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According to the 2024 Stack Overflow Developer Survey, 65% of professional developers worldwide used Python for development work in the previous year — making it the third most popular programming language overall, behind JavaScript and SQL.

Professionals at companies of all sizes use Python to manage data and create machine learning algorithms, among other data-oriented efforts. Learning Python can open the door to an in-demand, lucrative career as a Software Engineer or Data Analyst — or even help you streamline and broaden the depth of the work you do in your current career.

Do you want to learn Python? Then this article is for you. Below, we’ll be answering some of the top questions for beginning programmers at any stage of their professional careers, including:

• Is Python hard to learn?
• How long does it take to learn Python?
• Is Python worth learning?
• What can you build with Python?

Let’s dive in.

Is it worth learning Python in 2024?

Dutch programmer Guido van Rossum created Python in 1991. It’s still one of the most widely used programming languages today.

Many developers and tech companies prefer Python because its syntax is easy for humans to read. And they can use it to build scalable applications.

Here are four top reasons for learning Python in 2024.

1. High-demand

Professionals at companies of all industries and sizes — including Netflix, Amazon, and Reddit — use Python. So, it’s in high demand.

Moreover, Python is the most popular programming language among people learning to code. Approximately 62% of developers across all skill levels use Python.

An August 2024 LinkedIn search for jobs specifying Python skills in the UK returned 90,835 results overall.

How many is that? For example, you can compare that figure to the number of results in a search for jobs asking for C# (the ninth most popular coding language in the Stack Overflow survey) skills, which returned less than 10,000.

The bottom line is that among technical competencies, Python is a highly sought after skill.

2. Flexibility

Python’s versatility makes it one of the best programming languages to learn for aspiring tech professionals.

Learning Python doesn’t mean you need to pursue a job as a Python Developer. As for job prospects, Python skills are easily transferable, so you could work in data science, software engineering, marketing, or even artificial intelligence.

3. Lucrative job options

Not only are there many careers that need Python skills, but they also pay well. Mastering Python could help you land a job with a high-paying salary or even advance in your current company to greater responsibilities.

Some Python-adjacent titles in the UK net high median salaries. For example:

• Python Developer: £73,000 (source: devITjobs)
• Machine Learning Engineer: £60,000 (devITjobs)
• Data Engineer: £70,000 (itjobswatch)
• Full Stack Developer: £60,000 (devITjobs)
• Back-End Developer: £67,500 (devITjobs)

4. AI applications

ChatGPT, MidJourney, and other AI applications are booming. The size of the artificial intelligence market is projected to reach a staggering £145 billion in 2024, growing to a market size of close to £600 billion by 2030. To support that growth, companies will need to hire more Python Engineers.

Python powers many machine learning and artificial intelligence technologies. As adoption of AI tools grows among the general population, it stands to reason there will be opportunities at the intersection of Python and artificial intelligence for professionals in an array of fields.

Examples of Python in action

Companies typically use Python to develop back-end services. These services refer to server-side or behind-the-scenes functions, like retrieving information from databases and authenticating login information.

Businesses can also use Python to perform data analytics and learn more about customers.

Here are a few real-life examples of how companies are using Python:

• Netflix: The streaming service uses Python to create machine learning algorithms that personalise the user experience. Specifically, it creates personalised recommendations for shows or movies users should watch. Also, Netflix uses Python to manage its content delivery network, which stores content on servers around the world for streaming.
• Spotify: Spotify gathers and analyses data about users’ music preferences using Python code. Based on this information, it generates personalised recommendations for artists, songs, and people to follow.
• Amazon: The biggest online retailer in the world uses Python ML engine to analyse shopping and search habits to recommend relevant products and serve ads.

How long does it take to learn Python for beginners?

Python is one of the easiest languages to learn. It uses a simple and intuitive syntax — the way you arrange code — that resembles the English language. With regular practice every week, most novice coders can learn Python basics in three to six months.

If you’re new to coding and don’t know other programming languages, becoming proficient in Python will take longer. Mastering it will take years of practice and experience. While this might seem like a long time, Python is still faster to learn than more complex languages like Java or C++.

Is Python hard to learn?

It can seem intimidating to beginners. But is Python hard to learn?

Some languages are more difficult to learn than others. And everyone starts their coding journey in different places. But there are some tips that can make learning Python a little easier.

For starters, you can build your foundational coding knowledge before diving in. Many people find it helpful to start learning simple languages like HTML and CSS, which developers use to structure and style web pages.

How to learn Python

You don’t need a university computer science qualification to become proficient in Python.

Learning Python is similar to learning any other coding language. Start with the fundamentals like the syntax. Then, move on to practising real-life coding projects.

If you’re looking to uplevel your career with Python skills, these tips can make the Python learning process faster and smoother.

1. Build a foundation

Jumping straight into reading and writing complex Python code would be like picking up a novel in a foreign language and immediately understanding it. It’s not likely to happen, and you might feel overwhelmed and give up.

Instead, study the basic syntax — structures and rules — before you start writing code.

