Enterprise Data & AI is entering a more operational phase.
AI agents are moving into real business workflows. Ontologies are giving AI the context to understand how businesses actually work. Real-time data is connecting industrial operations with enterprise decision-making. Digital twins are bringing asset and engineering information together. And as these capabilities expand, governance and new delivery models are becoming just as important as the technology itself.
These were the conversations at the center of CoreShift 2026, Acuvate’s two-day Enterprise Data & AI forum held on September 15–16.
With 332 registrations, 232 unique attendees and eight focused sessions, CoreShift brought together enterprise leaders, customers, partners and technology experts to explore the technology and operating foundations shaping the next phase of Data & AI.
Here are eight key insights from the CoreShift 2026 sessions. You can also watch each session on demand and explore the discussions in full.
1. From AI Pilots to Enterprise-Scale Agentic AI
Getting an AI agent to work in a pilot is one challenge. Running it reliably in production is another.
The opening session looked at how enterprises can move Agentic AI from pilot to production. The key distinction was simple: a pilot proves possibility; production proves repeatability.
Once an agent reaches real users, enterprises have to account for more than its ideal path. They need to know how it responds to edge cases, what it must refuse, how releases are controlled, what each successful outcome costs, how latency behaves under load and who owns the agent once it is live.
The session brought these requirements together into six production-readiness areas: quality, release discipline, channel validation, economics, performance and operations.
2. Building Shared Business Context for AI
Enterprise AI does not struggle only because data is unavailable. It often struggles because the meaning behind that data is fragmented.
Different systems use different names. Teams define KPIs differently. Important relationships may exist only in people’s heads or across disconnected documents and applications.
The ontology session explored how a shared semantic foundation can give both people and AI a common understanding of the business. An ontology maps business entities such as plants, products, suppliers or customers, along with their relationships, rules and possible actions.
This matters because AI needs more than access to information. It needs context: how things are related, what business rules apply and what a particular term means inside the organization.
The session also looked at how Microsoft Fabric IQ, Foundry IQ, Work IQ and Web IQ can contribute different forms of context across structured data, documents, work patterns and external information.
3. Connecting Operational Technology with Enterprise AI
In industrial environments, some of the most valuable data is generated continuously by machines, sensors, historians and control systems.
The Real-Time Data & OT session focused on bringing that operational data into a broader Enterprise Data & AI environment.
That includes sensor selection, high-speed time-series streaming, edge processing, Azure IoT Operations and Microsoft Fabric Real-Time Intelligence. The aim is to make operational information available not only to plant systems, but also to AI models, agents, dashboards and enterprise decision-makers.
The broader architecture connects OT with enterprise applications, external information and business context, allowing users across operations, maintenance, engineering, finance and logistics to work from a more connected view of what is happening.
Real-time data becomes far more valuable when it is contextualized and connected to the decisions people actually need to make.
4. Data and AI Governance: Building Trusted Data Foundations for Enterprise AI
As AI agents gain greater access to enterprise data and systems, governance has to evolve with them.
The governance session started with a simple scenario: an AI agent has been running for months, sensitive data has gone somewhere it should not, and no one is quite sure who approved the agent or how to shut it down.
That raises fundamental questions around ownership, authorization, access, lineage, auditability and accountability.
The session outlined an AI governance framework covering governance principles, roles and ownership, agent registries, authorization, lifecycle management, security controls, model evaluation, lineage and continuous performance monitoring.
The message was clear: governance cannot be added after AI adoption has already scaled. It needs to be designed into the way agents are created, deployed and operated.
5. Industrial AI for Smarter Manufacturing Operations
Industrial AI is most useful when it is connected to the real operating environment of the plant.
The manufacturing session explored how operational data, enterprise data, AI, machine learning, machine vision, ontologies and agents can work together across manufacturing operations.
Use cases ranged from OEE and predictive maintenance to quality, asset performance and real-time decision-making at the edge.
Different data also needs different treatment. High-speed sensor messages can move through Microsoft Fabric Real-Time Intelligence, while larger inputs such as camera images may require machine vision models running closer to the equipment for immediate action.
The session also highlighted the importance of connecting these capabilities into a common Enterprise Data & AI platform rather than creating separate technology islands for every use case.
6. Forward-Deployed Engineering: The Operating Model for Enterprise AI
Many AI initiatives sit at the intersection of technology, process and people. Building the model is only part of the job. Forward-Deployed Engineering brings engineering closer to the business problem and the people doing the work.
Instead of beginning with a technology choice, the model begins with the workflow: What is the task? Who performs it? What data is involved? Where are the approvals? What outcome needs to improve?
The session described the “real” skeleton of an AI solution as starting with ROI, requirements, process and goals before getting into models, tools, orchestration and user experience.
