also sharpen your skills.

2. Data analysis

Employees spend an average of 14.31 hours weekly — over 30% of their time at work — on data tasks.  Yet Multiverse survey data found data analytics to be the biggest AI skill gap organisations face, with 52% of tech leaders and workers agreeing their businesses are lacking in this area.

You don't need a degree in data science to learn this skill. Accessible AI-powered tools like Microsoft Power BI and Tableau make collecting, processing, and analysing data easier than ever. They also allow users to create engaging data visualisations and reports.

Let’s say a Marketing Specialist wants to improve their social media campaigns. They could use Tableau to collect engagement data from Instagram and analyse it for trends. For example, they may discover that videos with music consistently perform better than posts with photographs. Based on this insight, they can create similar content to capture their audience’s attention more effectively.

Tableau and Power BI offer many free resources — including tutorials and community forums — to help you strengthen your data analytics skills.

3. Identifying AI workflows and implementing AI-related projects

While there are many useful AI tools, it can be difficult to weave them into your company’s existing workflows. Multiverse data shows 48% of tech leaders think their organisation can’t execute AI projects effectively.

Creating a project roadmap will help you spot opportunities and implement AI tools successfully. This framework should include these steps:

• Assess existing workflows to understand your organisation’s challenges and needs
• Identify areas where AI tools can provide advantages over conventional methods
• Form cross-functional teams that bridge data science, software development, and other departments
• Create clear guidelines for AI usage and data governance
• Rollout AI systems gradually to prevent disruptions
• Establish performance metrics to make sure your project is on the right track

4. Building AI features

Developing AI features is another critical skill gap identified by 26% of tech leaders and workers, according to the Multiverse ROI of AI report. This skill requires a basic understanding of machine learning algorithms and data analytics.

Luckily, you don’t have to start from scratch while creating AI features. Tools like PyTorch and TensorFlow offer libraries and extensions that simplify the process of building and training machine learning models. Software developers can use TensorFlow’s LiteRT library to integrate machine learning models into Android and iOS applications.

You don’t need a software engineering background to contribute to these projects. Low-code platforms like Microsoft Power Apps have intuitive, user-friendly tools that anyone can use to build AI features.

5. Ethics and responsible AI usage

The vast majority (93%) of workers surveyed by Multiverse believe they use AI ethically. But AI technology raises many ethical and legal challenges that aren’t always immediately apparent.

A 2024 study by the University of Essex discovered that AI hiring systems can “create algorithmic bias against women” by filtering applications based on gendered language. Along with bias, data privacy is another significant concern. Notably, Google faced a class-action lawsuit for using patient data from the Royal Free NHS Trust without consent to train its AI models.

AI ethics frameworks can help you navigate tricky situations and adhere to data privacy laws. The UK government has developed ten principles to guide the ethical use of generative AI, prioritising accountability, human control, and transparency. Similarly, the European Union created a human-centric framework for ethical AI usage.

Improving AI skills: Challenges and opportunities

Strengthening your AI skills takes effort, time, and the willingness to step outside your comfort zone. Taking advantage of online resources and seeking guidance from mentors will help you navigate the learning curve. Here are three options for levelling up your AI skills.

Completing self-taught courses

Many organisations have developed online courses that let you study artificial intelligence at your own pace. For example, AWS and Coursera offer classes on machine learning, natural language processing, and other AI fundamentals.

These courses are a convenient way to learn foundational AI skills while focusing on the areas most relevant to your professional development. However, they typically don’t offer personalised coaching or opportunities for hands-on practice.

Going back to school full-time

Some upskillers return to university to earn degrees in artificial intelligence, computer science, information technology, and related fields. These programmes have structured curricula and may offer experiential learning opportunities like group projects and internships.

But the cost of going back to school can be high. College students in England pay up to £9,250 annually in tuition, plus living expenses and other fees. And that’s not factoring in the lost wages from time spent studying instead of working, which can add up quickly over the course of a degree.

Learning on the job

Multiverse’s upskilling programmes provide a unique opportunity to learn artificial intelligence skills on the job. We offer 12 to 18-month programmes in AI, data analytics, and other tech disciplines — none of which require you to leave your current role to participate in.

Our AI-Powered Productivity programme empowers you to use generative AI tools to boost efficiency and output. You’ll learn how to integrate Microsoft 365 Copilot and other cutting-edge platforms in your everyday workflows. The course also covers crucial topics like AI ethics, data privacy, and performance metrics.

The AI for Business Value programme focuses on using artificial intelligence to spark innovation and optimise processes across the business. It combines AI fundamentals with business analysis skills, giving you the tools to drive organisational change. Plus, you’ll learn how to communicate the business impact of AI initiatives to non-technical stakeholders.

This modern approach combines the flexibility of online learning with the opportunity to receive personalised feedback from our dedicated instructors.

Unlike university and bootcamp students, you won’t have to pay a hefty tuition bill or reduce your earning potential. Our upskilling programmes are funded entirely by your employer, and you’ll keep earning a salary while you learn.

Stay competitive with in-demand AI skills

The AI revolution is already in full swing, and the job market is evolving at lightning speed as employers scramble to keep up. In this competitive environment, upskilling early can help you gain a head start and seize exciting — and potentially lucrative — career opportunities in data analytics, AI consulting, and other areas.

As Anna Wang, Multiverse Head of AI, observes, “It’s time to get employees up to speed on AI to even the data skills playing field and give individuals the opportunity to accelerate their careers.”

Multiverse’s upskilling programmes are the only way to gain AI mastery while earning a salary. You’ll study critical AI concepts and start applying your skills in the workplace from day one. Explore our AI for Business Value and AI-Powered Productivity programmes for more information, or fill out our quick application to get started today.

Tech company Multiverse, which has recently introduced powerful AI capabilities across its offerings, will launch the AI-Powered Productivity apprenticeship, the UK’s first accredited apprenticeship to fully embed Microsoft 365 Copilot. This program is eligible for public funding via the apprenticeship levy. The skilling of the wider workforce in AI tools is a crucial step to ensuring the productivity benefits are widely felt across the economy.

Research by Multiverse has found that more than half of workers (51%) have received fewer than 5 hours’ training on AI. 63% of tech leaders say the biggest blocker to further AI investment is their teams’ inability to fully use existing AI technology.