Many free online tutorials and videos explain how Python syntax uses structures like parentheses and quotes. This will help you make sense of what code means and how you can write your own.

Once you get the hang of Python syntax, you can expand your knowledge by studying built-in functions, or pieces of reusable code, that perform specific tasks.

2. Practice problem-solving

Many developers are strong problem solvers. Building your problem-solving skills will help you adapt to unique situations that the typical course or tutorial doesn’t cover.

For example, think about the common problems you may encounter at work and how you can use Python to solve them. You may not be able to anticipate every problem you might encounter, but you can build your problem-solving skills.

There are thousands of free practice problems online. For example, websites like Edabit and w3resource offer challenges with varying difficulty levels and provide thorough explanations of solutions.

3. Think about what you want to accomplish

Understanding syntax is the first step to learning Python. But it can be easy to get too caught up in trying to format your code perfectly.

As you work through Python coding problems, you may find it helpful to hand write an outline of what you want each line of code to do without worrying about syntax. This technique is called writing pseudocode, and even experienced Python Developers use it to plan out their programs.

Now, with AI tools, you can even ask an LMM to review your code for you. But you should always double check this work for accuracy.

4. Practice coding in Python consistently

Learning Python requires consistency.

Set aside time daily to practise, and dedicate at least a few days a week to learning Python.

It may be helpful to block off small amounts of time in your schedule. There are many free online Python courses and tools that you can use to practice practise, including Practice Python and HackerRank.

5. Join Python communities

Because Python is one of the most popular coding languages, many programmers have formed in-person and virtual communities dedicated to it. Joining a group can motivate you to keep learning and help you figure out solutions to challenging problems.

Here’s a few examples of helpful Python groups:

• PyLadies
• PySlackers
• r/Python and r/learnpython subreddits
• Real Python

Also, Pycon is an annual conference that brings together coding enthusiasts to discuss Python and its many applications.

6. Gain hands-on coding experience with an apprenticeship

An apprenticeship is an excellent way to learn Python and other programming languages.

Multiverse offers a variety of apprenticeship programmess that can help you upskill or reskill without the need to leave your current role. Some of these, such as the Advanced Data Fellowship, can help you develop your Python coding skills by completing real projects under the mentorship of coding experts.

In the Advanced Data Fellowship, apprentices learn to build intuitive dashboards with business intelligence tools, manipulate data using Python, and apply machine-learning algorithms for deeper insights. They’ll also develop skills in system security, technical requirements gathering, and systems development management.

Ideal for professionals aiming to elevate their organisation’s data-driven strategies and product enhancements, this apprenticeship leads to a Level 4 Data Analyst certification upon completion.

Want to learn more about Multiverse’s Advanced Data Fellowship and other opportunities to advance your career? Fill out our quick application to determine if you’re eligible.

Starting in September, the training will be delivered by Multiverse, a tech company delivering high-quality training through applied learning. Multiverse has trained more than 16,000 apprentices in data and digital skills since 2016.

Programmes will include the 13-month ‘AI for Business Value’ programme which trains apprentices to identify business value gains that can be achieved through using AI, giving apprentices the skills to leverage AI responsibly to drive business outcomes.

The degree-level Advanced Data Fellowship will empower apprentices to become leaders in data analysis and data science. Apprentices will build core capabilities in areas like statistical testing, data ethics, predictive modelling as well as data security - and will graduate with a BSc degree at the end of their programme.

The new Data Academy will train colleagues from a number of Mencap’s business functions, including Finance, IT, People, Quality, CEO Office, Governance, Communications, Advocacy and Activism, and Fundraising.

Jackie O'Sullivan, Executive Director of Strategy at Mencap said: “Investing in data skills isn’t just for big business, it’s pivotal for navigating the dynamic landscape in the Third Sector too. This new Data Academy will harness Multiverse’s expertise in critical areas such as AI and data literacy and develop our team’s skills. This will not only improve our business practices and help drive efficiency at scale, but it also represents a strategic investment in the skills of our colleagues that will support the attraction and retention of skilled and valued colleagues.”

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

Gary Eimerman, Chief Learning Officer at Multiverse said: "The effective use of data and AI has the potential to radically transform organisations. For a charity like Mencap, this could not only increase the number of people, families and carers that they can support through their exceptional services, but it’s also an investment in their employees: enriching the career trajectories of the team at Mencap.”

By building critical digital, data and AI skills in-house, Lewisham and Greenwich NHS Trust aims to reduce time spent on manual processes, enhance data-driven decision making, and ultimately improve patient outcomes.

The Academy will see more than 100 Trust colleagues trained on professional apprenticeships, across a diversity of job roles and functions. Frontline medical and clinical colleagues have enrolled on the programmes, as well as team members working in IT, quality assurance, administration and finance.

Apprenticeships will be delivered by the tech company Multiverse, best-in-class training in data analysis, visualisation and interpretation. Learners will be enrolled on the company’s new ‘AI for Business Value’ programme that will equip learners with the ability to drive improvement through the use of AI.

The training is fully funded by the Apprenticeship Levy.