The approach also emphasizes production delivery. Forward-deployed teams combine technical build skills with workflow understanding, product thinking, direct user feedback and governance awareness.
The objective is not simply to leave behind a prototype. It is to build a capability that can move into production and continue operating after handover.
7. Connected Digital Twins for Intelligent Industrial Operations
Asset-intensive industries often have information about the same equipment spread across P&IDs, historians, ERP systems, documents, images and maintenance applications.
Connected digital twins bring those sources together around the asset.
The Digital Twins session explored how engineering information, operational technology, enterprise systems and real-time sensor data can be connected to create a more complete operational view across manufacturing, energy, utilities and oil & gas.
A trusted Tag-ID registry provides a common identity for assets, while digitized P&IDs help connect engineering information to operational and enterprise data.
Instead of searching through multiple systems for information about a pump or valve, users can work from a connected view that brings together relevant PI data, ERP information, documentation and other sources.
That can support maintenance planning, turnarounds, predictive maintenance and faster operational decisions.
8. Scaling Enterprise AI: Customer Perspectives & Lessons Learned
The final executive roundtable brought the technology discussions back to the way enterprises actually operate.
One of the strongest themes was ownership.
Before automating a process, organizations need clarity on who owns the outcome, who has the authority to make decisions when a process crosses organizational boundaries and what measurable business result is expected.
The conversation also challenged the assumption that organizations always need another technology platform to make AI work.
Many enterprises already have identity in Entra ID, content in Microsoft 365, data in existing platforms, APIs, workflows and analytics. AI can sit across these capabilities through governed access rather than requiring every piece of enterprise information to be copied somewhere new.
The practical starting point is often simpler: identify the processes creating the most friction, understand what capabilities already exist and address what is genuinely missing.
Enterprise AI, in that sense, becomes as much an operating-model transformation as a technology transformation.
What CoreShift 2026 Brought Together
The eight sessions covered very different parts of the Enterprise Data & AI landscape, but they were closely connected.
Agents need trusted context. Context depends on connected and governed data. Industrial AI needs access to real-time operational information. Digital twins need reliable asset identities and engineering information. And none of these capabilities reach their full value without clear ownership, governance and an operating model that connects technology to real business processes.
That is the broader shift CoreShift 2026 explored: bringing Data, AI, context, operations and governance together so enterprises can put intelligence to work where it matters.
All eight sessions are now available on demand, including the presentations, demonstrations and executive discussions. Watch CoreShift 2026 On Demand and explore how these capabilities can apply across your organization to scale your Enterprise AI.
CoreShift 2026 - FAQs
CoreShift 2026 is Acuvate’s two-day Enterprise Data & AI forum covering Agentic AI, ontologies, real-time data, AI governance, Industrial AI, Forward-Deployed Engineering, digital twins, and enterprise AI operating models.
Enterprises need more than a working prototype. Production-ready Agentic AI requires quality evaluation, controlled releases, channel testing, cost visibility, performance monitoring, observability, and clear operational ownership.
An ontology is a business-language model that connects entities, relationships, rules, and actions across enterprise systems. It helps AI understand how the business actually works instead of reasoning over disconnected data.
A semantic layer gives AI a shared understanding of business concepts, definitions, relationships, and rules. This helps reduce ambiguity and enables more consistent answers, analytics, copilots, and AI agents across different data sources.
Real-time OT data connects sensors, machines, historians, and industrial systems with enterprise data platforms. This allows AI, dashboards, agents, and digital twins to support faster decisions across operations, maintenance, engineering, and other business functions.
An enterprise AI governance framework defines how AI agents are owned, authorized, secured, monitored, evaluated, and retired. It should also cover lineage, auditability, access controls, lifecycle management, and ongoing performance.
Industrial AI applies AI, machine learning, machine vision, real-time data, and edge computing to manufacturing operations. Common use cases include OEE improvement, predictive maintenance, product quality, asset performance, and real-time operational decision-making.
Forward-Deployed Engineering is an outcome-focused delivery model where technical teams work closely with business users and workflows. It starts with the process, requirements, ROI, and desired outcome before designing the AI solution.
Connected digital twins bring together engineering data, OT data, enterprise systems, asset information, and real-time sensor data around a common asset or process. This can improve maintenance planning, predictive maintenance, turnarounds, and operational decision-making.
An enterprise AI operating model defines how ownership, governance, technology, workflows, and measurable business outcomes come together. CoreShift highlighted that successful AI adoption depends as much on operating-model design as on the technology itself.
CoreShift 2026 covered eight areas: Agentic AI, ontologies and semantic context, Real-Time Data & OT, Data and AI Governance, Industrial AI, Forward-Deployed Engineering, Connected Digital Twins, and scaling enterprise AI.
All eight CoreShift 2026 sessions are available on demand through Acuvate’s CoreShift 2026 on-demand page.
