What are the main obstacles that are preventing AI startups from scaling up? – Thought Leaders

By Salvatore Minetti, CEO, Fountech.Ventures

The promise of artificial intelligence (AI) has undoubtedly captured the imagination of many investors over the past decade. Fuelled by strong public interest, the technology has become a real force for good, promising to deliver solutions with potential to solve some of the world’s biggest issues.

Relative to other emerging technologies, AI companies were the leading investment category globally in 2019, securing over $23 billion in financing according to Tech Nation.

However, AI companies require more than just investment to truly thrive in the current climate. Indeed, the issue is not so much the shortage of start-ups as it is the shortage of scale-ups.

To truly push this discipline forward, it is time that we ramp up our efforts to nurture only the most innovative businesses towards long-term success, so that they can become formidable companies. This begs the question: what are the obstacles holding AI businesses back from growing beyond the start-up phase?

Determining ‘true’ AI businesses

It is no secret that the tag ‘AI’ has become ubiquitous, with companies using the term left, right and centre in order to secure investment. The problem with this is that some companies without AI at their core are holding back progress in the sector at large, hindering the development of progressive solutions.

These issues with semantics make it more difficult for investors to determine which businesses actually use ‘true’ AI, and which don’t. Indeed, a recent MMC Ventures report revealed that two fifths of Europe’s AI start-ups don’t actually use AI in any of their products. Examples like this serve to highlight how pervasive the misuse of the term is. Undoubtedly, conflating the meaning of a product or service can not only lead to overspending and poor execution, but also a business’ ultimate downfall when it is outcompeted by those with more clarity and focus.

Investors would therefore do well to avoid this fate by vetting companies thoroughly early on in the process. This can be achieved by asking key questions, such as ‘does this company derive its competitive advantage from the use of AI?’, and ‘will this company propel the sector forward?’. This way, resource can be spent more valuably on companies with scalable technical solutions and real competitive edge.

Start-up stumbling blocks

In the deep-tech arena, ambitious young teams generally have the determination and technical expertise required to design and create an innovative product. However, powerful concepts aren’t always enough to guarantee the success of a new business venture, and too much focus on the technology could stymie its progress.

The lack of clear metrics for AI startups is particularly challenging; it is difficult to measure what makes a ‘good’ AI company. The hype surrounding AI and its growing popularity has also given rise to fierce competition, which means that founders need to be particularly attuned to the obstacles they will face.

Some fundamentals are important for every business. For one, entrepreneurs must be able to demonstrate that they are addressing a large and important problem – and show why they are in the best position to solve it. Perhaps even more importantly, businesses need to establish whether people will be willing to pay good money for their solution.

AI start-ups will generally fall at many of the same hurdles as their more traditional counterparts. Another CB Insights report revealed the most common reasons that budding entrepreneurs might fail on their way up to the top, which included a lack of market need for the product, not having the right team, and being out-competed by other businesses.

The first of these demands particular attention: the blight of so many tech startups is that they build the product, and then hope that somebody wants it. A failure to take the appropriate steps at the outset to understand the potential fit and demand means that the final product doesn’t ultimately capture the attention of the target market.

For AI businesses, however, there are additional elements that must also be considered. The team should be able to demonstrate that their AI is truly adding value to the data they are using – and not just being used as a smokescreen. Does the AI help explain patterns in the data, derive accurate explanations, identify important trends and ultimately optimize the use of the information?

If not, they must question whether they should really be selling themselves as an AI startup. There is a real risk that resources will be spent needlessly on building and marketing a solution that does not truly solve a problem using artificial intelligence. Ultimately, such businesses are likely to lose their vision over time and will fail to live up to the mark they might have envisaged for themselves. They may also struggle to secure funding; after all, most VCs will not want to risk an investment into a technology that is ambiguous.

Young teams also tend to face roadblocks when it comes to the financial side of things: AI start-ups are either under-funded from the outset or burn more cash than necessary. To achieve sustainable growth, fledgling companies need to be able to plan beyond the development budget and create a scalable commercial model that will stand the test of time. Granted, this is no easy feat with limited business nous.

Nurturing AI start-ups to success

Many of these missteps boil down to the fact that start-ups often fall short where appropriate mentorship and business acumen are concerned. Indeed, most would benefit from some additional expertise to navigate common stumbling blocks.

It is fundamental therefore that company founders work with third-party advisors to compensate for any gaps in knowledge. Young teams need mentors to help manoeuvre unfamiliar territory, and to provide additional legal, financial, and logistical guidance.

Ultimately, simply financing a project just isn’t enough. It is essential that we work to provide a more holistic model to support fledgling AI start-ups, so that companies are set on the path to commercially scalable projects. It is only by providing specialist support and assistance with the more fundamental aspects of business – as well as access to talent, capital and peer networks – that we can really push the needle forward in pioneering AI technology.