Three build vs buy mistakes that derail AI roadmaps (and how to avoid them)

Over the past decade working with global organisations, I've watched countless AI roadmaps stumble at the same hurdle: the build vs buy decision.

The biggest hurdle is (almost) always the decision-making process itself. Teams debate for months, make a call based on incomplete information, then realise 6-9 months later they've chosen wrong. Either they've spent a fortune building something a vendor could've delivered better and faster, or they've bought an expensive platform that doesn't fit their needs and requires massive customisation anyway.

The cost of getting this wrong is delayed delivery, missed opportunities, frustrated teams and leadership losing confidence in your AI strategy altogether.

Here are the three common mistakes and how to avoid them in your 2026 planning.

Mistake 1: Ignoring the hidden costs (especially for build)

This is the mistake that causes the most financial damage and it's surprisingly common.

Teams present business cases for building custom AI solutions based on development costs alone. They calculate engineering time, infrastructure, maybe some contingency and compare that to vendor licensing fees. Build looks cheaper, so they commit.

What they've missed:

• Ongoing maintenance and support. Someone needs to monitor those models, retrain them as data drifts, update them when business requirements change and fix them when they break. That's a permanent team’s job.

• Infrastructure and MLOps. You need robust infrastructure for model deployment, monitoring, versioning and rollback. If you don't have this already, you're building it from scratch.

• Regulatory compliance and audit. In financial services, AI systems need documentation, testing, validation, ongoing monitoring and regular audits. That's particularly true for high-risk use cases like credit decisioning or fraud detection. The compliance overhead for custom solutions is significant.

• Opportunity cost. Every hour your team spends building something that a vendor could provide is an hour they're not spending on use cases that actually differentiate your business.

I've seen build business cases completely reverse decisions when organisations calculate total cost of ownership honestly over three years (not just Y1). What looked like a £200K build vs a £500K vendor solution becomes a £800K custom solution vs £600K vendor solution, when you include everything.

How to avoid this mistake: Create a three-year TCO model for every significant build vs buy decision. Include development, maintenance, infrastructure, compliance, headcount and opportunity cost. If you can't articulate who will own and maintain the solution post-launch (including that responsibility in their actual job description), you don't have a complete business case yet.

Mistake 2: Building when you should buy (and buying when you should build)

The hardest part of build vs buy decisions is having the discipline to follow where the analysis leads, even when it conflicts with what people want to do (and who presented best during the discussion meeting).

I see two patterns repeatedly.

Pattern 1: Building commodity capabilities. Teams decide to build custom solutions for problems that are already solved by vendors. Document processing, customer service chatbots, standard analytics, meeting transcription, speech-to-text - standard use cases today that vendors have spent years and millions optimising. Your custom version won't be better, it'll just cost more and take longer.

The justification is usually "we want control" or "we have unique requirements." But when you dig into those unique requirements, they're often just configuration options that good vendors already support.

Pattern 2: Buying when they should build (or build with). The flip side is organisations that buy vendor platforms for genuinely differentiating use cases, then discover the platform can't actually deliver what they need. Now they're locked into an expensive contract and facing a complete rebuild anyway.

This happens most often when organisations buy based on vendor promises rather than proven capability in similar contexts. A platform that works brilliantly for tech companies might be completely wrong for regulated financial services, where compliance requirements, data governance and risk management are fundamentally different.

How to avoid this mistake: For each AI initiative, ask a simple question: does this use case create competitive advantage for our business, or is it table stakes that everyone in our industry needs?

If it's core to how you compete, creates unique customer value, or leverages proprietary data in ways competitors can't replicate, then build or build-with makes sense. You need control and customisation.

If it's table stakes (or close to), buy. Use vendor solutions and spend your budget on other AI use cases that actually differentiate you.

Mistake 3: Treating build vs buy as a binary choice

Most organisations frame build vs buy as an either/or decision. You're either building everything custom or buying off-the-shelf solutions. But that binary thinking ignores a third option that's often the smartest path: build with partners.

This isn't traditional outsourcing where you hand everything over and hope for the best. It's a deliberate partnership where you maintain strategic control and decision-making authority, but you're accelerating delivery by working with specialists who've solved similar problems before.

Build with works especially well for:

• Customising existing technology. Use cases that need tailoring but don't require you to invent new technology from scratch. A partner with deep experience in AI agents or specific AI use cases can build in weeks what would take your internal team months to figure out.

• Speed plus IP ownership. Situations where you want to own the intellectual property and maintain control, but you need to move faster than your hiring pipeline allows. Partners can staff up quickly, deliver the initial build, then hand over to your team with full documentation and training.

• Bridging capability gaps. If you're building your AI team (and who isn't in 2026?), partners can keep delivery moving while you recruit, and they can mentor your new hires as they onboard.

How to avoid this mistake: Don't limit yourself to build or buy. For each AI initiative in your roadmap, evaluate all three options against your specific constraints around speed, capability, competitive advantage and cost.

Get the full build vs buy micro-assessment

This article covers three of the most common mistakes, but there's more nuance to getting these decisions right, especially when you're balancing multiple AI initiatives, limited budgets and capability constraints.

We've created a micro-assessment with seven prompting questions that'll help you pressure-test your build vs buy decisions for 2026. These aren't theoretical questions, they're the ones we actually use when advising clients on their AI roadmaps.

Sign up for the micro-assessment using the form below - you’ll receive weekly emails from me (Ben), or my Co-Founder Mark, to support with your 2026 AI planning.

If you want to talk through your specific situation, my DMs are always open on LinkedIn. I read and respond to every message.