One of the best investments you can make is to upskill your team in artificial intelligence (AI) because the market is expected to grow from $200 billion in 2023 to $1.8 trillion by 2030. With its promise to permeate every area of an organisation’s operations, possessing the necessary skills to build production-ready AI systems, means you remain poised to leverage every opportunity. 

And the great news: upskilling your team doesn’t have to be a financial burden.

In 2017, the UK Government introduced the Apprenticeship Levy to drive investment in strengthening the country’s skills base. All organisations with staff costs over £3 million must pay 0.5% of their salary bill into a ring-fenced Apprenticeship Levy pot. Yet research from the Institute of Fiscal Studies shows that since its launch, the levy has raised £580 million more than has been allocated for spending on skills and training across the UK, suggesting many organisations are failing to invest in their future success.

But you can’t just invest in training for training’s sake. The programme needs purpose to ensure the content aligns to what the individual learner and your wider business want to achieve. 

As the UK’s only specialist AI apprenticeship provider, Cambridge Spark sets the gold standard in data, analytics and AI training - always first to market with recognised certifications and qualifications to help teams develop new skills. We believe the alignment between the training programme and learner and business needs is achieved through a simple 3-step process - which also enables you to demonstrate the impact of your investment.

Step 1: Determine why AI engineer training is necessary

Too often, training requirements are identified for short-sighted reasons, for example, in an attempt to quickly patch problem areas or jump on the latest trend. For training programmes to have the biggest impact, you need to take a longer-term view and consider how your business intends to grow, so you can account for its evolving needs. This is what helps you to identify the strategic imperative to upskill. For example, in ‘The AI advantage’ we highlighted several use cases and real-word examples for developing AI engineering skills, including enabling customer self-service, personalisation at scale, and accelerating the product development process. 

The best place to start is with your KPIs/OKRs, because they focus on what your team needs to deliver to move the strategic business dials that matter. Think about how to leverage AI to better support the team. For example, to automate mundane tasks, process bulk data in real-time, or identify trends earlier.

When you start by identifying potential use cases for AI (rather than focus on the technology’s capabilities), it makes finding the right training programme instantly easier, because you know what skills they need to acquire. For example, if you need your engineers to upskill so they can build production-ready AI systems, they need to learn about domains including practical machine learning (ML) techniques, neural networks and deep learning, generative AI (GenAI) and large language models (LLMs).

Step 2: Define what success looks like for your AI engineers

If you’ve started with your KPIs/OKRs, you already have the baseline defined and know how the training programme will contribute to the organisation’s overall vision of success. It means all products built because you upskilled the team in AI engineering will deliver regular value back to the business. Furthermore, because your AI engineering skills are being used to design and build products for a specific purpose, they are certain to be usable, useful, and used.

But success isn’t just about the business impact – it’s also about the personal impact it has on a learner.

As professionals, we all strive to do our best. At an individual level, upskilling to become an AI engineer can be a powerful motivating force, because it enables the person to solve more complex problems. While this intrinsic value can be trickier to measure and directly attribute, it does contribute to your organisation’s culture and reputation.

Step 3: Deliver feedback to the business

In this final step, you’ll want to establish a feedback loop within the business to highlight the value delivered. This is about more than metrics, it’s about the stories of what the business is able to achieve now because of its AI engineering capabilities.

For example, an AI engineer may have built a personal assistant, which reduced customer churn rates by 5%. But it also freed a customer service agent to work on a larger account and win a new client. Equally, a newly developed AI-powered recruitment tool may have sped up the CV screening process by 10%, but in the process it revealed potential bias, which led to the tool being retrained and the organisation reviewing its processes to place more emphasis on quality data.  

Feedback can be both positive and negative – sharing ‘wins’ and making sure the business doesn’t repeat past mistakes. It feeds into an organisation’s culture, fostering greater transparency, best practice and continuous improvement, which according to the Society of Human Resource Management, is hugely beneficial to attract and retain future talent.

The best path to AI engineering skills

Rather than blindly invest in any training you want to ensure you invest in quality training, which is guaranteed to deliver the results you need. According to McKinsey,

“Apprenticing offers hands-on learning to demystify change and role modelling to demonstrate hard-to-teach skills, such as problem-solving mindsets and how to use good judgment in evaluating code suitability.”

At Cambridge Spark, our approach maps your training outcomes to skills gaps across every function in your business. Training is delivered via live lectures, workshops, and self-paced e-learning, with learners supported by our expert lecturers, technical mentors and professionally trained coaches. Additionally, we grant access to our online learning platform, EDUKATE.AI, and community of 4,000+ learners and alumni.

Discover more about how Cambridge Spark has generated £350+ million in ROI for its clients.