Learning from the AI leaders

Unsurprisingly, lack of skills is cited as the biggest challenge. Issues around data governance and challenges around clear metrics follow the top challenge areas. All of these relate to the lack of experience with AI. As organisations embark on their journeys, they have to learn what is needed to ensure a successful project. 

When it comes to failure, leaders contend with issues including privacy or compliance, compared to the followers, where the biggest cause of failure is the inability to access data due to infrastructure restrictions.   

Having guardrails in place is key. “Two critical foundations for AI integration at a policy and governance level are that you have trust in your data and that the data is ethically managed,” says Deepak Ramanathan, Vice President of Global Technology Practice at SAS. He continues: “This demonstrates to your team and stakeholders that you are taking the appropriate actions to mitigate risk and liability. When it comes to Responsible AI, that includes not just those potential risks, but also the need to ensure that your models are driving accurate and actionable insights. At its core, Responsible AI begins with good policy and that flows onto rigorous technical execution, ensuring good governance is embedded at the heart of ‘AI Leaders’ systems.”