Databricks built the most capable AI platform the enterprise has ever seen at Data and AI Summit 2026. The data on who actually earns a return from it tells a more sobering story. Here is what changed at the summit, and what it means for leaders already on the platform. 

Ten minutes into the Data + AI Summit 2026 keynote, Ali Ghodsi, CEO of Databricks, said something most enterprise leaders were not prepared to hear: AGI is already here. Frontier models can now ace "Humanity's Last Exam", 2,500 hard questions across every discipline, and API costs have dropped 99% since 2023. The intelligence problem is solved.

Then he named the real problem.

"AI doesn't have an intelligence problem. It has a context problem."

That single line reframed four days of announcements. And if you attended the Summit with a Databricks investment already made, it should reframe the conversation you have next.

What 30,000+ Attendees Actually Heard

The Summit was not a product launch. It was a diagnosis.

Ghodsi's argument: enterprise agents keep failing not because the models are weak, but because they walk into your organization blind. They don't know which revenue metric your CFO trusts, why two dashboards contradict each other, or what your field technician actually does at 7 am before the first job. High intelligence with zero institutional context produces confident wrong answers at scale.

5 Takeaways from DAIS 2026 that Actually Matter

Databricks anchored its agentic story around three words it repeated all week: Context, Control, and Choice. A fourth ran quietly through every demo and every pricing slide: Cost. Those four Cs are the right lens for reading what shipped, because they map almost exactly onto the three reasons Gartner says projects die. Here are the five takeaways worth your time.

1. Context graduated from a project to infrastructure

The headline was Genie Ontology, a continuously learning context layer built from your governed data and human-annotated business semantics. It lets agents understand what a churned customer means or when your fiscal year starts, reasoning from enterprise truth rather than guesswork. Context is now a first-class part of the platform, which is the clearest signal yet that the industry has absorbed why so many early agents failed.

2. Governance and cost moved into the runtime

Unity AI Gateway became the single layer where models, agents, tools, and MCP services are governed, with spend caps, per-team budgets, and smart routing that sends each workload to the model that balances quality against price. This is Control and Cost made operational. It also makes a quiet point: on a consumption-priced platform, governing what your agents cost is the same task as protecting your margin.

3. The lakehouse became the place where agents act, not just analyze

Lakebase, Lakehouse//RT and the new LTAP architecture collapse the long-standing wall between operational and analytical data. Agents can now read and act on live operations at sub-second latency. The platform grew from a place where you study the business after the fact into a place where the work happens in real time.

4. Building agents got easy. Operating them is the hard part.

Agent Bricks matured into a full agent platform with more than 100,000 agents already built on it. Databricks itself framed the build loop as roughly 1% of the effort, with the other 99% going to deployment, evaluation, monitoring, context, and security. That framing is a gift to honest buyers, because it names exactly where pilots die.

5. Databricks climbed into the application and operations layer

With Genie One as an agentic coworker for business teams and CustomerLake as an agentic customer data platform, Databricks moved up the stack toward running operations itself. The platform set out to host the work, not just sit underneath the applications that do it.

The direction is unmistakable. On the Databricks platform, multi-agent workflows grew 327% in under four months, and companies that use AI governance tools deploy 12 times more AI projects into production. The platform shift is real.

But here is what the keynote did not tell you: closing the context gap is only half the equation. The other half is deciding who actually runs the operations once your agents have context. This is where most enterprises stall. Databricks deployed. Data pipelines unified. Models fine-tuned. Governance configured. And then the result that should stop any board meeting cold: 56% of CEOs told PwC their companies have realized neither higher revenue nor lower costs from AI over the past year, and only one in eight reported both. Because none of that setup is an outcome. It is a capability sitting at the edge of a workflow that still runs the old way.

The gap between Databricks production-ready and CFO-visible ROI is an execution gap. It needs a different kind of partner than the one that sold you the platform.

iOPEX: Built for the Gap

Gartner is specific about why agentic projects fail: escalating costs, unclear business value, and inadequate risk controls. Turn those three around, and you have the job description for the right partner. Someone who holds costs down, proves value in numbers, and owns governance, all of it inside your real workflows. Here is how iOPEX maps to the five takeaways above.

We make context engineering the actual work

Genie Ontology learns everything in your tables. The exception in a regional manager's head, the approval that shifts with who is asking, the workaround your team adopted when the documented process broke: that knowledge needs a human partner to capture and feed in. Our AI platform, ElevAIte, builds the connective tissue between your systems and your agents so they reason with high-fidelity business context. As one of our practice leads puts it, AI without context is guessing.

We price based on outcomes, which answers your Cost C

We commit to cost-to-serve, revenue realization, and SLA adherence, with delivery models engineered so that effort and cost fall as volume rises. In plain terms, we get paid when the number moves. That arrangement attacks two of Gartner's three killers at once, escalating cost and unclear value, and it sits naturally with the spend discipline Unity AI Gateway now enforces.

We run the operation, not just the build

Since the hard 99% lives in operating agents inside a live business, that is precisely what we do. Through our Agent Factory model and Forward Deployed Engineers, we embed multi-agent systems directly into revenue and service workflows on Databricks, using Unity Catalog, Genie, and Agent Bricks as the foundation. More than 1,000 iOPEX agents run in production today across service, revenue, finance, infrastructure, and CX, with governance and human oversight built into every one.

We move at the speed your consumption clock demands

Gartner's three killers all compound with time. Costs climb, value stays unproven, and risk controls lag the longer a project drifts. Our productized pilots are built to reach production before that happens. FieldPilot deployed to 1,000 field engineers in 12 weeks and has run in production since, with 92% overall AI accuracy and 100% accuracy on parts lookup. SuccessPilot and DemandPilot do the same across customer retention and demand generation. 

From Summit Floor to Business Outcome

Ghodsi's diagnosis was precise: the context problem is what separates AI experiments from AI operations. Databricks built the infrastructure to solve it.

What comes after context is consequence: agents that do not just know your business, but run parts of it. Revenue workflows that close faster. Service operations that self-correct. Customer success that acts before your CSM sees the churn signal.

That is not a platform capability. It is an operating model.

If you left Summit with a sharper picture of what your Databricks investment should be delivering, and a clearer sense of the gap between where you are and where the keynote said you should be, this is the conversation worth having. Talk to the iOPEX team →