Most agent demos work. Most agent projects never reach production. The gap between those two facts is not model quality, and Christian Kleinerman put a number on it: your proof of concept ran on five well-named tables, and production has a hundred thousand tables that nobody named carefully. Kleinerman is Executive Vice President of Product at Snowflake.

Here is how I opened the show.

Everyone’s talking about agentic AI but most enterprises aren’t ready. I’m Michael Krigsman and today on CXOTalk number 903 Christian Kleinerman Snowflake’s EVP of Product shares hard truths on what it really takes: data readiness, governance AI, economics, and workforce impact.

Michael Krigsman, CXOTalk episode 903

His central claim inverts the usual complaint about AI hype. The technology is running ahead of what enterprises can absorb, not behind it. He sells a data platform, so weigh his interest where it applies. The failure pattern he describes is one I have watched for twenty years under other names.

The technology is ahead of the enterprise, not behind it

I asked him to elaborate on the idea that the state of the technology is in advance of what we in the enterprise can absorb.

Where AI truly shines and truly has potential in the enterprise is when you make the context of that enterprise available to those state-of-the-art models where now I can go to a chatbot and ask a question and it’s not generic knowledge from the internet it is based on knowledge from my company maybe my customers maybe my products.

Christian Kleinerman, EVP of Product, Snowflake

The model is not the scarce asset. Context is. Any competitor can rent the same frontier model by the token. What nobody can rent is an organized account of how your company actually works, and that is the input the model needs in order to be worth anything to you.

Two things kill agent projects

Most of the scenarios where we see projects going wrong or projects getting shut down they run through 2 types of issues. Issue number one is correctness of results or correctness of actions.

Christian Kleinerman, EVP of Product, Snowflake

His second category was security, privacy, and access control. Sit with the first one, because the phrasing changed and the change is the whole story. For a decade we worried about correctness of results, which means a wrong number in a report that a human reviews before anything happens. Agents add correctness of actions, which means the system already did the thing. The error is not sitting on a screen waiting for review. It is in the world.

He drew the boundary himself. “Probably one of the last operations that will be fully automated with AI is gonna be things like a bank transfer or a wire transfer ’cause you wanna make sure that those things are a hundred percent correct.”

If the data is messy nobody can help you

This is in my mind the single biggest impediment for companies to tap into AI today. And it comes down to if the data estate of a company is not well organized or is not broadly accessible there is little magic that the AI can do. I like to say AI is a turbocharged super capable technology but it’s not magic.

Christian Kleinerman, EVP of Product, Snowflake

Then, shorter: “But if the data is messy nobody can help you.”

He offered a test that any executive can run this week without buying anything.

If you were to ask the best analyst in your company to answer a question would this best analyst know where to go know what to do? Then there’s a path for AI.

Christian Kleinerman, EVP of Product, Snowflake

If your best human analyst would not know which system holds the answer, the agent will not know either. It will produce something anyway, confidently, and that is worse than producing nothing.

An audience question got the honest answer

The sharpest exchange of the hour started with a question from the audience. Sai Penumuru asked on LinkedIn why so many projects get stuck at the proof of concept stage. Kleinerman traced it to a period when teams assumed AI was powerful enough that building a customer support agent, or a talk-to-my-database agent, would not be too hard.

And what we’ve seen I would say for sure last year plus is that by the time you try to roll those POCs or those organic efforts into production then you get the intersection with reality and reality is harsh.

Christian Kleinerman, EVP of Product, Snowflake

Reality is like “Oh yeah you tested it with 5 tables but we have a hundred thousand tables and they’re not clearly named.”

Christian Kleinerman, EVP of Product, Snowflake

That is the entire pilot graveyard in one sentence. The proof of concept ran in a clean room that a capable team built by hand. Production is the rest of the company, and the rest of the company was never tidied up for a machine to read.

Stop reading vendor claims and start testing

I asked him how business leaders can evaluate vendor claims about autonomy, about reasoning, about elaborate workflow capabilities, and see what is real.

My recommendation to everyone is go and try the technology. The beauty of this era of AI and agents is most products are a couple of prompts away from someone trying it.

Christian Kleinerman, EVP of Product, Snowflake

I restated it back to him: you have to do the empirical testing yourself, and you cannot rely on vendor marketing to tell you what will work in your environment and your use case. The economics of evaluation have flipped. When a trial is a couple of prompts away, the long formal bake-off becomes an expensive way to postpone finding out. It also removes the last excuse for buying on a slide deck.

I have been covering this failure for twenty years

I spent two decades writing about IT project failure for ZDNet and founded a consultancy, Asuret, to diagnose it. Every era has its big bang: the ERP program that goes live everywhere on one weekend, the data warehouse that will finally give everyone a single version of the truth. The pattern never changes. Organizations buy scale before they have earned it with one small thing that works.

Don’t take on large-scale AI rollouts. When you hear “We’re gonna buy a hundred thousand licenses of X Y Z solution and put it out there” and it’s like well do you even know if it works or not?

Christian Kleinerman, EVP of Product, Snowflake

Notice who is saying that. An executive whose company sells software is arguing against the large seat purchase, and the incentive runs the other way.

What to do with this on Monday

Run his analyst test on the use case you are about to fund. If your best human analyst would not know where to look, fix the data before you buy the agent. Put one small use case into real production, with real users and real consequences, rather than a demo that lives in a clean room, and scale only the parts that survive contact. Log what the agent does, evaluate it continuously, and watch for drift the way machine learning teams learned to watch for it, because a non-deterministic system does not stay correct on its own.

On people, Kleinerman keeps AI out of the decision seat: “I would think of AI for people related matters as an assistant not a decision-maker.” And if something failed recently, check the date on the failure. “Technology is advancing very fast. If you tried something 4 months ago and it didn’t work there’s a chance that it may be working today.”

Michael Krigsman

Michael Krigsman is an industry analyst and the founder and host of CXOTalk, where he has interviewed close to 900 C-level leaders since 2013. He spent two decades writing about IT project failure for ZDNet and founded Asuret, a consultancy built to diagnose why enterprise projects go wrong. He is based in Boston. More about Michael or get in touch.