Four gaps determine whether automated credit decisioning holds up: the data an organization trains on, who is accountable for the decision once the model is live, whether the right tool was matched to the right part of the problem, and what happens to a model as the market moves away from it.

We'll work through each gap using examples from equipment finance: what the post-COVID transportation cycle did to the data underwriting models are trained on, an overview of where LLMs fit versus machine learning models for credit decisions, and what it takes to validate an internal or vendor model against your own portfolio to build trust in the credit decisions.

We will conclude by discussing practical implementation details: where to invest before a model gets built, what factors should drive the build-versus-buy decision, and how to establish measurable goals for success and continue iterating on that success.

But these assets, like their cousins in smart agriculture and yellow iron, have new rules for financers. Customers strongly prefer pay-to-use versus pay-to-own. Venture capital partners of robotic OEMs want a focus on R&D, not production units on the balance sheet, blocking the traditional path to captive or usage-based financing. The autonomy and flexibility users require also complicate the collateral and residual value models of traditional equipment finance partners.

The situation is ripe for new funding and financing solutions to help robotics OEMs scale to meet emerging market demand. Many have turned to Hardware-as-a-Service (HaaS) to address the challenges of customization, technology obsolescence and recovery inherent in robotic assets.

This session, featuring a technology leader from the HaaS ecosystem, will explore the driving trends, the new requirements of both customers and vendors, and the experiences of early participants found in the HaaS1000.

This session resets the starting point. Ryan will walk through why the real starting line is the moment a customer asks for a quote, not the paperwork that follows it, and why that shift turns AI in origination into a distribution strategy instead of a back-office project.

From there, attendees will get hands-on. They'll map their own origination workflow to find where customer intent actually peaks, run automation candidates through a simple readiness filter, and work through a real decision from their own business to determine where the line between automation and human judgment should sit.

The session will close with the leadership shift happening now: managing a team of people and a set of agentic processes at the same time, and knowing which decisions belong to which.

This isn't a theory session. Everyone leaves with a framework they can use immediately and one concrete action to take back to the team.

The rush to adopt has revealed a gap that was always there: most leadership teams don't have a shared understanding of how their business actually works today — current processes, decision-making, culture, handoffs and assumptions baked into daily operations. Without that shared picture, AI doesn't solve problems. It accelerates the existing confusion.

You thought you were running a flat-course marathon. You're actually on a parkour course at a sprint pace, and your team doesn't have the same starting line, let alone a clear vision of the finish line.

This roundtable won't debate which tools to pick. It will ask the question most teams skipped: What does your leadership team need to understand and agree on before AI can actually deliver?

At NMEF, we took the opposite approach on purpose. We asked our people for ideas that solved one narrow, manual process — nothing grand. We built something small enough to test in a few days and used it to show our team the art of the possible. The results told us what to build next. No roadmap up front. Just a thing, ideation, then another thing.

The part most teams skip is proving any of it mattered. A prototype that "seems to be helping" isn't results, it's a vibe. This session covers how to scope a first AI project small enough to actually finish, how to define what "working" looks like before you start, and how to use those early, measurable wins to build the case — and the budget — for what comes next.

Identic AI moves beyond traditional AI assistants and even agentic AI. It creates a digital extension of human intelligence that can think, reason, communicate, and make decisions in ways that reflect the expertise and judgment of real people.

This high-energy session is a 40-minute keynote followed by 20 minutes of open Q&A. Joseph and Tawnya will explore how Identic AI has evolved from a bold concept into a practical business capability that can help organizations preserve institutional knowledge, accelerate decision-making, improve customer experiences, and scale expertise across teams. Together, they will challenge attendees to rethink what is possible when human intelligence becomes a strategic asset that can be deployed throughout an enterprise.

You will also see a live demonstration of EII (Enterprise Intelligence Infrastructure), the platform built to deploy Identic AI. Watch a leader’s digital twin brief him on priorities, explain the reasoning behind a past decision, evaluate a new business idea, and state its operating rules, including when a human must step in. EII works with the systems you already have.

If you’ve been wondering what comes after generative AI and agentic AI, this is the session that will show you what’s next.

The future isn’t AI replacing people. It’s people becoming exponentially more capable through Identic AI. Be among the first in the equipment finance industry to see it in action.

This interactive session will use the framework to map where leasing organizations are exposed today and where risk, opportunity and value may shift next. Participants will examine the familiar physical layers — power generation and delivery, cooling, networking and data-center infrastructure — alongside the less familiar economics of accelerators, model subscriptions and AI-enabled applications. The goal is not to predict winners. It is to identify what is being financed, what is being consumed and what has not yet been priced.

The conversation then turns to the existing portfolio. AI is increasingly embedded in ordinary equipment: machine tools, transportation assets, material-handling equipment, medical devices and agricultural equipment. When a meaningful portion of an asset's usefulness sits in software, data access or a cancellable subscription, conventional questions about residual value, remarketing and documentation become more urgent. Participants will consider where the value actually resides — and who bears the risk if it can be modified, withheld or switched off by a third-party provider.

Structured around Converge AI's TODAY → REVOLUTIONARY → ACTIONABLE methodology, the session will be discussion-led. Participants will compare observations, challenge assumptions and leave with a sharper view of the layers their organizations finance, use and need to investigate.

This interactive breakout will help attendees examine one practical AI use case through a simple readiness lens: what outcome they want, what the use case depends on, what needs to stay human, and what must be fixed before automation creates more risk than value.

The goal is not to slow AI down. It is to help leaders choose where to move now, where to pilot first, and where unresolved operating problems will quietly block scale.