Enterprise rollout playbook: pilots, governance, training

A good AI rollout isn't a launch event. It's a sequence of decisions, pilots, and adjustments that takes 6-18 months. Here's the sequence.

Phase 1: Discovery (weeks 1-4)

• Inventory current state. What AI is already in use (official + shadow)? What data sensitivities exist? What has legal / security reviewed?
• Stakeholder interviews. IT, security, legal, HR, finance, and 3-5 business units. Understand concerns and priorities.
• Define guardrails upfront. Acceptable-use policy, data classification, approved tools list. Short versions.
• Pick the first use cases. One or two, not ten. Criteria: clear ROI, moderate risk, enthusiastic team.

Output: a one-page plan, signed by sponsor.

Phase 2: Pilot (weeks 4-12)

• One team, one tool per use case. 20-100 users.
• Success metrics defined upfront. Measurable. If you can't measure it, you can't claim success.
• Training + support. Not "here's a link." Live sessions, office hours, a Slack channel.
• Weekly pulse. Surveys + usage data + support ticket volume.
• Iterate on setup. Rules, custom instructions, integrations. The pilot reveals what configuration actually matters.

Decision at end: expand, modify, or kill. Be willing to kill.

Phase 3: Expand (months 3-6)

Successful pilots go to adjacent teams:

• Next 3-5 teams. Similar profile to the pilot team.
• Training delivered by pilot champions. Peer credibility > top-down mandates.
• Case studies. Real stories from the pilot team. Concrete, not hypothetical.
• Refine guardrails. The pilot surfaced edge cases; policies update accordingly.

End state: ~500 users, real feedback, clear picture of value.

Phase 4: Scale (months 6-12)

• Org-wide deployment of tools that worked.
• Dedicated AI team (if not already) — platform, governance, enablement.
• Self-service adoption. Training becomes on-demand. Champions continue to support their areas.
• Vendor management. Formalize relationships, negotiate enterprise pricing, define SLAs.

Phase 5: Optimize (month 12+)

• Cost management. Utilization analytics; cut underused seats.
• Quality monitoring. Sampled output review; incident tracking.
• New use cases. Backlog of what's next.
• Model and tool refresh. Annual review of alternatives.

The governance that doesn't suck

Heavy governance early (review every AI request for 6 months) tanks adoption. Too-light governance (anything goes) creates incidents.

The right shape:

• Short, specific AUP (1 page).
• Pre-approved tool list. Additions via fast process (<1 week).
• Data classification in plain English. People can answer "can I put this in AI?" quickly.
• Incident process for when something goes wrong. Not to punish — to learn.

Change management essentials

• Leadership visible use. Executives using AI themselves matters.
• Middle manager enablement. They're the ones their teams ask.
• Peer champions per team. Credibility beats top-down.
• Celebrate success stories publicly. Specific, not generic.
• Honest about what AI doesn't do well. Overpromising is the fastest way to lose trust.

Common failure modes

• Big-bang launch. Everyone everywhere on day one. No time to iterate; first impressions bad; recovery hard.
• Pilot without expansion path. "We tried it, it was okay." Pilot done, nothing learned, nothing changed.
• Tool sprawl without governance. Every team picks their own; data exposure grows; IT can't manage.
• Governance without adoption support. Policies exist; nobody uses AI properly.

The role that matters most

Enterprise AI programs that succeed have a program lead with authority:

• Budget.
• Decision rights on tools and policies.
• Cross-functional convening power.
• Ear of senior leadership.

Without this, AI rollouts fragment across IT, HR, business units, and nothing coherent ships.

The timeline reality

Internally-honest timelines for a 5,000-person enterprise:

• 3 months: discovery + first pilot.
• 6 months: expansion, 500 users.
• 12 months: org-wide.
• 18+ months: optimization, second wave of use cases.

Execs want "6 months to be AI-first." That's rare. 12-18 is realistic and still fast.