How to Evaluate AI Implementation Partners: The 12-Question Checklist CTOs Use

The Question Most CTOs Skip Until It's Too Late

A 2024 McKinsey survey found that 47% of enterprises that abandoned AI pilots cited "poor vendor fit" as the primary cause, not budget shortfalls, not technical complexity. The partner looked credible in the pitch. The problems surfaced six months into delivery.

Vendor selection is where AI projects are won or lost before a single line of code is written. Yet most evaluation processes at mid-market companies in Latin America still rely on reference calls and demo scores. That is not enough when you are committing engineering bandwidth, data infrastructure, and sometimes multi-year contracts.

The following 12 questions give you a structured filter. Use them in the RFP stage, in discovery calls, and in final negotiations. Each question targets a failure mode that regularly kills enterprise AI implementations.

Questions 1 to 4: Technical Depth and Architecture Fit

1. Which models do you build on, and why did you choose them for this use case? A vendor who answers with a single model name, regardless of context, is selling you their preferred tool. A strong partner maps model selection to your latency requirements, cost constraints, and data sensitivity. For document processing workflows, a fine-tuned smaller model often outperforms GPT-4 class systems at one-fifth the cost.

2. How do you handle model drift over time? Models degrade as real-world data diverges from training distributions. Ask for the vendor's specific monitoring setup: which metrics they track, at what thresholds they trigger retraining, and who owns that process after deployment. If they cannot describe a concrete drift detection protocol, you are buying a static artifact, not a maintained system.

3. What does your architecture look like for our stack? Request a rough architecture diagram during the discovery call, not a polished sales deck. You want to see how their solution connects to your ERP, CRM, or data warehouse. Vendors who cannot sketch an integration pattern before the proposal stage typically rely on post-contract discovery, which translates into scope creep.

4. What is your approach to avoiding vendor lock-in? This question surfaces contract and architecture risk simultaneously. In 2026, the enterprise agentic AI market increasingly fragments around proprietary orchestration layers that make migration expensive. Ask whether their agent frameworks use open standards, whether your data and fine-tuned weights are portable, and what exit costs look like contractually.

Questions 5 to 8: Data Security and Compliance

5. Where does our data go during training and inference? This is non-negotiable for any company operating under Brazil's LGPD, Colombia's Ley 1581, or Mexico's LFPDPPP. You need to know: data residency location, subprocessor list, retention policies for inference logs, and whether your data is used to train shared models. Get these answers in writing before the NDA stage.

6. Do you have SOC 2 Type II, ISO 27001, or equivalent certifications? Certifications are not a guarantee of security, but their absence is a signal. For a 200-person fintech or a regional healthcare operator, an uncertified vendor introduces audit risk that your legal team will flag. Ask for the certification scope, not just the badge.

7. How do you handle PII in model inputs and outputs? Ask for a concrete example: if a customer service AI ingests a support ticket containing a national ID number, what happens to that string? Is it masked before reaching the model? Is it logged? Many vendors have not thought through this scenario, which means you will be designing the policy yourself after signing.

8. What is your incident response SLA for a data breach involving our environment? A 72-hour notification window is standard under LGPD. Ask whether the vendor's SLA aligns, and who the designated contact is for your account in an incident. Vendors without a named security contact at the account level are typically too early-stage for enterprise use.

Questions 9 to 12: Delivery Track Record and Commercial Terms

9. Show me a project that failed, and what you did about it. Every vendor will show you their three best case studies. Ask for a project that ran over budget, missed a milestone, or required significant rework. How a team handles failure tells you far more about operational maturity than their success stories. Vendors who cannot produce any example of a course correction have either no track record or no transparency.

10. What does your team structure look like for this engagement, and who are the people we will actually work with? Sales teams at AI consultancies routinely present senior engineers during the pitch and staff engagements with junior contractors. Ask for the names and LinkedIn profiles of the specific people who will work on your project. Confirm their availability before signing. This single question has saved several of our clients from costly mid-project team swaps.

11. How is pricing structured, and what triggers additional costs? AI implementation pricing models vary widely: fixed-fee, time-and-materials, token-based consumption, per-seat licensing, or some combination. Ask the vendor to walk you through three scenarios: the project runs on schedule, the project expands by 30% in scope, and you need to onboard a second business unit in year two. The answers reveal where the financial exposure sits and whether the vendor has thought through growth pricing.

12. What does success look like at 90 days, 6 months, and 12 months, and how do we measure it together? Vague success definitions are the most common source of post-contract disputes. A strong partner will propose specific KPIs tied to your business outcomes, not technical vanity metrics. For an accounts payable automation project, the right metric is cycle time reduction and exception rate, not model accuracy on a validation set. If a vendor cannot translate their technical outputs into your operational metrics, they will struggle to deliver business value.

How to Use This Checklist in Practice

Run all 12 questions across every vendor in your shortlist, and score each response on a consistent rubric. A simple three-point scale works: the vendor has no credible answer, has a partial or process-light answer, or has a documented and demonstrated answer.

Weight the questions by your organization's current risk profile. A company processing sensitive financial data should weight questions 5 through 8 heavily. A company whose primary concern is delivery speed and ROI should weight 9 through 12.

The goal is not to find a vendor with perfect scores. It is to identify where the gaps are before the contract is signed, so you can negotiate SLAs, add contractual protections, or decide the risk is too high.

Most mid-market companies in Latin America run one or two AI implementations per year. The cost of a failed implementation, including sunk engineering time, delayed roadmap items, and organizational skepticism toward future projects, typically exceeds the cost of a structured selection process by a factor of five to ten.

If you want a structured second opinion before your next AI vendor decision, the Kemeny Studio team runs AI readiness and vendor audits specifically for operations and technology leaders at growth-stage companies. Book a 30-minute diagnostic call at kemenystudio.com.

By the Kemeny Studio team