Michael E. Porter put it succinctly: “The essence of strategy is choosing what not to do.” This also applies to AI strategies:
“The problem is no longer finding opportunities for AI — it’s choosing among them. As the number of potential use cases continues to grow, many B2B organizations are failing to make conscious decisions about where AI can create differentiated value and where it adds little more than complexity. The hardest AI decision is no longer what to build — it’s what to ignore.“
Jack Welch said: “Strategy is simply resource allocation.” So, strategy raises the question: Which projects does your company prioritize? But the deeper and more crucial question is: “Are we prioritizing AI use cases based on business impact and customer value creation or simply on technical possibility?”
Technical feasibility is only one aspect. Economic viability and user desirability are at least as important:
“The irony is that technology is rarely the main obstacle. In our research, the organizations making the most progress are often less focused on the next AI capability and more focused on the conditions required for sustainable adoption, business impact, and organizational transformation.”
Data & AI Business Design therefore consists of three phases: Business, User, and Data & AI Understanding.
Read this week’s good read:
👉 https://www.forrester.com/blogs/too-many-ai-use-cases-too-little-impact/
