{"id":181,"date":"2026-03-12T10:00:00","date_gmt":"2026-03-12T10:00:00","guid":{"rendered":"https:\/\/www.intverse.io\/blog\/prepare-enterprise-data-for-useful-ai\/"},"modified":"2026-03-12T10:00:00","modified_gmt":"2026-03-12T10:00:00","slug":"prepare-enterprise-data-for-useful-ai","status":"publish","type":"post","link":"https:\/\/www.intverse.io\/blog\/prepare-enterprise-data-for-useful-ai\/","title":{"rendered":"Before an AI pilot, settle what the data means"},"content":{"rendered":"

Retrospective editorial \u2014 prepared in September 2026 and filed under 2026 for the IntVerse archive.<\/em><\/p>\n

Consider an assistant asked to explain why an order is delayed. Its answer could depend on order status in SAP, inventory in a warehouse system, and a carrier update. Bringing those records together is only part of the problem. The assistant also needs to know which status is authoritative, how recently it changed, and what the person asking is allowed to see.<\/p>\n

Choose a question with a decision behind it<\/h2>\n

Start with a narrow task rather than a broad promise to connect all enterprise data. Identify the person using the answer, the action they might take, and the consequences of a wrong or incomplete response.<\/p>\n

For an order-support assistant, an initial scope might be to summarize known exceptions and show their source records. Changing a delivery commitment is a separate capability with different controls. Keeping those scopes explicit makes evaluation more useful.<\/p>\n

Write down the business definitions<\/h2>\n

Terms such as customer, available stock, and completed order can mean different things across applications. Document the definitions needed for the chosen task, including identifiers, units, organizational boundaries, and status mappings.<\/p>\n

Assign an owner to resolve conflicts. If two systems disagree, the pilot needs a rule for presenting that disagreement or choosing an authoritative source. A plausible narrative should not hide an unresolved data conflict.<\/p>\n

Carry access rules through the workflow<\/h2>\n

Determine what information each user can retrieve and which actions require additional approval. Review permissions at the point where data is accessed, rather than assuming that an assistant\u2019s conversational instructions will enforce the organization\u2019s rules.<\/p>\n

Keep evidence of the records used to construct an answer. Source references and timestamps help reviewers distinguish an inaccurate response from an answer based on information that has since changed.<\/p>\n

Evaluate the difficult cases<\/h2>\n

Build a review set that includes missing records, stale updates, contradictory statuses, and questions outside the intended scope. Decide what a useful refusal or request for clarification looks like. Evaluate the workflow with the people who will actually rely on it.<\/p>\n

A practical next step:<\/strong> choose one operational question and identify the minimum records required to answer it. Review their definitions, permissions, and freshness before selecting a model or designing a conversational interface.<\/p>\n

Related reading: SAP Master Data Governance<\/a>.<\/p>\n

Talk with IntVerse about your connected enterprise.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

A focused review of ownership, business meaning, permissions, and freshness can expose the work an enterprise AI pilot actually needs.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-181","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t