Missing foundation of AI readiness

Artificial intelligence is rapidly becoming a central focus for countries, economies, and corporate boardrooms. Leaders are grappling with questions about which AI models to use, whether to deploy tools like ChatGPT or Claude, and how to develop an AI strategy for quick implementation.

However, these important questions often overshadow a more fundamental concern: an organization's actual readiness to benefit from AI. The challenge is not solely technological; it exposes a long-standing management issue concerning how organizations handle information. Many assume AI is the bottleneck, but the reality for numerous organizations, especially in developing economies like Pakistan, is that the organization itself is the limiting factor.

Consider a manufacturing company. An AI system could optimize production by analyzing numerous variables like employee skills, machine capabilities, and delivery commitments. Yet, its effectiveness depends entirely on the availability of reliable organizational data. If customer histories are incomplete, agent skills poorly documented, or performance data inaccurate, the AI has little to optimize. It can only enhance decision-making if the organization has already mastered the capture, maintenance, and integration of its dependent information.

Similarly, a call center could leverage AI to optimize customer interactions by considering factors such as customer history, issue complexity, and agent expertise. This advanced capability, however, is contingent upon having complete and accurate data. Without it, the AI's potential remains unrealized, highlighting that the true foundation for AI readiness lies in robust information management practices.