What the Floor at AI & Big Data Expo Tells Us About Where Enterprise AI Actually Is

Nobody at the expo asked "Which model is best?" They asked "Where does this break at scale, and how do we measure that early?" That shift in questions tells you everything about where the industry has moved.

The AI & Big Data Expo Global returned to Olympia London on February 4-5, 2026 — over 8,000 attendees, 200+ speakers, 150+ exhibitors, and seven co-located events spanning AI, cybersecurity, IoT, digital transformation, and intelligent automation. Speakers represented organizations from Citi and Jaguar Land Rover to Visa, Vodafone, and McKinsey.

Here's what stood out — not the announcements, but the signal underneath them.

The Demo Era Is Fading

Previous years of AI conferences had a predictable rhythm: polished demos, impressive benchmarks, vague promises about transformation. This year felt different. The energy was less "look what's possible" and more "here's what we shipped, here's what broke, here's what we learned."

The shift tracks with the data. Deloitte's 2026 State of AI in the Enterprise report, surveying over 3,200 senior leaders globally, found that worker access to AI rose by 50% in 2025. The number of companies with 40% or more of AI projects in production is expected to double in six months. Enterprise spending on generative AI solutions more than tripled from 2024 to 2025, reaching roughly $37 billion.

The money is real. The deployments are real. And the conversations have matured accordingly. At Olympia, nobody was trying to convince anyone that AI works. The question on the floor was whether it works reliably, at scale, under the constraints that enterprise environments impose.

The Five Themes That Dominated Every Conversation

Across hallway conversations, panel discussions, and exhibitor booths, the same themes kept surfacing. Not because they were on the agenda — because they're the problems teams are actually trying to solve.

Real Products, Not Slideware

The most valuable conversations happened with teams that had shipped something and could talk honestly about the gap between the proof of concept and production. KPMG's Q4 AI Pulse Survey captures why this matters: 65% of enterprise leaders cite agentic system complexity as the top barrier to deployment for two consecutive quarters. Building the agent isn't the hard part. Making it sustainable, scalable, and aligned with operational goals — that's where teams get stuck.

Gartner forecasts that 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from under 5% in 2025. The velocity is real, but so is the gap between "integrated" and "production-grade."

Governance, Data Rights, and Integration Debt

This was the undercurrent of nearly every serious conversation. As AI moves from experimentation to production, governance becomes the difference between scaling and stalling. Yet Deloitte found that only one in five companies has a mature governance model for autonomous AI agents. OneTrust's survey of 1,250 IT decision-makers reported that more than two-thirds say governance capabilities consistently lag behind AI project speed.

The World Economic Forum framed the challenge clearly in a January 2026 analysis: when governance is treated as an afterthought, it slows adoption and erodes trust. When it's designed into workflows from the start, it enables responsible deployment. The organizations winning with AI aren't those with the most models or the biggest budgets — they're the ones that built the operational foundation to deploy, monitor, and govern at scale.

Integration debt was equally present in conversations. Legacy data architectures can't power real-time, autonomous AI. Teams at the expo talked openly about the unglamorous work of data modernization, breaking down silos, and creating interoperable platforms — the infrastructure that makes AI deployable rather than merely demonstrable.

Agentic Workflows, Not Single-Model Tricks

The conversation has moved decisively beyond "which model" to "which architecture." Agentic AI — systems that can plan, execute, and adapt across multi-step workflows — dominated both the exhibition floor and the technical sessions.

KPMG's survey found that agentic AI is projected to be the top investment category in 2026, with half of executives planning to allocate $10-50 million to secure agentic architectures, improve data lineage, and harden model governance. But the survey also revealed a sharp rise in data privacy concerns (from 53% to 77%) and data quality concerns (from 37% to 65%) as agent-to-agent workflows expand the risk surface.

The practical implication: agentic workflows multiply both capability and risk. Teams that deploy agents without robust orchestration, observability, and access control aren't scaling AI — they're scaling fragility.

