The AI vendor landscape has a transparency issue — and enterprise buyers are starting to feel it.
It's become increasingly common for AI platforms to be sold on capabilities that exist only in theory at the time of signing. Features are described as "available," "ready," or "built" when the reality is closer to: we're confident we can build this, and you'll be one of our first production customers for it.
This isn't always bad faith. The pace of AI development is genuinely fast, and the pressure to win contracts is intense. But for the enterprise buyer, the gap between what was sold and what gets delivered can be enormously costly — in time, in internal credibility, and in real money.
How to Spot the Gap Before You Sign
There are a few signals worth paying attention to when evaluating any AI vendor:
Ask for live production references, not case studies. Case studies are polished post-hoc narratives. What you want to know is: has this vendor actually gone live with a customer in the same configuration you're buying — same CRM, same ticket volume, same use case? If the answer is no, you are the pilot, whether they say so or not.
Probe the integration depth specifically. It's easy for a platform to claim it integrates with your CRM. What's harder is integrating with your CRM, with your data structure, at your scale. Ask for specifics about what "integration" means in practice, and how they've handled edge cases.
Look at their team composition. A company with an extraordinary marketing presence but a thin customer success and solutions engineering team is a signal. Deploying AI in production is hard, and the vendors who do it well invest heavily in post-sale support. The ones who don't are often better at selling than delivering.
Ask what's on the roadmap — and what isn't. Ironically, the most trustworthy vendors are often the ones who are honest about what they can't do yet. A company that tells you "our voice capability is fully production-ready, but email is still coming" is showing you something valuable: they know what they have and what they don't.
The Crowded Market Reality
The voice AI and conversational AI space in particular has attracted enormous capital and enormous marketing budgets. Not all of that investment has gone into product depth. Some platforms are genuinely impressive in demos and thin in production. Others are so customized in their implementations that they function more like professional services firms than software companies — every deployment is bespoke, which creates real questions about supportability and scalability over time.
This doesn't mean the space is bad. It means buyers need to be more rigorous than the marketing suggests they need to be.
The organizations that navigate this well are the ones who treat AI procurement like infrastructure procurement: with skepticism, specifics, and a bias toward proven production deployments over impressive pitch decks.
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Frequently Asked Questions
Key questions on how to evaluate AI vendors honestly before committing to a deployment.
Ask for live production references in configurations that match yours — same CRM, similar ticket volume, same channel. If they can't name a current customer running exactly what you're buying, you're likely their first real deployment for that configuration. That's not automatically disqualifying, but it should change the contract terms, the implementation timeline expectations, and the level of vendor support you negotiate upfront.
The opposite, actually. A vendor who tells you clearly what they can and can't do today is demonstrating the kind of operational honesty that tends to carry through the entire relationship. The vendors to be more cautious about are the ones whose answer to every capability question is "yes" — often followed, months into implementation, by "we're still building that part."
A software platform has a repeatable product that can be deployed at scale without heavy customization for each customer. A professional services shop builds custom solutions for each client — which can produce impressive results but creates real questions about long-term support, scalability, and what happens when you need to modify or extend the system. Both models exist in the AI space. Knowing which one you're buying matters enormously for how you evaluate total cost of ownership.