Fast growth tech: why skills are the common denominator

Included amongst the UK’s fastest growing tech companies this year are familiar names such as Trainline, Depop, and Marshmallow. These European success stories are disrupting their respective markets – and despite a tough economic climate, they’re thriving.

The term ‘fast growth tech’ has become synonymous with innovation that delivers strategic value. The two go hand in hand, and customers buy from these companies because they offer the most cutting-edge, market-relevant products.

But pioneering technology, strong leadership, and strategic delivery don’t simply come to be. These things must be learned, and they’re all underpinned by a variety of different – but collectively essential – skills.

Rather than these companies being privy to some secret sauce, the common factor here is quite simple: they can all deploy the right skills, at the right time.

So how can fast growth scaleups build the skills they need to adapt, innovate, and grow?

1. AI skills mapping

The first step lies in accurately identifying the company’s most critical and time-sensitive skill gaps. The ones that are imperative for strategic execution.

This also presents the biggest challenge – and none more so than in a rapidly changing environment. The tech industry’s speed of knowledge change makes it especially difficult for leaders to pinpoint which skills they need, which already exist within their organisation, and where the gaps lie.

But unless tech companies get this right, learning and upskilling will always be disconnected from what the organisation is trying to achieve. And that’s a fast-track route to increased spend and inefficiency.

In the past, L&D teams have worked tirelessly to map skills manually – a monumental yet largely futile effort. Manual data capture and analysis is a lengthy process, and by the time it’s complete the results are already out of date anyway. What’s more, the business strategy – and the skills required to deliver it – is likely to have changed too.

Thankfully, AI skills mapping tools have all but removed the manual effort and complexity that prevents teams from auditing skills effectively. Equipped with this real-time data, organisations can access a fast, accurate and comprehensive view of employee skills across the board, mapped to job role and even proficiency level. Most importantly, they can see where the critical gaps lie, and they can work to close them at speed through skills-led learning.

2. Accessible (and discoverable) learning

The rise of digital learning has ensured an abundance of learning content that spans just about every subject, topic, and competence level. With the right learning strategy and tools, this content can also be delivered to learners, in context, and on demand, supporting the achievement of upskilling (and business) goals.

But let’s be very clear on this point: scaling tech companies don’t need more learning content, what they need to do now is make this content personalised, instantly accessible and discoverable to learners when they need it. Instead of having employees search through multiple sources, employers should be leveraging AI-powered search, skills mapping, and browser extensions to bring relevant content directly to the employees who need it.

Ensuring that learning is discoverable is also vital if employees are to engage, and keep engaging, with learning. In today’s AI-enabled world, employees have a low tolerance for anything that is passive or generic. People have become accustomed to personalisation, and that’s no bad thing. Learners want to see a quick return on their efforts, just as shareholders expect a strong return on their investment. And making sure that content is credible, accessible and discoverable will go a long way to achieving this.

See Also

Why early-stage startups must treat analytics as a day one priority

3. Hyper-personalised AI coaching

The last piece of the puzzle? Personalised teaching and coaching that brings learning to life in the form of demonstrable upskilling. We’ve always known that one-to-one coaching is highly effective for skills development but, until now, employers have never been able to deliver this at scale due to cost. It therefore remained a privilege for the elite few, not the many.

But with agentic AI, businesses can now provide every employee with hyper-personalised, one-to-one coaching at the touch of a button. These AI workplace coaches can teach, question, and adapt in line with the specific learner’s skill level, delivering the most relevant and verified knowledge, when they need it. They understand the individual’s capabilities, and they can connect them with the right content while providing the instant feedback and coaching that drives effective upskilling.

Now imagine this at scale, across geographies, languages, seniority levels and business functions. Through this lens, the opportunity to accelerate upskilling becomes crystal clear – and the business impact will be increased innovation, customer retention and market share.

Skills: common denominator and key differentiator

Alongside this, the rise of AI is also increasing the value of human insight and community. As the tech industry begins to reach product parity over the next few years, this will also become the new competitive moat. Tech companies will no longer compete on product innovation alone, and so distribution will become the key differentiator. In this landscape, those with the strongest networks, communities, reputations, and human-centric ethos will win – and it’s a point that only strengthens the case for skills as the most valuable growth currency.

The best tech companies recognise the need to be agile, innovative and strategic, of course. They know that these things set them apart in a saturated market. So the prevailing question now is this: Can scaleups secure their future success by building and deploying the skills that underpin each of these core performance drivers?