How much should a first AI project cost?

Costs vary widely by scope, but a well-defined pilot is far cheaper than an open-ended build and should be sized to answer one question. Expect the largest hidden cost to be data preparation and, after launch, ongoing inference and maintenance. Ask for a realistic run-rate estimate at your expected volume, not just development fees.

Should I build an in-house AI team instead of hiring a vendor?

It depends on whether AI is core to your product or a supporting capability. If it is central to your differentiation, building internal expertise eventually makes sense, though it takes time and competitive hiring. For most companies, a vendor is the faster way to validate value first, ideally with a handoff plan so your team can operate what gets built.

How do I protect my data when working with an AI vendor?

Start with contractual clarity on data ownership, retention, and whether your data can be used to train models beyond your project. Confirm where data is processed and stored, especially if you have regulatory obligations. Prefer vendors who can work within your cloud environment or offer clear data isolation over those who are vague about data flow.

What if the pilot succeeds but we cannot afford to scale it?

This is why run-cost estimates belong in the pilot phase, not after. A pilot that proves value but ignores production economics has only done half the job. A strong partner models scaling costs early so the go decision accounts for total cost of ownership, not just development spend.

How long before an AI project pays for itself?

Payback depends entirely on the use case, but the discipline that shortens it is choosing a problem with clear, measurable value from the start. Automating a repetitive, high-volume task tends to pay back faster than open-ended experimentation. Tie the pilot to a metric that translates directly into saved time or revenue, and payback becomes something you can actually track.

AI & Machine LearningAI adoptionvendor selectionmachine learning

How to Choose an AI Vendor Without Wasting Budget

Most AI vendor engagements stall in an expensive proof of concept that never ships. Here is a CTO's checklist for vetting AI partners on proof of value, data readiness, and real capability so you spend budget on outcomes, not demos.

CodonomyJuly 17, 20269 min read4 views

How to Choose an AI Vendor Without Wasting Budget

AI adoptionvendor selectionmachine learningprocurement

Written by

CodonomyEditorial Team

Insights from the Codonomy team on custom software, AI, automation, and digital growth for B2B companies.

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