Sales, Trends & Insights

Build versus buy: Why in-house technology gets more expensive over time

By Tony Smith — On July 8, 2026

A team of four people discussing at a table in a modern office setting, with one person working on a laptop.

For years, technology decisions were limited by what organizations could realistically build. Today, generative AI has changed that conversation. Internal teams can prototype tools, connect workflows, and create useful systems faster than ever before. What once required months of engineering effort can now take weeks, sometimes less.

That speed is exciting, but it also makes the decision to build feel more practical than it used to. When a team can solve a narrow problem quickly, the natural question becomes: Why buy what we can build ourselves? But that question misses the bigger picture.

The real cost of in-house technology rarely shows up on day one. It shows up later, when the system needs to scale, integrate with more tools, and support more teams. The build versus buy pitfall is the belief that the first release tells you what the solution will cost. In reality, the first release is just the beginning.

Build versus buy starts after launch

The appeal of building is easy to understand. Internal solutions can feel faster, more tailored, and more controlled. Teams can focus on a specific problem, avoid vendor evaluations, and create something that fits the way they work today.

But needs change, systems evolve, and AI capabilities move quickly. A solution that worked well for one team or use case can quickly spiral beyond its original scope.

That’s where the math changes. McKinsey found that 10–20% of technology budgets intended for innovation are diverted to resolving technical debt, while that same debt can account for 20–40% of the total value of a company’s technology estate.

The hidden cost of building is that the work never stops. Every custom integration, permission model, workflow, and AI enhancement needs care. And the more the system matters, the more expensive it becomes to maintain.

The total cost of ownership is bigger than the build

A homegrown solution often starts with a focused goal. Maybe the team needs a better content repository, a faster way to personalize assets, or a custom AI agent to help sellers find answers.

Those are valid needs but, over time, each narrow solution creates new questions:

Can it support more teams without creating duplicate work?
Can it keep content accurate, governed, and easy to find?
Can it connect to the systems people already use?
Can it adapt when buyer needs, products, or regulations change?
Can AI produce relevant answers with the right context and guardrails?
Can leaders see what’s working and what needs attention?

Forrester has warned that unmanaged technical debt can increase operational and maintenance costs, limit business opportunities, and create less resilient and secure systems. That’s why total cost of ownership needs to include more than the initial build. It should account for the people, user support, and opportunity cost required to keep the solution useful.

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A smarter repository isn’t enough

Finding the right content is only part of the job. Go-to-market teams also need content that fits the situation, speaks to the audience, and helps move the work forward.

A repository can store assets, while a connected system can guide people toward the right asset, for the right audience, in the right moment. A connected system can also bring content, learning, coaching, engagement, and analytics together, so teams understand what works and where to improve.

AI makes this even more important. AI is only as valuable as the context it can access and the guardrails around how it works. A disconnected AI tool may summarize content or answer basic questions. But without governed content, integrated workflows, and shared data, it can’t consistently support the needs of a modern revenue organization.

Speed now can slow innovation later

The biggest risk of building in-house isn’t failure. As more teams depend on the system, every change becomes more complex. Internal teams have to manage bugs, update AI capabilities, respond to user feedback, and keep the experience aligned with business needs. When the original builders move to new projects or leave the company, institutional knowledge can disappear with them.

In the end, engineers spend more time maintaining what already exists and less time building what could move the business forward.

Building may still make sense for capabilities that create unique competitive advantage. But for the connective layer that supports go to market teams, the smarter question is whether internal resources should own every future enhancement, integration, support ticket, and governance decision.

A platform changes the economics

The Seismic Enablement Cloud™ gives organizations a unified, AI-powered foundation for connecting content, learning, coaching, buyer engagement, and insights. Instead of stitching together isolated tools or maintaining custom systems, teams can work from a shared environment built to evolve with changing business needs.

A platform gives teams a governed structure for working together, while allowing internal talent to focus on the areas where the business truly differentiates.

The better build versus buy question

The question of building or buying is less about your team’s ability to build a solution. It depends more on your team’s appetite for owning the long-term cost of keeping it current, governed, secure, and valuable.

Speed matters in the AI era, but sustainable speed comes from systems that can keep pace with change. The organizations that get this right will be the ones that invest in systems that reduce complexity, connect teams, and make AI more useful with context behind it.

Ready to rethink the long-term cost of in-house technology? See how Seismic helps teams scale smarter with a platform built to grow with them. Get a demo today!