Luka Crnkovic-Friis, CEO of Peltarion Reveals How to Get Your Developer Spend on AI right

It has become almost clichéd to say AI has the power to transform our lives. Over the past few years, headlines have lauded the many and multi-faceted ways in which artificial intelligence can solve society’s woes.

It can have such a positive impact that in 2021, estimates predict that increasing AI usage across businesses could create $2.9 trillion of business value, and add 6.2 billion hours of worker productivity. Yet despite the potential for such gains, there still remains a disconnect between ambition and execution in businesses of all sizes, but particularly among SMBs.

A recent study found that almost 85% of business leaders believe AI will give their companies a significant competitive advantage, but only one in five has incorporated AI into their operations or processes. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but even then, only half actually have one. When such huge firms, with such huge operations aren’t making the switch, what chance do smaller companies have?

At the heart of the disconnect is resources.

The Problem with AI Investments

Investing in AI can be notoriously complex and expensive, even for the largest companies. This means that businesses of all sizes can be reluctant to take the leap, even when the potential benefits are so large.

The first problem often comes down to deciding what to use AI for. The sheer potential of AI and machine learning is what makes it so overwhelming. When you factor in the costs involved, most companies don’t have the time or the resources to experiment with different use cases before finding the best fit. Especially SMBs.

Then there is the problem of staffing. The complex nature of building AI services and products requires a specific set of skills, managed by a team of experts. Even where businesses can afford to hire teams of people to work on AI projects, finding these experts is tricky. The very best are hard to come by, want high salaries and are more likely to want to work at larger firms. Yet another stumbling block for the smaller firms.

And finally, there is the inherent resistance to change. Making significant shifts in business direction has historically been slow – after all, it took almost three decades for the business world to switch from steam to electricity, despite the huge benefits we now know the latter brings. A similar trend is being seen with adoption of AI; the ROI on a successful AI project is extremely high but the problem has been scaling it in organisations of all sizes.

This all adds up to a vicious cycle in which businesses can’t, or don’t want to invest in AI, which in turn hinders AI progress, causes AI to further “fail” on its promises, and which makes businesses reluctant to invest.

Breaking the Cycle

The first step towards breaking this cycle for businesses of all sizes, but particularly smaller firms with smaller budgets, is to get familiar with AI. There are a number of free, online courses including by Google and Coursera that debunk the myths surrounding the technology, and how to use it. The biggest of which is that you need to spend a lot to get a lot back.

It’s also worth starting small. Adding AI to your operations and processes needn’t be an all or nothing decision. There are likely myriad ways in which AI can make incremental as well as large gains for your business. Take the time to list areas where processes are still manual, slow, or expensive and look for relevant AI solutions. This not only makes it easier to determine how to use AI, but it helps keep any costs associated with it manageable.

Then there are online tools and services that allow you to experiment with AI applications without having to invest in any of the necessary AI infrastructure, and without the need to hire teams of experts. These services allow you to build AI models using your own data. Once you feel confident enough, you can deploy these models and use them in your business. All while getting support from the teams behind these tools.

Small businesses have often tried to try to deploy AI from scratch by themselves but it often ends up being too complex and expensive. Not only can it be the opposite, but when the wider issues surrounding resistance to change are factored in, smaller businesses are, in many ways, better positioned to win. They’re more agile; more responsive. They likely have more immediate and direct uses for AI, because they’re more likely to be using manual processes and this makes them more likely to see the biggest gains.

Making smart investments, rather than unnecessarily large investments, is the key to not only unlocking the power of AI for a business – of any size – but is key in helping to unlock its power for the wider world.