Top Reasons AI Projects Fail #5: Reinventing the Wheel
December 5, 2025 | 4 Minute Read
Innovation can be a double-edged sword. When teams pursue originality at all costs, they often end up duplicating capabilities that proven tools already deliver. The result is a year of engineering effort spent recreating what a foundation model or SaaS API could have done in weeks.
In this part of our Top Reasons AI Projects Fail series, we explore how to balance customization with pragmatism and why velocity often beats vanity in real-world AI delivery.
Why Reinventing the Wheel Is So Costly
Building everything from scratch feels empowering, but it rarely pays off. The AI ecosystem moves faster than most internal development cycles can keep up with, and by the time a bespoke solution ships, it’s already behind the market.
Devlin Liles, CCO, Improving
When organizations over-customize, they consume budget and time that should go toward unique value, such as integrations, proprietary data, or domain-specific improvements. The foundation of AI success is not novelty. It’s acceleration.
Why This Happens
Reinvention doesn’t stem from incompetence; it comes from a mix of culture, curiosity, and risk aversion.
Innovation bias. Teams equate “custom” with “better,” assuming pre-built components are less sophisticated.
Not-invented-here syndrome. Internal teams resist external services out of fear of dependency or loss of control.
Poor time-to-value awareness. Leadership measures progress by features built, not by capabilities delivered.
Lack of a buy-build-blend framework. Without a clear evaluation process, every solution defaults to “build.”
When speed and differentiation are both goals, misaligned incentives can quietly derail both.
How to Prevent This Failure
Avoiding reinvention requires discipline and decision frameworks that prioritize outcomes over originality. The goal isn’t to eliminate customization, but to earn it.
Start Close to Off-The-Shelf. Use cloud services, foundation models, vector databases, and orchestration frameworks as your baseline. Design with what already works.
Adopt a Buy-Build-Blend Approach. Buy when time-to-value matters most, build when defensibility requires it, and blend when integration delivers the best of both.
Customize Only Where it Counts. Invest effort in domain grounding, proprietary tools, or unique datasets that provide a truly competitive advantage.
Prioritize Time-To-Value Over Feature Count. Deliver something useful fast, prove ROI, then harden and expand.
Plan Differentiation Phases. Establish early wins with standard tools and schedule later sprints for hardening, compliance, or unique extensions.
Showcase Value Through Contrast. At one Improving engagement, a client in the legal sector spent millions by building a custom knowledge system that was still unready for production. By contrast, our team delivered a working version using a commercial LLM with SharePoint retrieval in weeks. We later layered in custom redaction and compliance modules to differentiate, achieving the same outcome for a fraction of the cost.
Starting from proven components doesn’t limit creativity; it funds it. Speed to impact builds trust and secures future investment.
Key Takeaways
Reinventing the wheel drains time, money, and momentum. AI success depends on recognizing when existing tools already solve most of the problems.
Start near the shelf and get to 80 percent quickly.
Use the buy-build-blend model to guide decisions.
Customize only when it creates defensible value.
Focus on time-to-value instead of novelty.
Build differentiation after proving impact.
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