The apprenticeship will see learners develop the skills to boost their output at work by using Microsoft 365 Copilot, while understanding the ethical and data protection implications of using AI. It will be delivered using Multiverse’s measured, applied, guided, and equitable approach, which incorporates personalised, on-the-job learning to maximise business impact. The programme is suitable for a wide range of roles and levels of experience.

Launched in 2023, Microsoft 365 Copilot embeds generative AI into Microsoft’s suite of productivity apps – Word, Excel, PowerPoint, Outlook, Teams, OneNote, OneDrive – to unleash creativity, unlock productivity, and uplevel skills.

Microsoft expanded its skilling program Get On – established in 2020 to empower 1.5 million individuals with tech skills by 2025 – with the added aim of equipping 1 million more people with the AI skills ranging from AI fluency to technical and business transformation.

Microsoft UK CEO, Darren Hardman, said: “To fully capitalise on AI's economic potential and drive growth, we must equip people with the necessary knowledge and tools. By investing in AI skilling, we not only enhance our own capabilities but also drive innovation and productivity across the entire economy.

“The AI-powered Productivity apprenticeship from Multiverse is a great example of a programme that places AI and Microsoft 365 Copilot at the heart of building the skills for the future. We are excited to see the impact of this programme on the future workforce.”

The UK’s first edtech unicorn, Multiverse, is a tech company that identifies, closes and prevents skills gaps, through personalised, on-the-job learning, and is one of the world’s largest apprenticeship providers.

AI-Powered Productivity joins a suite of AI apprenticeships launched by Multiverse, including AI for Business Value and Transformative Leadership, targeted at individuals across every age and every stage of a business.

Multiverse has trained people at 1,500 organisations including the NHS, KPMG, and Capita.

Multiverse CEO, Euan Blair, said: “We know that Gen AI will unlock a surge of productivity in UK businesses, but it requires a combination of the right tools and the right skills to be successful.

“That’s why businesses that want to win in the AI age must make a deliberate effort to upskill and reskill workers with what they need to harness this opportunity. We’re taking market-leading tools like Microsoft Copilot and empowering workers to drive real outcomes using them.

“Not only will it enable businesses to get the best out of AI, but it’ll also set individuals up with the skills to drive their careers for years to come.”

Businesses and organisations can enrol their employees onto the programme, where they will cover modules on AI technologies, prompt engineering, data privacy, and tool utilisation. Participants will learn to measure the impact of AI on their roles, advocate for its use in the workplace, and follow ethical practices.

Employers will be able to fund the programme fully from their Apprenticeship Levy, an additional payroll tax, which is ringfenced for apprenticeship training. The Levy is currently set at 0.5% of an employer’s annual pay bill and applicable to employers with an annual pay bill of over £3 million.

Let’s take a closer look at how professionals use maths for data science and how much you’ll need to know to pursue a career in this exciting field.

How do Data Scientists use maths?

A Data Scientist's primary role is to mine, examine, and make sense of data. Maths plays a role in each of these stages.

Data Scientists use mathematical skills to:

• Understand and use machine learning algorithms
• Analyse datasets from various sources
• Identify patterns in data
• Forecast trends and growth

Data Scientists also use mathematical functions to perform data analysis and apply machine learning techniques like clustering, regression, and classification.

Clustering

Clustering is a way to organise data into clusters or groups that share similarities with each other. It involves some calculus and statistics. A clustering algorithm organises data into these groups to identify trends and reveal insights at the surface level.

For example, a company with a large customer base can use clustering to segment customers based on their demographics or areas of interest. When you are promoting products, you can better personalise your marketing messages based on data points like customer location, behaviour, interests, and more.

Regression

Regression analysis is a way to measure how certain factors impact outcomes or objectives. In other words, it shows how one variable impacts another. It uses a combination of algebra and statistics.

Data Scientists use regression to make data-driven predictions and help businesses make better decisions. For example, they can use regression to forecast future sales or to predict if a company should increase the inventory of a product.

Classification

Data classification is the process of labelling or categorising data to easily store, retrieve, and use it to predict future outcomes. In machine learning, classification uses a set of training data to organise data into classes. For instance, an email spam filter uses classification to detect if an email is spam or not.

Foundations of data analysis

All data professionals need a solid grasp of essential mathematical concepts, but that’s only part of the skill set needed to analyse data effectively. The ability to work with diverse types of information and create data visualisations are also crucial for gaining meaningful insights.

Working with different data types

Data Analysts and Data Scientists handle a wide range of data types, including:

• Categorical data: Qualitative information that can be represented by a name or symbol, such as customer demographics and types of products
• Numerical data: Quantitative information, such as conversion rates and sales revenue

You should know how to use Structured Query Language (SQL) to manage categorical and numerical data. This language allows you to query, organise, and filter information in relational databases.

Data visualisation

Data Scientists often transform datasets into accessible graphic representations. These visualisations can reveal previously unnoticed patterns or anomalies in datasets. They also allow data professionals to communicate their findings with non-technical stakeholders.

Platforms like Microsoft BI and Tableau use machine learning models and mathematics to analyse data. They also have intuitive interfaces that allow you to design interactive dashboards and data visualisations. For example, you could use line graphs to represent economic trends over time.

You should also learn how to use data visualisation libraries in Python. Popular frameworks include Gleam, Matplotlib, and Plotly. They have built-in templates and themes that you can use to create polished visualisations quickly.

What types of maths do Data Scientists need to know?

Luckily, you don’t need to be a mathematician or have a Ph.D. in mathematics to be a Data Scientist. Data Scientists use three main types of maths—linear algebra, calculus, and statistics. Probability is another maths data scientists use, but it is sometimes grouped together with statistics.

Linear algebra

Some consider Linear Algebra the mathematics of data and the foundation of machine learning. Data Scientists manipulate and analyse raw data through matrices, rows, and columns of numbers or data points.

Datasets usually take the form of matrices. Data Scientists store and manipulate data inside them and they use linear algebra during the process. For example, linear algebra is a core component of data preprocessing. It’s the process of organising raw data so that it can be read and understood by machines.

At a minimum, Data Scientists should know matrices and vectors and how to apply basic algebra principles to solve data problems.

