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The End of AI Experimentation: What Davos 2026 Revealed About Enterprise AI's Next Phase

  • Writer: Raghav Kumar
    Raghav Kumar
  • 1 day ago
  • 5 min read

Updated: 5 hours ago


The World Economic Forum's "Scaling AI: Now Comes the Hard Part" panel at Davos 2026 didn't declare AI startups dead. It did something more significant… it marked the end of the experimentation phase.


Enterprises are done with pilots. Production is where most AI strategies are breaking.

“Everybody talks about AI, the impact of AI, but where is the value? Where is it in dollar figures? And this is what we are able to establish.” - Amin Nasser, CEO of Aramco

It’s Pretty Clear What’s Already Failing


Products that wrap GPT or Claude behind a polished UI are struggling. OpenAI and Anthropic ship updates weekly. Any differentiation based purely on interface design disappears just as fast.


If your moat is UX, you're renting space in someone else's product roadmap. And the landlord is actively building everything you offer.


Enterprises aren't buying interfaces anymore. They're buying systems that integrate deeply, execute reliably, and hold up under operational pressure. The shift from "can this generate a good response" to "can this survive our compliance review" represents a fundamental change in buying criteria.


The Patterns That Are Working


Four distinct categories of winners are emerging from Davos conversations and enterprise deployments:


  • Orchestration platforms that own the full lifecycle. Configuration, user acceptance testing, cutover, hypercare - all the unglamorous work that determines whether AI actually ships. Real workflow control, and not prompt tooling.

  • Vertical models trained on proprietary data. These are industry-specific models that general-purpose LLMs will never see. They’re not just slight improvements or a fine-tuned GPT-5 or an Opus 4.5. They're models trained on data sets that incumbents control and competitors don’t have access to.

  • Autonomous systems that act without constant human prompting. A WEF release on scaling artificial intelligence cites Foxconn and Boston Consulting Group scaling an "AI agent ecosystem" to automate approximately 80% of decision workflows and unlock an estimated $800 million in value. This isn't an outlier, rather, the direction the entire market is moving.

  • Compounding moats. Products that get meaningfully harder to replace with every customer because they accumulate workflow intelligence, distribution leverage, and security trust.


The Shift to Agentic AI



This shift showed up everywhere, from panel discussions to how Davos itself was run. The Forum and Salesforce deployed an AI agentic concierge called EVA for the event. Salesforce CEO Marc Benioff emphasized that EVA represents "far more than a chatbot," positioning it as evidence that the "agentic enterprise" is a new architecture, not just a feature.


Thoughtworks launching AI/works this week fits exactly into this pattern — agentic systems aimed at legacy modernization. The focus has shifted entirely from smarter prompts to software that executes end-to-end workflows. And this execution requires infrastructure that compounds in value.


Trust Becomes the Bottleneck


“For [agentic commerce] to work, we need to invest in trust. You need to trust your agent… merchants need to trust… and your bank needs to trust that when they get a request to authorize a transaction on your behalf, that you really wanted that to happen.” - Ryan McInerney, CEO of Visa

For all the talk about capabilities, the most repeated word at Davos wasn't "intelligence" or "automation." It was "trust."


Mastercard's approach to agentic commerce crystallizes why. They're not trying to build the best checkout experience. They're building the trust layer that makes agentic commerce possible at scale. As Mastercard executive Sherri Haymond told Axios, "Agentic commerce will only scale at the speed of trust."


Security and governance concerns are forcing companies to pause deployments even when the technology works. For CIOs, that means 2026 roadmaps will prioritize platforms that ship with identity, policy, and auditability baked in, not added later as a control tower.​ For CISOs, the bar has shifted from ‘can we block this?’ to ‘can we continuously see, govern, and revoke every action an agent takes across systems?’ For CMOs and business owners, the question is no longer whether AI can generate content, but whether they can put their brand and customer experience in the hands of autonomous systems without introducing new risk.


EY's Raj Sharma captured the core enterprise concern at Davos: AI agents have "no name" - the identity and lifecycle management gap is making chief information security officers hit the brakes.


KPMG US CEO Tim Walsh pointed to an even deeper discontinuity: "Quantum breaks everything." Firms aren't just hardening current environments. They're keeping data on-premises longer because they know the security assumptions built into today's deployments won't survive the next wave of technological change.


From Speed to Compounding Advantage


The real survival mechanism in AI has fundamentally shifted from speed to compounding advantage.


Systems that learn from every deployment, and architectures that teams cannot realistically build in-house, are what create switching costs rooted in operational integration, not UI preference.


Accenture CEO Julie Sweet distilled the governance principle emerging from these deployments: "It's human in the lead, not human in the loop." The distinction matters. "Human in the loop" implies constant supervision. "Human in the lead" means AI systems operate with significant autonomy within boundaries set by human judgment.


This is the bet we're making at Beacon.li: enterprise AI orchestration that deploys in days rather than months, where every implementation teaches the system how large organizations actually run. 


Under the hood, the platform builds a living knowledge graph capturing configurations, runbooks, edge cases, and failure modes across implementations, to ensure orchestration quality improves with each new customer and each additional go-live.


The learning compounds, switching costs increase because Beacon.li becomes embedded in day-to-day operations, and the moat deepens with every deployment rather than every feature release.


The Question That Will Separate Winners from Losers


What gets meaningfully harder for competitors to replicate each time you onboard a customer?

If the answer is still "a better interface on top of someone else's model," Davos just explained why that won't hold.


The market is sorting itself in real time. Products built on borrowed infrastructure are facing a reckoning. Companies building genuine orchestration capabilities, proprietary data advantages, and compounding operational moats are getting renewed and funded.


The age of AI experimentation is over. Production is where strategies either prove out or break. And production requires infrastructure that doesn't just work once but instead get stronger with each additional deployment.


The uncomfortable truth is that most AI startups are solving yesterday's problems with yesterday's assumptions about what enterprises need. The winners will be those who understood early that enterprises want systems that make their operations fundamentally more capable, not better chatbots.


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