Editorial draft: review before publishing.
Generative AI has lowered the barrier to building impressive demos. The harder step is turning a demo into a system people rely on every day — one that is accurate enough, affordable, secure and maintained.
Data comes first
AI assistants are only as useful as the information they can reach. Organizations that invest in well-governed, searchable data — documents, tickets, knowledge bases — see far better results than those that start with model selection.
Measure before you scale
Evaluation is the most underrated part of AI engineering. Defining what “good” looks like, building test sets from real cases and monitoring quality after launch turns AI from an experiment into a product.
Plan for cost and capacity
Inference costs grow with usage. Choosing the right model size, caching, routing and deciding where workloads run — global cloud, regional infrastructure or both — are architecture decisions, not afterthoughts.
Assign ownership
Successful AI programmes have a clear owner, a cross-functional team and a governance process that covers privacy, security and responsible use from the start.