Meera Nair, Chief People Officer at Lewisham and Greenwich NHS Trust, said: "I am thrilled to announce the launch of our Data & Digital Academy. This initiative empowers our employees with the skills to make better data-driven decisions, positively impacting the patients we serve, saving time in their day, and developing into the practitioners of the future.

“By unlocking the value of data, we aim to improve patient and community outcomes, both directly and indirectly. Our goal is to enable data-driven decision making to improve pathways, drive efficiencies and identify opportunities. We are committed to building a data and digital-first organisation and are thrilled to have Multiverse support us on this journey."

Alice Long, Apprenticeships Lead at the Trust, said: “We are excited to launch the Data and Digital Academy, a transformative partnership with Multiverse, which will help us empower our staff with the data and AI skills to drive cutting-edge healthcare decision making. Our aim is to for our colleagues to have the skills to help them transform the way they work, deliver best in class patient care and foster innovation at the Trust."

Since 2020, Multiverse has partnered with over 60 different NHS bodies, and more than 1000 NHS employees have enrolled on Multiverse programmes. Research by the company has found that UK employees working in healthcare are spending more time on data tasks than any other sector, but 29% of that time is spent unproductively.

Euan Blair, CEO of Multiverse, said: "I could not be prouder of our work with the NHS, where enhanced skills in data and AI have the potential to save lives, and better support patients and communities.

"Lewisham and Greenwich NHS Trust has recognised that emerging tech and data have the potential to help clinicians treat more patients, more reliably - improving outcomes and helping all of us lead healthier lives. The Data and Digital Academy will drive skills that will serve both their colleagues and patients for years to come."

The programmes aim to equip team members from various business functions with advanced, industry-relevant data capabilities.

Programmes will be delivered by the tech company Multiverse and include the Advanced-Data Fellowship. In this degree-level program, participants will develop skills in areas like statistical testing, data ethics, predictive modelling, and data security.

Staff are enrolled on the 15-month Data Fellowship which focuses on comprehensive training in data analysis, where they will master data wrangling and analysis techniques.

The Data & Insights For Business Decisions programme is a 13-month course designed to impart both core technical skills required to transform data into insights and softer skills such as building narratives and presenting findings.

These programmes are launched to improve data-driven decision-making at Hyde and promote efficiency within the business. The programmes will also boost the skills of apprentices who are enrolled.

Multiverse is a tech company delivering high-quality education and training through a unique professional apprenticeship model. It offers apprenticeships targeted in areas including software engineering and data analytics.

Neal Ackcral, Chief Operating Officer at Hyde said: “Using data more effectively will undoubtedly help us improve our service for customers. Understanding their needs and working more efficiently will ultimately help us do more for them. It will also help build a more positive data culture throughout our organisation and support those who wish to enhance their data skills.”

Gary Eimerman, Chief Learning Officer at Multiverse, said: "Our partnership with Hyde is driving data skills transformation throughout their ranks. With this apprenticeship programme, Hyde Housing is not only investing in operational efficiency, they're also enriching the career trajectories of its team members. It's a solid step towards a more data-driven housing industry."

The UK is home to some of the world’s most prestigious universities, leading education, research and innovation on a global stage.

However, Higher Education Institutions (HEIs) are grappling with data skills gaps in the workforce – so much so that qualitative research from WONKHE and Advance HE reported the sector is facing ‘a crisis in data skills.’

This shortage of data skills can undermine student outcomes, hold back progress and result in universities falling short of the expectations of students, funders and even regulatory bodies.

So, what can universities do?

By improving data skills, university leaders can make better data-driven decisions and promote the best outcomes for students and staff.

In this article, learn how universities can close the HEI skills gap and address challenges in higher education with employee upskilling.

Digital transformation presents new challenges and opportunities to universities

Digital transformation has made higher education more reliant on data to optimise processes and inform decision-making.

However, there are data skills shortages in the workforce. And extensive recordkeeping requirements for enrolment, academic histories and research data make it challenging for HEIs to streamline data management. Staff are often left trying to manage complex data environments without the required skill sets.

As a result, the opportunity to make university workforces more productive through digital transformation has not yet been realised. By building workforce data skills, university leaders can improve data quality and how it is used.

Democratising data skills

One of the largest barriers to success is the culture around data skills. Currently, most workplaces view data as the domain of a ring-fenced IT department. It’s not usually a priority to train the wider workforce in data literacy.

However, to run a modern university, data skills are needed across departments.

Upskilling in the workforce spreads skills and increases knowledge sharing across the organisation. By boosting data literacy, capabilities can sit across every function, rather than just a time-poor IT team.

The business case for upskilling university workforces

Data upskilling programmes enable teams to build internal capability without relying solely on expensive hiring drives. This way, HEIs can improve the ways they work not only for enterprise-level ‘big data’ solutions but for everyday activities too.

