Ask three departments what counts as an active customer and you may get three different answers. In a region full of airlines counting loyal travelers, telecoms counting active lines and banks counting relationships, those definitions run through every system. People live comfortably with that ambiguity. Agents cannot. When an AI system reads that a customer churned or that revenue was recognized, it needs one agreed meaning, connected to the right data and the right rules, or it will act with great confidence on the wrong one.

This keynote explores the semantic layer, the shared map of business definitions, relationships and policies that sits between your data and your agents. We look at how leading organizations are building it, who should own it, and why getting definitions right is fast becoming the cheapest way to make AI more accurate, more explainable and less expensive to run.

Here is a simple test for any AI program. If your main model provider doubled its price, changed its terms or became off limits in one of your markets, how long would it take to move? For many organizations the honest answer is months, because prompts, evaluations, fine tuning and workflows were all built around one vendor without anyone deciding that on purpose.

This keynote panel treats portability as a discipline rather than a hope. Our panelists share how they run switching drills, which parts of the stack they insist on owning, the contract clauses that protect their exit, and how they balance the convenience of one provider against the freedom to leave.

The race for ever larger models makes the headlines, but inside many North Texas banks, energy companies, airlines and insurers a quieter story is unfolding. Smaller models trained for a single domain are proving faster, cheaper and easier to govern, especially for numerical work like fuel and demand forecasting, pricing and risk, where a confident guess is worse than no answer at all.

Our panelists share where they swapped the giant for the specialist, what it did to accuracy and cost, and how to decide which problems truly need the biggest brain in the room.

Since January 2026, companies that build or use AI in Texas have had a new set of ground rules. The Texas Responsible AI Governance Act sets out basic expectations for how AI is developed and deployed, names a short list of uses that are off limits, and created a state AI council along with a sandbox where companies can test new systems with more room to experiment.

This panel looks at what the law means in practice for everyday enterprise AI, how it sits alongside rules in other states and countries, and how to build one governance model that works wherever your AI travels. A practical conversation for organizations that want to innovate with confidence and keep trust at the center.

Everyone has a demo. Far fewer have a reliable way to tell whether an agent is accurate, safe and improving over time. As North Texas companies move agents out of pilots and into customer service, operations and finance, that question has become a conversation for the boardroom.

This session explores practical evaluation, from test sets built on real work to human review loops and the metrics that deserve a place on an executive dashboard.

Some of the most valuable data in the enterprise is the data you are not allowed to touch. Patient records, customer conversations, fraud cases, the rare failure that happens once a decade. Synthetic data offers a way through, realistic records that carry the patterns without the people, which matters all the more under the Texas Data Privacy and Security Act.

This fireside chat separates the promise from the pitfalls. Where synthetic data is already speeding up projects in regulated industries, how to prove it is both useful and truly private, and what happens to models that learn too much from machine made examples and too little from the real world.

Your agents are starting to call tools, negotiate with other systems and hand tasks to agents built by partners and vendors. Behind every one of those conversations sits a new layer of protocols and connectors that most enterprise architectures never anticipated.

This session demystifies the emerging standards that let agents work together, and shows how to adopt them with clear rules for trust, logging and accountability from the very first day.

Most of the AI conversation has lived on screens, in chat windows, documents and dashboards. The next chapter steps into the distribution centers, rail yards, airport hangars, oil fields and factory floors that make North Texas one of the busiest logistics hubs in the country. World models help machines understand space, time, cause and effect, so robots and autonomous systems can rehearse an action before they take it.

Our panel explores where physical AI is already creating value across Texas, what it demands from data, power and infrastructure, and how leaders prepare their operations, safety teams and people for machines that move.

Every organization has a few people who simply know. The aircraft mechanic who can hear when an engine is not quite right, the field engineer who knows every quirk of a pipeline, the underwriter who senses a bad risk before the numbers show it, the operations lead who remembers why a rule was written twenty years ago. Much of what they know was never written down, and many of them are approaching retirement.

This closing conversation explores how leaders are using AI to listen to, record and preserve that expertise while those experts are still in the building. How to invite people to share what they know rather than feel replaced by it, how to turn stories and judgment into something a new colleague or an agent can learn from, and how to honor the people whose wisdom built the company.