Calculus

Data Scientists use calculus to analyse rates of change and relationships within datasets. These maths skills help them understand how a change in one variable — such as changing customer preferences — affects another variable, like sales revenue.

Before you begin your data science journey, you should master the two main branches of calculus: differential and integral.

Differential calculus

Differential calculus studies how quickly quantities change. Data Scientists should learn its foundational concepts, including limits and derivatives. Python libraries like NumPy and SymPy can speed up this learning process by performing complex calculations efficiently.

Data professionals apply differential calculus to optimise machine learning models and functions. For instance, gradient descent calculates the error between the predicted and actual results. This method allows neural networks and other types of algorithms to adjust their parameters iteratively, reducing errors and improving performance.

Integral calculus

Integral calculus analyses the accumulation of quantities over a specific integral. To effectively apply this technique, you must understand definite and indefinite integrals. Familiarity with Python libraries like SciPy can also help you calculate integrals.

Data professionals use this branch of mathematics to solve many problems in data science, such as forecasting the demand for a product and analysing revenue. Machine learning algorithms also use integral calculus to calculate probability and variance.

Probability and statistics

Probability and statistics go hand in hand. Data professionals use these mathematical foundations to analyse information and forecast events.

Statistics is the branch of mathematics that collects and analyses large data sets to extract meaningful insights from them. Data Scientists use statistics to:

• Collect, review, analyse, and form insights from data
• Identify and translate data patterns into actionable business insights
• Answer questions by creating experiments, analysing and interpreting datasets
• Understand machine learning and predictive models

Here are a few examples of statistics principles you’ll need to know to break into the data science field:

• Descriptive statistics - Analyses a dataset to summarise its main characteristics, like mean and mode
• Inferential statistics - Extrapolates from known data to make predictions or generalisations about a larger population
• Linear regression - Predicts the relationship between an dependent variable and two or more independent variables
• Statistical experiments - Know how to create statistical hypotheses, do A/B testing and other experiments, and form conclusions

In contrast, probability is the likelihood that an event will occur. Data professionals use this method to analyse risk, forecast trends, and predict the outcomes of business decisions.

Data Scientists need to know these basics of probability:

• Distributions - Summarizes all the possible values in a dataset and the frequency with which they occur
• Statistical significance - Measures the likelihood that a relationship or result isn’t random
• Bayes' Theorem - A mathematical formula used to calculate the likelihood of an event based on prior knowledge and the probabilities of related events
• Hypothesis testing - Determines whether your assumptions about a particular population or dataset are supported by evidence
• Probability theory - Calculates the likelihood of different outcomes of random events or uncertain situations

Keep in mind that how much maths you need to know may also depend on your role. For example, a junior Data Analyst focuses more on analysing trends. Although they still need to know how to extract data and interpret information, they work less with complex mathematical concepts. Unless they need to work with machine learning algorithms, they’ll use maths for data science less than a senior-level Data Scientist.

This is more of an introduction than an exhaustive list of how much maths is involved in data science. If you are interested in learning data science and the maths that Data Scientists use, Multiverse offers a Data Fellowship and a Data & Insights for Business Decisions program.

Data Scientists roles: salary, job titles, and more

Modern businesses generate and collect enormous amounts of data, such as financial transactions, healthcare records, and social media posts. They need workers with hard data skills to analyse this information effectively and support data-driven decision-making.

In the UK, the surging demand for data professionals has far outpaced the available workforce. A study commissioned by the Department for Digital, Culture, Media and Sport found that UK businesses are seeking to fill 178,000 to 234,000 roles requiring hard data skills. However, 46% of the surveyed companies reported difficulty finding qualified candidates within the last two years.

This talent shortage has led many UK businesses to offer competitive salaries and other perks. According to Indeed, the average salary for Data Scientists in the UK is £51,000. To attract candidates with specialised data skills, employers may also offer hybrid or remote arrangements, generous leave policies, and additional benefits.

Professionals often begin their careers as junior Data Scientists or Analysts, but this field has many opportunities for advancement. Here are three job titles you could pursue as you gain experience:

Senior Data Scientist

A Senior Data Scientist leads long-term projects and supervises Junior Data Scientists. They also communicate findings to stakeholders and guide data-driven decision-making. For instance, a Senior Data Scientist might use machine learning algorithms to detect fraud and help business leaders develop new cybersecurity policies.

Salary:

• Starts at - £61,000
• Average base salary - £73,000
• Top earners make up to - £87,000

Source: Glassdoor

Machine Learning Engineer

A Machine Learning Engineer builds, deploys, and maintains machine learning applications. They use maths and data science to design and train machine learning models.

Salary:

• Starts at - £41,000
• Average base salary - £55,000
• Top earners make up to - £73,000

Source: Glassdoor

Data Architect

A Data Architect designs and maintains data structures, databases, and data pipelines. They’re responsible for integrating data from different sources so data flows smoothly throughout their organisation.

Salary:

• Starts at - £55,000
• Average base salary - £68,000
• Top earners make up to - £85,000

Source: Glassdoor

Boost your skills with comprehensive Data Scientist training

A strong understanding of maths is essential for machine learning and data science roles. It can help you solve problems, optimise model performance, and interpret complex data that answer business questions.

You don’t need to know how to solve every algebraic equation — Data Scientists use computers for that. However, you should become familiar with the principles of linear algebra, calculus, statistics, and probability. You don’t need to be an expert mathematician, but you should broadly enjoy maths and analysing numbers to pursue a data science career.

Multiverse’s Data Fellowship and Data & Insights for Business Decisions programs can help you learn the basic maths concepts you need to know. However, the focus is on how to apply those maths skills in data science.

The Data Fellowship guides you through the fundamental principles of data analysis, including identifying and solving real world problems with data. Our modules cover advanced analytics and statistical methods, data visualisation, data management, and other critical topics. You’ll sharpen your skills by participating in data analysis and statistics hackathons.

The Data & Insights for Business Decisions program teaches you how to transform raw data into meaningful insights. You’ll learn how to use popular data analytics tools — including Excel and PowerBI — to clean and manipulate data. The program also teaches you how to tell compelling stories with data and foster a data-driven culture in your organisation.

Upskillers don’t pay for tuition — programs are free. You actually get paid to work in a data role and learn while you complete the program. You’ll also start immediately applying your new skills by working on real projects for your employer, accelerating the learning process.