For example, administrative staff may introduce automation for data processing tasks they would usually perform manually

When staff feel greater ownership over data, they become more interested in finding areas to apply it. Over time, an upskilled university workforce can build better services for students without relying on the IT team as an island of data skills. These are some of the key benefits:

Upskilling creates new opportunities for staff

According to higher education think tank HEPI, members of the GuildHE group saw their student-staff ratio double from 8.3 to 17 between 2014 and 2021. Many HEIs face high employee turnover rates that make it difficult to provide the best quality of service, and at the same time, could damage remaining employees’ work-life balance.

Improving data skills can create new progression paths for employees and increase retention. By building their capabilities, staff become empowered to get the most from their technology, feel more satisfied at work, and are more likely to remain in their jobs.

Upskilling underpins a better student experience

Data skills enable universities to understand student needs and make data-driven decisions to improve experience. Students become more engaged, leading to higher levels of satisfaction – and better National Student Survey (NSS) scores.

As a metric used by many prospective students to decide whether a university is right for them, improving student satisfaction can also improve future enrolment levels.

Upskilling enables optimised resource allocation

As declining international student applications force universities to stretch budgets further, upskilling teams to leverage data-driven insights can be help to improve how existing resources and talent are used. HEIs can also draw on their Apprenticeship Levy funds to pay for the cost of training at no commercial cost to staff or the university. Find out more and read our guide to the Apprenticeship Levy here.

How Goldsmiths University unlocked new benefits from closing skills gaps

Multiverse helps universities build digital skills in the workforce through dedicated upskilling apprenticeship programmes.

Goldsmiths, University of London, partnered with Multiverse and invited staff across all functions to enrol in the Data Academy. Here, they learn skills including analytics, AI and predictive modelling that can assist with their day-to-day tasks.

Throughout the programme, employees at Goldsmiths learned how to:

• Build a data culture – upskilling has empowered individuals across the organisation to become ‘digital professionals’, reducing their reliance on the IT department.
• Create new efficiencies –teams at Goldsmiths have optimised internal processes using skills learned on the programme, streamlining workflows like manual data entry.

David Minahan, Chief Information Officer at Goldsmiths, said:

“Since beginning with the Data Academy, we’ve felt the benefit of improved day-to-day data capabilities across the organisation. Individuals have started thinking in ways they wouldn’t have before, identifying opportunities and working on achieving the outcome themselves.

"For example, I recently spoke to one of my colleagues in the accommodation department, who used to have to transpose data from one spreadsheet to another and into a system on a regular basis. He’s now developed a Python script to automate this.

"Everyone has their own example of a piece of automation that has helped them to streamline their tasks. You’re creating a more efficient organisation, and that starts with individuals.”

Upskilling the workforce at your university

Data skills have transformed how staff at Goldsmiths work, take ownership of data, and provide the best possible experiences for students.

Learn more about how Multiverse can support your university to identify skills gaps and build new opportunities through apprenticeship programmes.

AI is here to stay, and holds real potential for businesses and employees that know how to use it. It’s likely your teams are already using AI to improve workflows or efficiencies, and you might have employees experimenting with AI tools on-the-job.

But the skills needed to leverage the technology to its full potential don’t begin and end with prompt engineering. For AI usage to be effective and strategic there are other skills employees can build – and will likely need very soon for organisations to remain competitive.

That’s because the increasing use of AI in the workplace has arrived hand-in-hand with an ever-pressing skills gap. In fact, nearly half (45%) of leaders name AI as their most significant skills shortage, according to our Preparing for the AI Revolution report – one that must be addressed if businesses are to get the most value from the technology.

Here are four things your team needs to understand to drive real value with AI, this year and beyond:

How to collect, clean, and prepare data

AI has exciting potential applications for data analysis. It can help businesses quickly transform large datasets into actionable insights and predictions, and increase the speed and accuracy of data-driven decision-making. But the ability to drive real value comes from the state of your data.

Before everything else, data must be collated, cleaned and prepared for analysis. Only then can it support AI use cases effectively and deliver the outcomes businesses want and need.

For this to be possible, upskilling your teams in core data skills is vital. Whether that’s understanding the data lifecycle in relation to AI or being able to evaluate your organisation’s data infrastructure effectively, it’s about making sure your teams are skilled in the fundamentals of AI and data.

If your employees are struggling to get to grips with the data that underpins your AI tools, check out our beginner’s guide to data analysis methods here.

How to experiment with AI safely and ethically

As businesses implement new AI-enabled solutions, processes and policies need to evolve, to help employees navigate the application of these emerging technologies.

Setting out clear guidance is the first step towards helping employees understand the organisation's stance on AI and any approved tools they can use to experiment.

Adhering to this guidance is fundamental. But, employees today must also know how to identify ethical risks and considerations in AI applications themselves. That means having the skills to mitigate biases and risks associated with AI.

Upskilling employees to be able to experiment with AI safely and ethically will help to arm your organisation with technically minded individuals. Ones that can take advantage of opportunities while also implementing fair, transparent and accountable practices in AI algorithms and decision-making.

How to identify opportunities for AI

There’s a lot of potential for skilled employees to deeply understand business needs. From aligning AI solutions directly to business problems, to optimising processes and implementing AI projects that deliver on tangible business impact – and ROI.

