Starting Small but Supported

The graveyard of enterprise AI is filled with ambitious projects that tried to boil the ocean. The survivors started with a puddle.

The Scale Paradox

There’s a persistent myth in enterprise AI: to get real value, you need to go big. Enterprise-wide rollout. All departments. Full integration. Massive budgets.

This approach has a spectacular failure rate.

The paradox: the bigger you start, the less likely you are to finish. Complexity compounds. Stakeholders multiply. Scope creeps. And by the time you’ve built the comprehensive solution, the requirements have changed.

Knowledge engineering amplifies this risk. Unlike generic software, knowledge systems depend on organizational context that varies wildly between teams, departments, and use cases. What works for the risk assessment team won’t work for customer service — not because the technology is different, but because the knowledge is different.

The smart path is counterintuitive: start small, but don’t start alone.

The Pilot Trap

Let’s be clear about what “starting small” doesn’t mean.

Many organizations run AI pilots that are doomed from the start:

The Science Project: A small team explores what’s technically possible with no clear business outcome. They build something impressive that never reaches production.

The Shadow IT Experiment: Someone in a department starts using AI tools without infrastructure, governance, or support. It works until it doesn’t — and then there’s no path forward.

The Vendor Demo: A vendor runs a proof-of-concept with their data and their experts. It looks great. But when you try to replicate with your data and your people, it falls apart.

The Isolated Success: A team gets something working but doesn’t document how. When they move on, the knowledge of how to maintain it walks out with them.

Each of these is “starting small” in a way that guarantees you stay small — or fail entirely.

Starting Small, Done Right

The alternative is a structured approach that treats the pilot as a stepping stone, not a destination:

graph TB
TITLE["<b>FROM PILOT TO SCALE</b>"]
TITLE --> START

START["<b>START</b>"] --> P1["<b>PHASE 1: DESIGN</b><br/>• Select use case<br/>• Map knowledge<br/>• Define metrics<br/>• Establish governance"]

P1 --> P2["<b>PHASE 2: PILOT</b><br/>• Extract knowledge<br/>• Build minimal MVP<br/>• Validate w/ users<br/>• Measure outcomes"]

P2 --> D1{"<b>GO/NO-GO</b><br/>Decision #1"}
D1 -->|"✓ GO"| P3["<b>PHASE 3: OPERATIONALIZE</b><br/>• Harden for prod<br/>• Integrate<br/>• Train users<br/>• Feedback loops"]

P3 --> D2{"<b>GO/NO-GO</b><br/>Decision #2"}
D2 -->|"✓ GO"| P4["<b>PHASE 4: SCALE</b><br/>• Replicate<br/>• Build capability<br/>• Expand coverage<br/>• Center of Excellence"]

D1 -->|"✗ NO-GO"| END1["<b>Stop or Redesign</b>"]
D2 -->|"✗ NO-GO"| END2["<b>Stop or Rethink</b>"]

style TITLE fill:#fef2f2,stroke:#dc2626,stroke-width:4px,color:#991b1b,font-size:18px
style START fill:#fef2f2,stroke:#dc2626,stroke-width:3px,min-width:150px
style P1 fill:#fff7ed,stroke:#f97316,stroke-width:3px,min-width:200px
style P2 fill:#fef2f2,stroke:#dc2626,stroke-width:3px,min-width:200px
style P3 fill:#fff7ed,stroke:#f97316,stroke-width:3px,min-width:200px
style P4 fill:#d1fae5,stroke:#10b981,stroke-width:4px,min-width:200px
style D1 fill:#fef3c7,stroke:#f59e0b,stroke-width:3px,min-width:150px
style D2 fill:#fef3c7,stroke:#f59e0b,stroke-width:3px,min-width:150px
style END1 fill:#fee2e2,stroke:#dc2626,min-width:130px
style END2 fill:#fee2e2,stroke:#dc2626,min-width:130px
linkStyle 3,5,6,7 stroke:#1f2937,stroke-width:3px,color:#1f2937

Phase 1: Design (Before You Build Anything)

