In the world of AI, many discuss “Business Value” but only few can clearly articulate its actual meaning.
The paper “Artificial Intelligence and Business Value: a Literature Review” by Ida Merete Enholm, Emmanouil P., Patrick Mikalef and John Krogstie offers an insightful organizational framework connecting AI with business value. Here, we are sharing our interpretation (and according drawing) of this framework
AI Enablers
Sustainable AI value demands certain enablers. It’s not just about having the right technology; it also involves cultivating a company culture and leadership that embraces AI. Furthermore, ethical considerations and regulatory compliance in AI are equally crucial.
𝐈𝐦𝐩𝐚𝐜𝐭𝐬
AI’s application brings specific impacts (referred to as “First-order Effects” in the study). This includes generating novel insights enhancing business decisions, increasing process efficiency, and potentially transforming business processes.
𝐆𝐨𝐚𝐥𝐬
These impacts facilitate the achievement of business goals (termed “Second-order effects” in the original study), such as operational, financial, market, and sustainability performance improvements.
𝐑𝐢𝐬𝐤𝐬
It’s important to acknowledge that AI can also lead to unforeseen negative consequences. Issues like eroding trust in products or services and potential harm to corporate reputation are real concerns.
Conclusion
This framework is a guide to connecting the dots between the necessary investments for long-term value (enablers) and the immediate and long-term effects (impacts and goals), all while mitigating unintended negative consequences. Thinking about those pieces of the puzzles should lead to an effective AI data strategy.