Exploring AI: Working With Vendors – IA Magazine

Artificial intelligence has officially moved out of the “interesting conference topic” stage and into the daily operations of agencies, carriers, MGAs, and just about every office with a keyboard and a budget. Vendors now promise faster submissions, cleaner claims notes, smarter underwriting support, sharper marketing copy, better customer service, and dashboards that look suspiciously like they were designed to impress your CFO and your teenager at the same time.

And yes, some of those promises are real. AI can absolutely speed up repetitive work, improve search across documents, summarize conversations, flag unusual patterns, and help teams get to a decent first draft faster. But working with AI vendors is not like buying a fancier stapler. It is closer to hiring a very fast intern who may be brilliant, may be weirdly overconfident, and definitely needs supervision.

That is why the smartest organizations are not asking only, “What can this tool do?” They are also asking, “What data does it touch? Who is accountable if it goes sideways? How do we test it? What happens when the vendor changes the model? And can we unplug this thing without setting the building on fire?”

If you are exploring AI vendors, especially in regulated or client-facing businesses, here is the practical playbook: stay curious, stay skeptical, and never let a slick demo do the job of due diligence.

Why Working With AI Vendors Feels Different From Traditional Software

Traditional software usually behaves like a very literal employee. Give it the same inputs, and it gives you the same outputs. AI systems are more like talented improvisers. That flexibility is what makes them useful, but it is also what makes them risky. Outputs can vary. Performance can drift. A model that behaves beautifully in a sales demo can get strange when it meets your real workflows, your messy documents, and your gloriously unhinged customer emails.

For insurance organizations, the stakes are even higher. AI may influence underwriting support, claims triage, fraud review, customer messaging, marketing, or internal productivity. Even if a vendor says its system is “just an assistant,” that assistant can still shape decisions, create bias, mishandle personal information, or generate nonsense with unsettling confidence. In other words, the risk is not limited to the model. It lives in the full chain of data, prompts, people, policies, integrations, and decisions.

This is the first rule of working with AI vendors: you can outsource the tool, but you cannot outsource accountability. If the output affects customers, employees, compliance, or reputation, it is still your problem, even if the vendor brings the fancy logo and the aggressively optimistic sales deck.

What to Ask Before You Fall in Love With the Demo

1. What exactly is this product doing?

Start by forcing clarity. Is the tool generating text? Ranking claims? Recommending actions? Summarizing calls? Extracting data from PDFs? Detecting anomalies? “AI-powered” is not a job description. You need the actual workflow.

A vendor may present one polished headline feature, but behind the scenes the product may rely on multiple models, third-party APIs, speech-to-text engines, retrieval systems, or subcontractors. If the stack looks like a Russian nesting doll of vendors inside vendors, congratulations: you now have supply chain risk wearing a startup hoodie.

2. What data goes in, where does it go, and what happens to it?

This is where grown-up questions begin. Ask whether prompts, uploaded files, customer records, call transcripts, or policy documents are stored, retained, reviewed by humans, or used to improve the vendor’s models. Ask whether data is segregated by tenant, encrypted in transit and at rest, and restricted by role. Ask whether the vendor supports zero-retention or similar enterprise controls where appropriate.

If the answers get foggy, that is a signal, not a mystery. A responsible vendor should be able to explain data flows in plain English. If they cannot describe what happens to your information, they probably should not have it.

3. How was the model tested for your use case?

Generic benchmark scores are fine for conference panels and LinkedIn posts, but they do not prove the tool works in your environment. You want to know how the product was evaluated for the actual job you are considering.

Ask for testing methodology, error rates, known limitations, hallucination rates where relevant, bias assessments, drift monitoring, and examples of failure modes. A good vendor will not claim perfection. A good vendor will tell you where the tool performs well, where it struggles, and where human review is non-negotiable.

4. What documentation can you provide?

Ask for model cards, system cards, security documentation, privacy terms, acceptable use policies, incident response commitments, audit summaries, and change-management procedures. Think of documentation as the difference between “trust us” and “here is how this machine behaves.”

If a vendor is serious about enterprise adoption, it should not panic when asked for evidence. Mild sweating is fine. Panic is not.

The Contract Section Where Everyone Suddenly Becomes Very Interested

Once the demo sparkle fades, the contract becomes the real personality test. This is where you learn whether the vendor is prepared for adult supervision.

Data rights and model training

Spell out who owns the inputs, outputs, derivative work, and usage data. Clarify whether your data can be used for model training, fine-tuning, benchmarking, product improvement, or abuse monitoring. Do not assume the default terms say what you hope they say. Hope is not a control framework.

Security and privacy obligations

Require baseline security standards, access controls, logging, breach notification timelines, subprocessors disclosure, and data deletion procedures. If the tool handles regulated or sensitive information, make sure the privacy commitments match the risk. A vendor’s homepage can say “security first” all day long. The contract should explain what that actually means on Tuesday at 2:14 p.m. when something breaks.

Performance, accuracy, and acceptable use

Most vendors avoid guaranteeing output accuracy, and that is not shocking. AI is probabilistic. But the contract can still define service levels, uptime, support, escalation paths, and intended use restrictions. If a tool is unsuitable for final claims decisions, legal advice, or customer eligibility determinations without review, write that into policy and process.

Intellectual property and indemnity

AI raises uncomfortable IP questions, especially when outputs may resemble protected content or when training data provenance is unclear. That does not mean every AI tool is a legal landmine, but it does mean you should ask direct questions about training data sources, output safeguards, and indemnity structure. The legal landscape is still evolving, which is lawyer-speak for “please do not freestyle this part.”

