Enterprise AI.

Enterprise AI is artificial intelligence deployed inside large organisations at scale. It runs in operations, in products, in customer processes and in the decisions managers take every day. The distinction that matters is not the technology but its position: enterprise AI is embedded in how the organisation works, rather than confined to experiments, innovation labs or isolated tools.

That position brings obligations. Systems must work with existing data and core applications, meet security and regulatory requirements, and be owned by someone answerable for their outcomes. Enterprise AI is therefore as much an organisational discipline as a technical one — closer to running a critical business function than to launching a product feature.

Many organisations have run dozens of AI pilots; far fewer have turned them into systems the business relies on. The cause is rarely model quality. The usual blockers sit elsewhere: data that is fragmented, incomplete or not accessible in the form a system needs; the difficulty of integrating into core systems such as ERP, CRM and operational platforms; and unclear ownership once the pilot team moves on.

Governance and change management complete the picture. A pilot can run under exceptional conditions; production must satisfy risk, legal and compliance functions, and it must change how people actually work. Without a named owner, a budget beyond the trial and a plan for the people affected, promising results stay on slides.

Value appears in familiar places: lower cost, higher revenue, faster decisions, less capital tied up in inventory and receivables, and better control of risk. What has shifted is how organisations account for it. Counting use cases or active users says little; the more serious question is how AI moves the P&L and EBITDA.

This requires baselines before deployment, attribution that finance teams accept, and the discipline to stop initiatives that do not deliver. Organisations that treat AI as an investment portfolio — with explicit expectations, review points and reallocation — tend to concentrate resources on the few applications that change results, rather than spreading them thinly across many.

Before committing to scale, a few questions separate substance from activity. Which decisions or processes will actually change, and for whom? How will value be measured, and against which baseline? Who owns the outcome — not the technology, but the business result?

Boards should also ask how risk and regulation are handled, including obligations under the EU AI Act for systems used in Europe, and how models will be monitored once live. Finally: what does it cost to run? Compute, licences, integration and the people needed to maintain a system over years are often underestimated at the pilot stage, and they determine whether a promising case remains attractive at scale.

Enterprise AI is one of the four themes of Davos Catalyst, alongside AI infrastructure, sovereign and defence AI, and AI and capital. The summit brings corporate decision-makers together with the builders and investors who shape what is possible, in private conversations and one-to-one meetings rather than on stage.

Davos Catalyst takes place from 12 to 15 January 2027 at the Rätia Center in Davos Platz. Participation is by invitation or application only.