Adoption is broad — results are uneven
McKinsey's 2025 State of AI survey found that 88% of organizations regularly use AI in at least one business function, up from 78% a year earlier — yet only about a third have begun scaling it across the enterprise. The gap usually comes from starting with the wrong processes. The best first projects are repetitive, text-heavy, easy to check, and low-risk if something goes wrong.
Ten good places to start
Each of these is common across industries and easy to measure:
- Lead intake and routing — classify inquiries and send them to the right person instantly
- Answering common customer questions from your own approved content
- Document intake — extract details from forms, invoices, and applications
- Meeting notes and action items — summaries sent to attendees automatically
- Follow-up drafting — first drafts of emails and quotes for staff to review
- Recurring reports — pull data from several systems into one summary
- Invoice and expense processing — match, categorize, and flag exceptions
- Scheduling — coordinate appointments and reminders across calendars
- Internal knowledge search — let staff ask questions of policies and manuals
- Support triage — tag, prioritize, and route tickets before a person picks them up
How to choose your first project
Score each candidate on three things: hours spent per week, how easy it is to check the output, and the cost of a mistake. Start with high hours, easy checking, and low risk. One well-measured win builds the case for the next.
Guardrails before you launch
Responsible automation is what separates projects that scale from projects that stall:
- Use enterprise AI services that don't train on your data
- Keep sensitive or regulated information within approved systems
- Add human review wherever accuracy matters
- Limit what each automation can access
- Log what the system does so you can audit it
- Write an internal AI-use policy your team understands
Measure the result
Agree on success measures before building — hours saved per week, response time, error rate, or leads handled — and compare against a baseline after launch. If you can't measure it, it's hard to justify scaling it.
Most successful first projects don't replace people. They remove the repetitive parts of a job so your team spends more time with customers.
Not sure where AI will pay off in your business? Our AI opportunity assessment ranks your processes by time saved, risk, and cost to build.