What happened at HumanX
A small but useful signal emerged from HumanX this week. In TechCrunch's conference report, the product that kept coming up in hallway conversations, vendor booths, and panel mentions was Claude, not ChatGPT. That does not prove Anthropic has won the enterprise market, but it does show where a lot of practitioners currently feel they get the best day-to-day leverage: coding help, document-heavy reasoning, and agentic workflows that are close to real work.
That distinction matters. HumanX is not a consumer popularity contest, it is a business conference where operators compare what actually gets adopted inside teams. According to the report, agents were one of the dominant themes of the event, especially for coding and task automation. In that setting, repeated mention of one tool is less about brand and more about perceived usefulness under production pressure.
The other part of the story is what was missing. OpenAI still has enormous distribution, funding, and mindshare, but the conference chatter described in the article suggests many business users no longer treat it as the default choice for serious workflow automation. Instead, the market is starting to split between general AI awareness and specific tools people trust for getting work done.
Why this matters now
Enterprise AI is moving into a more practical phase. For the last two years, the market was driven by demos, benchmark screenshots, and a race for general mindshare. That phase created awareness, but it did not answer the question that matters to a COO or CTO: which tools fit existing workflows, reduce manual work, and can be governed safely at scale? Conference buzz like this is one of the first signs that buyers are starting to answer that question with usage patterns rather than slogans.
It also says something important about agents. The strongest wedge for agentic AI is not a futuristic autonomous employee. It is software that performs bounded work inside an existing process: drafting code changes, analyzing documents, preparing structured outputs, or helping teams move faster through repetitive steps. When practitioners consistently favor one model family for those jobs, it suggests the market is rewarding reliability, context handling, and workflow fit over raw hype.
There is a second lesson here for European businesses. If the real enterprise market is being shaped by which models can plug into daily operations, then model lock-in becomes a strategic risk. Today one provider has momentum. Tomorrow another provider releases a better model, a cheaper model, or an open model you can run inside your own perimeter. The companies that benefit most will be the ones that build around interchangeable model layers, not those that wire their processes directly into one vendor interface.
Laava's perspective
At Laava, we read this story less as "Claude is winning" and more as "the market is finally rewarding production utility." That is a healthy shift. Enterprises should care far less about who wins the weekly leaderboard and far more about which architecture lets them process documents, reason with context, and take action in ERP, CRM, and email systems with guardrails in place.
This is exactly why we build model-agnostic systems. If a workflow benefits from Claude today, we can use Claude. If a client needs sovereign deployment with Llama, Mistral, or another open model tomorrow, the system should not need to be rebuilt from scratch. The durable value is not the API endpoint. It is the way context is structured, business rules are enforced, and integrations are wired into the actions the agent is allowed to take.
The HumanX signal also reinforces a point we keep making to clients: AI adoption does not fail because teams picked the wrong homepage. It fails because the surrounding system is vague. No metadata. No evaluation set. No approval gates. No clean handoff into systems of record. Once those pieces are engineered properly, switching models becomes a business decision instead of a rewrite project.
What you can do now
If your organization is experimenting with enterprise AI, this is a good moment to separate assistant usage from actual workflow automation. Ask a simple question: where does the model create a measurable business outcome? That could be extracting data from incoming PDFs, drafting replies from a knowledge base, or preparing updates for a CRM. If the answer is still "it helps people brainstorm," you are not at the production stage yet.
Then design the thin slice properly. Choose one process, define the sources of truth, decide where human approval is required, and keep the model layer replaceable. That is how you get from conference enthusiasm to boring, reliable automation. If you want to map a real process and see whether an AI agent can deliver ROI in four weeks, that is exactly the kind of roadmap session Laava runs.