The first step is to apply here. If accepted, you’ll start learning data science and get on-the-job training at a company that pays you for your time.

The short answer: It depends.

High-growth industries, like tech, may pay over £25,000 for entry-level apprenticeship roles. Apprenticeship opportunities in other industries might pay less. Your apprenticeship wage also depends on what company you work for and the level of your position.

To help you understand how much you could earn as an apprentice, we'll guide you through the following:

• What’s the apprenticeship minimum wage?
• Can apprentices earn more than the minimum wage?
• Multiverse apprenticeship wages
• How much money will you make as an apprentice?
• Apprentice working hours
• What else are apprentices entitled to?
• Is an apprenticeship right for you?
• What happens when I finish my apprenticeship?
• How to start your apprenticeship journey

What’s the apprenticeship minimum wage?

The apprenticeship minimum wage is the basic hourly amount employers must pay apprentices. The minimum pay depends on your age and how long you’ve been an apprentice.

For example, in 2024, the apprentice wage for those aged under 18 is £6.40 per hour. But if you’re 19 or over and have completed your first year, you’re entitled to the National Minimum Wage for your age group.

Here’s a breakdown of the minimum hourly wage for apprentices depending on your age and year of study. Note: these figures represent the minimum hourly wage for 2024. The 2025 minimum wages, which go into effect in April 2025, are also listed.

In the UK, the National Minimum Wage is updated each April. Apprentices aged 21 and over who’ve completed the first year of their apprenticeship are eligible for the National Living Wage.

Can apprentices earn more than the minimum wage?

The National Minimum Wage for apprentices is the minimum your employer must pay you. Many organisations (including Multiverse) pay you much more than the National Minimum Wage rate to complete your apprenticeship. For instance, if you’re entering a high-growth and in-demand field like tech, wages tend to be higher than the minimum.

Multiverse apprenticeship wages

At Multiverse, the companies we work with pay a minimum of £18,000 a year. But you’ll find roles on our platform that pay £25,000 or more per annum (per year). We focus on the skills of the future, offering high-quality apprenticeship opportunities across key sectors like Business, Digital and Tech.

Multiverse programmes include:

• Software Engineering (Level 4)
• Advanced Data Fellowship Whole (Level 4-6)
• Digital Business Accelerator (Level 3)
• Business Transformation Fellowship (Level 4)

How much money will you make as an apprentice?

Let’s break it down. Your apprenticeship salary is the amount an employer pays you yearly before income tax and other deductions like National Insurance. How much income tax you pay depends on which tax band you’re in, and your total earnings determine your tax band.

You’re likely in the basic band if you’re working in an entry-level role. In the basic band, you’re taxed on income between £12,571 to £50,270. You don’t pay tax for income below £12,570 (your tax-free Personal Allowance limit). The UK Government taxes earnings in this threshold at 20%.

You’re in the next tax band (the higher rate) if you earn above the basic rate threshold. In the higher rate tax band, you’ll be taxed 40% for income between £50,271 and £125,140. An additional rate of 45% applies to incomes over £125,140.

Now for the maths. Let’s say your salary is £20,000 per annum (per year), and you’re doing an apprenticeship lasting 15 months. Yearly you’ll take home around £17,624 after tax and National Insurance. Monthly you’ll take home around £1,468. Throughout your entire apprenticeship, you’ll earn £22,020.

Apprentice working hours

To complete your apprenticeship full-time, you are typically expected to work at least 30 hours per week. However, if you have specific circumstances (for example, if you’re a carer for a family member), you may be able to work part-time. For part-time apprentices, such as those working 16 hours per week, the apprenticeship duration will be extended to ensure adequate training time.

Your employer must follow employment regulations regarding your working hours:

• Under 18: As a young worker, you can legally work a maximum of 8 hours per day and 40 hours per week.
• 18 and Over: For adults, the standard working limit is an average of 48 hours per week, though you may choose to opt out if desired.

In addition to your set working hours, apprenticeships require that you dedicate at least 20% of your working time to training or studying for your qualification. In a Multiverse programme, you’ll typically spend at least one day a week studying toward your apprenticeship qualification.

What else are apprentices entitled to?

Whether you complete your apprenticeship full-time or part-time, your employer will pay you for working and training hours. Aside from being paid to complete your apprenticeship, you’re legally entitled to employee benefits like holidays, sick pay and rest breaks.

Training

As an apprentice, you’ll be paid for your time at work. You’re also paid for the time you’re in coaching sessions and bootcamps with industry experts (off the job training). You’ll spend 80% of your time working for your employer and  20% of your time doing off the job training. You’ll also be paid for time working towards English and Maths qualifications if they’re part of your apprenticeship.

Holidays

As a full-time apprentice, you’re entitled to a minimum amount of paid holiday. For each year of your qualification, you’ll get at least 20 days of holiday pay plus bank holidays. Many employers provide apprentices well above the minimum paid holiday and offer company-wide shutdowns once a year.

Sick pay

If you’re too ill to work, sick pay offers peace of mind. You’re entitled to Statutory Sick Pay (SSP) as an apprentice. The minimum amount is £116.75 a week for 28 weeks. Some companies offer sick pay schemes that pay more than the basic weekly amount. For example, an employer might offer up to two weeks of paid sick leave at your usual weekly rate.

Rest breaks

You’re legally entitled to rest breaks at work like any other employee. If you’re under 18 and your working day is longer than 4.5 hours, your employer must give you a 30-minute break. If you’re 18 or over, you’ll get a 20-minute break if you work more than six hours daily. As with holiday and sick pay, many companies will offer apprentices above the minimum amount. For instance, you might get up to an hour for lunch and shorter breaks throughout the day.

Is a Multiverse apprenticeship right for you?

There’s never been a better time to start your apprenticeship journey. A Multiverse apprenticeship enables you to learn the skills you need to level up your career without taking time away from your current role. To top it off, you’ll continue to be paid for the time you spend learning on the job. All training is paid for by your employer once they partner with Multiverse.

Train in high-growth industries

Apprentices are in demand across the board, especially in high-growth sectors. Let’s take the tech industry as an example. In 2024, there were 122 “unicorn” startups — startups with a valuation of $1 billion (roughly £770 million) — in the UK alone. The UK tech sector is still growing in 2024, and companies need new, diverse talent. AI is also driving high levels of investment by tech companies. According to Multiverse data, 81% of tech leaders plan to increase investments in AI — including on human capital — over the next three years.

