# How to Evaluate AI Vendors When You Don't Have a CTO

Canonical: https://granular.to/blog/evaluate-ai-vendors-without-cto
Published: 2026-05-19
Updated: 2026-05-19
Author: Trey
Category: Operator's view
Tags: ai-agents, automation, operations, professional-services

> A practical five-question vendor evaluation framework for mid-market operators who lack a CTO or IT department, grounded in failure-rate data from RAND, MIT, and S&P Global.

> **TL;DR.** Eighty percent of AI projects fail to deliver value, and mid-market companies are especially vulnerable because most vendor evaluation frameworks assume you have a CTO, a procurement team, and an IT department. Here are five questions any operator can ask in a vendor meeting, no technical background required: Does this vendor know my industry? Will it connect to my existing systems? What happens after the demo? Who owns the code and data? What does this actually cost over three years?

You do not need a CTO to evaluate AI vendors. You need the right five questions, asked in the right order, by someone who understands the operation. That person is you.

Every vendor evaluation guide on the internet is written for a technical audience: CTOs evaluating architecture, IT directors assessing security posture, procurement teams running RFPs. None of it speaks to the VP of Operations at a [$50M distribution company](/) who got asked by the board, "What are we doing about AI?" and now has six vendor demos on the calendar and no framework for telling them apart.

This is that framework.

## The Failure Rate Is Real, and It Is Worse for Mid-Market

Before we get to the questions, the context matters. [RAND Corporation research](https://mybusinessfuture.com/en/80-ai-failure-rate-2026-how-rand-and-gartner-expose-the-ai/) found that more than 80% of AI projects fail to reach meaningful production. That is twice the failure rate of non-AI IT projects. The root causes are not technical: data is not ready, decision governance is unclear, and use cases are too broad.

For mid-market companies, it gets worse. [S&P Global's 2025 survey](https://www.ciodive.com/news/AI-project-fail-data-SPGlobal/742590/) of over 1,000 IT professionals found that the share of companies abandoning most of their AI initiatives surged from 17% in 2024 to 42% in 2025. The average sunk cost per abandoned initiative: $7.2 million for enterprises, roughly $1.1 million for mid-market firms. That is real money, and most of it is gone before anyone realizes the project is failing.

![Printed AI vendor proposal on a desk with sections highlighted in yellow and red pen annotations in the margins](/images/blog/evaluate-ai-vendors-without-cto-proposal-markup.jpg)

MIT's NANDA Initiative [reported in 2025](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/) that 95% of generative AI pilots fail to deliver measurable financial return. Ninety-five percent. If you bought a piece of equipment with a 95% failure rate, you would fire the salesperson who sold it to you. But AI vendors keep selling pilots because the pilot is where the money is made.

## Five Questions That Replace a CTO

### 1. Do you know my industry?

This is the single most important question, and it is the one most mid-market operators skip. AI is not general-purpose in the way vendors imply. A tool trained on retail data does not understand construction takeoffs. A chatbot built for SaaS customer support does not know how to parse an insurance claim.

Ask the vendor to name three customers in your industry and revenue band. Not "we work with manufacturers." Names. Ask for a reference call. If they hesitate, that tells you everything.

The reason this matters: [Horvath's 2025 study](https://news.az/news/germanys-mitt-elstand-slows-ai-investment-in-2025-study) of 200 mid-market firms found that AI spending actually fell to 0.35% of revenue, down from 0.41% the prior year. The firms that cut spending cited a common reason: AI vendors did not understand their operations well enough to deliver value. Industry knowledge is not a nice-to-have. It is the difference between a working tool and an expensive experiment.

### 2. Will it connect to what I already have?

Your ERP is not going away. Your accounting system is not going away. Your CRM (or the spreadsheet that functions as your CRM) is not going away. Any AI tool that requires you to rebuild your data infrastructure before it works is a project, not a product.

Ask specifically: "What systems have you integrated with in production, not in a demo?" Push for details. Do they connect to QuickBooks Enterprise? To Sage? To Procore? To your industry-specific vertical software? "We have an open API" is not an answer. An open API means *you* do the integration work, which means hiring someone to do the integration work, which means the $2,000/month tool actually costs $2,000/month plus $30,000 to $50,000 in integration fees.

This is where [the build-vs-buy decision](/blog/build-vs-buy-ai-mid-market-guide) becomes concrete. Generic AI tools rarely integrate cleanly with industry-specific systems. Purpose-built tools for your vertical are more likely to connect to the platforms you already use.

### 3. What happens after the demo?

The demo is a controlled performance. The data is clean, the prompts are pre-tested, and the results look magical. The question is what happens when the demo ends and real data enters the system.

[Research from TrustPath](https://www.trustpath.ai/blog/spotting-red-flags-how-to-evaluate-ai-vendors-and-avoid-costly-mistakes) documented a case where a vendor's demo showed 95% accuracy that dropped to 68% in production. That gap is common and predictable, because demos are rehearsed and production is not.

Ask for a paid pilot on your data, not theirs. Two weeks, scoped to one workflow, with a defined success metric you agree on before it starts. If the vendor will not do a paid pilot, they know their product will not survive contact with your reality.

Questions for the pilot phase:
- What data do you need from me, in what format?
- Who on your team manages the pilot, and how often will they check in?
- What does "success" look like, and who measures it?
- If the pilot fails, what do I owe?

