AI can generate answers. But enterprise value is driven by decisions at speed
There’s a moment every enterprise eventually reaches with AI.
It starts with momentum - copilots deployed, models integrated, dashboards illuminating insights in real time. The organization feels faster, more intelligent, more automated. And yet, when it matters most - when supply chains are disrupted, demand shifts unpredictably, or critical planning decisions must be made, the AI stalls. It responds, but it doesn’t decide.
Because most enterprise AI wasn’t built for the business. It was built for language.
This is where the inflection point begins.
In Emerging Tech: AI Vendor Race — Break Boundaries by Adapting DSMs to Emerging Use Cases1, Gartner examines how organisations are applying domain-specific models to forecasting, planning, and operational decision-making. The report explores emerging approaches such as time-series foundation models.
Gartner names Wipro as a sample vendor in this report. We feel this reflects a broader approach moving from isolated AI deployments to integrated, domain-aligned intelligence layers that can simulate outcomes, automate workflows, and support high-stakes decision-making.