Get paid to learn

All apprentices get paid to work and learn. Some industries pay more than others. Companies with a skills gap will happily pay you to complete your apprenticeship and gain industry expertise in your field.

What happens when I finish my apprenticeship?

After you finish your apprenticeship, you may be in a position to grow your career through a new role or promotion. Promotions usually come with a pay rise as compensation for your increased experience and responsibilities. Having a new qualification will help you now and for the rest of your career.

Start your apprenticeship journey

If you're looking to gain new and exciting skills on the way to future-proofing your career in the dynamic tech industry, apply for a Multiverse programme in minutes today.

Without it, a lack of clear vision, skills, and data literacy will hold back growth – with companies unable to turn an exponential explosion of data into a competitive advantage.

By 2030, GDP could increase by as much as 26% from AI productivity gains, according to PWC. This expansion will only come if workers have the skills to input clean data into AI models.

It means companies with a strong data culture will have the upper hand as AI adoption takes hold.

In this article, we’ll explore what a data culture is and the practical steps for building one from our data experts.

What is a data culture?

Data culture is where data is deeply integrated into all aspects of an organisation’s operations and decision-making, with every individual fluent in what data means for their role.

The ingredients of a strong data culture include:

• Data-driven decision-making: A commitment to make decisions based on data rather than intuition or guesswork.
• Widespread data literacy: Any employee, at any level, can read, understand, create, and communicate data.
• Data governance and trust: Solid data governance frameworks ensure data quality, security, and compliance – creating trust for anyone using it.
• Data accessibility: There’s transparency in how data is collected, processed, and used. There are no silos, with data readily accessible to anyone.
• Data is seen as a strategic asset: There’s a clear understanding of how data contributes to success and competitive advantage.

In a strong data culture, the average employee lives and breathes data within their day-to-day tasks. Managers use data to inform decisions. And senior leaders underpin the wider business strategy with data.

Why build a data culture? Benefits and examples

Nearly nine out of ten (88%) business transformation initiatives fail to achieve their original goal, according to Bain & Company. For many companies, this is because they lose focus on maintaining and developing their new capabilities.

A data culture overcomes this, with teams ready to take on new tools and change their ways of working. Benefits include:

• Greater productivity and operational efficiency: Employees can easily process and visualise data, saving time on every data task. Examples include using predictive analytics to optimise the supply chain or improving inventory management by accurately forecasting demand.
• No more ring-fenced data teams: With widespread data literacy, employees can self-serve insights, reducing the load on internal data specialists – who in turn gain time back to focus on more complex initiatives in the overall data strategy.
• New opportunities: When encouraged to work with a data-first mindset, employees can accelerate the speed of projects and uncover new revenue stream opportunities through advanced insights – bringing new ideas and products to market faster.

Five steps to build a strong data culture

Once you’ve identified your current state, be bold in your ambition. A strong data culture is not the destination, it’s a journey. Here’s how to bring everyone along the way:

1. Align a data culture with your business goals

Start with a clear rationale for your data culture. Assess the internal data capabilities and employee skills you would need to establish one. Set out the benefits for the business as a whole, as well as the benefits for individual functions and role types.

2. Spot skills gaps and spread data learning across all levels

Assess your training needs by identifying data skills gaps. A skills matrix is a simple framework to map out your state of play, helping you target learning opportunities for all employees at any seniority level. Building data capabilities at all levels of the org chart means everyone takes a stake in supporting culture change, rather than creating silos.

3. Help employees understand the value of data

When employees see the value data can create, more will look at how their data skills can be applied to improve their roles. When a data culture takes hold, this mindset supports data-driven decision-making. Managers and leaders will act on real insights rather than hearsay, making decisions more targeted and impactful.

4. Create space to share ideas and best practices

Cross-functional data projects and creating Centres of Excellence (COEs) can help to build good data practices across the workforce. By offering opportunities for teams to collaborate with data, knowledge sharing and data-driven efficiencies break out of silos.

5. Measure the impact of your data culture

Transparency and reporting back progress to the whole business creates a feedback loop grounded in data, showing success and keeping everyone bought in. One example is CBRE, which measured the time saved on run-rate processes and calculated the overall time and financial savings for the business.

Discover how CBRE built data skills across every level of the business to create a self-sustaining data culture.

The need for upskilling in a strong data culture

Across the workforce, data skills are in high demand and short supply. According to our Skills Intelligence Report, 25 days of productive time are lost to data skills gaps annually. More than half (57%) of workers have no – or just basic – Excel skills. Some 86% have no Python skills.

Upskilling is one way to bridge this gap: by expanding skills and knowledge to better meet the demands of evolving job roles. It’s a route that helps your existing workforce make the most of vast internal and external datasets, readying them for the rise of artificial intelligence.

When coupled with continuous learning, these training tactics can help employees make the most of data, supporting a strong data culture.

Start shaping your workplace data culture with Multiverse

Need more advice on building a thriving data culture?

Multiverse can help. Learn more about our range of employee training and data upskilling programmes.

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

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

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

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

1. Overestimating AI maturity

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

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

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

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

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

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

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

2. Difficulty securing investment and demonstrating ROI

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

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

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

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

3. Employees still lack access to formal AI training

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

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

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

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

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

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

Learn more about your AI maturity

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

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

Tell us about your recent career change at Multiverse.

I joined Multiverse in May 2022 as a Coach in what was then the Data Literacy Programme (DLP). During our first DLP cohort, this programme went from strength to strength, developing into the Data and Insights for Business Decisions (DIBD) programme we know today!

I was invited to apply for an Operations Executive role pilot because of some analysis work I’d done alongside my coaching role. It was a brilliant opportunity to get stuck in with the first iterations of projects to support coaches with our new approach to organising and maintaining high-quality service during our programmes, which are now rolling out across the business.

How are you enjoying your new team and remit?

I've been in the role for six months and I'm loving it! It's great to be able to have an impact on the success of thousands of apprentices, rather than one cohort. I've also gone from quite a structured role as a coach, with defined apprentice touch points, to a role where no two days or weeks are the same. I've found this really refreshing and energising, working with so many more great colleagues across the organisation.