But the fundamentals of business analysis with AI require employees to apply different techniques, approaches and understandings to a variety of scenarios. For example, being able to conduct comprehensive analyses of internal and external business environments to gain strategic insights. Or the ability to surface business pains and gather input on potential solutions from key stakeholders.

Being able to spot opportunities for AI means having employees in-house who are skilled in not only evaluating the state of your business, but also in understanding its needs.

How to manage and communicate organisational change around AI

Whether it’s the ability to identify opportunities or implement new solutions, extending AI use throughout your organisation is vital for driving future progress.

But managing change through AI initiatives isn’t simple.

Communicating critical information about AI projects to technical and non-technical stakeholders is an ongoing challenge for those using data and AI in their everyday roles. But it’s inherently important to the overall success of AI usage in your organisation.

For instance, being able to effectively describe the process for taking an AI project from idea to implementation is vital to secure buy-in from key stakeholders across the business. Or the ability to identify different models for ways of working on AI projects that enable teams to collaborate more efficiently.

While communication is typically considered a ‘soft skill,’ it’s fundamental to delivering ROI and measuring the business impact of AI initiatives effectively.

If you’re looking to upskill your employees with soft skills training, check out our article here.

Drive business value with AI

Check out our AI for Business Value programme and help your team leverage AI responsibly to drive business outcomes.

Tell us about your recent career change at Multiverse.

I recently moved across from the Delivery side of the business to our Sales team. When I joined Multiverse three years ago, it was as a Data Coach delivering our apprenticeship programmes, and after 12 months I was promoted to Technical Lead. Coach roles are a fantastic opportunity to interact with our product, understand our learners and customers, and be at the frontline of making our mission come to life. It was in my Technical Lead role that I started collaborating with the Sales team, working alongside them to develop our programmes and ensure they continued to meet customer and learner needs, as well as providing them with the right resources to communicate the value of Multiverse to potential customers.

I felt I was at the point in my career where I could take a risk and make a change and ultimately, I was lucky to have the opportunity to move internally at Multiverse. Throughout my onboarding into the Sales team, I was enabled to learn our Sales process and playbook That was about 15 months ago, and it’s been an amazing rollercoaster journey since then - I am now an Account Executive (AE) sitting within the mid-market region, and carrying my own quota!

How are you enjoying your new team and remit?

It’s been brilliant for my self-development; I’ve never learned so many things so quickly. Sales is exactly the change I was looking for - the role is demanding and you need to build new skills and resilience to achieve success. While part of a team, as an AE you’re very independent, so you get out what you put in. I’ve had to adapt to a new environment, and I’ve been taught a lot of lessons about setting yourself up for success; it’s all about laying the right foundations, having patience, and setting a strategy with a result in mind. I have brilliant colleagues and regional directors who push me to be better and to learn, its been a really exciting change of perspective.

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

I think the main challenge, which I’ve already alluded to, has been throwing myself into a high-intensity, successful, and demanding sales environment with no prior sales experience. It has been a huge change, from understanding losses are part of the role and building my resilience to deal with rejection, to finding my own sales process, to keeping on top of the strategic pivots we have made as a business and GTM team. Here, you transition fairly quickly into carrying a quota - which is exciting, because you are trusted to just get into the work, but also intimidating at first. I am grateful to have had a lot of support in my new role, from our Marketing teams who make great materials and case studies, to our Operations teams, our Enablement teams for coaching, and from my colleagues and leaders in Sales.

Besides this, I have also focused on improving my organisation skills to ensure I operate a more proactive mindset. It’s easy to focus on the ‘now’ and be reactive, but long-term thinking is crucial to ensuring personal success and ultimately Multiverse achieving its revenue targets.

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

I would sum it up as ‘exposure’. You support your own development, both in your current and future roles, by gaining exposure across the business. Widening your understanding helps you have an idea of what you might want to do in the future, as well as how to improve in your current role. Make the most of your internal network by talking to as many people as possible; in my experience, everyone at Multiverse is happy to chat and share their insights, so don’t be afraid to ask for someone’s time!

If you can, get involved in cross-functional initiatives and projects - working cross-team is a great way to gain real insight into other business areas and what the roles actually involve. I was lucky to have a brilliant manager in Delivery, Lily, who was amazing at aiding my development and finding opportunities for me to gain exposure. She was also supportive and understanding when I expressed my desire for change, and we’ve kept in touch since. Talk to your manager or team lead, or seek out a mentor internally - having a sounding board can be really helpful in supporting you to take action from your learnings, and their network can help you identify those projects. The dream role doesn’t always appear overnight, but seeking exposure and learning opportunities will help you find it.

Freddy’s journey from Coach to AE showcases our Career Mobility approach at Multiverse and highlights the importance of the right mentality when wanting to execute a career change. Want to join a company where career mobility is a priority? We’re hiring.

Hosted at Multiverse HQ in Paddington, the sold-out event drew a large and diverse crowd, predominantly SaaS Customer Success professionals keen on gaining insights and networking opportunities.

