Everyone runs AI pilots. Fewer deploy to production. Here's what separates successful projects from shelf-ware.
The Production Gap
Research consistently shows a significant gap between AI pilots and production deployment:
- Many companies have AI pilots running
- Fewer have AI integrated into daily operations
- The gap between "works in lab" and "works in business"
Why Projects Get Stuck in Pilot
| Reason | Impact |
|---|---|
| No production plan | Pilot designed as experiment |
| Missing governance | Compliance blocks deployment |
| No operations team involved | Nobody to run it |
| Integration complexity | Can't connect to systems |
| No success metrics | Can't prove value |
| Security concerns | Stuck in review |
| Change management missing | Users don't adopt |
Pilot Thinking vs Production Thinking
| Pilot Thinking | Production Thinking |
|---|---|
| "Does it work?" | "Does it scale?" |
| Quick prototype | Robust architecture |
| Happy path testing | Edge case handling |
| Manual oversight | Automated monitoring |
| Few users | All eligible users |
| Experiment governance | Enterprise governance |
| Success = works once | Success = sustainable value |
Characteristics of Successful Projects
Projects that make it to production share these traits:
- Production requirements from day one: Not added later
- Governance built in: Compliance from start
- Operations involved early: Not handed over at end
- Clear success metrics: Business outcomes defined
- Integration planned: Architecture for scale
- Rollback strategy: What if it breaks?
- Change management: Users prepared
The Failure Points
Projects typically fail at specific stages:
- Idea to pilot: Never starts
- Pilot to scale: Works for 10 users, not 1000
- Scale to production: Infrastructure issues
- Production to adoption: Users don't use it
- Adoption to value: No ROI realized
How to Beat the Odds
1. Design for Production
- Build as if it will run forever
- Monitoring, logging, alerting
- Error handling at every step
2. Get Governance Done Early
- Security review before not after
- Compliance checkpoint in timeline
- Risk assessment documented
3. Plan Integration
- API access negotiated
- Data pipeline ready
- Rollback integration tested
4. Involve Operations
- Ops team from kickoff
- Runbook created during pilot
- Support process defined
5. Measure Real Outcomes
- Business metrics not technical
- Baseline before starting
- Ongoing measurement plan
Questions to Assess Readiness
- Who will run this in production?
- What happens when it fails?
- How will we know it's working?
- What's the rollback plan?
- Who approves the deployment?
- How do users get trained?
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