Build vs. Buy in 2026: The CFO Reality Check

Artificial intelligence has changed the narrative around building software. It has not changed the structural realities of owning it.

The current cycle in technology commentary suggests that SaaS economics are broken, internal engineering is suddenly cheap and AI agents can replace vendors altogether. The claim is seductive: if code can be generated in minutes and integrations can be scaffolded automatically, why pay subscription margins to a third party?

This argument collapses the moment you evaluate it through the lens of finance operations.

The build versus buy decision has never hinged on whether an organization can technically construct a tool. It has always hinged on whether that organization is prepared to assume long-term responsibility for governance, security, evolution and institutional risk.

AI meaningfully lowers the cost of initial development. It does not eliminate the lifecycle burden that defines ownership.

For finance leaders evaluating planning, consolidation or broader EPM workflows in 2026, the fundamentals remain intact.

AI has compressed the front end of software development.

Prototypes can be generated quickly. Integration scripts can be scaffolded. Data transformations can be automated. The cost of reaching a functional Version 1 has fallen dramatically.

But Version 1 has never been the cost center that matters.

The enduring cost of software is not writing code; it is maintaining institutional trust in that code. Trust depends on auditability, data lineage, access control, regression testing, documentation and the ability for multiple stakeholders to understand how the system behaves under edge conditions. None of these have been eliminated by AI.

In many cases, they become more complex. AI-generated systems can function correctly while remaining poorly understood by the organization. That is acceptable for internal utilities. It is unacceptable for systems that influence forecasts, executive reporting, or regulatory disclosures.

AI reduces the friction of building. It does not reduce the consequences of ownership.

When stripped of ideology, the build-versus-buy decision rests on a small number of structural variables.

• Time-to-value. Buying delivers operational capability in weeks. Building, even with AI, requires scoping, iteration, testing, and alignment. Timelines rarely compress as much as initial enthusiasm suggests.
• Total cost of ownership. Internal systems accumulate invisible obligations: maintenance cycles, security reviews, feature requests, model adjustments, performance tuning and user support. Vendors amortize these across thousands of customers; internal teams bear them alone.
• Talent durability. AI can assist engineers. It cannot assume accountability. When a key builder leaves, institutional risk becomes real. Finance systems cannot depend on tribal knowledge or undocumented logic.
• Security and compliance. SOC 2 controls, GDPR alignment, audit trails and role-based access governance are not one-time implementations. They are ongoing disciplines. Vendors exist largely to absorb that burden at scale.
• Integration fragility. Internal builds rarely fail because the interface is inadequate. They fail because ERP schemas evolve, dimensional models change, APIs break, or reconciliation logic becomes brittle. Maintaining data plumbing over multiple fiscal cycles is significantly harder than generating it.
• Opportunity cost. Every engineering hour allocated to internal finance infrastructure is an hour not spent on product differentiation or revenue-generating capability.

AI accelerates development. It does not neutralize these structural variables.

Generic commentary about “just building it internally” assumes enterprise software is a thin application layer. EPM is not.

EPM platforms encapsulate decades of domain complexity: multi-scenario modeling, driver-based forecasting, dimensional hierarchies, consolidation logic, intercompany eliminations, FX handling, workflow orchestration, and audit traceability. These are not isolated features; they interact continuously across reporting cycles.

Organizations that attempt to build their own planning or consolidation engines often succeed initially. For a period, the system works. The breakdown occurs as the business scales, entities multiply, reporting dimensions expand, and compliance scrutiny increases. The architecture that supported $50 million in revenue becomes fragile at $300 million.

The migration to a formal EPM platform usually follows not because the company lacked engineering capability, but because sustaining financial logic at scale is an ongoing systems discipline rather than a one-time development project.

This pattern has repeated for two decades. AI has not eliminated the underlying complexity.

There are environments where building is strategically sound.

Early-stage organizations with simple planning processes, stable engineering teams and a short operational horizon may benefit from internal tooling. In companies under roughly 150 employees, the governance burden is lower, dimensional complexity is manageable and the planning cycle may not justify enterprise-grade infrastructure.

In these cases, building can function as a temporary bridge. The key is recognizing it as scaffolding rather than foundation. The internal system should be treated as a transitional asset with a defined sunset horizon, not as permanent infrastructure.

Building also makes sense when addressing niche workflows that are poorly served by existing vendors. Even then, the organization must accept explicit ownership of lifecycle risk.

The distinction is not pro-build or pro-buy. It is pro-clarity about responsibility.

The most expensive elements of internal systems rarely appear in the initial business case.

Over time, support responsibilities shift toward finance teams. Documentation gaps create friction during audits. Shadow versions of models proliferate. Integration scripts degrade quietly until reconciliations fail. Security reviews grow more demanding as the organization matures. A single engineer becomes a bottleneck for changes.

These costs typically materialize 12 to 24 months after launch. By then, the internal system is deeply embedded, and replacement is disruptive.

The lesson is not that internal builds are inherently flawed. It is that they carry delayed risk that must be priced in honestly.

A simplified structural pattern emerges:

• Small organizations with low process complexity and strong engineering depth may build temporarily.
• Mid-market organizations with moderate complexity and multi-year horizons generally benefit from buying.
• Enterprise organizations with high complexity and regulatory exposure almost always benefit from buying.
• AI-native startups may adopt a hybrid approach: building differentiated workflows while relying on established systems for core financial infrastructure.

The decision aligns less with ideology and more with organizational maturity, complexity and risk tolerance.

The most durable model in 2026 is not build or buy. It is structured systems augmented by flexible AI-native workflows.

Modern EPM platforms increasingly serve as the governed, auditable, enterprise-grade container for financial logic, while simultaneously exposing extensibility layers that let teams automate, script and accelerate processes far beyond what was possible even three years ago.

Vendors are not standing still. They are absorbing the AI wave, embedding agentic capabilities, expanding integration surfaces and enabling customers to tailor workflows without compromising security or data integrity.

The result is a hybrid architecture. You get the stability and trust of a formal EPM foundation paired with the speed and adaptability of AI-driven customization.

This is where build and buy converge, not as competing philosophies but as complementary layers in a modern finance stack.

AI will reshape software categories. It will compress implementation timelines, increase buyer expectations and eliminate vendors that rely on inertia rather than value. It will also make integrations more automated and reduce friction in deployment.

What it will not eliminate is the need for governance, structured data models, reliability, and domain expertise.

SaaS is not being replaced. Weak SaaS is being exposed.

The vendors that survive will be those that integrate AI in ways that reduce operational burden while preserving institutional stability. Competitive pressure will intensify. The structural need for systems of record and systems of planning will remain.

The build-versus-buy decision in 2026 is not radically different from 2016. What has changed is the speed at which prototypes can be generated and the confidence with which they are presented.

The core question remains unchanged: does the organization want to own the infrastructure or the outcome?

For most mid-market and enterprise finance teams, long-term infrastructure ownership carries risk and distraction that outweigh the perceived savings of building. Buying allows internal talent to focus on analysis, insight, and strategic value creation rather than system maintenance.

AI has altered the surface of the debate. It has not altered the economics of responsibility. That is the reality CFOs must anchor to.

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