Build vs. Buy

Enterprise AI leaders face a central tension: the existential risk of being left behind versus the business risk of investing without a clear path to ROI. The biggest AI risk today isn’t just a bad bet, it’s a slow one.

For most companies, the reality of scaling AI is a cycle of progress hampered by:

• A 70-80% project failure rate.

• Bottlenecked teams that create huge hidden costs.

• Stalled projects that lack clear, quantified business value.

These problems force organizations to confront the traditional “build vs. buy” dilemma. Building alone is slow and risky, causing many of the stalled projects and bottlenecks listed above.

This high failure rate is precisely what makes the “buy” option so tempting. It’s why every customer is asking: “Why not just use an OOTB model from OpenAI or Anthropic? Isn’t that ‘good enough’?”

While both approaches can work for simple use cases, each presents significant challenges for high-value AI. Building alone is too slow. But buying alone and subscribing to a generic model locks you into rigid, short-term solutions, creates tech debt, deepens vendor lock-in, and can’t evolve with your business.

The answer is to “buy the build”: buy platforms and partners that give you the ability to build custom AI systems in-house.

For organizations with strong technical talent, building in-house has historically been the winning strategy. It’s a proven model for maintaining control, customization, and owning core IP. It is perfectly logical to assume the same will hold true for AI.

The challenge is that generative AI is different in a few critical ways, presenting new, high-friction obstacles that cause this trusted approach to fail at alarming rates. This is why research from MIT shows that the companies least successful at deploying AI were the ones who tried to build tools themselves, without outside help.

This high project fallout rate is a symptom of two deep, strategic issues:

1. Applying AI to the Wrong Problems: Success requires deep expertise to identify which business challenges are truly suited for AI. As Scale CEO Jason Droege noted in a recent interview, many companies fail because they try to apply the technology to the “wrong kind of problem.” Without expert guidance, teams invest heavily in solutions that AI can’t solve effectively or that don’t deliver meaningful business value. This strategic misstep is often compounded by underestimating the team required to succeed.

2. The True Team Cost and Adoption Risk: A successful AI initiative requires more than just engineers. Without a product-centric team that includes Product Managers and Designers, solutions lack user focus, suffer from low adoption, and fail to be integrated into core business processes.

These challenges are consequential. Every month lost to a slow or stalled build is lost revenue, unrealized efficiencies, and a diminishing competitive advantage.

The urgency to buy a solution is often a reaction to a problem already underway: 78% of knowledge workers are bringing their own AI tools to work. This “shadow AI” creates serious risk, pressuring leaders to find an official solution. This pressure typically pushes organizations down two common, but flawed, paths.

The first path is simply licensing a foundational model from a major provider. However, as our customers have pointed out, this approach might benefit the employee, but not the company. A model is just an engine, not a complete vehicle. It lacks the critical orchestration and intelligence layer required for enterprise-grade security, integration, and reliable performance.

Worse, this approach means you are failing to capture your own IP. The data generated from your employees’ interactions provides a one-time benefit and then vanishes. It isn’t used to improve your own system. This is why it is critical to own your data, reports, and the feedback loops. Without them, your firm’s knowledge stays scattered, and each employee’s work fails to strengthen your own.

The second path is taking a step up to a packaged tool or service, which has its own set of limitations. These solutions are built for the 80% of a problem that is common to all customers. A business’s true competitive advantage, however, lies in its unique 20%, which is almost always tied to its complex and proprietary data environments. These generic tools simply can’t handle that level of specificity.

Ultimately, both of these paths lead to a tech debt mortgage. Rigidity forces teams to create brittle workarounds. As the business evolves, it inevitably outgrows the tool, leading to a costly “rip-and-replace” project that erases any initial savings.

The winning strategy resolves the false choice by providing a unified, foundational platform that combines the speed of “buying” with the advantage of a “build” solution.

This “build and buy” model is guided by a core principle: Own what creates a unique business advantage and partner for what provides speed and expertise. This means investing in a centralized AI platform while co-developing the specific application logic that sets the business apart.

This is the strategy that allows you to centralize to lead the market, not chase it, turning your firm’s expertise into true leverage and enabling the reuse of firm-specific patterns. This collaborative model pairs our AI expertise with an organization’s invaluable domain knowledge. A successful AI tool requires an organization’s internal experts to give feedback and constantly improve the system.

Most importantly, this hybrid model is designed to win executive approval. It replaces purely conceptual arguments with the quantified ROI projections leaders require to fund a project. By building on a proven platform with a clear scope for custom development, it delivers a concrete investment case with a predictable path to value.

What leaders need is a proven partner who can guide them to success without an ulterior motive, like locking them into more compute, selling model credits, or profiting off training with their enterprise data.

Scale activates the “Build and Buy” strategy by acting as that partner, combining our technology, our team, and our access to the latest research. This hands-on, co-development approach ensures our partners build valuable, proprietary IP tailored to their exact needs, not a generic solution that can’t evolve.

• How We Accelerate Speed-to-Value: Our foundational platform and forward-deployed teams eliminate common bottlenecks, moving projects from concept to production in months, not years.

• How We Reduce Risk: We bring experience from solving the hardest, most complex AI problems, which is supported by our world-class evaluation suite, security, and red-teaming services. This expertise helps identify the right problems to solve and avoids costly failures.

• Engagement Flexibility: Our “mix-and-match” model adapts to each team’s needs. We can fill roles a team doesn’t have—providing a full pod of MLEs, SWEs, and PMs—or work alongside the roles they do, augmenting their existing talent to be maximally capital-efficient.

• Deployment Flexibility: A huge advantage over rigid SaaS solutions, we provide maximum flexibility with how and where solutions are deployed: in a customer’s own environment, in our cloud, or in a hybrid model to meet any security or data governance requirement.

Together, these elements ensure the result isn’t just a functional tool, but a proprietary AI asset that grows in value.

The era of exploration is over and the mandate is now measurable ROI. The best way to move forward is to build smarter, agentic solutions that learn directly from your best people.

At Scale, we translate our deep expertise from working with frontier AI labs into a co-development partnership that converts your team’s knowledge into a proprietary, continuously improving asset. Let’s identify one high-value business outcome and build your first agentic solution, turning your team’s unique expertise into a durable competitive advantage.

If you’d like support evaluating the build-versus-buy decision for your organization, we’re here to help. The companies that get the most out of AI are the ones that think foundationally and long-term and Scale partners with you at every step, ensuring you make the right strategic choices and capture lasting value from your AI investments.

CTA: Visit us to learn more about the enterprise AI offering and request to speak to the team here: https://scale.com/genai-platform

Scale is fueling the generative AI revolution. Built on a foundation of high-quality data, frontier-grade expertise, and deep partnerships with leading model builders, Scale enables enterprises to build, evaluate, and deploy reliable AI systems for their most important decisions.

Working with Scale, organizations can rapidly develop custom AI agents that learn their unique workflows, tools, and skills—powered by the Scale GenAI Platform, the industry-leading platform for building and controlling advanced, continuously improving agents.

Learn more about our approach for enterprise AI transformation: https://scale.com/enterprise/agentic-solutions