AI is no longer optional. But it’s also not a plug-and-play tool—it requires a structured approach. Whether your organization is just exploring AI or scaling from pilots to enterprise-wide adoption, success depends on understanding where you are on the journey and what comes next.

Wharton professor Ethan Mollick makes an important point: employees need the freedom to experiment with AI openly and without fear. When that experimentation is paired with a clear strategy and governance, adoption accelerates and new possibilities emerge. But no consultant or vendor can do the work for you. The only way to unlock AI’s potential is by doing—by testing, learning, and refining.

Here are three stops you should expect along the way.

1. Table Stakes — Building a Solid AI Foundation

Every transformation begins with groundwork. Without it, pilots turn into “random acts of AI” that create noise but little value.

• Executive Sponsor: Is there a senior leader accountable for AI adoption? Without one, AI efforts stall in middle management.

• AI Council: Do you have a cross-functional team governing priorities? This prevents departments from pulling in different directions.

• AI Charter & Policy: Are there shared principles and guardrails? Clear documentation builds trust and reduces ethical risk.

• Use Case Pipeline: Are you identifying and scoring opportunities? Many organizations confuse experimentation with strategy.

• Pilot Projects: Are you testing in controlled environments before scaling? Pilots should generate data, not just hype.

Example: A professional services firm created an AI Council and piloted generative AI for proposal drafting. Within six months, they cut prep time in half—but more importantly, the council ensured those gains didn’t stay siloed in one practice group.

2. Leading Policies — From Pilots to Sustainable Practices

Once a foundation exists, the challenge is turning experiments into systems. This is where many companies stall.

• ROO + ROI Mapping: Are you measuring both operational and financial returns? If not, you risk killing projects that create value but don’t show up immediately in dollars.

• Scaling Projects: Have you defined thresholds for moving pilots into production? Without this, teams keep tinkering without impact.

• AI Literacy & Training: Are employees empowered, or fearful? Encouraging “secret cyborgs” (employees already experimenting) often surfaces the most practical use cases.

• Cross-Department Collaboration: Are departments sharing data and lessons? AI thrives on connected insights; silos suffocate it.

Example: A mid-market manufacturer scaled a successful customer-support pilot, integrating it into ERP workflows. Operational ROI (faster ticket resolution, fewer escalations) convinced leadership to expand AI to other functions.

3. Next Horizon Practices — Pioneering the Future of AI

Organizations that mature in AI don’t stop—they institutionalize. They turn adoption into advantage.

• Continuous Learning Journeys: Are skills refreshed regularly? AI tools evolve monthly; yesterday’s literacy can’t keep up.

• AI Talent Recruitment: Do you have dedicated expertise where needed? Generalist curiosity helps, but advanced use cases require specialists.

• Innovation Incentives: Are employees rewarded for AI-driven improvements? Without incentives, experimentation fizzles.

• AI Transparency Reports: Are you showing stakeholders how AI is used responsibly? Trust is as important as efficiency.

Example: A global media company publishes an annual AI report detailing use cases, safeguards, and lessons learned. The transparency has strengthened employee buy-in and built credibility with clients.

Moving Forward
The path to AI transformation isn’t linear, but it is navigable. By building a foundation, scaling responsibly, and adopting forward-looking practices, organizations can unlock AI’s real potential. The question isn’t whether AI will reshape your business, but how—and how quickly—you’re willing to adapt.