Any career change comes with its challenges, what did you have to overcome and how did you approach this?

One of the best things about working at Multiverse is the wealth of experience you're surrounded by all the time, and how willing people are to share it with you. I have had to adapt to thinking on a larger scale - instead of imagining solutions to problems my cohort of apprentices were facing, which I would devise and implement entirely on my own, I now work on fixing problems for thousands of apprentices.

I have to think through how effectively solutions can be adopted by coaches with competing demands on their time and attention. I've borrowed an old Nike slogan - 'Just do it!' - and lean heavily on colleagues who have expertise and skills that I'm still developing.

If you could share one piece of advice for someone looking to develop their career or move internally, what would it be?

Volunteer for things! I only got the Operations Exec Pilot opportunity because I asked my Delivery Lead if there was anything else I could be doing to develop, so make sure you put yourself out there and say yes!

Alex’s journey from Data Coach to Operations Executive showcases our Career Mobility approach at Multiverse and highlights the importance of keeping an open mind and jumping at opportunities when wanting to make a career change. Want to join a company where career mobility is a priority? We’re hiring.

Artificial intelligence and machine learning are distinct but related concepts. AI refers to advanced software that imitates how humans process and analyse information. Machine learning is a subtype of AI that uses algorithms–or sets of rules–to perform specific tasks.

These technologies have many innovative uses in finance, healthcare, logistics, and other industries. But the number of people with artificial intelligence and machine learning skills has not kept up with soaring demand. A 2024 Red Hat survey found that 81% of UK Information Technology Managers see a critical AI skills gap, with 40% citing talent shortages as their main obstacle preventing their organisations from using AI to its full potential.

Expanding your AI and machine learning skills can help you keep up with the evolving tech landscape. We examine the differences between these technologies, applications in the workforce, and more.

What’s the difference between AI and Machine Learning?

Artificial intelligence is a broad term for software that mimics how humans perform complex mental processes. This technology analyses information, learns from its experiences, and solves problems.

Machine learning is one of the most popular branches of AI. This approach uses algorithms — or instructions — to guide decision-making and execute tasks. All machine learning is AI, but not all AI programmes use machine learning.

Businesses often use artificial intelligence without machine learning for repetitive or straightforward tasks. Some people call these applications “Good Old-Fashioned Artificial Intelligence” (GOFAI) because they don’t learn from data like machine learning algorithms. For example, GOFAI chatbots use rule-based systems to respond to customer inquiries. These chatbots provide pre-scripted answers but can’t learn from previous interactions or adapt to different contexts.

However, some organisations create hybrid systems by combining machine learning with symbolic — or rules-based — AI. These models rely on machine learning algorithms to process data, but they also use symbolic reasoning techniques to interpret information based on predefined knowledge.

This dual approach allows hybrid AI systems to mimic human reasoning and solve more complex problems. For example, Google DeepMind has developed geometry-solving software that blends neural networks with a symbolic AI engine. The neural networks use their “intuition” to guess the best way to solve a geometry problem, while the symbolic AI engine generates solutions based on this reasoning.

What is AI?

Artificial intelligence refers to machines and software that imitate human cognitive functions. This technology performs advanced processes that traditionally relied on human intelligence. For example, AI software can identify patterns in large datasets, recognize faces in photographs, and give personalised recommendations.

Businesses use advanced computer systems and infrastructure to build AI applications. On the hardware front, Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) enable machine learning models to process large amounts of data efficiently. GPUs have powerful parallel processing capabilities, perfect for analysing images and videos. By contrast, TPUs perform complex computations at lightning speed, allowing neural networks to learn faster.

Additionally, many organisations use cloud computing to optimise their AI software. Cloud platforms like Google Cloud enable businesses to draw on remote training data for machine learning models without investing in expensive infrastructure. Users can also access additional resources — such as cloud storage solutions and analytics tools — to improve their AI operations. This flexibility lets businesses scale their AI applications up or down as needed, boosting performance and reducing costs.

Popular AI business applications

One of the most popular types of AI are large language models (LLMS). Engineers use vast quantities of human-generated content to train OpenAI’s ChatGPT and other LLMs. The models learn context and language from the data and use this knowledge to respond to human input.

Engineers also use AI to create robots that respond to their environments and perform intricate tasks. For instance, AI-powered vacuum cleaners avoid obstacles in their paths, while AI surgical robots assist surgeons with operations.

Additionally, AI enables researchers to develop autonomous systems that operate without human guidance. Autonomous drones and vehicles use algorithms and sensor technologies to make real-time decisions and navigate their environment.

Smart assistants have also gained widespread popularity. Applications like Siri and Google Assistant use natural language processing to interpret and respond to human input. Users can ask these assistants to perform many functions, such as adding tasks to their calendars, controlling smart devices, and setting timers.

Finally, retailers and streaming services often use AI-powered recommendation engines to personalise the customer experience. For example, Amazon uses machine learning algorithms to analyse customers’ browsing behaviour and suggest relevant products. The retailer also uses an LLM to tailor product descriptions for individual consumers.

What is Machine Learning?

Machine learning is a subset of AI that uses algorithms to create intelligent systems that learn from datasets. The algorithms detect patterns in data, make predictions based on historical trends, and complete tasks. They refine their performance over time as they receive more data, so humans don’t need to tweak the programming.

There are three main types of machine learning with different applications:

Supervised learning

An algorithm processes datasets with historical inputs and outputs and identifies their relationships. The software generalises and extrapolates this knowledge to predict future outputs. Organisations use supervised learning to teach algorithms to classify items, detect abnormal data points, and forecast future trends.

Unsupervised learning

The algorithm looks for connections and patterns between unlabeled data points and generates insights into the dataset’s structure. For instance, an algorithm could analyse website traffic and sort customers into groups based on browsing behaviour.

Reinforcement learning

The algorithm gains positive or negative reinforcement from its environment and adjusts its behaviour accordingly. AI-powered robots and self-driving cars use reinforcement learning to learn new tasks and optimise performance. The streaming service Spotify also uses reinforcement learning to provide increasingly accurate personalised recommendations.

