In the rush to harness the power of AI, companies are heavily investing in evaluating and probing AI solutions to grapple with the transformative nature of GenAI. AI has the potential to enhance operational efficiencies and act as a springboard for new products and services. However, its integration can be disruptive, often necessitating organizational transformations as it impacts everyone from developers to end-users. Consequently, organizations should adopt a systematic approach to implementing AI capabilities, measuring and adjusting their strategies based on its adoption and its influence on business outcomes.
In this extended blog post, we discuss how to systematically tackle AI transformation, reshaping the operational model, enhancing efficiency, and unlocking additional value propositions. This serves as a thought exercise to assist in formulating an AI strategy that aligns with industry trends and adoption levels. Given that AI is a rapidly evolving domain, the insights here should be viewed as current reflections, subject to change as the landscape shifts.
Executing a company’s vision for AI requires a framework that, at its core, drives innovation through a mechanism to create ideas, prioritize them, test or fail proof of concepts, and execute select minimum viable products (MVP) towards a programme of transformation and industrialization.
On the other hand, AI is not simply a solution in search of a problem. Instead of trying to force AI services and models into your organization without a clear plan, it’s important to first identify your business requirements and then match them with the right AI solution. The use-case based approach shown in the figure below has been drawn with this strategic intent in mind.