Enterprise AI Beyond the Pilot
A practical look at the three forces defining enterprise AI right now, and how organizations across industries can turn promising experiments into scalable business capabilities.
Enterprise AI now turns on three questions: Can you trust the data? Can the system survive production? And which technologies are ready to earn investment? Too often, organizations tackle these questions separately. Data teams fix quality issues project by project, AI teams optimize prototypes in isolation, and innovation roadmaps chase what is newest rather than what is ready. But successful AI depends on all three working together.
Strong data foundations support reliable systems. Production discipline turns a successful demo into an operational capability. A clear readiness filter keeps investment focused on technologies that can deliver practical value. Get the sequencing wrong, and even well-funded initiatives stall before they scale.
Data Excellence:
Build AI-Ready Foundations
Poor-quality, inconsistent or disconnected data does not simply slow AI projects down; it limits how far they can grow. When governance, security and visibility are added later, problems often become clear only once a system is live, operating at scale or interacting with customers.
The organizations making the most progress treat data quality as a core business capability, not something every AI project has to repair for itself. They make information easier to access, connect and trust, while building governance, security and end-to-end monitoring into the foundation from the start.
The gap between them is where many enterprise AI initiatives stall. Getting a copilot or agent to perform well in a controlled demonstration is the easier part. Getting it to behave reliably inside a legacy CRM, a regulated workflow or a team that did not ask for it is the real work.
Once AI is influencing customers, revenue or operational decisions, it needs the same discipline as any other production system: clear reliability targets, active monitoring, accountable ownership and a defined response when performance falls outside acceptable limits.
Reasoning models, multimodal AI and multi-agent systems are moving from research into real-world testing. At the same time, specialized hardware, distributed computing and synthetic data are changing what organizations can build and how much it costs to run.
The challenge is not keeping up with everything new. It is knowing what is ready to deliver value today, what deserves structured experimentation and what should remain on the watchlist. Whatever the technology, it still needs trusted business data, practical economics and a path to safe, reliable scale.
Trusted data. Production discipline. Practical innovation.
Enterprise AI scales when all three reinforce one another.
Without trusted foundations, deployment becomes fragile. Without clear ownership and measurement, pilots struggle to survive contact with real operations. Without a readiness filter, innovation budgets drift toward novelty rather than value.
From trusted data to production discipline, and what to build next.
Data quality is both the top governance priority and the most commonly cited challenge in managing data for AI.
Register your interest and secure your place today.
THE AI SUMMIT NEW YORK — MINI REPORT SERIES
INTRODUCTION
1
Every AI initiative eventually runs into the same wall: the data underneath it.
Applied AI:
From Pilots to Measurable Business Value
2
A working prototype and a production system are not the same thing.
Next Generation:
What's Shaping the Road Ahead
3
Separating real breakthroughs from short-lived hype is becoming a core technical skill.
Whether the goal is proving lineage to a regulator, scaling a copilot across a contact center or catching defects on a production line, the fundamentals remain the same. The organizations setting the pace are those turning AI from a collection of experiments into a repeatable business capability.
Register to attend
December 9-10, 2026 | Javits Center, New York
Can you trust the data?
Quality, lineage, access and governance.
Can AI survive production?
Reliability, monitoring, ownership and adoption.
What should you build next?
Readiness, value, cost and scalability.
DATA SIGNAL
Source: Omdia research on data governance. 60% name data quality a key governance priority; 17% cited quality and consistency as the leading challenge, ahead of data security at 14%.
In financial services, this means proving a clean audit trail behind model decisions across legacy and cloud systems. In healthcare and pharma, it means connecting patient and clinical-trial data without weakening the controls around it.
INDUSTRY LENS
The AI-Ready Data Test
Accessible
Teams can find and use the right data without creating uncontrolled copies.
Governed
Ownership, permissions, security and policy are built in from the start.
Traceable
Data can be followed from source to model output, supporting monitoring and auditability.
Continue the conversation at The AI Summit New York: Data Excellence Stage
Rebuilding the Enterprise Data Foundation for AI
Beyond RAG: Building the Context Layer for Enterprise A
Why Data Observability Matters
Why attend: Hear how regulated industries are strengthening data quality, auditability and accountability before AI reaches production.
A retailer deploying a shopping assistant has to manage traffic spikes, incomplete inventory data and brand tone in full view of the customer. A telecoms provider automating network operations has to prove that an agent will not degrade service while it encounters the edges of a live network.
INDUSTRY LENS
The Production-Readiness Test
Bounded use case
Start with a high-frequency, high-friction task rather than trying to automate an entire function.
Reliability target
Define service-level objectives, error tolerances and the conditions that trigger intervention.
Accountable owner
Make one team responsible for performance, escalation and improvement after launch.
Outcome metric
Measure the business result inside systems leaders already trust, not activity in an AI-only dashboard.
The teams shipping AI that lasts tend to start narrow and specific, keeping a human meaningfully in the loop while they prove reliability and value. They then scale one validated use case into the next, building depth before breadth.
Data quality is both the top governance priority and the most commonly cited challenge in managing data for AI.
Source: Omdia research on data governance. 60% name data quality a key governance priority; 17% cited quality and consistency as the leading challenge, ahead of data security at 14%.
DATA SIGNAL
Measured Return
$1.49 earned for every $1 invested
Average among organizations that have quantified their generative AI return.
VALUE SIGNALS
Expected Return
Up to 47% over the next 12 months
Senior executives' expectation for agentic AI investments.
Source: Omdia, "The ROI of Gen AI and Agents 2026," global survey of 2,050 AI decision-makers.
Domain-Specific AI: Building Moats with Proprietary Data and Expertise
Why attend: Explore the operating practices that separate an impressive prototype from a reliable, measurable system that teams can use every day.
The practical takeaway: The organizations turning pilots into lasting systems are not necessarily the ones with the most impressive demo. They are the ones with a clear plan for what happens after it.
A simple path to scale
Start narrow
Choose one bounded workflow with visible friction and a clear owner.
Prove value
Track reliability and the business outcome before expanding scope.
Scale deliberately
Reuse the operating model, controls and lessons in the next use case.
A practical
readiness filter:
Test now
Use cases with trusted data, bounded risk, clear ownership and a measurable outcome.
Watch
Wait
Capabilities showing promise but still needing better cost, control or integration evidence.
Technologies without a clear business case, operating model or path to reliable scale.
On the factory floor, manufacturers are combining vision, sensor and text data to identify defects earlier. In healthcare, teams are exploring reasoning models to support specialist decisions where accuracy is critical and a clinician remains responsible for sign-off.
INDUSTRY LENS
The AI data center chip market is expected to grow from $207 billion in 2025 to $286 billion by 2030.
Source: Omdia AI data center chip market forecast. This growth is linked to expanding AI applications, reasoning models and continued infrastructure investment.
INFRASTRUCTURE SIGNAL
MOVE BEYOND THE PILOT
See how technical and data leaders are building enterprise AI that can be trusted, deployed and scaled.
10
At The AI Summit New York, explore the architectures, governance choices and deployment practices behind AI systems that deliver measurable business impact.
Learn what works
Hear practical lessons from organizations moving AI from experimentation into production.
Compare what is next
Assess emerging technologies against real requirements for value, control and scalability.
Build the right connections
Meet the technical leaders, innovators and solution providers shaping enterprise AI.
Continue the conversation at
The AI Summit New York: Applied AI Stage