Running an AI Pilot Program: A Practical Playbook

A good AI pilot does one job: produce credible evidence that a tool and workflow combination is worth rolling out — or worth stopping. Most pilots fail to do this. They either run too small to generate signal, too long to maintain focus, or with success criteria so vague that the post-pilot meeting becomes a vibe check. This playbook lays out how to run one that gives leadership a clear decision.

We have run dozens of these for Melbourne and Australian businesses across professional services, retail, manufacturing and not-for-profit. The pattern below is what reliably works.

Define the decision the pilot is answering

Start with the decision, not the technology. Write one sentence:

"By [date], we will decide whether to roll out [tool] to [population] for [workflow], based on [metric] reaching [threshold]."

For example: "By 15 August, we will decide whether to roll out ChatGPT Enterprise to all 42 client-facing consultants for proposal drafting, based on average proposal turnaround time reducing by at least 30 percent."

If you cannot fill in those brackets in week one, you are not ready to pilot. Spend another two weeks in discovery instead.

This framing forces honesty about what the pilot is actually for. It is not a technology evaluation. It is a business decision with a deadline.

Pick the right scope

The sweet spot for an Australian SMB pilot is:

• One primary workflow, with one or two adjacent extensions if natural
• One or two teams, totalling 8 to 15 participants
• Six to eight weeks end to end
• One primary metric plus two or three secondary indicators

Common mistakes:

• Picking a glamorous workflow (board pack generation) instead of a high-volume one (customer service responses)
• Including too many teams, so no one feels ownership
• Stretching the timeline to "give people a chance to ramp up", which kills urgency
• Tracking 12 metrics, none of them rigorously

The workflows that pilot well share three traits: high frequency, measurable quality, and visible turnaround time. Customer service responses, proposal drafting, content production, and routine analysis all qualify. Strategic planning, executive coaching and creative breakthroughs do not.

Set the team up properly

A pilot is a small change programme. It needs:

• Executive sponsor. Reviews progress fortnightly. Owns the rollout decision.
• Pilot lead. Runs the day-to-day. Typically operations or functional manager.
• Participants. Volunteer if possible, but ensure a mix of enthusiasts and sceptics. A pilot of only enthusiasts produces misleading data.
• Champion or coach. Available for office hours twice a week. Can be internal or external.
• Measurement owner. Often the pilot lead, but separately named. Owns the baseline and the dashboard.

Brief everyone in week one with a written one-pager: scope, metrics, schedule, escalation path. Pilots fail more often from communication gaps than from technology limitations.

For the broader context, see the pillar on AI enablement for teams.

Establish the baseline before the tool goes live

This is the step almost everyone skips. Before participants get access, spend a week measuring the current state of the workflow:

• How long does it take, end to end?
• How many touch-points, handoffs and rounds of review?
• What is the quality benchmark? (Customer satisfaction, error rate, win rate, internal review pass rate.)
• How does the team feel about the workflow on a 1 to 5 scale?

Without a baseline, any improvement claim post-pilot is contestable. With a baseline, the conversation is short.

A simple baseline survey of 5 to 10 questions, combined with a fortnight of timekeeping on the workflow, is usually enough. Do not overbuild this.

Run the work

Weeks 1 to 2 are setup and baseline. Weeks 3 to 6 are active use. Weeks 7 to 8 are analysis and decision.

During active use, four rituals matter:

1. Weekly office hours. 30 minutes, optional, for participants to bring real work and get help.
2. Weekly Slack or Teams thread. Quick wins, blockers, prompts that worked. Low ceremony, high signal.
3. Mid-pilot check-in. End of week 4. Adjust scope or metrics if needed. Be willing to kill a use case that is not working.
4. Lightweight metrics tracking. Weekly snapshot of the primary metric. Not a science project — a thermometer.

Avoid the temptation to add new use cases mid-pilot. If the team is finding adjacent wins, document them for the rollout phase but do not let them dilute the primary measurement.

Decide and document

At the end of week 8, run a 90-minute decision meeting with the sponsor, pilot lead, and measurement owner. Three possible outcomes:

• Proceed to rollout. Primary metric hit threshold; team wants to keep using the tool. Move to enablement planning.
• Iterate. Signal is positive but not conclusive. Define a focused 4-week extension with tightened scope.
• Stop. Metric did not move enough, or the workflow does not fit. Document learnings, redeploy budget.

Write a one-page decision memo. Include the baseline, the result, three things that worked, three that did not, and the recommended next step. Circulate it. This artefact pays compound interest — six months later you will refer back to it constantly.

For what to measure once you do roll out, see measuring team AI adoption metrics. For the change-management overlay on the rollout phase, see change management for AI adoption.

A worked example

A Melbourne professional services firm of 60 staff piloted ChatGPT Enterprise with 12 consultants for proposal drafting. Baseline: average proposal took 4.5 hours, with 1.7 rounds of partner review. Pilot goal: 30 percent reduction in time, no increase in review rounds.

Result after eight weeks: average time 2.6 hours (a 42 percent reduction), review rounds steady at 1.6. Win rate over the same period was statistically unchanged. The firm rolled out to all 42 client-facing staff over the following six weeks, with a champion in each practice group and a shared prompt library seeded from the pilot.

Total pilot cost including consulting was around $22,000. Estimated annualised time recovered post-rollout: roughly 4,200 hours.

That kind of evidence makes the rollout conversation short.

The Australian context

Two local notes. First, the Voluntary AI Safety Standard expects organisations to demonstrate proportionate testing before scaled deployment. A documented pilot is exactly the kind of evidence that satisfies that expectation. Second, for firms with privacy-sensitive workflows — health, legal, financial — the pilot is also the moment to test that data classification and tool configuration genuinely meet Privacy Act obligations. Better to find issues in a pilot of 12 than after rollout to 200.

What to do next

If you have a workflow in mind but no pilot scope, draft the one-sentence decision statement first. If the sentence is hard to write, the pilot is not ready. Once you have it, the rest of the playbook above is largely mechanical. The pillar on AI enablement for teams covers where pilots fit in the broader programme.