Select the right use case. Not every use case is a good pilot. The ideal first project has:

• Clear, measurable business value
• Accessible subject matter experts
• Bounded scope (not “transform everything”)
• Low regulatory risk for experimentation
• Visible enough to attract attention, but not so critical that failure is catastrophic

Map the knowledge landscape. Before extraction begins, understand what you’re dealing with:

• What knowledge types are involved? (Refer to Module 2)
• Where does the knowledge live? (Refer to Module 1)
• How current does it need to be? (Refer to Module 4)
• What’s the capability decomposition? (Refer to Module 3)

Define success metrics. If you can’t measure improvement, you can’t prove value. Metrics should cover:

• Business outcomes (time saved, errors reduced, decisions improved)
• Knowledge quality (accuracy, currency, coverage)
• User adoption (usage rates, satisfaction, feedback)

Establish governance. Even a pilot needs rules:

• Who owns the knowledge assets created?
• Who can modify them?
• How are changes reviewed?
• What happens when someone leaves?

Phase 2: Pilot (Build, But Build Right)

Extract knowledge with rigor. Use proper methods (expert interviews, decision journaling, scenario elicitation) — not just document scraping. The pilot is your chance to learn what extraction approaches work in your organization.

Build minimal, but production-quality. The pilot solution should be simple in scope but robust in execution. Cutting corners on quality just means rebuilding later.

Validate with real users. Not a demo for executives — actual use by the people who will depend on it. Their feedback is data.

Measure ruthlessly. Capture everything you need to make the go/no-go decision. What worked? What didn’t? What surprised you?

→ GO/NO-GO Decision #1: Did the pilot prove value? Is the knowledge extraction approach validated? Are users adopting?

Phase 3: Operationalize (Make It Real)

Harden for production. The pilot revealed edge cases, failure modes, and integration needs. Address them before expanding.

Train users properly. Not just “here’s the tool” — help them understand when to use it, when to override it, and how to provide feedback.

Establish feedback loops. The knowledge system should improve over time. Build mechanisms for users to flag issues, suggest improvements, and contribute updates.

→ GO/NO-GO Decision #2: Is the solution production-stable? Are feedback loops working? Is the organization ready to support expansion?

Phase 4: Scale (Replicate What Works)

Extract patterns, not just solutions. What did you learn about knowledge extraction that applies to other use cases? Document the approach, not just the output.

Build internal capability. Each project should leave your organization more capable of doing the next one. If you’re still entirely dependent on external help, you haven’t scaled — you’ve just repeated.

Create a center of excellence. As you accumulate experience, centralize the expertise. Not to control all projects, but to accelerate them.

The Support Question

“Starting small” doesn’t mean “starting alone.” The organizations that scale successfully get help at the right points:

The goal isn’t to outsource forever — it’s to build capability while delivering value. Each engagement should leave you more independent, not less.

What “Supported” Looks Like

The right support relationship has specific characteristics:

Methodology transfer, not just delivery. You should understand why decisions were made, not just receive outputs.

Documented artifacts. Everything created should be documented well enough that your team can maintain and extend it.

Explicit capability building. Each phase should include training or shadowing so internal staff learn the approaches.

Decreasing dependency. The support model should explicitly plan for handover and independence.

Honest assessment. A good partner will tell you when you’re not ready to scale, when a use case is too ambitious, or when internal capability gaps need addressing first.

The Uncomfortable Truth

Most organizations underinvest in the early phases and overinvest in scale. They want to skip the messy work of knowledge mapping and go straight to the AI rollout.

This is backwards.

The quality of your knowledge engineering is determined in Phase 1. Everything after that is execution. If you don’t understand your knowledge landscape, the best AI in the world can’t help you.

Start small. Start right. And get help where it accelerates you without creating dependency.

Want to go deeper?

A Design Workshop is Phase 1 done right. We work with your team to select the right pilot use case, map the knowledge landscape, and design an extraction approach that sets you up for success — not just in the pilot, but in the scale that follows.

Book a Design Workshop →