Start with the work, not the model
AI creates value when it improves a specific decision, removes repetitive effort, or makes institutional knowledge usable. If you cannot name the work, you are not ready to pick a technology.
← Insights
Short answer
Where should a company start with AI?
Start with the business problem and operational reality — not with a model or a vendor pitch. Identify where judgment, repetition, or knowledge access creates measurable cost or delay.
Map processes and systems first
Understand where people spend time, which systems hold truth, and what data is reliable. Many “AI projects” fail because the underlying process or data foundation is unclear.
Prefer a small, real pilot
Choose one constrained workflow with clear owners and success criteria. Prove impact before scaling architecture or buying platforms you do not yet need.
FAQ
Do we need a data warehouse before any AI?
Not always. You need enough trustworthy data for the use case. Strategic AI programs usually require better data foundations; quick wins may not.
Should we build custom AI or buy a product?
Buy when a proven product fits the problem. Build or customize when your process, data, or control requirements are unique.