Our panel consisted of:
Eliza Cheyney - Manager, EMEA Commercial Customer Success, Salesloft
Sarah Patel - Head of Customer Success, BlueOptima
Amy Newbury - Head of Customer Success, Kleene
Nick Cornforth - Manager, Customer Success, Adobe

Moderating the panel was Theo Vadgama, Regional Director of Enterprise Customer Success at Multiverse, who also shared Multiverse’s perspective on the evolving role of Customer Success.

The event was kicked off by Jimmy Lee, Multiverse’s SVP of Revenue Operations. Jimmy has founded, scaled, and sold 3 Silicon Valley startups, and is a leading figure in the Customer Success and Revenue Operations space.

Jimmy began with a rousing overview of the industry’s current landscape. He highlighted the economic environment and the revenue pressures faced by many SaaS companies today, underscoring the increased importance of Customer Success (CS). As he pointed out, retaining existing customers is as important as acquiring new ones. According to Jimmy, the best CS teams now serve as high-touch, critical advisors who ensure renewals become seamless non-events.

Something everyone on the panel agreed with was the changing perception of CS. Traditionally seen as a support-only function, CS is now increasingly viewed as an essential part of the sales team, integrating with clients early on — right alongside Account Executives. This strategic alignment develops customer champions from the outset, eliminating stop-start handovers and enabling CS teams to have greater influence on customer satisfaction and retention.

When discussing enablement in the evolving landscape, the panelists were asked about the strategies they're employing to support their teams:

• Deeper discovery of customer pains: The panel emphasized the importance of a thorough and continuous understanding of customer challenges. It’s no longer sufficient to identify just one problem; teams must delve deeper to uncover multiple pain points and address them proactively.
• Reinforcing the CS-Sales relationship: Strengthening the partnership between CS and Sales was universally acknowledged as vital. The panelists discussed the need for consistent communication and collaboration, ensuring both teams work towards common goals to enhance the overall customer experience.
• Weekly risk reporting and forecasting: Regular risk assessment and forecasting have become crucial. Panelists stressed the discipline required to identify and attach risk factors as early as possible, enabling teams to manage potential issues before they escalate.
• Driving accountability with metrics: Establishing clear metrics and holding teams accountable to them was another key takeaway. Metrics provide a tangible way to measure performance and drive continuous improvement across CS teams.

Despite the event's focus on change and adaptation, the general sentiment among both the panelists and the audience was one of excitement. The conversation was overwhelmingly positive, with panel members expressing their enthusiasm for the evolving landscape. They noted the transformation within the profession highlighting the influx of diverse profiles, such as consultants, who are bringing fresh perspectives and skills.

The shift towards a more sales-oriented function and the accompanying increase in responsibility has empowered CS leaders and their teams. This empowerment allows them to deliver greater value and foster stronger accountability within their organizations. The panelists stressed CS is not merely a remedy for a poor product or an unsatisfactory sales experience. Instead, it plays a crucial role in enhancing already exceptional products and effective sales teams, making their work even more rewarding.

Ultimately, this optimism and positive momentum in the CS field suggest a promising future, positioning Customer Success professionals as key drivers of growth and customer satisfaction. If you want to join a CS team where the above is certainly true, Multiverse are hiring.

In its current form, the Apprenticeship Levy is a tax on UK employers, with funds exclusively earmarked for apprenticeships training. Labour’s goal is to broaden its use – creating more opportunities for adults in the UK to gain new skills.

There’s still a lot we don’t know about the future of the Growth and Skills Levy. But, to help employers unpack what a reformed Levy could mean for their business, here’s what we know so far:

What we know about the Growth and Skills Levy

The goal to reform the Apprenticeship Levy into the new Growth and Skills Levy sits at the heart of Labour’s mission to boost skills in the UK. As a key manifesto commitment, Labour plans to broaden flexible access to adult training in the hope that it will open up opportunities for growth across the workforce.

The intention of the reform is not to reduce the number of apprenticeships, but to increase flexibility. Eventually, the new Levy could allow businesses to spend some of their Levy contributions on non-apprenticeship training, with a portion still reserved for apprenticeships.

So far, the Government has not announced any non-apprenticeship training. Instead, they have announced new ‘Foundation Apprenticeships’. These are targeted at young people, with the goal of providing a broad curriculum and developing both employability and job-specific knowledge skills. The first seven foundation apprenticeships will be available from August 2025, with more likely to follow.

To support this change, employers will be asked to fund more of their Level 7 apprenticeships outside of the Levy. From January 2026, new Level 7 apprentices will only be eligible for levy funding if they are aged 16-21.

A new minimum duration for apprenticeships has also been announced. Apprenticeships can now be as short as 8 months, provided they still meet specific requirements. If you are interested in learning more about these changes, please reach out to a member of the team.

What is Skills England?

The Government has also created a new agency, ‘Skills England’, with the functions of the Institute for Apprenticeships and Technical Education (IfATE) transferring to Skills England in June 2025.