Evaluation, Trust, and Production Survival

If there was one question that defined the expo floor, it was this: "Does this survive production?"

The concern is well-founded. KPMG's global study found that 66% of employees rely on AI output without validating accuracy, and 56% report making mistakes because of it. An MIT study found that only 5% of custom AI projects reach production. More than half of companies using AI have experienced at least one negative incident — inaccurate outputs, biased results, or system failures.

Evaluation isn't a nice-to-have. It's the gate between a working demo and a deployed product. Teams at the expo were asking specific, operational questions: What's our accuracy threshold before this goes live? What's the escalation path when the agent fails? How do we detect drift before users notice?

These are product management questions as much as engineering questions. And they're the questions that separate teams shipping AI from teams demonstrating it.

Buyers and Builders in the Same Room

One of the rarest and most valuable dynamics at the expo was the proximity of buyers and builders. Enterprise leaders evaluating AI solutions were in the same sessions as the teams building them. That compression eliminates the translation layer that usually distorts both sides of the conversation.

Buyers weren't asking for capabilities. They were asking for evidence: deployment timelines, governance frameworks, failure modes, cost structures, and references from comparable environments. Builders who could speak to production realities — not just model performance — commanded attention.

The Question Nobody Asked

During conversations, not a single person opened with "Which model is best?"

That question has become irrelevant at the enterprise level. Models are commoditizing. The differentiation has moved to orchestration, data quality, governance, evaluation, and integration — the operational infrastructure that determines whether an AI system delivers value or generates liability.

The question teams are actually asking is: "Where does this break at scale, and how do we measure that early?"

That's the right question. It implies a set of product decisions that most teams still need to formalize: what's the failure mode we're most concerned about, what's our detection threshold, what's the human escalation path, and what's the kill criteria if outcomes don't materialize.

What This Means for Product Managers

The expo confirmed something that's been building throughout 2025 and into 2026: the bottleneck in enterprise AI has shifted from "can we build it?" to "should we deploy it, and under what conditions?"

For product managers navigating this landscape, the signal from the floor points to five priorities.

Start with the workflow, not the technology. If you can't describe the specific process you're changing — the steps, the people, the handoffs, the failure points — you don't have a product decision to make. You have a technology exploration. Those are fine, but don't confuse them with product strategy.

Quantify the baseline before you build. Every conversation at the expo that led somewhere productive started with numbers: current processing time, error rate, cost per transaction, customer satisfaction score. Without a baseline, you can't measure improvement. Without measured improvement, you can't justify continued investment.

Design governance in, not on. The teams that are scaling AI successfully treat governance as architecture, not policy. Access controls, audit trails, escalation paths, and human-in-the-loop checkpoints are built into the system from day one — not bolted on after the first incident.

Make evaluation a first-class product decision. Define what "good enough" looks like before you ship. Set accuracy thresholds, failure-mode categories, and monitoring criteria. Treat evaluation as a product capability, not a QA checkpoint.

Set explicit kill criteria. Every AI deployment should have pre-defined conditions under which you shut it down. If adoption doesn't reach X% in Y weeks, if error rates exceed a threshold, if user trust scores decline — pull it. The discipline to kill underperforming AI is as important as the ambition to deploy it.

The P&L Test

Events like this are valuable only if you use them to pressure-test real product decisions. The expo floor reinforced a principle that applies beyond any single conference:

If the workflow doesn't change, the P&L won't change.

AI that doesn't alter how work gets done — that doesn't remove steps, reduce errors, compress timelines, or enable decisions that weren't previously possible — is technology in search of a problem. The enterprise buyers at the expo know this. The teams that earned their attention were the ones who could draw a straight line from deployed AI to a changed workflow to a measurable business outcome.

The demo era is fading. The deployment era demands a different set of skills: operational rigor, governance design, evaluation discipline, and the clarity to know when something doesn't deserve to exist.

That's where the value is now. Not in which model you choose, but in how clearly you've defined what success looks like — and how honestly you'll measure whether you've achieved it.