Your next big AI decision isn’t build vs. buy — It’s how to combine the two

A year ago, agentic AI lived mostly in pilot programs. Today, CIOs are embedding it inside customer-facing workflows where accuracy, latency, and explainability matter as much as cost.

As the technology matures beyond experimentation, the build-versus-buy question has returned with urgency, but the decision is harder than ever. Unlike traditional software, agentic AI is not a single product. It’s a stack consisting of foundation models, orchestration layers, domain-specific agents, data fabrics, and governance rails. Each layer carries a different set of risks and benefits.

CIOs can no longer ask simply, “Do we build or do we buy?” They must now navigate a continuum across multiple components, determining what to procure, what to construct internally, and how to maintain architectural flexibility in a landscape that changes monthly.

Know what to build and what to buy

Matt Lyteson, CIO of technology transformation at IBM, begins every build-versus-buy decision with a strategic filter: Does the customer interaction touch a core differentiator? If the answer is yes, buying is rarely enough. “I anchor back to whether customer support is strategic to the business,” he says. “If it’s something we do in a highly specialized way — something tied to revenue or a core part of how we serve clients — that’s usually a signal to build.”

IBM even applies this logic internally. The company uses agentic AI to support employees, but those interactions rely on deep knowledge of a worker’s role, devices, applications, and historical issues. A vendor tool might address generic IT questions, but not the nuances of IBM’s environment.

However, Lyteson cautions that strategic importance isn’t the only factor. Velocity matters. “If I need to get something into production quickly, speed may outweigh the desire to build,” he says. “I might accept a more generic solution if it gets us value fast.” In practice, that means CIOs sometimes buy first, then build around the edges, or eventually build replacements once the use case matures.

Another useful insight can be taken from Wolters Kluwer, where Alex Tyrrell, CTO of health, runs experiments early in the decision process to test feasibility. Rather than committing to a build-or-buy direction too soon, his teams quickly probe each use case to understand whether the underlying problem is commodity or differentiating.

“You want to experiment quickly to understand how complex the problem really is,” he says. “Sometimes you discover it’s more feasible to buy and get to market fast. Other times, you hit limits early, and that tells you where you need to build.”

Tyrrell notes that many once-specialized tasks — OCR, summarization, extraction — have been commoditized by advances in gen AI. These are better bought than built. But the higher-order logic that governs workflows in healthcare, legal compliance, and finance is a different story. Those layers determine whether an AI response is merely helpful or genuinely trusted.

That’s where the in-house build work begins, says Tyrrell. And it’s also where experimentation pays for itself since quick tests reveal very early whether an off-the-shelf agent can deliver meaningful value, or if domain reasoning must be custom-engineered.

Buyer beware

CIOs often assume that buying will minimize complexity. But vendor tools introduce their own challenges. Tyrrell identifies latency as the first trouble spot. A chatbot demo may feel instantaneous, but a customer-facing workflow requires rapid responses. “Embedding an agent in a transactional workflow means customers expect near-instant results,” he says. “Even small delays create a bad experience, and understanding the source of latency in a vendor solution can be difficult.”

Cost quickly becomes the second shock. A single customer query might involve grounding, retrieval, classification, in-context examples, and multiple model calls. Each step consumes tokens, and vendors often simplify pricing in their marketing materials. But CIOs only discover the true cost when the system runs at scale.

Then comes integration. Many solutions promise seamless CRM or ticketing integration, but enterprise environments rarely fit the demo. Lyteson has seen this play out. “On the surface it looks like plug-and-play,” he says. “But if it can’t easily connect to my CRM or pull the right enterprise data, that’s more engineering, and that’s when buying stops looking faster.”

These surprises are shifting how CIOs buy AI. Instead of purchasing static applications, they increasingly buy platforms — extensible environments in which agents can be orchestrated, governed, and replaced.

Remember the critical roles of data architecture and governance

Most IT leaders have figured out the crucial role of data in making AI work. Razat Gaurav, CEO of software company Planview, compares enterprise data to the waters of Lake Michigan: abundant, but not drinkable without treatment. “You need filtration — curation, semantics, and ontology layers — to make it usable,” he says. Without that, hallucinations are almost guaranteed.

