Enterprise AI value realization faces cost and visibility challenges

Enterprises are entering a new phase of AI adoption, shifting focus from building capabilities to maximizing return on investment. Challenges like AI sprawl, escalating inference costs, and a lack of visibility into AI's actual value are becoming board-level concerns. Many organizations are questioning their initial investments, especially as the cost of GPU computing remains high and the initial experimental phase gives way to production realities. The current procurement model, largely based on token consumption, is being re-evaluated as companies explore options for owning or renting their own infrastructure and utilizing more cost-effective open-source models.