1. Agentic workflows replace single-prompt tools
The first wave of business AI was a chat window. The current wave is software agents that carry multi-step work: reading documents, updating systems, and escalating exceptions to humans. The productivity gains come from redesigning the workflow around the agent, not from the model itself.
Start with one process that has clear rules and measurable output — claims intake, invoice matching, first-draft reports — and deploy an agent with guardrails before widening its scope.
2. Smaller, purpose-built models win on cost
Frontier models grab headlines, but most business tasks don’t need them. Right-sized models fine-tuned or configured for a specific job routinely match big-model quality on narrow tasks at a fraction of the operating cost, with faster responses and simpler security reviews.
- Route easy tasks to small models and keep large models for complex reasoning.
- Measure cost per completed task, not cost per token.
- Re-evaluate model choices quarterly — the landscape shifts fast.
3. AI governance is becoming a purchase requirement
Enterprise buyers increasingly ask vendors how AI is used in their product, where data flows, and who reviews automated decisions. Teams that can answer with documentation win deals; teams that improvise answers lose them. Treat an AI governance pack — model inventory, data-handling policy, human-review rules — as sales infrastructure, not red tape.
4. Data readiness beats model choice
Most stalled AI projects fail on data plumbing, not intelligence. Inconsistent records, undocumented pipelines, and unowned datasets will sink a proof of concept that would have succeeded with a modest model and clean foundations. A data-readiness assessment is the cheapest insurance an AI program can buy.
5. Skills transfer, not just tool deployment
The transformation projects that stick are the ones where internal teams can operate and improve the system after the consultants leave. Budget for enablement — training, documentation, and a run-book — from day one, and measure success by what your team can do unaided six months later.
Key takeaways
- Pick one high-rule process and deploy an agent with guardrails.
- Match model size to task; optimize for cost per completed task.
- Document AI governance before your customers ask for it.
- Invest in data foundations before model sophistication.
- Fund training so the capability stays after the project ends.