Databricks Summit 2026: AI Is Everywhere Except Your Bottom Line. Here's Why.
This is Part 2 of our two-part breakdown of the 2026 Databricks Data + AI Summit. Part 1 covers the platform and product releases: Genie Ontology, Unity AI Gateway, Reyden, and more. This post looks at the harder problem: turning AI investment into business outcomes.
"AI is everywhere except my bottom line." That phrase kept coming up at this year's Summit: in sessions, in hallway conversations, and in the research presented as part of the enterprise AI navigator work.
The feature releases were substantial and we cover them in Part 1. But Databricks also created space across sessions for a harder conversation: the organizations struggling most with AI aren't short on tools. They're short on clarity about where AI creates value, and the organizational discipline to get there. That thread ran through nearly every meaningful discussion we had all week.
The Pilot Trap
The most common pattern we see, and what Summit conversations confirmed, is a proliferation of pilots that were never designed to become production systems. Teams spin up a proof of concept, show it works in a controlled environment, and then hit a wall when it comes to scaling, governing, or integrating it into daily operations.
The problem usually isn't technical. The pilots are constructed to prove a capability, not to answer the harder questions: What does this replace? How does it fit into existing workflows? What's the ROI, and over what timeframe? Who owns it when it breaks?
When those questions don't have answers, you get stuck. As we've written before, AI readiness starts well before you pick a model or spin up a pilot. It starts with having data that's trustworthy, governed, and structured to support AI in the first place.
Where Most Organizations Are Right Now
The Summit research painted a clear picture of where enterprises currently stand. Around 80% of companies are at the early edge of an agentic strategy or have none at all. Even among those with a strategy, roughly 65% said their infrastructure isn't ready to execute on it.
That second number is underappreciated. You can have excellent strategic clarity and still be paralyzed by a data foundation that wasn't built for the speed, quality, and governance requirements that AI demands. Strategy and infrastructure readiness have to move together, and in most organizations, they haven't.
This is exactly why Databricks' investments in tools like Genie Ontology and Unity AI Gateway matter as much as they do. They're not just features. They're the infrastructure layer that makes AI strategies executable.
Context Is More Than a Data Problem
In Part 1, we covered how Databricks framed context as the core bottleneck in enterprise AI and why tools like Genie Ontology exist to solve it at the infrastructure level. But context has a people and process dimension that's just as important, and harder to ship as a product feature.
When Databricks talks about context, they mean getting the right data definitions, permissions, and business semantics into your AI systems. When organizations fail at AI adoption, the context problem is usually one layer up: the people building and deploying these systems don't share a common understanding of what problem they're solving, what success looks like, or how the work will change when AI is in the loop.
We see this constantly. Two teams using the same word and meaning completely different things. A business stakeholder and a data engineer who have never aligned on what the AI is actually supposed to do. An executive sponsor who approved the budget but hasn't been brought into the design decisions that will determine whether anyone uses it.
This is the context gap that no platform release fixes. It has to be built through deliberate process: alignment sessions, shared definitions, and a clear answer to the question Databricks kept surfacing all week. What are you actually trying to agentify, and why?
Starting Small, on Purpose
A theme that surfaced in a public sector deep-dive at the Summit deserves more attention: the deliberate small start. The instinct in large organizations is often to architect the enterprise-wide AI transformation first and then execute. For organizations where trust in AI is low or governance requirements are high, the opposite approach often works better. Start small, in a contained domain, with a trusted group of stewards.
The goal isn't to stay small. It's to build a proof point that's trusted enough to replicate. That result often becomes the template that pulls the rest of the organization along.
The Trust Problem Is Underrated
People don't trust what they don't understand, and if they don't trust it, they won't move. This came up in almost every AI adoption conversation we had at the Summit, and it was validated explicitly in session after session.
It's especially true for operational teams being asked to act on AI-generated outputs. If a recommendation comes from a system they can't interrogate, they'll route around it and keep doing things the way they always have. The AI investment sits unused.
Trust is also asymmetric, and this isn't unique to AI. It's a general pattern with software. One bad answer, especially a confidently wrong one, does more damage than a dozen good ones can repair. Once a tool burns someone, they rarely come back to check if it's improved. People who had a frustrating experience six months or a year ago aren't waiting around to give it a second chance. They've moved on, and the tool usually has to earn its way back through someone else's recommendation rather than their own curiosity.
Meeting people where they are isn't a soft consideration. It's a prerequisite for adoption. The organizations actually seeing returns from AI investments have spent as much energy on change management, explainability, and trust-building as they have on the underlying technology. Getting it right the first time matters more than most rollout plans account for. There's rarely a clean second chance.
Meeting Organizations Where They Are
Organizations arrive at this moment from very different starting points. The feature releases like Genie Ontology, Unity AI Gateway, and Omnigent are built for teams ready to unlock the full potential of a governed, well-structured data foundation. The more pressing question for most enterprises is how to get there.
Data and AI maturity isn't binary. It runs a wide spectrum, from teams still consolidating data sources and establishing basic governance, to organizations running production ML pipelines and experimenting with multi-agent workflows. Where you are on that spectrum should determine what you focus on next. The organizations seeing the best results are the ones honest about that assessment rather than skipping steps to chase the latest capability.
For teams earlier in the journey, the priority is still foundational: clean, trusted, well-documented data, consistent definitions, and governance that doesn't require heroic effort to maintain. These aren't glamorous investments, but they're what determine whether AI tools actually work when you deploy them. For teams further along, the Summit releases open up real possibilities.
Databricks' platform is built to meet organizations across that entire spectrum. The more important question for any given team is honest self-assessment: where are we today, and what does the next step actually look like from here?
What This Means for Your AI Strategy
The organizations seeing real returns from AI investments share a few traits: they're clear about what they're agentifying and why, they've built trust with the people who will actually use the outputs, and they've designed their governance and infrastructure to move at the speed the strategy requires.
None of those are technology problems. They're leadership, process, and people problems, which is exactly why the technology alone isn't delivering the bottom-line impact the hype promised.
If your organization is sitting on pilots that haven't turned into production systems, or debating an AI strategy without a clear infrastructure plan to back it up, the path forward isn't more tools. It's figuring out where AI actually creates value in your business, and what it takes to get there. The Databricks platform gives you an exceptional foundation. The work of deciding what to build on it is still yours to do.
Brooklyn Data helps data and AI teams move from strategy to production. If you're working through these questions right now, we'd like to talk.