Skills England will develop a single picture of national and local skills requirements, bringing together businesses, providers, unions, Mayoral Combined Authorities (MCAs) and national government to assess the skills the economy needs.

Skills England will also shape the future of the Growth and Skills Levy, holding a list of approved qualifications and training that businesses will be able to spend Levy money on. The list will be developed in collaboration with businesses and experts.

What could this mean for employers?

Labour’s mission statement refers to the vital need for upskilling and training – alongside apprenticeships – to meet the needs of developing technology in the workplace.

Under the current system, Apprenticeship Levy-paying employers are only using 55.5% of available funds, on average.

By creating more flexibility over how the money is spent, the new Growth and Skills Levy could help some employers utilise a greater proportion of their Levy funds – with training that meets specific business needs and skills gaps. For example, it could provide an opportunity to level-up teams with shorter courses in technical skills, such as AI and data. These are vital areas that will be necessary for future business success and to maintain a competitive edge.

This isn’t just beneficial for employers. Employees also stand to benefit from increased investment in training opportunities – being empowered to learn new skills and feeling valued by their company. For employees, upskilling means opportunities to continuously learn and progress in their roles – which also helps improve retention. In fact, we see 94% of individuals remain at their employer beyond their Multiverse apprenticeship.

What do employers need to consider now?

The Growth and Skills Levy is a commitment from the Labour Party to upskilling employees. Fundamentally, the new policy should not change how employers should think about their investment in training: through the lens of increasing employees’ access to gain in-demand skills.

The Multiverse view

With careful implementation, new opportunities could be created for all workers across the economy – delivering ROI for employers and supporting a culture of work-based lifelong learning.

And while we don’t have all the answers just yet, the key to making a reformed Levy successful will be in making sure it's designed with the support and expertise of employers.

Read more about our perspective on the new Levy in our Skills Mission Report.
Want to speak to us about the Levy or other ways to support upskilling in your workplace? Get in touch.

Last updated: 12 June 2025

Prompt engineering involves giving precise and detailed instructions to generative AI tools to produce high-quality outputs. For example, you can use prompt engineering to help an AI tool generate a complex snippet of code or a detailed image. This process helps you get the desired output instead of vague or incorrect results.

Professionals in many industries use prompt engineering to obtain the best results from AI tools. But is prompt engineering a viable career path? This guide explores prompt engineering techniques, current career opportunities, and salary expectations.

Why does prompt engineering matter in the context of generative AI?

Why is prompt engineering important?

Let’s cover some basics. Generative AI models use natural language processing to interpret prompts – or inputs – from users. They then draw on vast databases to produce relevant outputs. But AI models don’t always generate the desired output, especially when asked to perform complex tasks.

Say you prompt ChatGPT to create a lesson plan for a college class on Indigenous novels. A poorly written prompt could cause the AI system to generate content that addresses younger students or uses culturally insensitive stereotypes.

Prompt engineering allows you to craft effective prompts that produce more accurate responses. It also reduces the amount of time you spend fact-checking and revising the outputs generated by AI systems.

What is prompt engineering used for? This discipline has applications in a broad range of professional and creative contexts. Here are a few use cases:

• Audio production - Video Game Developers and Video Editors can prompt generative artificial intelligence tools to create narration and sound effects.
• Code generation - Software Developers use prompt engineering to write code more efficiently. For example, a developer can input specific requirements into GitHub Copilot and receive relevant code snippets.
• Content creation - Prompt engineering allows professionals to generate tailored articles, emails, and other content. For instance, a marketer could ask ChatGPT to write an informative blog post with specific keywords.
• Data analysis - Data Analysts prompt large language models (LLMs) to interpret datasets and generate data visualisations. Generative AI services can also suggest new ways to analyse datasets, leading to fresh insights.
• Image generation - Prompt Engineers can ask text-to-image models to create custom images based on written descriptions. Companies use these generative AI services to create infographics, logos, and other marketing materials.

Examples of prompt engineering with specific tools

By way of example, here are two types of AI-generated outputs — one text and one visual — made by feeding prompts into popular generative AI models.

Prompt engineering with ChatGPT

ChatGPT is an AI chatbot powered by a large language model. It has many applications, from answering questions to writing resumes.

Suppose you want to use ChatGPT to write a jingle for your organic soap business. Here’s the output ChatGPT generates if you input this generic prompt: “Write a commercial jingle for my soap company.”

This output addresses the prompt but lacks humour and emotional appeal. It also doesn’t target a specific audience or reference identifiable products.

Crafting effective prompts lets you generate a more precise and tailored jingle. For example, you could break down the process of creating the jingle into intermediate steps, such as:

• Create a funny and upbeat jingle for Rachel’s Herbal Suds. Focus on the brand’s use of natural ingredients and commitment to sustainability.
• Address the jingle to eco-conscious women in their early twenties who care about gentle skincare and health.
• Include at least two soap puns.
• Limit the jingle to 15 lines or less.

