Data leaders have spent years helping people understand and use enterprise data. Now they face another challenge: helping AI understand the business. An AI system needs more than access to accurate records. It must know what the data means, how business concepts relate, which source to trust, and what rules apply to the task at hand. Data leaders must also connect those technical requirements to an AI strategy that the business can fund, govern, and evaluate.

Those issues run through the agenda at Technology & Innovation Forum East, taking place November 4–5 in New York City. Through a dedicated data and AI track, certifications, workshops, and an award presentation, data, AI, and analytics leaders can assess their capabilities, work on current priorities, and examine technologies likely to affect their plans. Here are data and AI topics to consider as you build your agenda.

Find The Gaps In Your AI Readiness

Before expanding an AI program, leaders need an honest view of what the organization can support. The Technology & Innovation Forum Certification: Data & AI Readiness provides a structured assessment of the capabilities behind AI adoption. It helps participants examine where existing data, governance, technology, and organizational practices support their goals and where weaknesses could constrain them.

Bring a priority initiative to mind. What data does it require? Who owns that data? Which governance practices apply? Does the organization have the skills and operating structure to move the initiative into production? Those questions turn “Are we ready?” into a more useful discussion about what a specific program requires next.

Build An AI Strategy Before Adding More Projects

Individual AI projects are easy to approve. A coherent portfolio is harder to build. In the Design A Transformational AI Strategy workshop, attendees will respond to a fictional CEO mandate to transform a company with AI in 18 months. Working as an executive team, participants will use a realistic business scenario and a one-page template to connect AI choices with company priorities. They will then defend their strategies and compare the trade-offs involved.

The value for data and AI leaders is the discipline of making choices across technology, governance, investment, and business priorities before new projects accumulate. Participants leave this analyst-led session with a completed one-page strategy they can use as a model for similar conversations inside their organizations.

Make A Plan For AI-Ready Data

With a strategy in place, the next question is whether the data can support it. The Build A Plan For AI-Ready Data workshop separates the learning into two parts. First, organizations must address familiar but unfinished priorities such as data fabric, governance, quality, timeliness, integration, accessibility, and security. Second, agentic systems need capabilities that many existing data environments were not designed to provide. These include semantic layers, knowledge graphs, and vectorized representations of unstructured information that give agents context, relationships, and memory.

This is a hands-on working session, not simply a presentation about the importance of data quality. Attendees can use it to identify which parts of their current foundation need attention and which newer capabilities belong on their roadmap.

Help AI Understand The Business

An AI system can retrieve the right information and still interpret it incorrectly. It may not know which definition of “customer” applies, how two business entities are related, or which policy governs a particular action. Employees resolve questions like these through experience. AI needs the organization to represent that knowledge more explicitly.

Get Started On A Journey To Build An AI Context Layer explores how knowledge graphs can serve as the enterprise’s digital twin of meaning, grounding agentic AI in governed, contextual, and explainable business knowledge. Attendees will learn how knowledge graphs improve the accuracy, governance, and scalability of agentic AI initiatives. For many data leaders, this represents a significant evolution of their role. The challenge is no longer simply managing data. It is making business knowledge understandable to machines.

Reassess Your AI Platform Strategy

Once organizations understand the data and context that their AI applications require, they must decide how their platform environment will support them. AI Platforms Are Transforming Themselves And The Way Enterprises Build And Deliver Value explores the changing platform landscape and the agentic architectures beginning to emerge within it. The session also looks at how enterprises can rationalize AI across the platform investments they already have.

That last point deserves attention. The goal is not to add every promising capability to the technology stack. Data leaders should examine whether a platform fits their data architecture, supports reliable and explainable applications, and reduces rather than compounds fragmentation. The agenda for Technology & Innovation Forum East provides the tools and insights to help achieve this.

Learn From An Organization Producing Results

Eventually, every data and AI program must answer a straightforward question: What changed for the business? Celebrating Excellence In Data And AI: The 2026 Data & AI Impact Award Winner will feature an organization that has operationalized data, expanded AI initiatives, built trust, and delivered measurable enterprise outcomes.

Listen for what happened between the original idea and the final result. How did the organization define the business problem? Which governance or leadership choices mattered? What did it measure? Where did the program encounter resistance? Those details often offer more value than a polished account of the technology alone. Seeing winning examples of success at Technology & Innovation Forum East puts real-world strategies into clear view.

Put Quantum Computing In Perspective

Most of this agenda path addresses work that data and AI leaders are doing now. The Quantum And AI Opportunity looks further ahead. The session begins with an overview of quantum computing and its current and future trajectory. It then examines potential applications, with an emphasis on AI use cases, while separating plausible opportunities from hype. Audience questions are encouraged.

The immediate goal is not to create a quantum investment plan. It is to understand the technology well enough to recognize relevant developments, ask better questions, and avoid reacting to every announcement as if it demands a new strategy.

Build Your Agenda Around The Work You Need To Advance

These seven agenda picks at Technology & Innovation Forum East cover different stages of the data and AI leader’s work. As part of a wider data and AI agenda, they address a defining challenge for data leaders: Giving AI reliable data is only the beginning. AI must also understand the surrounding business context, operate within clear boundaries, and contribute to an outcome that the organization values.

At Technology & Innovation Forum East, you can use the agenda to determine which part of that work needs your attention now. Check out the full agenda and explore ticket options for you and your team.

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