### 4. Who owns the code and the data?

This question matters more than most operators realize. [S&P Global found](https://www.spglobal.com/market-intelligence/en/news-insights/research/ai-experiences-rapid-adoption-but-with-mixed-outcomes-highlights-from-vote-ai-machine-learning) that 87% of organizations express concern about vendor lock-in, and 45% say it has already blocked them from adopting better tools.

![Close-up of a contract signature page with a pen resting on the dotted line, shallow depth of field](/images/blog/evaluate-ai-vendors-without-cto-contract-detail.jpg)

When you feed your operational data into an AI vendor's platform, three things can happen:

1. **Best case:** Your data stays yours, is exportable in a standard format (CSV, JSON), and the vendor deletes it when you cancel.
2. **Common case:** Your data is used to train the vendor's model. Your operational intelligence improves their product for everyone, including your competitors.
3. **Worst case:** Your data is locked in a proprietary format. Switching vendors means starting from zero.

Ask for the Data Processing Agreement before you sign anything. Read it, or have your attorney read it. The key clauses: data ownership, data portability, deletion timeline post-cancellation, and whether your data is used for model training. Italy [fined OpenAI EUR 15 million](https://www.intelligentcio.com/eu/2024/10/03/how-ai-is-creating-a-new-era-of-cloud-vendor-lock-in/) for data handling violations. If a $100 billion company gets it wrong, a startup AI vendor can too.

### 5. What does this actually cost over three years?

AI vendors are masters of the low anchor price. The subscription is $2,000 per month. But [enterprise AI implementations typically cost 3 to 5 times the advertised subscription](https://xenoss.io/blog/total-cost-of-ownership-for-enterprise-ai) when you factor in integration, customization, and ongoing maintenance.

Here is what the real cost stack looks like:

| Cost Category | Typical Range | Notes |
|---|---|---|
| Monthly subscription | $1,000 to $5,000 | Often per-seat or consumption-based |
| Integration/setup | $10,000 to $50,000 | Legacy system integration adds 40-60% |
| Data preparation | $10,000 to $90,000 | 96% of businesses underestimate this |
| Training | $500 to $1,500 per user | Plus ongoing time cost |
| Annual maintenance | 15-20% of build cost | Recurring, often unbudgeted |
| Year 2-3 price increases | 10-30% annual | Consumption-based pricing is volatile |

Ask the vendor for a three-year total cost projection, not an annual price. [CloudZero's research](https://www.cloudzero.com/state-of-ai-costs/) found that 53% of AI vendors now use consumption-based pricing, up from 31% the prior year. One mid-sized SaaS firm in their study saw monthly costs jump from $2,000 to $18,000 during a peak season. If your vendor uses consumption pricing, ask for a historical cost curve from a customer your size.

The honest comparison is not "AI tool vs. no AI tool." It is "AI tool total cost vs. the operational cost of the problem it solves." If [your quoting process](/blog/quoting-process-costing-you-jobs) costs you $200,000 a year in lost jobs and delayed quotes, a $120,000 three-year AI investment makes sense. If the problem costs you $30,000 a year, a spreadsheet fix is the right answer.

## The Vendor Meeting Cheat Sheet

Print this. Bring it to every demo.

| Question | Good Answer | Red Flag |
|---|---|---|
| Name three customers in my industry | Specific names, offers reference calls | "We work across many industries" |
| What systems have you integrated in production? | Names your ERP/CRM specifically | "We have an open API" |
| Can we do a paid pilot on my data? | Yes, with defined success metrics | Wants a 12-month contract first |
| Show me the Data Processing Agreement | Provides it immediately | "Our legal team will send that later" |
| Three-year total cost including integration? | Provides itemized breakdown | Only quotes monthly subscription |

## FAQ

**Do I need technical knowledge to evaluate AI vendors?**
No. The five questions above are business questions, not technical ones. You understand your operation, your data, and your budget. That is the expertise that matters.

**How long should an AI vendor pilot last?**
Two to four weeks, scoped to one specific workflow. Anything longer is a paid engagement disguised as a pilot. Anything shorter is not enough time to test with real data.

**What is a reasonable budget for a mid-market company's first AI project?**
Plan for $50,000 to $150,000 all-in for the first year, including setup, integration, and training. That range covers most focused tools for a single workflow. If a vendor quotes significantly less, ask what is not included.

**Should I hire a consultant to help evaluate AI vendors?**
Only if the consultant has implementation experience in your industry, not just strategy experience. A consultant who has never built an AI tool for a contractor or distributor will give you the same generic framework you can find online.

You do not need a CTO to make a smart AI decision. You need the discipline to ask hard questions, the patience to pilot before committing, and the willingness to walk away from a vendor who cannot answer the five questions above. The technology is not the hard part. The evaluation is.

If you are in the middle of vendor evaluations and want a second opinion from people who build these tools for mid-market operations, [book 30 minutes with us](/).

---

## Keep Reading

- **[Build vs. Buy AI: What No One Tells Mid-Market Leaders](/blog/build-vs-buy-ai-mid-market-guide)**. Once you have evaluated vendors, the next question is whether to buy off the shelf or build something custom. This post covers the real tradeoffs.
- **[What AI Actually Costs for a Mid-Market Company](/blog/what-ai-actually-costs-20-person-shop)**. A deeper dive into the cost stack for mid-market AI projects, with specific numbers by company size and use case.