AI has quickly transitioned from experimental pilot projects to boardroom priorities. One key question for Enterprises when firms are racing to scale AI across all functions is: Should you buy a solution that already exists or build your own AI platform? There is no one answer. You will determine the best approach based on the balance of costs, risk, and long-term value creation. Here we offer an easy to use framework for strategically considering this decision.
Long term vision and competitive edge
The first step is to ask a fundamental question: What role will AI play in the long-term trajectory of the business? If AI is expected to serve as a strategic moat—where intellectual property, proprietary models, and differentiation are critical then building in-house becomes more compelling. On the other hand, if AI is primarily viewed as an enabling utility to enhance existing processes, then purchasing vendor solutions may be sufficient.
Measuring whether this vision is being realized requires more than traditional ROI. Companies must track how much AI contributes to revenue growth through better personalization, how much it reduces costs through automation, and how effectively it accelerates innovation cycles compared to peers. Control over intellectual property and independence from vendors are equally important yardsticks. In this way, the AI platform is not simply an IT expense but an investment in the company’s long-term strategic position.
Innovate the Decision in Unit Economics
CFOs are uniquely positioned to review the hype by grounding the build versus buy conversation in unit economics. The crux of the issue is: How much does each insight, model, or automated process cost? A custom-built platform might spread costs over thousands of use cases, ignoring upfront investments in infrastructure, data pipelines, and specialized AI talent. Off-the-shelf solutions offer predictable subscription pricing and fast exits to market, but they can exaggerate per-use economics with growing adoption.
For example, a retail enterprise building its own recommendation engine might incur high lump-sum costs but yield proprietary insights differentiating the customer experience for years, while buying a pre-existing recommendation platform achieves valuable realization quicker but may inflate cost per transaction with rising numbers of users. If we assess the lifetime value of the AI capabilities being deployed in relation to the cost of each output, CFOs deed the conversation from the speculative potential of technology to something tangible in financial myopia.
Scoring on Risk-Adjusted Value
Not all values are equal. A build vs. buy choice will consider both potential upside and risk associated with execution. Building in-house offers strategic control, customization and potential unique differentiation. With in-house solutions come considerable execution risk: not attracting, over project, and taking a longer path to achieving value. Buying a solution offers quicker deployment, vendor-tested capabilities and less immediate execution risk, but it may also come with less flexibility, dependency on vendor roadmaps, and reliance on vendor concentration risk. A structured scoring model can eliminate some of the uncertainty.
Each option can be evaluated across dimensions that can include the following:
- Strategic Control: How important is it to the enterprise to have control of data, models and IP?
- Time to Value: How quickly can an enterprise expect to start to derive measurable outcomes?
- Scalability: Can the solution scale easily and efficiently as more users adopt it?
- Vendor Risk: Is there a single vendor dependency?
- Talent Dependency: Can the enterprise attract and retain the right AI skillsets?
By scoring each dimension, CFOs can be able to compare different scenarios and
develop an understanding and perspective that aligns financial planning to
intended strategic risk appetite.
Hidden Expenses
The greatest blunders relating to AI platform decisions happen because hidden expenses are ignored. Just because budget models suggest that one direction is less costly, that means little when it comes time to execute.
For build scenarios, hidden expenses are ongoing maintenance, compliance obligations, continuing updates to account for fast emerging AI models, and a retention premium for the limited supply of AI skilled talent. Although enterprises may engage in an evaluation of the anticipated ongoing expenses to have an internal platform, enterprises may understate the long-term costs from trying to keep an internal platform competitive in an expanding marketplace.
For buy scenarios, hidden expenses manifest differently. Integration costs, vendor lock-in, limits to customization, and the escalating high costs of licensing as you increase the adoption of the vendor’s tools can negate the discounted starting cost. In addition to a distorted view of TCO, enterprises may incur missed opportunity costs if they find themselves constrained to the pace of the vendor’s evolving technology versus their own speed of evolution.
