Insights

Insights and practical guidance

Every article carries key takeaways and a source field, so it can be circulated and checked internally.

The blocker is rarely model capability. It is process ownership, data readiness and integration.

Ten questions that should have answers before the first use case goes live.

Starting with an integration layer and master-data discipline delivers faster than a full replacement.

AI Transformation Glossary

The same term often means different things across departments. These are the definitions we work with.

AI transformation: The process of embedding AI into everyday processes, systems and governance — measured by outcomes, not tool purchases.
Readiness assessment: A structured review of data, process, systems and risk to determine which AI use cases are feasible now.
AI agent: A software role that completes multi-step tasks using defined rules and tools, still bounded by human review and permissions.
Human-in-the-loop: A design that retains human review or approval at higher-risk or customer-facing steps.
Systems integration: The engineering work that lets CRM, ERP, commerce platforms and workflows exchange consistent data.
Master data management: Establishing a single trusted version and maintenance rules for core records such as customers, products and suppliers.
AI governance: The rules and review cadence covering who may use which data, how output is checked, and how issues are logged and escalated.
Adoption rate: The share of staff actually using the new process or system — a key indicator of whether a project worked.
Retrieval-augmented generation (RAG): Retrieving relevant content from your own documents before generating an answer, reducing unsupported responses.
Data residency: Where data is physically stored and processed, often constrained by contract or regulation.

Sixty minutes to review your systems, your most time-consuming processes and your goals — followed by initial observations and a suggested scope.