Most enterprises operate across dozens or hundreds of systems. Taxonomies differ, fields drift, and data interrelationships are rarely explicit. Agentic reasoning fails when applied to inconsistent or siloed information. That’s why vendors like Planview and Wolters Kluwer embed semantic layers, graph structures, and data governance into their platforms. These curated fabrics allow agents to reason over data that’s harmonized, contextualized, and access-controlled.

For CIOs, this means build-versus-buy is intimately tied to the maturity of their data architecture. If enterprise data is fragmented, unpredictable, or poorly governed, internally built agents will struggle. Buying a platform that supplies the semantic backbone may be the only viable path.

Lyteson, Tyrrell, and Gaurav all stressed that AI governance consisting of ethics, permissions, review processes, drift monitoring, and data-handling rules must remain under CIO control. Governance is no longer an overlay, it’s an integral part of agent construction and deployment. And it’s one layer CIOs can’t outsource.

Data determines feasibility, but governance determines safety. Lyteson describes how even benign UI elements can cause problems. A simple thumbs up or down feedback button may send the full user prompt, including sensitive information, to a vendor’s support team. “You might approve a model that doesn’t train on your data, but then an employee clicks a feedback button,” he says. “That window may include sensitive details from the prompt, so you need governance even at the UI layer.”

Role-based access adds another challenge. AI agents can’t simply inherit the permissions of the models they invoke. If governance isn’t consistently applied through the semantic and agentic layers, unauthorized data may be exposed through natural-language interactions. Gaurav notes that early deployments across the industry saw precisely this problem, including cases where a senior executive’s data surfaced in a junior employee’s query.

Invest early in an orchestration layer, your new architectural centerpiece

The most striking consensus across all three leaders was the growing importance of an enterprise-wide AI substrate: a layer that orchestrates agents, governs permissions, routes queries, and abstracts the foundation model.

Lyteson calls this an opinionated enterprise AI platform, a foundation to build and integrate AI across the business. Tyrrell is adopting emerging standards like MCP to enable deterministic, multi-agent interactions. Gaurav’s connected work graph plays a similar role inside Planview’s platform, linking data, ontology, and domain-specific logic.

This orchestration layer does several things that neither vendors nor internal teams can achieve alone. It ensures agents from different sources can collaborate and provides a single place to enforce governance. Moreover, it allows CIOs to replace models or agents without breaking workflows. And finally, it becomes the environment in which domain agents, vendor components, and internal logic form a coherent ecosystem.

With such a layer in place, the build-versus-buy question fragments, and CIOs might buy a vendor’s persona agent, build a specialized risk-management agent, purchase the foundation model, and orchestrate everything through a platform they control.

Treat the decision to build vs buy as a process, not an event

Gaurav sees enterprises moving from pilots to production deployments faster than expected. Six months ago many were experimenting, but now they’re scaling. Tyrrell expects multi-partner ecosystems to become the new normal, driven by shared protocols and agent-to-agent communication. Lyteson believes CIOs will increasingly manage AI as a portfolio, constantly evaluating which models, agents, and orchestration patterns deliver the best results for the lowest cost.

Across these perspectives, it’s clear build-versus-buy won’t disappear, but it will become a continuous process rather than a one-time choice.

In the end, CIOs must approach agentic AI with a disciplined framework. They need clarity about which use cases matter and why, and must begin with small, confident pilots, and scale only when results are consistent. They should also build logic where it differentiates, buy where commoditization has already occurred, and treat data curation as a first-class engineering project. It’s important as well to invest early in an orchestration layer that harmonizes agents, enforces governance, and insulates the enterprise from vendor lock-in.

Agentic AI is reshaping enterprise architecture, and the successful deployments emerging today aren’t purely built or purely bought — they’re assembled. Enterprises are buying foundation models, adopting vendor-provided domain agents, building their own workflows, and connecting everything under shared governance and orchestration rails.

The CIOs who succeed in this new era won’t be the ones who choose build or buy most decisively. They’ll be the ones who create the most adaptable architecture, the strongest governance, and the deepest understanding of where each layer of the AI stack belongs.