Using a CFO point of view helps surface the hidden variables early in the process and help quantify them in the financial models. The overall TCO often has a very different look than the original budget number.
A Hybrid Playbook
Increasingly, the best option may not be binary but hybrid. Companies can purchase proven AI platforms where capabilities are commoditized and readily available, while building proprietary solutions where there is a strategic differentiation.
Take the financial services sector. A bank may purchase a vendor’s AI-powered fraud detection system to leverage the size and pace of improvement, and also build a bank-owned AI model for personalized credit risk assessment to ensure differentiation in customer experiences and lending strategy. In healthcare, a provider may purchase a vendor solution for standardized imaging AI and build a custom model to help meet local population health challenges.
A hybrid playbook also provides financial flexibility. Vendors solutions can typically be accounted for as an operating expense giving your organization more flexibility while in-house builds can be categorized as a capital investment and a strategic asset to further enhance the enterprise’s evaluation. This way, CFO’s can maximize short-term performance with long-term positioning.
Make the Decision an Enterprise Strategy
CXOs should consider the build-versus-buy decision as a strategic enterprise effort instead of a narrow IT investment. Compared to ERP or CRM systems, the growth of AI platforms is comparable to foundational and critical infrastructure. The choice between build and buy has implications for speed to innovation, customer experience, regulatory compliance, and competitive positioning.
For example, consumer-facing businesses focused on retail and travel may care more about fast AI-driven personalization, thus leaning toward “buy”. Whereas a defense or energy company that is highly compliant and cares about data sovereignty may lean toward building proprietary systems. Decision making regarding build versus buy is an enterprise-wide issue and needs to reflect on the enterprise set of priorities and the surrounding regulatory, competitive, and financial context of the enterprise.
Factor
Build
(In-House)
Buy
(Vendor Solution)
Hybrid
Approach
Long-Term Vision
AI as core differentiator, IP-driven moat
AI as enabler, commoditized capability
Balance: build core, buy utilities
Measurement
Revenue uplift, cost savings, innovation velocity, IP ownership
Faster adoption, market parity, vendor benchmarks
Mixed metrics (internal ROI + vendor benchmarks)
Unit Economics
High upfront, lower long-term per use
Predictable pricing, rising per-use costs
Balance CAPEX & OPEX
Time to Value
Slower, depends on talent & infra
Fast, plug-and-play
Mix of quick wins + long-term build
Control & IP
Full control, differentiation possible
Limited control, vendor roadmap driven
Control in core areas, outsource the rest
Risk
Talent, delays, overruns
Lock-in, vendor dependency
Diversified risk
Hidden Costs
Maintenance, compliance, retention premiums
Integration, licensing escalation
Shared risks
Strategic Fit
Best for compliance-heavy, differentiation-driven industries
Best for speed & commoditized use cases
Best for large diversified enterprises
Strategic Takeaways
Center the build versus buy discussion on unit economics, adjust hard costs of variables to output metrics to compare opportunities more clearly.
- Value risk-adjusted value, not just raw ROI, track both upside potential and execution risk.
- Estimate hidden costs early, include financial costs for compliance, integration, customization, and talent retention in models.
- When possible, use a hybrid approach, combine vendor expediency and internally developed differentiation to accommodate resiliency, and, flexibility.
- Connect with enterprise priorities, ensure the decision is framed larger in a growth, compliance, and competitive strategy.
Closing Remarks
The choice to build or buy an enterprise AI platform is more about the finance and stewardship side of the equation rather than about technology. The difference becomes much clearer when executives begin examining the choice through unit economics, risk-adjusted value, and hidden costs. Leaders may illuminate the right path for organizations depending on their AI objectives looking for both efficiency and differentiation. In many instances, the hybrid route will emerge as a less risky path forward for CFOs that may provide speed to the SaaS solution but without long-term strategic control.
Companies that grapple with this choice will be able to optimize not only across cost structures but also permanently plant themselves at a sustainable advantage in an AI-fueled economy.
