Currently, most companies are pondering how they can best take advantage of AI at their own scale. Applications and experiments are thus flourishing, with varying degrees of success. Very often, the frustrations are commensurate with the hopes. And with good reason: AI, however "intelligent" it may be, can ultimately only do one thing—work from the data we provide it with. To capitalize on it, we therefore need centralized data of sufficient quality and quantity, and derived from a wide range of sources. In many organizations, however, this data is scattered among various business functions, each of which has its own systems.

So, before considering sophisticated generative AI set-ups, it is useful to carry out a quick diagnosis of your organization. Is tacit knowledge sufficiently formalized? Is it centralized? Are data collection and processing methods sufficiently standardized? Given the nature and quantity of the data collected, is there a risk of triggering biased responses from your AI system? Would you benefit from access to additional sources? This upstream work is essential to ensuring the quality of the AI's responses and maximizing its potential to help decision-making.

Source:  Harnessing AI to accelerate digital transformation, The Choice by ESCP, July 2023.