Build vs. buy AI: A CIO's decision matrix

Understanding the build option

Not every AI capability needs to be purchased from a vendor. For some organizations, building in-house is the right path for competitive differentiation.

When building makes sense

The strongest case for custom AI development is when existing solutions cannot meet specific business requirements. Off-the-shelf AI products generalize by design. They must work across many companies, which means they sacrifice specialization, workflow nuance or proprietary data logic that creates a competitive advantage.

"CIOs should ask: are we buying intelligence, or are we buying standardization where our business actually needs specialization?" said Oscar Marin, managing director at EY Technology Consulting.

Organizations with in-house AI talent and technical expertise also gain long-term strategic control. Duvvuri calls these capabilities industry native. Owning those data and intelligence layers rather than renting them preserves differentiation that competitors using the same packaged programs cannot replicate.

Challenges and considerations

Development time and upfront costs run higher than most organizations project. Ongoing maintenance, model updates, talent acquisition and retention compound the investment beyond the initial build. What organizations consistently underestimate is the people.

"Talent, business readiness and data are the biggest factors that define success and are mostly underestimated," said Darshan Naik, chief growth officer for technology at Capgemini Americas.

Understanding the buy option

Purchasing an existing AI platform is the right call for a significant portion of enterprise use cases, and the wrong call for others.

When buying makes sense

Buying AI services makes the most sense when speed and proven capability are the main requirements for a project. For organizations with limited in-house AI expertise or resources, buying provides access to model performance that few enterprises can recreate internally. For standard use cases, the vendor market offers options that can be deployed quickly, so teams can focus on core business rather than technology development.

"If success depends on access to the best frontier model performance, that usually points toward buy," Marin said.

Challenges and considerations

The risks of buying AI programs include dependency, customization limits and data control. A Zapier survey found that nearly three out of four survey respondents said losing their primary AI source would negatively affect day-to-day operations, and only 6% said they could walk away without disruption. Vendor programs are designed to be more general-purpose, which can force organizations into a more generic operating model. Ongoing licensing and subscription costs will also grow at scale. Data security and compliance concerns add further risk.

"Critical signals and workflows become trapped inside proprietary platforms, limiting data reuse across use cases and narrowing future architecture choices," Duvvuri said.

CIO's decision matrix

Most failed build-or-buy decisions start the same way.

"One of the most common mistakes is treating build-versus-buy as a purely technology decision," Naik said.

Consider the following criteria:

• Strategic alignment. If the capability shapes how the company competes, buying it gives every competitor that has access to the same platform an edge. "If it is industry-native and differentiation-critical, default to building the data and intelligence layers and then buying and activating the commoditized layers for speed," Duvvuri said.
• Technical complexity. The more AI needs to embed into proprietary workflows, domain-specific data or unique decision logic, the less a packaged program can deliver. "If success depends on how AI is embedded into a specific workflow, operating model or data context, that points much more toward build," Marin said.
• Resource availability. Organizational maturity, talent and data readiness are the most reliable predictors of success, according to Naik. Without them, the build path fails before it delivers value.
• Time-to-market. Buying almost always wins when speed is the determining factor. Jim Rowan, principal and U.S. head of AI at Deloitte Consulting LLP, noted that 75% of organizations still fail to move from proof of concept to enterprise scale, regardless of the path, citing Deloitte's AI Infrastructure Survey.
• Total cost of ownership. Evaluating one use case at a time obscures economics. "Deciding one use case at a time hides the portfolio-level reuse economics where the biggest cost and speed advantages usually live," Duvvuri said.
• Scalability and flexibility. Tools that work for a team or a pilot often become cost-inefficient, especially when governance overhead scales nonlinearly.
• Risk tolerance. Governance frameworks embedded from day one deliver better compliance and adoption outcomes than those added after deployment, according to Rowan.

Score each of the following criteria against your organization's current reality. Four or more signals in one column are a strong indicator. No single criterion is disqualifying on its own.

Hybrid approaches: The middle ground

Many production AI deployments combine build and buy rather than choosing one or the other.

"The real question is not simply whether to build or buy AI," Marin said. "It is which parts of the AI stack you should buy, and which parts you should build to preserve differentiation."

