Ford's Kevin Dunbar on Transforming Fleet Management with AI-Powered Intelligence

As businesses look for ways to improve fleet efficiency, reduce operating costs and simplify day-to-day operations, artificial intelligence is emerging as a valuable tool for turning vehicle data into actionable insights.

Ford Pro AI was recently named AI-Powered SMB Solution of the Year by the 2026 AI Tech Ascension Awards. The platform is designed to help fleet managers access critical information faster, identify potential issues and make more informed decisions without navigating complex dashboards or reports.

We sat down with Kevin Dunbar, General Manager for Ford Pro Intelligence, to discuss how Ford Pro AI is addressing real-world fleet management challenges, the importance of accurate and reliable AI insights, and how intelligent technology can help businesses operate more efficiently.

Ford Pro AI was named AI-Powered SMB Solution of the Year. What problem did you set out to solve?

Kevin Dunbar: Fleet managers have plenty of data. What they often do not have is the time to sort through it.

They are managing vehicles, drivers, maintenance, costs and customer needs. We surveyed fleet managers, they told us they spend an average of 23 hours a week on administrative tasks across areas such as maintenance, dispatch, budgeting, human resources, and customer service. They estimate that AI could reduce the time they spend on those tasks by about 40% or more.

We designed Ford Pro AI to make their existing fleet data easier to access, enabling them to act quicker. A fleet manager can ask a question in plain language, get a clear answer based on their fleet’s distinct information, and be directed to the relevant area within Ford Pro Telematics Software to take the next step. The goal is simple: less time digging and more time running the business.

There are a lot of AI tools on the market. What is different about Ford Pro AI?

Kevin Dunbar: This is not a general-purpose chatbot that has been adapted for fleets. It was built specifically around the work fleet managers do every day.

Ford Pro AI combines Ford’s commercial vehicle experience and proprietary vehicle data with a multi-agent architecture built on clean, structured information from each customer’s own fleet. It’s a closed model that cross-checks responses against customer-specific data rather than using pooled fleet data, information from the internet, or generic industry benchmarks.

That matters because the questions are specific. A customer may need to know which vehicles require service, what is behind an engine fault, which vehicles are being underused, or where fuel and repair costs are increasing so they can act quickly to save on fuel costs. Ford Pro AI helps turn those questions into information the fleet manager can act on.

What does Ford Pro AI help fleet managers do today?

Kevin Dunbar: It helps them get answers faster across three areas: vehicle health, driver behavior, and reporting and costs. A fleet manager can ask which vehicles need service, whether vehicles were used outside operating hours, which vehicles recorded speeding or harsh-driving events, how much the fleet spent on fuel or repairs, or whether certain assets are being underused.

Ford Pro AI is  read-only. It does not take action on a customer’s behalf, but it can direct the customer to the relevant area of Ford Pro Telematics Software to review more information or complete the next step. That keeps the fleet manager in control.

Why is customer-specific data so important when applying AI to fleet operations?

Kevin Dunbar: Because fleet decisions have real operational and financial consequences. A generic answer is not enough when a vehicle may need service or a business is trying to understand why its costs are changing.

Ford Pro AI is built on data from the customer’s own fleet. Its multi-layered architecture uses multi-agent validation and clean, structured data models to reduce the potential for hallucinations and errors.

We also believe the customer has to remain part of the process. Ford Pro AI can surface information and point to a potential issue, but the fleet manager is responsible for reviewing that information and deciding what to do next.

Some people worry that AI will replace jobs. How do you think about the relationship between AI and fleet managers?

Kevin Dunbar: Fleet management is a hands-on job. It involves people, vehicles, customers, and situations that do not always fit neatly into a dashboard. AI can help with the data work. It can spot a trend, surface an issue or pull together information that might otherwise take time to find. But it cannot calm a driver after an accident, manage a customer issue, negotiate with a supplier or understand all the context behind a decision. Those parts of the job require experience and judgment.

