Enterprise AI: Build or Buy?
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Many enterprise AI initiatives promise reusability but end up creating silos instead. See why “build once, use everywhere” usually fails, and what a true multi-use case AI platform actually requires.
Being the bearer of bad news is never fun, but letting you continue down a path of delusion is far more dangerous. With that said, we need to discuss a promise you’ve been sold about AI platforms that handle infinite use cases. They told you that all you have to do is build it once and you’ll be able to use it everywhere.
It's honestly a compelling pitch. Develop an AI capability for one department, then extend it across the organization. Amortize the investment. Multiply the value. The business case writes itself.
The truth is however, six months later, reality usually looks different. The customer service team has one AI initiative. Sales has another. Operations need something built entirely separate. Each requires its own data pipeline, its own integration work, its own dedicated resources. The AI that was supposed to unify your enterprise has instead fragmented it further.
And this pattern repeats across industries. According to MIT's NANDA research, 95% of enterprise AI pilots fail to deliver measurable ROI. This makes total sense when you consider that S&P Global found that 42% of companies abandoned most of their AI initiatives in 2025, up from just 17% the year before. The average for proof-of-concepts isn’t any better as organizations scrap 46% of AI before they ever reach production.
The problem isn't ambition. The problem is that reusability was treated as an eventual benefit rather than a foundational requirement. Reusability isn't a deployment problem. It's an architecture problem that begins before the first line of code.
Enterprise AI investments rarely get approved on the strength of a single use case. The business case that launches most initiatives depends on projected scale. The vision is to build the capability once, deploy it many times, and watch the returns compound.
This assumption feels reasonable. Software, after all, is inherently replicable. So stakeholders naturally extend this logic to AI. The vendor pitch reinforces it and the organizational pressure to justify AI investment through projected scale creates its own momentum.
Before you know it, the business case gets written around multi-use-case deployment, even when the technical approach doesn't support it.
Menlo Ventures found that organizations have identified an average of 10 potential AI use cases. But only 24% are prioritized for near-term implementation, and a third remain stuck in prototype or evaluation. So as you can see, these dishonest deals keep widening the gap between identified opportunity and realized value.
When AI projects fail to scale across use cases, the usual explanations focus on execution. Either insufficient resources, competing priorities, or lack of executive sponsorship become the scapegoat. These factors matter. But they obscure a more fundamental issue. Most enterprise AI implementations are architecturally incapable of reuse from the moment they're designed.
AI solutions become welded to their training data and source systems in ways that traditional software does not. Fine-tuning a model for one use case creates dependencies that break portability. The prompts are optimized for specific data structures. The outputs are formatted for particular workflows. The evaluation criteria reflect one team's definition of success.
An AI solution that works generically works poorly in practice. But optimization for one context creates rigidity. The customer service AI that excels at ticket classification may be useless for sales forecasting, even if the underlying model could theoretically support both. MIT's research revealed that large enterprises take an average of 9 months to scale AI from pilot to production.
Different teams select different tools, clouds, and frameworks. Each choice makes sense in isolation. Collectively, they create an environment where no shared foundation exists for models, data access, or observability. The growth of AI agents illustrates this fragmentation. Every department wants one. Sales buys a prospecting bot. HR buys a policy bot. Customer support buys a chatbot. This looks like progress. It’s actually friction.
Research from Writer's 2025 enterprise AI report found that 72% of executives say their company develops AI applications in silos. Meanwhile, 68% report that generative AI has created tension or division between IT teams and other business areas. The technology that was supposed to break down barriers is instead reinforcing them.
When you look at these factors in aggregate, you realize these aren't failures of execution. They're predictable outcomes of how most organizations approach AI.
The shift from a single-use to a multi-use case AI deployment requires rethinking how AI capabilities are structured, deployed, and governed. Let’s take a look at the different variables that actually make this possible.
Reusable AI treats capabilities as composable components. Data extraction is a building block. Document processing is a building block. Knowledge retrieval is a building block. Each can be configured for specific contexts without being rebuilt for each one. This modularity enables recombination without reconstruction.
Blueprints accelerate this process: pre-validated patterns that encode architectural decisions, integration approaches, and governance requirements. They provide starting points that teams can customize rather than reinvent.
Reusable architecture separates what changes from what stays constant. Model selection should be independent of use-case implementation. If swapping from one language model to another requires rebuilding applications, the architecture is too tightly coupled. LLM-agnostic approaches allow organizations to evolve their AI capabilities as the technology landscape shifts.
To that point, data integration should function as a shared service, not per-project work. When every AI initiative begins with a data engineering sprint, reusability is impossible by definition.
And last but not least, observability should span all AI applications from a single layer. Fragmented monitoring creates blind spots. Unified observability and reporting enables performance comparison, drift detection, and governance enforcement across the portfolio.
Scaling AI requires scaling trust. That means centralized visibility into all deployments, consistent security and compliance posture, and the ability to update, retrain, or swap components without cascading changes.
Gartner predicts that over 40% of agentic AI projects will be canceled by 2027 due to escalating costs, unclear business value, or inadequate risk controls. The projects that survive will be those with governance embedded from the start, not bolted on after problems emerge.
The myth isn't that reusable AI is impossible. Organizations are deploying multi-use case AI platforms today. The myth is that reusability happens automatically, that it emerges from successful pilots without deliberate architectural investment.
Every AI investment is either building toward a scalable foundation or adding to fragmentation. There’s no neutral option. The pilot that succeeds in isolation but can't extend across the enterprise hasn't created value. It has created technical debt with a positive demo.
The longer organizations delay architectural decisions, the harder consolidation becomes. Each siloed initiative adds integration complexity. Each point solution introduces another data model, another security boundary, another governance gap. The cost of unification grows with every deployment.
Only 31% of businesses have successfully scaled AI to production, according to KPMG. The majority remain trapped in pilot purgatory, proving value in controlled environments but unable to extend it across the enterprise.
The question isn't whether your AI will work. It's whether it will work once or work everywhere. Let us help you make the right architectural choices today, so you can provide unparalleled value to your customers tomorrow.
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