The role of Deep Learning in AI and Machine Learning

Deep learning is a specialised field within machine learning that uses many layers of neural networks for sophisticated pattern recognition and problem solving. It’s designed to mimic how the human brain processes information, learns from experience, and applies reasoning.

Traditional machine learning algorithms rely heavily on human intervention to learn and often struggle to process unstructured data efficiently. In contrast, deep learning models can interpret many data types and automatically improve their performance with minimal human input.

Deep learning has a wide range of applications across various industries, including automotive, aerospace, healthcare, and security. For example, autonomous vehicles use deep learning to automatically detect and avoid obstacles in the road. Similarly, computer vision programmes use deep learning to recognize faces and classify images.

Machine Learning and AI: How do businesses use them?

Organisations in all industries leverage AI and machine learning to improve their operations. These technologies complete repetitive tasks faster and more accurately than humans, enhancing productivity. Businesses also use AI and machine learning to drive innovation and develop more efficient processes.

To develop and use AI applications effectively, professionals need diverse tech skills. For example, Marketers should be proficient in prompt engineering to effectively use generative AI tools to develop personalised marketing campaigns. Meanwhile, strong data analysis skills enable Supply Chain Managers to use AI for demand forecasting and inventory optimisation.

Here are five use cases for AI and machine learning in different sectors.

Autonomous threat detection

Cybersecurity professionals use AI and machine learning to detect cyber threats more efficiently. This technology autonomously monitors computer networks and systems for abnormal behaviour and data points. Algorithms analyse these anomalies to determine if they’re caused by cyber attacks and trigger defence mechanisms.

Autonomous threat detection lets organisations respond more quickly to cybersecurity incidents. For example, Horizon3.ai’s NodeZero Autonomous Security Platform detects attackers and automatically diverts them to decoy systems, preventing them from accessing critical data. The platform also improves and adapts in response to emerging threats so organisations can stay two steps ahead of cybercriminals.

Diagnostic imaging

AI and machine learning have revolutionised medical imaging. Radiologists and other healthcare professionals use this technology to capture and reconstruct diagnostic images. For example, AI software can create synthetic images based on a single image, so patients spend less time in the radiology department.

AI also helps clinicians analyse images for lesions, tumours, brain aneurysms, and other conditions. In some cases, this technology may detect abnormalities missed by human eyes. This increased precision leads to faster and more accurate diagnoses and improves patient outcomes.

Personalised marketing campaigns

Marketers use artificial intelligence and machine learning to create more effective and targeted marketing campaigns. Machine learning algorithms analyse behaviour, demographics, and other data to gain insights into customers’ preferences. Companies use these findings to provide personalised product recommendations and promotions.

For example, Brewdog uses AI software to personalise its email marketing campaigns based on customers’ recent purchases, web activity, and other data. In a recent experiment, the company found that its personalised campaigns generated 13.8% more revenue than non-personalized ones.

Businesses also use AI to automate time-consuming marketing processes. AI-powered chatbots answer questions from prospective customers, while generative AI tools create articles and other marketing content. These innovations let marketers focus on tasks that require a human touch, like nurturing client relationships and developing the perfect brand voice.

Supply chain optimization

Products often travel through convoluted global supply chains before they reach customers. AI helps organisations streamline and optimise these processes so goods reach their destinations as efficiently as possible.

Sophisticated machine learning algorithms analyse historical data and forecast future trends. These models predict changes in customer demand, the availability of raw materials, and other market dynamics. Businesses leverage this data to anticipate supply chain fluctuations and respond proactively. For example, Unilever uses an AI application called Scoutbee to scrape web data to find alternative suppliers if demand for a product spikes or their usual distributors aren’t able to meet inventory needs.

Fraud detection

Any organisation can fall victim to internal and external fraud. AI fraud detection tools use machine learning algorithms to analyse data and identify suspicious or anomalous patterns. These applications also generate detailed reports that help humans investigate potentially fraudulent activity.

For instance, the UK government developed the Single Network Analytics Platform (SNAP) to detect fraud and organised crime. This AI system analyses data from the World Bank and other sources to detect suspicious activity and networks. With this tool, public sector organisations can effectively detect fraudulent claims and safeguard public funds from criminals.

Ethics in AI and Machine Learning: What professionals need to know

According to Multiverse’s ROI of AI report, 93% of professionals are confident that they use artificial intelligence ethically. However, despite this optimism, researchers and tech experts have raised alarms about the ethical dilemmas associated with this technology.

Bias is one of the most significant ethical challenges posed by AI. Models trained on biased datasets can perpetuate racism, sexism, and other forms of discrimination. For instance, an UberEats courier recently won a lawsuit after the company’s “racially discriminatory” facial recognition system barred him from accessing the platform. This case illustrates how AI systems that make automated decisions based on physical appearance can reinforce inequities.

Data privacy is another pressing concern. Many people worry that artificial intelligence tools collect and use their personal data and intellectual property without consent. In 2024, for example, the UK Information Commissioner’s Office revealed that LinkedIn had been training its AI models with user data without explicit consent. In response to these findings, the social media platform agreed to suspend this training until further notice.

Ethical frameworks can guide professionals as they develop and use AI and machine learning tools. For example, the UK government has created a seven-point framework to help civil servants use this technology responsibly. This blueprint promotes data integrity, fairness, transparency, and other key principles.

Demand for AI and Machine Learning jobs

The widespread adoption of AI and machine learning has opened new career opportunities in every industry. The World Economic Forum’s Global Risk Report 2024 predicts that the demand for AI and Machine Learning Specialists will increase by 40% by 2027.

Data science is one of the fastest-growing AI-related professions. Data Scientists use machine learning algorithms to interpret complex datasets and help business leaders make informed decisions.

Data Analysts and scientists rank sixth on the Future of Jobs Report 2023’s list of the fastest-growing occupations between 2023 and 2027. These professionals also command healthy salaries. Glassdoor data indicates Data Scientists in London earn a median salary of £60,000.

Additionally, LinkedIn’s 2024 Jobs on the Rise Report lists Artificial Intelligence Engineer as the tenth-fastest growing career. These experts use programming languages and technical skills to build, train, and maintain AI software. According to Glassdoor, Artificial Intelligence Engineers in London earn an average salary of £64,000.

