Things Enterprises Look for in an AI Platform

Things Enterprises Look for in an AI Platform

Gartner has warned that more than 40% of agentic AI projects could be cancelled by the end of 2027, with escalating costs and inadequate risk controls among the causes. Selection is one way to reduce that risk, by choosing a platform for its operating model instead of its demonstrations. The things enterprises look for in an AI platform tend to be unglamorous: control over data and models, and proof that the system holds up under load.

What Is an Enterprise AI Platform?

What separates a platform from a collection of AI tools is a common control layer. An enterprise AI platform provides governed access to company data and a choice of models, then adds one consistent way to build and monitor applications, so each project does not reinvent security and oversight. In practice, it is judged on how many teams can use it safely, not on how impressive any single model looks.

Why Enterprises Need an AI Platform

IBM's 2025 breach research found that 63% of organisations had no governance policy to manage AI or detect shadow AI. Without a platform, each department buys its own tool, and company data leaves through channels security never approved. Centralising access is why enterprises treat an AI platform as infrastructure and not as a software purchase.

Key Things Enterprises Look for in an AI Platform

The things enterprises look for in an AI platform fall into eight areas, each tied to a failure that costs organisations time or money later.

Scalability and Performance

IDC projects a tenfold rise in agent usage and a thousandfold rise in token and API call loads among Global 2000 companies by 2027. A platform that performs well in a pilot of fifty users can stall at fifty thousand, so buyers ask for load testing on their own workloads and cost controls such as quotas and model routing. Latency commitments matter as much as throughput, since an assistant that takes half a minute to answer simply goes unused.

AI and Generative AI Capabilities

Enterprises want to swap models per task without rebuilding applications, because the best model for summarising contracts is rarely the best one for customer chat. Model flexibility is now a standard scorecard item, alongside retrieval over internal documents, since answers grounded in company data are the ones a business can verify.

Integration and Interoperability

A platform is only useful if it reaches the systems where work happens. Buyers check for native connectors to tools such as Salesforce and ServiceNow, and for hooks into identity and security monitoring so existing controls apply to AI traffic as well.

Security and Data Protection

IBM found that 13% of organisations had suffered a breach of an AI model or application, and 97% of them lacked proper AI access controls. Buyers therefore look for role-based access and clear data-residency options, along with assurances that prompts are not used to train external models.

Governance and Compliance

Regulation keeps moving, which makes audit trails more valuable than any single feature. Under the EU's final Digital Omnibus, high-risk obligations under Annex III now apply from 2 December 2027, not 2 August 2026, yet some transparency duties for synthetic content begin on 2 December 2026. A platform should log decisions and produce evidence on request.

Automation and Orchestration

Agent capability is where marketing outruns product. Gartner estimated that only about 130 of the thousands of vendors claiming agentic capabilities were building something that deserved the label. Buyers should test whether a platform can run multi-step workflows with approval points and rollback, not just chain prompts together.

MLOps and Model Management

Models drift, and the first sign is usually a business metric slipping, not an error message. Enterprises therefore look for versioning and continuous evaluation against live data, with tracing across multi-step workflows, so a team can say which model produced which output and when performance began to change. Rolling back to an earlier model version should take minutes, not a formal change-request cycle.

Usability and Technical Support

Adoption depends on whether business teams can build without waiting for data scientists. Low-code interfaces and a named support contact with defined response times carry real weight in selection, because a platform that only specialists can operate ends up serving a handful of projects.

Common Enterprise AI Platform Challenges

Forrester's 2026 research suggests roughly three-quarters of enterprises are adopting agentic AI, yet only a sliver run it in real production. Forrester points mainly to the difficulty of proving value, but data quality also widens the gap. A platform inherits whatever inconsistencies sit in source systems, so the same supplier recorded under three names produces three different answers. Verified business data of the kind Dun & Bradstreet maintains gives platforms a consistent company-level reference to resolve those records against. Unclear ownership of each use case then compounds the problem, and usage-based pricing means a successful pilot can become a budget overrun once adoption widens.

How to Evaluate and Select an AI Platform

Start with two or three workflows that matter and run a time-boxed proof of value against them. The things enterprises look for in an AI platform become comparable only when every vendor is scored on the eight areas above using the same test data. Ask for audit-log samples and reference customers at similar scale. Exit terms matter too, since they show how easily models and data can be moved. Dun & Bradstreet's trusted business intelligence and continuous monitoring help organisations keep the data behind that evaluation, and the platform that follows, accurate and current.