The expected growth in AI business is simply not happening at the anticipated rate. Consequently, if someone is an AI professional, there’re not many big projects that are coming their way. I’d like to talk about why this is the case, and if you’re an AI professional, how to prepare.”
The first challenge is lack of skillset in the IT employee base. Are employers upskilling their employees? Are they investing enough? AI requires specialized knowledge: statistics, algorithms and computer science, not civil or mechanical engineering. So, well-intentioned employees starting on the AI journey get stuck. Additionally, there is only a small talent pool. 500,000 jobs are available in AI, but only 50,000 computer science grads per year, out of which only 20-30% are of some quality.
Second, companies need to pick processes carefully that they want to incorporate AI into. If not, maybe RPA or analytics is a better fit. For example, for routine invoice processing, payroll processing, etc., RPA can fit the bill nicely.
Third, the lack of infrastructure sets us back as a country in terms of AI adoption. Basic telephone access and WiFi are missing; how can we talk about drones and video processing in AI?
If you’re an AI professional, the key is to learn the specific skills, and encourage your company to select incoming employees from the computer science, applied mathematics or statistics background. Second, help your business leaders select the correct business processes for AI enablement. Finally, learn to work with the reality of low-quality infrastructure, and pick use cases that have a high chance of success even with the inferior infrastructure.