5 reasons why AI adoption is really a question of effective leadership

In the race to put AI to work, organisations are naturally focused on the tech and data management side of implementation. But as I see it, right now, AI adoption is mostly a leadership challenge. One that requires innovation know-how, empathy and good storytelling skills. Why? I'm giving you five reasons below:

Reason 1: Most AI use cases are too expensive right now. 

A recent study by the Massachusetts Institute of Technology (MIT) concluded that AI is still far too expensive to replace current workplace activities at scale. If you want to move fast, you need to know how to build an agile innovation funnel – one  that helps you determine which AI use cases have real potential to scale and which ones are better off in a prototyping state for now. 

Reason 2: AI is a great catalyst for reimaging roles across your company.

Between 40 and 60 percent of all jobs in the world will likely be impacted by AI in the future. But changing job requirements, skills gaps and the upskilling revolution were huge issues even before the AI onset. Leaders can seize this moment to invest in equipping teams with the skills they need to be successful in the future.

Reason 3: Think responsible use, data security and compliance at the beginning of your AI journey - not midway. 

This is existential! Winning organizations will take the time needed to build a solid foundation for AI adoption. This requires both a strong base of structured data and responsible AI leadership -  crucial at a time where there are still so many gray areas. 

Reason 4: AI scares many employees. 

And because it’s scary, implementation of new AI-powered solutions needs to go hand in hand with effective internal storytelling. Pushing forward without this element in place could jeopardize the success of this change process. Why are certain use cases exciting? How is this empowering employees or benefitting customers? How are we dealing with risks or potential negative consequences? It is important to address such questions proactively.  

Reason 5: Implementing AI data requires adjusting the operating model and ways of working. 

In order to really create benefit, AI and data efforts will need to be fully integrated across the organisation in ways that often go against the usual corporate structures, country kingdoms and silos. Working on AI use cases can be a powerful moment to enable people in your organisation to work together more effectively across departments and regions. 

Agree or disagree with the above? What do you see as the main challenges around AI integration? Drop me a comment.