Here’s the final answer when you input this prompt:

This output references the specific brand and includes phrases designed to appeal to the target audience, such as “Join the sudsy revolution.” Subsequent prompts could use different keywords to change the output or ask the AI to address the audience more subtly than "eco gals in your twenties."

Prompt engineering with Magic Media

Magic Media is a free text-to-image model that uses artificial intelligence to translate natural language into visual art.

Say you want to create an image of a cute dog with soap for your company’s marketing materials. Let’s start with a basic prompt: “Create an image of a dog with soap.” Magic Media generates this image:

This image satisfies the prompt but may not fit your desired aesthetic. It also doesn’t make the soap look appealing, so it’s not useful as a marketing image.

A Prompt Engineer can create a more detailed prompt, such as: “Create a marketing image of a cute poodle with soap. Make the soap look lavish and sudsy.”

In response, Magic Media generates these images:

These images depict adorable dogs with a variety of soap products. You could further engineer the prompt to change the background colour, type of soap, and art style.

How to get the most out of prompt engineering: Best practices

Because AI tools rely on natural language inputs, you don’t necessarily need a computer science degree to become a successful Prompt Engineer. Below, we detail some best practices that will help you create prompts to achieve your desired outcomes.

Define the objective

Identify your goal before you start creating prompts. Specific objectives will help you develop focused instructions to guide the AI system.

Here are a few examples of possible goals:

• Prompt the AI to create a dark, dramatic soundtrack for a movie trailer.
• Develop prompts for an AI tutor that explains complex physics concepts to students.
• Craft prompts for a legal AI to summarise Supreme Court decisions.

Provide relevant context

Unlike humans, AI technology doesn’t have previous experiences and contextual understanding. Prompt Engineers must provide relevant background information to get accurate answers. This context could include:

• Constraints
• Desired formats, such as charts or poems
• Expected length
• Industry-specific technical knowledge
• Relevant events
• Tone

Use clear and concise language

Large language models understand human text, but they can’t read the user’s mind. Avoid ambiguity by using concise and precise language.

For example, “Write a two-paragraph email explaining that a shoe order has been delayed due to weather” will generate a more detailed response than “Tell someone their order got delayed.”

Refine your prompt based on the AI’s response

Effective prompt engineering requires iterative refinement. Keep adjusting your prompts in response to the AI’s outputs until you get highly relevant responses. Changing even a single word can drastically alter the outcome, so never settle for the first response.

Experiment with different prompt engineering techniques

Prompt Engineers use many prompting techniques, including:

• Chain of thought prompting - Split complicated tasks into intermediate steps to help large language models engage in complex commonsense reasoning.
• Few shot prompting - Include specific examples to help generative AI models understand the type of response you want.
• Least to most prompting - The Prompt Engineer instructs the AI model to break a problem into subproblems and solve them in order.
• Zero shot prompting - Create simple AI prompts with minimal context.

Understand the limitations of the AI model

Researching the limits of the AI model’s ability will allow you to set realistic expectations. For instance, text-to-image models often generate nonsensical text that the Prompt Engineer must manually replace or erase.

Analyse the final product

Large language models and other AI systems can provide inaccurate or biassed information. Always check AI generated output carefully for errors and hallucinations.

Prompt engineering as a career

Prompt engineering is a relatively new discipline, but people with AI skills can pursue several career opportunities in this area. Here are two prompt engineering jobs:

Prompt Engineer

A Prompt Engineer uses natural language to create effective AI prompts. They also evaluate AI systems to identify technical issues and improve performance.

Prompt Engineers may work for creative companies to design AI images, videos, and other content. Other companies hiring Prompt Engineers include consulting and tech firms.

According to Glassdoor, the average base salary for Prompt Engineers in the UK is £52K. However, Prompt Engineers also average an additional £9k in bonuses and other non-salary compensation.

Prompt Engineers typically need these skills:

• In-depth understanding of artificial intelligence and machine learning principles, such as supervised and unsupervised learning
• Knowledge of prompting techniques
• Natural language processing skills, including sentiment analysis and text classification
• Proficiency in Python and other relevant programming languages

AI Security Engineer

An AI Security Engineer develops security measures to protect AI systems from cybersecurity threats. For example, they can mitigate prompt injection attacks by manually verifying outputs and filtering user inputs to block malicious prompts.

Glassdoor reports that AI Engineers earn an average salary of £51K, while Information Security Engineers earn nearly £62k on average.

AI Security Engineer roles require expertise in these areas:

• Cloud security principles
• Cybersecurity measures, such as encryption and vulnerability management
• Natural language processing and neural networks

Uplevel your AI skills with Multiverse

Prompt engineering is one of the newest career paths in artificial intelligence. The demand for these professionals will likely grow as more companies rely on generative AI to create content and drive innovation.

Multiverse’s free AI Jumpstart module allows apprentices to study prompt engineering, machine learning, and other AI skills while completing a related apprenticeship program. Apprentices also learn how to ethically use AI models in their current roles for free— all while receiving their regular salary.

Complete our easy application to learn more about how Multiverse can help you achieve your career goals in the tech industry and beyond.