Last year, the American tech company Cisco surveyed over 8,000 companies worldwide about their preparedness for artificial intelligence. Only 14% of global organisations say they are fully prepared to implement and actively use AI. On the other hand, more than half of the respondents admit they’re worried: 61% believe they have just one year to deploy an AI strategy before feeling the negative impact on their business. The results show that there is still some work ahead of us. While companies understand the importance of AI, most aren’t ready yet to work with the technology. 

Cisco notes that 97% of global companies have felt in the last six months the urgency to implement AI tools. Yet, 86% aren’t ready to leverage the full potential of this technology. Clean data and a solid IT infrastructure are the backbone of any AI system. But, as the survey shows, that’s precisely where the issues start for many businesses.

> 81% of respondents admit their data is scattered across the organisation. In other words: their data is stuck in different silos. The lack of central data management poses a couple of problems. One: fragmented data makes it very difficult to exploit the power of AI fully. Two: gathering data, cleaning it and bringing everything together in one system takes a lot of time. Time, many companies recognise, they don’t have. 

> 95% of respondents acknowledge that AI will heavily impact their current IT infrastructure, but only 17% say their systems are scalable and flexible enough to handle the complexities of artificial intelligence. Many companies will have to invest in IT and upgrade their current networks before they can even begin playing with AI integrations. And we all know, IT upgrades cost bags full of money and time. 

Walk, don’t run 

While it’s clear that organisations still have a long way to go, that doesn’t mean you should start to run before you can walk. The hype gives companies the impression that whatever they do with AI needs to be big and bold. They get started without being fully prepared or completely understanding the potential. The result: the AI project fails — wasting time, money and maybe even hope and ambition. 

Instead, start small. Go for the quick wins. Look for what will bring you the most value in the shortest amount of time. Experiment, win some and lose some, try again. If you take it one step at a time, the impact of a failed experiment won’t be as big. You’ll learn more from it than you’ll lose. What’s more: these small steps will help change the perception of your AI readiness. You might think your company isn’t ready, but you’ll soon see the success of the experiments: they are proof that you are. 

These smaller projects are your foundation. Once the foundation is strong and secure enough, you can build on top of it. Slowly but steadily, you’ll create a powerful AI system without risking too much.