Generative AI (Gen AI) adoption is accelerating, but execution still lags. Gartner predicts that in 2025, at least 30% of Gen AI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value.
These are real concerns, but they don’t have to be roadblocks. By focusing on high-value, feasible use cases, businesses can start small and scale as they refine their data and AI strategies. Moreover, Gen AI itself can be leveraged to address data-quality issues, like assisting with data cleanup and analyzing data to attach relevant metadata. This approach not only mitigates data challenges but also accelerates the development of AI-driven solutions.
Read this eBook to learn what’s required to bring a high-impact Gen AI use case from pilot to production, how to rethink your data readiness, and how to build momentum using the data you already have.
