Resist temptation to rapidly roll out AI – executives must focus on talent and training

AI has been at the top of any executive’s to-do list over the last few years. Every director has to work with it, and every firm has to integrate it. Investors, consumers, and boards are demanding action and, often, CEOs have little choice but to oblige. 
 
The whirlwind surrounding AI right now is powerful. The perception of being seen as behind the curve on AI could prove costly. 
 
But within all this frenzied activity, lies a hidden danger that is already hurting bottom lines. The rush to roll out digital transformations, the white-hot crunch for AI talent, and the urge to integrate AI in every facet of a business’ operations has led to mistakes. 
 
Failed digital transformation projects are already estimated to cost up to $2trn globally by 2026. And the hype around AI does not look like it will dissipate anytime soon, so we can expect to see more high-profile teething errors, and more revenue leaking out through avoidable mistakes. 
 
Executives are trying to run before they can walk. They are under pressure to integrate AI as soon as possible, but most do not have the required foundations in place. Most digital transformation projects today are hastily thought out, knee-jerk reactions to impatient investors and boards. 
 
CEOs have to try and remove themselves from this pressure cooker – they must take a step back and think about the long-term impacts of these deeply costly projects. If they did, they’d see that the first step to sustainably integrating AI into their operations is to invest in talent and training first, and then build out the actual AI technology. 
 
AI systems are vast, complex, and cross-cut departmental boundaries. Rapidly rolling them out, without the right experts behind them, and without the broader cultural environment to cope with the changes, will inevitably leave you floating adrift listlessly. 
 
The brave decision now is to take a beat and reallocate investment away from AI itself and towards talent and training that lays the groundwork for AI integration down the line. This will be a difficult sell to boards and it will take a steady hand from executives. 
 
But should they succeed, their firms will be the best placed to drive sustainable, long-term value in the AI age and weather the storm of inevitable further technological disruption down the line.

In terms of talent acquisition, there needs to be a re-focusing. Rather than hiring candidates with past technical experience now, to insulate their firms in the long-term, executives should be deploying a skills-first approach. 
 
As the pace of technological development increases, a candidate’s ability to un-learn and re-learn is far more important than experience of any single technology or system. The people who drive the most value for their firms are the ones who can rapidly learn and implement new skills, not necessarily those with deep expertise in one area. 
 
This should feed down, even to the level of graduate recruitment. A diversity of graduates is absolutely essential. Only taking in those from engineering, mathematics, or economics courses will hamstring you.  
 
Grads with backgrounds in arts and humanities have the critical thinking and adaptability that will prove so valuable in the AI-driven corporate environment. Understanding AI governance, AI regulation, and AI safety will define business success over the coming years and grads with the ‘soft’, creative skills will be at the centre of this. 
 
As well as recruitment, executives have to be focused on training and re-training. The pace of change in AI is unprecedented. New models are coming out at a rate of knots, regulatory frameworks are built every day, and AI startups are launching by the thousand. Staying on top of these changes requires a culture of learning, unlearning, and relearning – and this is set from the top down. Regular training courses, lectures with external experts, and workshops will all instill a culture of continual adaptation. 
 
CEOs cannot be blamed for rolling out rapid AI projects – at the end of the day, they have to answer to their investors or boards. But they can also sell a different story. One that still ends with AI-fueled transformation and growth – but starts with laying the foundations of proper talent and training.