Build, Buy, Partner: How to Navigate GenAI Without Getting Stuck

It started with a slick demo.
A GenAI-powered interface that responded to prompts, surfaced smart product recommendations, and looked like a shortcut to the future. But three weeks in, no one could quite answer the question:

Who owns this now?

This is a common story.

Everyone’s racing to prove they can do something real with GenAI. Whether it's a conversational storefront, a dynamic UI layer, or an intelligent discovery tool, the pressure is the same: deliver quickly, impress stakeholders, and show tangible results.

But under that urgency sits a harder question: Should we build, buy, or partner?

One team recently had to navigate that decision—across vendor lock-ins, advisory support, internal tooling, and tight timelines. What they learned wasn’t about picking the right model—it was about picking deliberately, with eyes wide open.

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The "Partner" Phase: Fast Momentum, Hidden Constraints

The team started by working with two kinds of partners:

1. A GenAI platform provider, offering a prebuilt, no-code solution for conversational product discovery.
2. A strategic consultant (in this case, me), brought in to help guide architecture and evaluate tooling.

At first, the arrangement worked. The platform helped them demo something fast. Strategic support helped shape roadmap conversations. But as needs evolved, constraints emerged:

• The platform was a black box—hard to customize, impossible to adapt to new data sources or client needs.
• Prototyping velocity slowed when the vendor couldn’t support fast turnarounds.
• My role, scoped for advisory rather than embedded support, couldn’t fill delivery gaps.

These weren’t failures—they reflected a common tradeoff: partners help you start, but they can’t always help you grow. Without internal ownership, external support hits a ceiling.

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The "Build" Pivot: Real Ownership, Real Tradeoffs

To gain more control, the team shifted to building an internal GenAI framework—focused on flexibility, speed, and reusability.

They explored:

• Vertex AI for model orchestration
• Vercel AI SDK and CopilotKit for conversational interfaces
• Firecrawl for dynamic scraping
• A modular UI system—composable blocks that could be arranged on the fly

They also leaned on tools like Cursor to speed up development. While effective, Cursor—like many AI-native dev tools—created friction when used without clear architectural scaffolding. Fast became messy without strong instructions or guardrails.

Other challenges surfaced:

• Too many architectural decisions with too little time
• Internal developers strong on frontend but still new to GenAI orchestration patterns
• A tight deadline: ~3 weeks to produce something meaningful

Still, building gave the team something essential: agency. Even with imperfections, they owned their decisions—and their path forward.

Lesson: Building gives leverage, but only if scoped tightly. Not every idea needs to be a platform. Some just need to prove what's possible.

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The "Buy" Track: Good for Learning, Limited for Differentiation

Alongside their build and partner tracks, the team explored buyable tools: GenAI playgrounds, hosted APIs, and UI kits.

These were great for learning:

• Fast prototyping
• Lightweight validation
• A sense of what's possible

But they weren’t production-ready:

• Limited integration paths
• Minimal UI flexibility
• No control over behavior or data flow

Lesson: Buying is useful for exploration—not for strategic differentiation. Use it to learn, not to lead.

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Why Strategy Still Needs Ownership

Across all three paths, one insight became clear:

Tools don’t build products. People do.

This team had strong instincts, motivated people, and promising prototypes. But shifting roles, unclear vision, and fuzzy ownership slowed momentum.

• Some teammates weren’t sure what product they were building.
• Others didn’t know who to ask for decisions.
• Advisors could suggest, but not unblock.

Managing directors noted another issue: there was no internal change ambassador.
No one person was explicitly responsible for helping the team translate ideas into momentum, connect the dots across stakeholders, or maintain continuity between prototypes and product strategy.

That role—part translator, part champion, part connective tissue—is often the difference between exploration and execution. Without it, even strong teams can stall.

As one person put it: “We're just building it out. But I'm not sure where it's going.”

This isn’t unusual. It’s exactly why GenAI work—fast-moving, multidisciplinary, experimental—needs clarity and continuity early.

Strategy without delivery is suggestion. Tooling without ownership is potential. And without someone to carry the thread, innovation drifts.

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A Practical Playbook for Moving Deliberately

1. Clarify What Each Mode Offers

• Buy to explore quickly and learn cheaply.
• Partner when you have access, alignment, and shared incentives.
• Build when control and long-term adaptability matter.

None are wrong—but each has different requirements.

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2. Define Boundaries, Not Just Ambitions

Prototype ≠ product. Demo ≠ roadmap. Decide upfront:

• What’s disposable?
• What needs to scale?
• What’s here to prove a point?

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3. Keep Things Modular

Use abstraction layers—model routers, agent harnesses, UX wrappers—to protect flexibility.
Decouple logic from infrastructure. Avoid hardwiring decisions.

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4. Focus on the UX Delta

Most GenAI demos today are glorified search or form-fillers. That’s fine—as long as you know what better looks like:

• Adaptive refinement
• Intent modeling
• Context-aware layouts
• Conversational memory

These aren’t polish—they’re value.

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Final Thought: It's Not the Mode—It’s the Momentum

The real mistake isn’t building or buying or partnering. It’s assuming any one of those will carry you to a finished product.

If you’re working in GenAI—especially in ambiguous, fast-moving environments—ask:

• Do we control this?
• Can we adapt it?
• Who owns the next decision?

Demo fast. Build smart. Partner with intention.
But always, always own your momentum.