Change management and termination rights

AI products change fast. Models get swapped, features appear overnight, and yesterday’s “beta but production-ready” tool becomes today’s “surprise workflow disruption.” Your agreement should require notice for material changes, document review rights when risk shifts, and a clean exit path if the product no longer fits your standards.

What Good Governance Looks Like Inside Your Organization

Even the best vendor cannot rescue a bad internal process. Before implementation, decide who owns governance. That usually means a cross-functional group involving technology, security, legal, compliance, risk, operations, and the business team actually using the tool. If nobody owns the decision, everybody will enthusiastically assume somebody else does.

Set a tiered review process based on use case sensitivity. An AI tool that drafts internal meeting notes should not require the same scrutiny as one influencing claims communications or underwriting workflows. Create an inventory of approved tools, define prohibited uses, document where human review is required, and train employees on what not to paste into public tools.

For agencies and insurers, one especially useful principle is simple: third-party AI should be held to the same or similar standards as internally built AI. If your internal governance would require privacy review, bias testing, legal review, and monitoring, vendor tools should not get a free pass just because they arrived with a contract and a booth at a conference.

How to Run a Pilot Without Becoming the Vendor’s Unpaid QA Team

Pilots are good. Blind faith is less good.

Run a limited pilot with a defined use case, a small user group, sanitized or properly approved data, and clear success metrics. Measure time saved, error rates, user satisfaction, escalation frequency, and output quality. Compare AI-assisted performance against your current process, not against a fantasy version of your process where everyone is efficient and never forgets passwords.

A strong pilot also includes adversarial testing. Try messy documents. Try ambiguous prompts. Try edge cases. Try inputs that tempt the system to overreach. If the tool will face rude real-world data after launch, give it rude real-world data before launch.

Most important, define the stop conditions in advance. What level of error, drift, instability, or unexplained behavior would pause deployment? Teams love go-live plans. They need just as much discipline around no-go plans.

Post-Launch: The Part Too Many Teams Forget

Buying the tool is not the finish line. It is the start of continuous oversight. Monitor for drift, rising exception rates, data leakage, strange outputs, biased patterns, user workarounds, and vendor changes. Review logs. Reassess when the use case expands. Reassess when new integrations are added. Reassess when your team starts saying things like, “We now use it for way more than we originally intended.” That sentence should trigger a meeting.

Also remember that human behavior changes around AI. Staff may over-trust polished answers. Review steps may quietly disappear because the output sounds confident. People may use unofficial tools when official ones feel slow or restrictive. Governance has to address the humans in the loop, not just the software in the cloud.

Responsible AI vendor management is therefore not a one-time procurement event. It is vendor oversight, model oversight, workflow oversight, and human oversight tied together with documentation and common sense.

Practical Experience: What Teams Learn After the AI Honeymoon

Here is the part that rarely makes it into glossy product brochures: working with AI vendors is usually less like flipping a switch and more like learning the temperament of a very clever new colleague. Early excitement is real. Teams see the tool summarize calls in seconds, pull key facts from long documents, or generate clean first drafts that would have taken a human much longer. The room gets that wonderful, dangerous energy of, “This changes everything.” Sometimes it does. More often, it changes several things, improves a few, annoys a few, and exposes a bunch of process problems you already had but were previously able to ignore.

One common experience is that AI makes weak processes look faster before it makes them look better. If your intake workflow is inconsistent, your document library is messy, or your escalation rules live in three spreadsheets and one person’s memory, the vendor’s tool may still produce quick results, but those results can be uneven. The lesson is not that AI failed. The lesson is that automation loves structure. If your house is messy, the robot vacuum is not your enemy. It is just now emotionally involved.

Another common experience is that the best vendor relationships become unusually collaborative. The useful vendors are the ones who admit where the model struggles, help define boundaries, share testing logic, and respond like partners when issues appear. The frustrating vendors are the ones who answer every concern with “our customers love it” and treat governance questions like a personal insult. In practice, teams learn to value boring maturity over flashy ambition. A vendor who can explain retention settings and change logs may be more valuable than one who promises artificial general magic by Labor Day.

Organizations also discover that employee training matters almost as much as vendor quality. A good tool used carelessly creates risk at speed. Teams need to know when to trust AI, when to verify it, when to escalate, and when to ignore it completely. The best rollouts usually come with plain-language rules: do not enter restricted data into unapproved systems, do not treat generated output as final truth, do not remove human review just because the paragraph sounds polished, and do not let convenience outrun judgment.

Finally, experienced teams learn that success with AI vendors is usually quieter than the hype. It looks like fewer manual keystrokes, faster document review, cleaner handoffs, more consistent summaries, and better time spent on work that actually needs human expertise. It is not always cinematic. Nobody hears dramatic music. But when vendor selection is disciplined, contracts are clear, pilots are honest, and monitoring continues after launch, AI becomes what businesses actually need: useful, governed, and a lot less likely to create tomorrow’s apology email.

Conclusion

Working with AI vendors is no longer a futuristic exercise. It is a present-day management challenge, especially for organizations that handle sensitive data, customer interactions, regulated processes, or high-volume decisions. The best approach is not fear and it is not blind enthusiasm. It is structured curiosity.

Ask harder questions. Demand documentation. Test the product in your world, not the vendor’s dream world. Make the contract specific. Keep humans accountable. And monitor after launch like you expect reality to remain reality, which, historically, has been a pretty strong bet.

Because in the end, the winning strategy is not just adopting AI. It is adopting AI without letting hype write checks that governance has to cash.