We designed Ford Pro AI to extend what fleet managers can do, not replace them. If it can take some administrative burden off their plate, they can spend more time with their teams and  customers.

Why is now the right time to bring AI into fleet management?

Kevin Dunbar: Fleet vehicles are generating more information than ever. But more information doesn’t automatically make a fleet easier to manage. Fleet managers don’t need another dashboard to check. They need a clearer picture of what needs attention and what to do next.

AI can help close the gap between having information and knowing what to do with it. Instead of searching across screens or digging through reports, fleet managers can ask a question in plain language such as “Which vehicles need service this month?” or “Where can we reduce fuel use and costs?”

The value isn’t just getting an answer. It’s helping managers identify priorities and make informed decisions with less effort spent finding and interpreting the information.

Fleet operators bring the expertise. AI helps put the fleet’s information to work.

What do you see as the future of AI in fleet management?

Kevin Dunbar: The best technology should make the work simpler, not add another layer of complexity. AI should continue to focus on solving practical customer problems. Can we help fleet managers understand what needs attention? Can we give them clearer information to support decisions about uptime, safety, utilization and cost? Can we help them be more proactive and stay ahead of issues?

We are not interested in AI for AI’s sake. The measure of success is whether it helps give a customer their time back, keep vehicles moving, serve their customers, and run the business.

Mezmo CEO Tucker Callaway on the Future of AI Agents, Observability and Autonomous IT Operations

Mezmo's AURA, an open-source SRE agent harness designed to help teams build and deploy production-ready agents within their existing environments, was recently named AI-powered Enterprise Agent Solution of the Year by the 2026 AI Tech Ascension Awards. We spoke with Tucker Callaway, CEO of Mezmo, to discuss how AI agents are reshaping observability, the challenges of moving from AI demonstrations to production deployments, and why governance, transparency and trust will be critical to the future of autonomous IT operations.

How do you think the rise of AI agents will impact observability?

I think observability is headed for a pretty fundamental reset. Historically, observability has been built to help humans understand systems through dashboards, queries, and investigation. As agents take on more operational work, that model changes. Agents need useful context delivered directly into the workflow. Agents don’t work that way. They need useful context delivered directly into the workflow.

That means the value starts to shift. Storing massive amounts of data and giving people better ways to search it becomes less important. Turning noisy telemetry into focused evidence becomes much more important. Agents don’t need more data. They need the right data.

We’ll also need to observe the agents themselves. What did the agent see? Why did it make that decision? What tools did it use? Did it stay within the boundaries we set? Observability becomes as much about establishing trust in autonomous workflows as monitoring the underlying infrastructure. ⁠

There’s a lot of energy around AI SRE right now. How can companies separate reality from hype?

The demo is easy. Production is hard.

You can connect a model to a few tools and generate a pretty impressive investigation. The real question is whether it works reliably in your environment, with your data, when something important is actually failing.

I’d look at practical outcomes. Did it reach a correct, validated conclusion faster? Can the SRE interrogate the result and see the evidence? Can it reuse evidence and outcomes from previous incidents? Can the team control what it’s allowed to access and what it’s allowed to do?

Companies also shouldn’t have to choose between a passive copilot and an agent with unrestricted access. Start with investigation. Measure the results. Require approval for consequential actions. Give the agent more authority as it proves itself. That’s how you turn an interesting demo into something your on-call team will actually trust. ⁠

⁠ ⁠

⁠Do you think enterprises feel pressure to adopt AI solutions? Are there concerns they should be mindful of about moving too fast?

There’s definitely pressure. Every executive team is asking how quickly the company can take advantage of AI, and development teams are already shipping faster because of it.

The concern is that operational maturity may not keep pace. More code means more releases, services, dependencies, and change entering production. Companies can end up creating more operational complexity than their teams can reasonably understand or manage.

