AI Adoption Without the Hype
A practical framework for evaluating where AI adds value in your business—and where it's just expensive novelty.
Every client meeting now includes the question: "What about AI?" Usually followed by vague excitement or vague fear, depending on the headlines that week.
Here's how we cut through the noise.
The Value Assessment Framework
Before any AI discussion, we ask four questions:
1. What's the Current Cost?
AI makes sense when there's a significant existing cost to reduce. That cost might be:
- Time spent on repetitive tasks
- Error rates in manual processes
- Expertise bottlenecks (only 2 people can answer certain questions)
- Customer friction (long wait times, generic responses)
If you can't quantify the current pain, AI won't have measurable impact.
2. What's the Acceptable Error Rate?
AI systems make mistakes. Period. The question is whether those mistakes are acceptable.
| Use Case | Typical Error Rate | Acceptable? |
|---|---|---|
| Email categorization | 5-10% | Usually yes |
| Document summarization | 10-15% need edits | Often yes |
| Customer-facing answers | 5%+ | Rarely |
| Medical/Legal advice | Any | Never |
We don't build AI systems where the error consequences exceed the value of automation.
3. Do You Have the Data?
AI needs examples to learn from. If you don't have:
- Documented processes
- Historical decisions with outcomes
- Feedback loops to measure quality
...then you're not ready for AI. Start by documenting what you want to automate.
4. Who Maintains It?
AI systems degrade without maintenance. Models drift. Edge cases accumulate. Someone needs to:
- Monitor accuracy metrics
- Review flagged edge cases
- Update training data
- Explain decisions when challenged
If you don't have this capacity, consider whether the project is sustainable.
Where We See Real Value
Based on actual implementations:
Document Processing: Extracting structured data from unstructured documents (invoices, contracts, forms). High volume, well-defined outputs, human review for edge cases.
Internal Knowledge Search: Making existing documentation searchable with natural language. Lower risk than customer-facing, high value for organizations with years of accumulated knowledge.
Triage and Routing: Categorizing incoming requests to route them efficiently. Humans still handle the requests; AI just reduces sorting time.
Content Drafting: Generating first drafts of repetitive content (job postings, product descriptions, response templates). Always with human editing.
Where We Push Back
Customer-Facing Chatbots: Unless you're prepared for public failures and have comprehensive fallback systems, the reputational risk rarely justifies the cost savings.
Decision Automation: Any process where the AI's decision directly triggers action without human review. The "automation bias" problem is real—people trust AI outputs they shouldn't.
"Innovation Theater": AI projects that exist primarily to appear innovative. If the use case wouldn't make sense without the AI buzzword, it won't make sense with it.
The Honest Conversation
When clients ask about AI, we often end up recommending simpler solutions:
- Better search on existing content
- Automated workflows with traditional logic
- Improved documentation and training
- Process redesign before automation
Sometimes the unsexy answer is the right one. AI is a tool, not a strategy.
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