Multiverse’s upskilling programmes can help you gain the essential skills to thrive in the evolving job market and pursue AI-related roles. Our AI for Business Value programme teaches you how to implement AI solutions to boost operational efficiency and drive organisational change. Similarly, the AI-Powered Productivity programme focuses on AI literacy, empowering you to use AI solutions to improve efficiency.

These programmes are fully funded by your employer and allow you to gain hands-on experience in your current role. You’ll get at least three hours of protected learning time weekly to complete structured training modules and collaborative projects. You’ll also practise applying your new AI and ML skills in the workplace, accelerating your professional development.

Future proof your career with in-demand skills

AI will disrupt approximately 40% of jobs worldwide, according to a 2024 report by the IMF. This statistic may sound alarming, but this technology will likely change most jobs, not eliminate them. Developing AI and machine learning skills will allow you to adapt to the evolving workforce and fill critical skills gaps.

Immerse yourself in the latest AI and machine learning developments with Multiverse’s free bite-sized AI training. These innovative training modules provide fast, actioned-oriented lessons on foundational AI principles, prompt engineering and teach you how to apply AI ethically in your current career.

Ready to become an AI expert? Talk to your employer about our AI for Business Value apprenticeship to start your journey.

Baroness Lane Fox is a serial entrepreneur and tech leader with three decades of experience - guiding multiple companies to public markets. She now serves as President of the British Chambers of Commerce, Chancellor at The Open University, and co-chair of a new government panel tasked with driving improved adoption of technology in the public sector. In addition, she currently serves on the board at Chanel and previously served on Twitter’s board (now X) for almost 7 years until 2022.

The company has also appointed Jillian Gillespie as Chief Financial Officer. Jillian joins from MongoDB, the developer data platform with a market capitalization of $20 billion, where she was Senior Vice President of Finance and Operations. She led the company through major milestones and international expansion over ten years, from its Series F funding round in 2013 through to IPO in 2017, followed by success as a public company.

Multiverse is a tech company that identifies, closes and prevents skills gaps, through on-the-job learning, apprenticeships programmes, and a personalised AI platform. The appointments come off the back of two consecutive record breaking quarters for the company. In October, the company launched an ‘AI-Powered Productivity’ apprenticeship, the first apprenticeship in the country to fully embed training on Microsoft 365 Copilot. AI training programs now make up 22% of Multiverse’s revenue.

Multiverse's research shows 64% of businesses lack confidence in deploying AI and associated technologies – a skills gap that has become more acute with rapid technological advancement. They also support Multiverse's continued expansion in the United States, where 87% of business leaders believe they have skill gaps. The company already partners with more than 1,500 companies across the US and UK.

Euan Blair, founder and CEO of Multiverse: "Multiverse has the capacity to be a generational British tech success story - ensuring people globally can embrace tech with confidence by embedding learning in tech, data, and AI into their daily work. In Martha and Jillian, we're adding two exceptional leaders who understand both the scale of the global skills crisis and how to build and scale transformative solutions. As we expand our footprint with new products and partnerships, their experience in scaling high-growth tech companies will help us seize this moment and reshape how organisations develop talent in the AI era."

Baroness Martha Lane Fox: “The promised gains from technology will never be delivered unless people have the skills to take advantage of them. This is becoming urgent in the boardrooms of every organisation, and Multiverse is perfectly positioned with its model of continuous, applied learning. Across the UK we won’t unlock growth without giving employers access to the skills they need to thrive. The debate about skills reform sorely needs the voice of employers, and I’ll be working to deliver it, alongside companies themselves and learners’’.

Jillian Gillespie, Chief Financial Officer of Multiverse: "I am thrilled to join Multiverse at such a pivotal moment in its journey. What really attracted me is the opportunity to collaborate with such a talented, genuine, and ambitious team in a fast-growing, dynamic, and rewarding business. I firmly believe that applied, on-the-job learning represents the future of workforce development and I look forward to applying my experience to an exciting new challenge.”

Today, the first cohort of learners graduate from tech company Multiverse’s degree apprenticeships. This marks the first time an independent apprenticeship provider has awarded its own undergraduate degrees.

Forming part of Multiverse’s commitment to promoting equitable access to economic opportunity, the graduating cohort from the Advanced Data Fellowship Level 6 programme will be the first of 850 learners on Multiverse’s degree apprenticeship programmes to receive a Multiverse-awarded degree. The National Student Survey from this programme scored higher than all other providers offering the same standard, with an overall satisfaction rating of 89.5%.

With businesses citing data skills gaps as a key barrier to AI success, and half of employees unable to use data to make analysis more efficient or automate processes, degree apprenticeships offer a way for employers to upskill their workforce while solving real business challenges in the face of rapid technological change.

During the programme over half secured promotions, while the whole cohort have benefited from earning a salary and developing valuable real-world experience while they learned. By comparison, 1.8 million people in the UK are currently saddled with more than £50,000 in student debt. Half of Multiverse’s degree apprentices have not previously pursued higher education, and 30% meet one or more markers of socio-economic disadvantage. This demonstrates how apprenticeships can provide a lever for social mobility alongside their outcomes for business value.

Not only have these apprentices boosted their career prospects, they have also driven value for their employers: apprenticeships generate around £28 for every £1 invested. Tangible projects that this cohort of apprentices have completed include building an invoice reading app using the ChatGPT API and developing a dashboard for the new revenue system that reduced the percentage of hotels with a failed stage gate by 16%.

Liam Cottrell, an apprentice at Mars UK, said: “I was Mars’ first digital apprentice and I’ve been amazed at how much I’ve learnt throughout the process. Never thought I’d be able to build my own data pipeline to help with a work project, which I did as part of the data engineering module.”

Euan Blair, founder and CEO of Multiverse, said: “I couldn’t be prouder of the apprentices graduating today. CEOs tell me time and time again that they learnt their most important skills on the job, so giving learners the opportunity to apply practical skills to real-world projects is key. We set out to deliver degree-level apprenticeships at Multiverse not because of any attachment to the concept of a degree, but because we believed these programmes could deliver real world value that accelerates careers, and delivers value to employers. These apprentices have proved that.”

Multiverse has partnered with more than 1,500 companies across the US and UK including Meta, Citigroup, KPMG, Capita, and Just Eat, with 16,000 apprentices now in its community. Multiverse apprentices have tracked more than £2 billion in return on investment.