I don’t think the answer is to wait. Start with a bounded workflow and be clear about what success looks like. Decide what the agent can access, what requires approval, and how its decisions will be recorded. Plan for what happens when it’s wrong, because eventually it will be.

The simplest way to put it is: humans set the boundaries, agents do the work. Autonomy is where this is going, but agents have to earn trust along the way. ⁠

⁠ ⁠

⁠You recently announced an open-source project called AURA for building production-ready agents. Can you explain more about the project and its benefits?

AURA is our open-source SRE agent harness. It runs in the customer’s infrastructure and coordinates workflows across the observability tools, runbooks, ticketing systems, collaboration platforms, and models they already use.

We built it because generic agent frameworks leave a lot of production work to the customer. SRE teams need to inspect how the agent reached a conclusion, control the systems it can access, and adapt workflows to their own environment.

Open source matters when an agent is working in production. Teams need to see how it operates and understand why it made a decision. I don’t think companies will be comfortable putting a black box in the middle of incident response.

AURA handles the workflow. The Mezmo platform provides the governance, auditability, memory, and operational context around it. Teams can begin with investigations and gradually expand the agent’s authority as confidence grows. ⁠

⁠ ⁠

What does Mezmo see as the core challenges it hopes to help solve in 2027 when it comes to building with AI in the world of software?

The big challenge will be operating everything AI helps us build.

Software is becoming much easier to create and change. That doesn’t make it easier to run. In fact, the operational surface area is likely to grow much faster than our ability to understand it manually.

Agents will need focused context instead of unlimited access to raw data. Enterprises will need a reliable record of what each agent saw and did. They’ll also need governance that keeps people in control without asking someone to approve every individual tool call.

That changes the role of the SRE. People move up the stack and spend more time defining policy, boundaries, and success conditions. The agent handles more of the investigation and operational work within those constraints.

I don’t think the winners will simply have the smartest agent. They’ll have the best environment for putting agents to work safely: useful context, clear controls, an auditable record, and a way for each workflow to earn more authority over time.

VitalEdge Technologies’ Shriram Rajagopal on Transforming Equipment Dealership Operations with Agentic AI

VitalEdge Technologies' VitalityAI was recectly named AI-powered Enterprise (Agent) Solution of the Year by the 2026 AI Tech Ascension Awards. Designed specifically for equipment dealerships and rental operations, VitalityAI brings specialized AI agents directly into existing business systems to help teams improve efficiency, uncover opportunities and make more informed decisions across service, parts, rental, sales and finance. We spoke with Shriram Rajagopal, Chief Product and Technology Officer at VitalEdge Technologies, to discuss how the company is applying agentic AI to solve industry-specific challenges, the importance of connected intelligence and human oversight, and what’s next for AI-powered dealership operations.

VitalityAI was named AI-powered Enterprise (Agent) Solution of the Year. What is VitalityAI, and what problem was it built to solve?

Equipment dealerships run some of the most complex operations in any industry. They manage multi-location businesses where service, parts, rental, sales and finance are tightly connected, and where a delay in one area shows up as downtime for a customer in the field. Yet the data that could guide those decisions is often scattered across systems, branches, OEM systems and reporting tools. Teams can see what happened last month, which is reactive and retrospective. However, they do not always know what needs attention today and in order to be proactive.

VitalityAI is our answer to that gap. It’s an operational intelligence platform with a cohort of specialized AI agents built specifically for equipment dealerships and rental operations. Launched in March 2026, it’s embedded directly in our e-Emphasys ERP, IntelliDealer DMS and connected applications. The agents analyze dealership data, surface risks and opportunities, and recommend next steps right where people are already working.

There’s no shortage of AI assistants on the market. What makes VitalityAI different from a general-purpose tool?

The difference is context. A general-purpose assistant doesn’t understand what washout analysis is, why rental utilization matters to cash flow, or how a spike in service demand affects parts inventory and technician schedules. A dealer would have to build all of that in themselves, including the data model, the integrations, the governance and the workflows.