 Hybrid options include:

• Vendor platforms with custom integrations. By using a vendor platform as the foundation and adding custom orchestration, organizations can accelerate development of standard components while maintaining competitive advantages that packaged programs cannot deliver.
• Open source deployment. Open source AI models enable in-house customization without vendor dependency, giving organizations architectural control and the ability to fine-tune on proprietary data.
• Managed AI services. Third-party providers deliver capability faster than internal hiring allows, bridging the gap while internal teams build the skills to take ownership.
• Consulting partnerships. External specialists help organizations build infrastructure and governance frameworks required to eventually manage AI development independently.

Implementation considerations

Building internal AI capabilities requires the following three processes, which organizations consistently underestimate:

• Hiring AI talent for sustainment, not just delivery.
• Establishing a disciplined development lifecycle.
• Creating governance frameworks before deployment.

AI procurement strategy should be treated as an ongoing operational discipline, with defined ownership, exit criteria and periodic vendor reviews built in from the start.

"Today, organizations can build AI-enabled capabilities much faster than before, especially when they use a disciplined lifecycle with strong architecture, testing, governance, and human oversight," Marin said.

Two implementation steps are often treated as afterthoughts, whether the organization is building or buying.

Governance cannot be retrofitted. Creating ethical AI frameworks and security blueprints before deployment, not after, is a critical foundation. "Those that bolt on security later can undermine adoption and create vulnerabilities," Rowan said.

Vendor selection requires the same rigor. Evaluating vendor credibility means assessing stability, not just capability. Zapier's research found more than a third of enterprise leaders are concerned about a single point of failure in their AI vendor relationships, and 32% specifically worry about a vendor shutting down entirely. Forty-four percent of enterprises have responded by using multiple AI vendors simultaneously to spread that risk, and 42% maintain contingency plans for pricing changes or outages.

Emily Mabie, a senior AI automation engineer at Zapier, said there are questions to answer when managing vendor relationships. These questions include "Who actually owns the vendor relationship? If the service starts slipping, what's the exit plan?"

She also suggests that contract and SLA negotiations should include explicit data portability terms and exit provisions. "What happens to my operations if this vendor goes out of business, raises its prices or gets acquired?" Mabie said.

Case studies and real-world examples

EY's partnership with 8090, an AI-native software development company, demonstrates what the build approach looks like when delivery discipline is in place. Together they developed EY.ai PDLC, an AI-native product development lifecycle that combines architecture, governance, automated testing and human oversight. The result compresses what traditionally took months into days or weeks.

"That is the important signal from EY's work with 8090: the opportunity is not just faster coding, but a more structured AI-native lifecycle for building enterprise solutions," Marin said.

The practitioners and analysts who advise on these decisions daily see consistent failure patterns on both sides. "Unsuccessful investments typically emerge as pilots in purgatory," Rowan said.

The build failure pattern is recognizable across client engagements.

"A common pattern is spreading effort too thin, touching many workflows without fully transforming any, rather than proving depth in a single end-to-end use case," Duvvuri said. "The organization underestimates sustainment: the ongoing engineering, AI/MLOps, and governance required as models, data and requirements change."

Production deployments also often expose what pilots hide.

"A failed build often looks like early excitement followed by problems scaling, governing, integrating and sustaining the solution in production," Marin said.

Making the right choice for your organization

The right decision depends on organizational maturity, competitive context, data readiness and risk tolerance. Locking into long-term commitments before the fundamentals are in place creates unnecessary risk.

"Without embedding enterprise-specific context into AI tools, value realization tends to be slow, fragmented, and significantly more expensive than expected," Naik said.

Continuous evaluation is the discipline

AI technologies and vendor capabilities are evolving so quickly that today's build decision may have a strong alternative within a year. "Just as important is maintaining flexibility in the approach, as AI technologies and vendor capabilities continue to evolve rapidly," Naik said.

The decision is strategic, not technical

Balancing innovation with practical business outcomes requires treating build-or-buy as an ongoing architecture question, not a one-time procurement event.

"The key is to avoid treating build-versus-buy solely as a technology decision," Rowan said.
"Rather, it should be seen as a strategic decision and an investment in organizational transformation that aligns people, processes, security and governance."

Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.