Learning from the AI leaders

AI adoption is still nascent. Newly released research from SAS’s Data and AI Pulse Survey 2024 Asia Pacific finds that only 18% of organisations can be categorised as AI leaders, where the organisation has an AI strategy and long-term investment plans in place. 

Research from IDC predicts that we will move from the experimentation phase, “the GenAI  scramble” that we saw in 2023 and 2024, and mature into the adoption phase in 2025/26 before moving into AI-fuelled businesses in 2027 and beyond.   

So what are the leaders doing differently? The SAS research project explores this in detail. From the outset, the leaders have a strategic goal and  a clarity of purpose, focusing their investment efforts on driving new revenue growth, increasing operational efficiency, and increasing profits. Followers tend to have too many projects as well as unclear goals and expectations. 

The expectations for AI are high, with 40% of the survey respondents expecting a return of three times or greater ROI, and it is this expectation that is driving investment, with 43% of organisations planning investment increases of over 20% over the next twelve months. Shukri Dabaghi, Senior Vice President of APAC & Emerging EMEA at SAS, comments “This ties into the idea that we are in the midst of an ‘AI gold rush’ across the Asia Pacific region. These ROI expectations exist despite many surveyed organisations not having a clear AI strategy. Without a clear strategy and roadmap in place, it is likely that there will be some disillusionment with AI. Our study has shown that ‘AI Leaders’ understand the importance of Responsible AI and governance. This enables these organisations to shift their focus from tactical AI initiatives and focus on more complex and transformational AI use cases.”  

Across industry verticals, healthcare and life science lead the way with 38% of companies having either integrated or transformative approaches to AI, followed by insurance and banking with 37% and 30% respectively.  

When it comes to investments, the difference between the leaders and followers is predominantly that the leaders can now run AI projects themselves. They have invested in the skills and capabilities to be able to run projects internally, compared to the followers who are relying on external sources to enable them to deliver. 

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.” 

What is clear from the research is that the capabilities change as organisations mature in their AI experience. Those that are leading are exploring the ways they can measure the impact of AI deployments and are embedding an AI architecture across their organisations, compared to those at the beginning of the journey, who are building the team and developing a governance framework for deployment.  

Sameer Thakkar, Vice President of Marketing for APAC & Emerging EMEA at SAS comments: “In the next twelve months, I expect to see a stronger focus on data and how data can be turned into actionable insights. This will be key as organisations will be looking to implement a portfolio of AI uses cases with clear alignment to creating business value.”