VitalityAI is designed around dealership roles, data structures and decision points from day one. We have agents for service, parts, rental, sales, finance, equipment diagnostics and customer engagement, and they speak the language and operating logic of the industry. That means dealers get relevant, actionable recommendations without first having to teach the AI how their business works. VitalityAI is embedded in the user workflow capturing the right data at the right time. This seamless user experience drives adoption to enable AI to effectively use that data in a timely manner.

VitalityAI is described as an “orchestrated” agent platform. Why does orchestration matter in a dealership?

Because no department in a dealership operates in isolation. When service demand rises, it changes parts requirements and technician capacity. When rental fleet availability shifts, it affects customer commitments. When equipment goes down, it affects the customer’s productivity and the dealer’s relationship with that customer.

Our agents share dealership context and coordinate across functions, so the platform can connect those dots instead of reporting isolated metrics department by department.

And because everything runs inside the ERP, DMS and rental systems dealers already use, they don’t need separate dashboards, disconnected AI tools, or manual effort to move data from one place to another.

What does this look like in practice for dealership teams?

Insights Agent is a good example. A dealership leader or functional manager can ask a question in plain language about financial performance, labor efficiency, technician productivity, rental utilization, washout analysis or fleet performance, and get an answer without waiting for someone to build a report. That cuts down on repeated data pulls, spreadsheet work and manual interpretation.

On the service side, Tech Assist helps technicians move faster from customer complaint to root cause, with guided troubleshooting for complex repairs and more consistent diagnostics. In our ROI model, a dealership using Tech Assist could save an estimated 30 minutes per technician per day, improve first-time fix rates by 10% and improve warranty recovery by 30%. With skilled technicians in short supply across the industry, getting more out of every hour of their time makes a real difference to margins and to customer uptime.

For fleet managers and rental leaders at heavy equipment dealerships losing margin to reactive, spreadsheet-driven rate management, VitalityAI’s Rental Rate Advisor is an AI-powered pricing tool that delivers a three-tier rate model, live market benchmarks, and a controlled publish workflow, all inside the platform they already use.

Unlike generic rate modules or disconnected analytics tools, Rate Advisor is built around the fleet manager’s actual workflow. Pricing that used to take days takes minutes, with guardrails that protect margin at every level.

On the sales side, opportunity scoring predicts how likely a deal is to close, so reps can focus on the opportunities most likely to generate revenue or work on the specific factors that would raise the dealer’s chances of winning. Lead scoring ranks leads by their likelihood to convert, improving sales productivity and conversion rates. The Customer Briefing Agent turns reps’ conversations with customers and prospects into CRM actions by automatically creating leads, contacts, tasks, and follow-ups. The Sales Research Agent improves conversion and reduces attrition by delivering AI-driven insights on accounts, pipeline trends, and growth opportunities to speed up sales planning and execution.

How does VitalEdge approach trust and human oversight with agentic AI?

We believe AI in the dealership should make people better at their jobs, not take major decisions out of their hands. VitalityAI provides explainable recommendations, so users can see why the platform is suggesting a particular action, and human oversight is built into the operating model, building trust while maintaining required gate checks. Dealers are making decisions that affect customer relationships, OEM relationships and their own financial performance. They need to understand and stand behind every one of those decisions, and our platform is designed to support that accountability rather than bypass it.

What’s next for VitalEdge?

We have had a steady progression of innovation which we keep building: delivering practical capabilities on a defined roadmap rather than one-off features.

Looking ahead, we see a future where every role in the dealership has an intelligent partner that understands the business, connects activity across departments and helps people act with confidence. We have finance and accounting related AI agents that help with preventing fraud, highlighting customer credit risks, accelerating month end closes, identifying potential revenue leakage and cost overruns. Our goal is for dealers to spend less time hunting for answers and more time serving customers and keeping their equipment running.