Getting ROI from AI

humans. The challenge lies in closing this gap by ensuring that AI outputs are relevant and actionable for business stakeholders. This requires a thorough understanding of both the technical capabilities of AI and the business context in which they operate. By focusing on human-centric design and promoting AI literacy, organizations can enhance the success rate of their AI initiatives.

AI Literacy: The New Digital Literacy

As AI becomes increasingly integrated into business operations, the need for AI literacy becomes vital. Kal suggests that the 2020s will be known as the era of AI literacy. This involves understanding probability-based workflows and the relationship between data quality and AI performance. As Kal states, "The quality of data and the relationship between data and the performance models that we use in the wild" are vital. AI literacy equips stakeholders with the necessary skills to critically evaluate AI solutions, identify potential biases, and make informed decisions. By investing in AI literacy, organizations can empower their workforce to effectively and responsibly use AI technologies.

Practical Applications: Healthcare and Advanced Analytics

In sectors such as healthcare, AI holds huge potential to transform operations and improve outcomes. Kal shares examples from healthcare projects, where AI models predict patient readmissions and identify high-cost procedures. However, he emphasizes that the success of these models hinges on human intervention and contextual understanding. For instance, a readmissions model was successful because it provided not only predictions but also explanations of the factors contributing to high-risk cases. This enabled healthcare providers to make informed decisions and adapt interventions accordingly. Similarly, in advanced analytics, AI solutions must be aligned with business objectives and designed to enhance human decision-making processes.

Designing AI for Trust and Action

Trust and actionability are key components of successful AI deployment. Kal advocates for designing AI solutions that inspire trust and facilitate decision-making. This involves introducing stress testing into risk management processes, as exemplified by OpenAI's research on stress testing models. By understanding the limitations and potential biases of AI models, organizations can mitigate risks and design solutions that are reliable and trustworthy. Kal concludes with a call to action: "Let's try to make AI a little bit more boring." By demystifying AI and focusing on practical applications, organizations can truly utilize the potential of AI to drive substantial business outcomes.