How a Fractional CTO Evaluates AI Build-vs-Buy Decisions

Buying an AI product can leave you with a new subscription and the same manual work. Building your own can leave your engineers maintaining capabilities you could have bought. Before committing to either, you need to know what will improve for the business, how soon, and what your team will have to take on to make it work.

For a startup or growing business, my recommendation is to evaluate existing products and managed services first, then justify custom development against a specific need they cannot adequately meet. That need might involve the customer experience, integrations, security requirements, or a capability central to the business.

Consider a hypothetical claims team that wants to read submitted documents, extract key information, and route each submission to the right adjuster. Its decision provides a useful way to work through the trade-offs.

Start with the work that needs to improve

Before comparing vendors, establish where time is going today. Are adjusters retyping information, chasing missing documents, or waiting for someone to assign the claim? Each problem could require a different solution.

Define the intended result in operational terms: shorter intake time, fewer transcription errors, or less manual review. Record a baseline and identify who will be responsible for changing the process.

Also check what existing software can already do. A better intake form, a configurable routing rule, or an unused feature in the current platform may address part of the problem.

McKinsey's report published in March 2025 found that workflow redesign had the strongest association with self-reported impact on operating profit from generative AI among the factors studied. The business case needs to explain how people's work will change once the technology is available.

Separate the product decision from the model decision

“Build AI” can describe very different levels of responsibility.

A custom product can use an existing model. Hosting that model yourself is a separate choice, and neither option requires training a foundation model from scratch.

For the claims team, document extraction and routing should be evaluated separately. An extraction service might perform well while an existing claims platform handles assignments. A custom review screen could be worth building if it combines source documents and extracted fields in a way that materially reduces adjuster review time.

Before building custom routing, check whether an existing product can express the required rules, including exceptions, permissions, and approval steps.

Test the options against your actual workflow

A demonstration shows what a product can do under selected conditions. Before committing, test the uncertainty that could change your decision. Set a budget and a decision date for the pilot.

For the claims team, use a representative set of documents that includes poor scans, missing fields, unusual formats, and conflicting information. Establish the permitted handling of sensitive data before sharing samples with a vendor.

Before testing, have experienced staff establish the expected results for the evaluation examples, including how to handle missing or ambiguous information. Agree on pass/fail criteria with the people who will use the system:

• Quality: Set minimum accuracy thresholds by field and consequence. A wrong claimant identifier deserves different treatment from a minor formatting error.
• Review effort: Set acceptable review times for routine and difficult submissions, including checking and correction.
• Exceptions: Define the required handling of missing or conflicting information, failed processing, and service outages, then test those cases.
• Integration: Verify that results reach the claims system with the required permissions and audit history.

Keep a subset of the examples out of development and evaluate every candidate against that same set and the agreed criteria. Compare results with the current process, including the work people do after the AI finishes.

If an existing product meets the agreed requirements, there needs to be a clear business reason to build an alternative. If the available options fall short, prototype the specific capability you would need to build before approving a larger project.

Compare total cost and the capacity to maintain it

Estimate when each option could deliver its first validated benefit, including integration and adoption time. Account for the cost of waiting, such as continuing manual processing during implementation.

Compare the first year of implementation and operation. For an investment expected to last several years, compare both options over the same period, making growth and staffing assumptions explicit. Test the costs at plausible growth and peak volumes. Include subscriptions or usage fees, integration, staff training, human review, maintenance, and support.

For a custom solution, identify who will own it after launch. Ask what else those engineers will have to postpone. A small API bill can sit inside an expensive operational commitment if the workflow needs frequent attention.

Use the pilot to estimate cost per successfully completed submission. Include correction and review time; a cheap extraction call does not establish a cheap process.

For example, saving two minutes of net staff time on each of 1,000 monthly submissions would recover about 33 hours of capacity. The business case should explain how those hours will be used: clearing a backlog, improving response times, or supporting more volume. Cash savings require a corresponding change in spending.

Higher volume can change the comparison, but it does not automatically justify building or self-hosting. Recalculate using actual pricing, performance, and staffing requirements. Review the business case after launch as usage, operating costs, and performance change.

Understand the responsibility and dependency you accept

Security and data requirements should be conditions an option must satisfy before it makes the shortlist. For sensitive workflows, establish what data the service stores, whether it may use your data for model training, who can access it, and what retention and deletion controls are available.

Review the actual service and agreement. In particular, distinguish ownership of your data from rights to use or export a customized model. Guidance on AI vendor agreements from Ward and Smith identifies inputs, outputs, fine-tuned models, and rights at termination as separate issues to address.

Then estimate what replacing the vendor would involve. For the claims team, that could mean exporting documents and correction history, adapting an integration, and testing another service against the same evaluation set.

An internal interface can reduce code changes when switching providers. Data portability, differences in model behavior, and contract terms still need their own assessment. Invest in an exit path proportionate to the risk; supporting several providers from launch adds its own cost.

What a useful CTO recommendation should contain

A build-vs-buy evaluation should leave leadership with a decision it can act on:

• The recommended approach and rationale, including which requirements ruled out the alternatives.
• The first delivery phase, its acceptance criteria, and the work being deferred.
• The cost assumptions and ongoing owner, including internal effort and expected review work.
• The conditions for reconsidering, such as an unmet requirement, a material pricing change, or evidence that another approach performs better.

For the hypothetical claims team, a reasonable initial recommendation might be managed document extraction, routing through the existing claims platform where it fits, and a small integration to close the remaining gap. Adjusters would review the extracted information during the pilot. Reducing that review would depend on demonstrated quality, error detection, and time savings.

That recommendation remains conditional on the pilot. If the available products cannot satisfy a critical requirement, custom development may be justified from the start. If the measured benefit is too small, deferring the project is a valid outcome.

These decisions are part of broader technical leadership. In the healthcare AI platform I helped guide, vendor evaluation sat alongside product planning, privacy, integrations, and delivery. Established AI services supported a custom experience; the surrounding product and operational responsibilities still needed deliberate design.

The same discipline applies to planning an MVP: commit enough to validate the opportunity, and expand when the evidence supports it.

If you are weighing an AI vendor proposal against a custom build for a specific workflow, AI Workflow Design starts with that workflow, its constraints, and your goals, and recommends an approach your team can implement and maintain.