Practical AI Deployment: Why Most Organizations Get It Wrong
By Jean-Luc Martel —
Every organization wants to "leverage AI." Most are approaching it backwards.
The typical pattern:
- Identify a use case
- Acquire AI tools
- Deploy to existing workflows
- Wonder why adoption fails or results disappoint
The problem isn't the AI. It's that AI capabilities expose organizational dysfunction that was previously hidden or tolerable.
What AI Actually Requires
Effective AI deployment demands:
Clean data pipelines
Not "big data." Not "data lakes." Clean, well-structured data with clear lineage and governance. Most organizations discover their data is:
- Siloed across incompatible systems
- Inconsistently formatted
- Missing key metadata
- Subject to undocumented transformation logic
AI doesn't fix this. It reveals it brutally.
Clear decision authority
AI systems provide insights, predictions, or recommendations. Someone has to act on them. In most organizations, decision authority is ambiguous, distributed, or politically contested.
Deploying AI without resolving this just moves the bottleneck. You get better predictions that no one has authority to implement.
Feedback loops
AI systems improve through feedback. This requires:
- Capturing outcomes
- Linking them back to predictions
- Updating models systematically
Most organizations can't do this because they don't measure outcomes consistently, or the time lag between prediction and outcome spans multiple reporting cycles and organizational changes.
The Implementation Reality
Successful AI deployment looks less like:
- "We're using GPT for customer service"
And more like:
- "We restructured our customer service data architecture, clarified decision protocols for escalation, implemented outcome tracking, and now we're using AI to optimize routing and response generation."
The AI is the easy part. The organizational infrastructure is the hard part.
Where to Actually Start
If you're serious about AI deployment:
Start with high-frequency, low-stakes decisions
Build feedback loops where:
- Outcomes are visible quickly (hours to days, not months)
- Wrong predictions have limited downside
- You can iterate rapidly
Solve the data problem first
You don't need perfect data. You need:
- Consistent data
- Known data lineage
- Clear ownership
- Version control
Make decision authority explicit
Document:
- Who makes what decisions
- What triggers a decision
- What information is required
- How outcomes are measured
The Honest Assessment
Most organizations aren't ready for AI deployment because they're not ready for systematic decision-making. AI just makes this painfully obvious.
The good news: fixing these problems makes the organization more effective even without AI. The AI just amplifies what's already there—good or bad.
What Success Looks Like
Organizations that actually benefit from AI:
- Had decent processes before AI
- Use AI to make those processes faster and more consistent
- Continuously measure and improve based on outcomes
- View AI as tooling, not magic
It's less exciting than the hype suggests. It's also more valuable.