AI and the CDO: Questions to Ask BeforeYou Buy

Key Takeaways

• Data quality comes first. AI models amplify existing data problems. Ask about data maturity, not just model accuracy.
• Governance is not optional. Establish roles, ownership and decision rights before implementation. This is a CDO problem, not an IT detail.
• Team readiness is a risk factor. Budget for upskilling. Vendor training is not enough. Your team needs domain expertise to own the deployment.
• Vendor maturity does not equal fit. A mature vendor in a different domain may not understand your industry's constraints and compliance requirements.
• Implementation risk is predictable. Ask vendors about failure modes, rollback plans and integration complexity. Get concrete timelines and assumptions in writing.

Start With Data Quality, Not Model Accuracy

Vendors will show you benchmark accuracy scores. Set them aside for now. Your first question should be: "What does your implementation assume about our data quality?"

Many AI implementations struggle because the underlying data is fragmented, inconsistent or incomplete. A model trained on clean data performs well. A model trained on your actual product database, with missing values, duplicate records and inconsistent formatting, performs poorly.

Before you evaluate any AI tool, run a data quality audit. Find out:

• How complete your key attributes are, domain by domain.
• How many duplicate or conflicting records exist across systems.
• Whether definitions (product, customer, category) are the same in every system.
• Who owns each dataset and who can approve its use for AI.

Then ask the vendor: "Given this data quality baseline, what performance can we expect in the first six months?" If they promise immediate results, they either do not understand your data or they are overselling. If spreadsheets still hold much of your product data, start with Excel vs PIM.

Governance Must Move as Fast as AI

This is where many organizations stumble. They buy an AI tool, the vendor deploys it, and then nobody knows who owns the outputs. Is it the CTO? The CMO? The compliance team?

Governance is not a post-implementation problem. It is a pre-purchase requirement. Before you sign, establish:

• Who owns the AI outputs and who is accountable when they are wrong.
• Who approves the data used for training and production.
• Which outputs need human review before they reach customers.
• How decisions are logged so they can be audited later.

Ask the vendor: "Does your platform support role-based access control? Can I restrict who approves AI outputs? Can I audit every decision the model makes?" If they cannot answer cleanly, that is a red flag. For the underlying principle, see AI without data governance is noise.

Ask About Your Team's Upskilling Timeline, Seriously

Vendors include "training" in their implementation packages. Often that means a short workshop on how to use the interface. That is not enough.

Your team needs to understand:

• How the model uses your data and where its limits are.
• How to spot drift, errors and biased outputs.
• How to adjust prompts, rules or configurations without the vendor.
• How AI outputs fit into existing workflows and approvals.

This takes months. Budget for it. Ask the vendor:

• What training do you provide beyond the interface?
• How long until our team can run the solution without you?
• What ongoing support is included, and what costs extra?

Evaluate Vendor Fit, Not Just Vendor Maturity

Maturity is not the same as fit. A vendor might be mature in one vertical (for example, e-commerce product descriptions) but out of its depth in yours (for example, pharmaceutical supply chain).

Ask:

• How many implementations have you done in our sector?
• Can we speak to references with a similar data landscape?
• Which of our compliance requirements does your product already support?

Do not accept generic answers. Push back. If they say, "We support any use case," they do not understand your constraints.

Implementation Risk Is a Vendor Question, Not an IT Problem

Many organizations treat AI implementation as a standard tech rollout. It is not. The failure modes are different, and the risks are higher.

Ask the vendor:

• What are the most common reasons your implementations fail or stall?
• What is the rollback plan if outputs degrade in production?
• Which integrations are in scope, and who builds them?
• What data quality thresholds does your timeline assume?

Get concrete timelines. Get failure scenarios in writing. Get commitments about data quality thresholds. If they cannot provide these, do not buy. The same discipline applies to any platform purchase; see how to brief a PIM vendor.

Build Your Vendor Assessment Checklist

Before you take the first vendor call, create a simple scorecard. Score each vendor on:

Some vendors will rush you through selection. They want the sale. A significant AI investment deserves rigor: take the time to evaluate, talk to several vendors, make reference calls and run a proof of concept with your actual data. The cost of a bad choice is higher than the cost of a thorough evaluation.Worth knowing

Conclusion

Buying AI is not like buying software. You are not just buying a tool; you are making a bet on your organization's data maturity, governance structure and team capability. The CDO's job is to make sure you make that bet with your eyes open.

Before you talk to vendors, audit your data. Before you sign, establish governance. Before you go live, upskill your team. Throughout, push vendors for concrete answers about implementation risk and failure modes.

If a vendor cannot answer these questions clearly, they do not understand your problem. Move on. If you want an independent view of your product data before buying AI for it, talk to a PIM consultant.