Episode Summary

Kevin Dunbar joins Maribel Lopez to discuss how AI is revolutionizing commercial fleet management through Ford Pro Intelligence. With nearly two decades of experience at companies like Cisco and Palo Alto Networks, Kevin shares insights on how Ford's commercial division is processing over a billion data points daily to help fleet operators optimize operations, reduce costs, and improve safety.AI with Maribel Lopez: Transforming Fleet Management with Kevin Dunbar

Guest: Kevin Dunbar, General Manager of Ford Pro Intelligence
Host: Maribel Lopez, Founder of the Data for Betterment Foundation and Lopez Research

Key Topics Covered

Ford Pro Intelligence Platform

• Commercial division serving business and government customers
• Comprehensive ecosystem from vehicle upfitting to fleet management
• Data services, telematics software, and fleet controls
• Updated from last earnings to 757,000 and 24% yoy growth. (vs. 675,000+ subscribers with 20% growth rate.)

Data at Scale

• Processing over 1 billion connected vehicle data points daily
• Sensor data ranging from tire pressure and GPS to seatbelt activity and driver behavior
• Clean, structured data transformation into actionable insights

AI Applications in Action

• Digital vehicle walkarounds replacing 20-minute manual processes
• Predictive maintenance moving customers from reactive to proactive service
• E-switch assist tool using machine learning for electrification decisions
• Connected uptime system achieving 98% vehicle availability

Tangible Business Impact

• 10% reduction in insurance costs through safer driving coaching
• 20% improvement in driver safety metrics
• 25% reduction in speeding incidents
• 80% reduction in cost downtime
• 10-20% total cost of ownership reduction

Notable Quotes

"We want to make sure that their Ford vehicle works as hard for their business digitally as it does mechanically." - Kevin Dunbar

"It's not just about having data. It's about having clean, structured data." - Kevin Dunbar

For more episodes of "AI with Maribel Lopez," visit Lopez Research and follow our latest insights on AI transformation across industries.

About Ford Pro and Ford Pro Intelligence

Ford Pro is helping commercial customers transform and expand their businesses with vehicles and services tailored to their needs. Ford Pro Intelligence is Ford’s comprehensive solution for fleet digitalization and operational efficiency, combining connected vehicle data, telematics tools, and smart management software under one platform

Follow Kevin at https://www.linkedin.com/in/kevin-dunbar-78343558/

Follow Maribel at https://www.linkedin.com/in/maribellopez/

#FordProIntelligence #FordPro #FleetManagement #Fleets #DataSecurity

Episode Overview

In this episode, Maribel Lopez sits down with David Singer, Global Vice President and Go-To-Market Strategy at Verint, to explore the rapid evolution from generative AI to agentic AI and how organizations can successfully implement AI solutions that deliver real business outcomes.

Key Topics Discussed

The Evolution from Generative to Agentic AI

• Generative AI: Excellent at answering questions and synthesizing information from knowledge sources
• Agentic AI: Takes the next step by actually executing actions autonomously, not just providing recommendations
• The critical difference: autonomous decision-making versus rules-based automation

Building Trust in Autonomous AI Systems

• Start with human-in-the-loop monitoring for training and validation
• Gradually reduce oversight from constant monitoring to spot checks
• Apply quality monitoring practices to AI agents similar to human agents
• Consider AI agents as "silicon-based employees" requiring training, access controls, and performance management

Successful AI Implementation Strategies

Start with Clear Outcomes: Define specific business goals before selecting technology

• Focus on solutions that deliver outcomes, not just impressive technology
• Begin with well-understood processes that can be enhanced rather than completely reimagined

Three Proven Starting Points:

1. Call Wrap-up Automation: AI-powered summarization reduces agent workload
2. IVR Modernization: Convert top call flows to agentic conversational AI
3. Quality Management: Scale from monitoring 1-3% of calls to near 100% coverage

Vendor Selection Criteria

• Proven outcomes at scale: Look for vendors with demonstrated success stories and customer references
• Technology adaptability: Choose providers who can evolve with the rapidly changing AI landscape
• Production readiness: "POCs are easy, production is hard" - prioritize vendors with production deployment experience

Change Management for AI Adoption

• Deploy solutions that genuinely help employees first
• Build internal champions through positive early experiences
• Scale gradually to maintain trust and adoption

Key Insights

• Employee Experience Drives Customer Experience: AI solutions that improve employee satisfaction often lead to better customer outcomes
• Observability is Critical: Comprehensive monitoring and quality management become essential as AI systems gain autonomy
• Outcomes Over Technology: Success comes from focusing on business results rather than being enamored with the latest AI capabilities

About the Guest

David Singer is the Global Vice President and Go-To-Market Strategy at Verint, where he focuses on delivering AI-powered outcomes for customer experience automation. Verint has been incorporating AI into their platform for over a decade, evolving from call recording and workforce management to comprehensive CX automation solutions.

You can follow David here: https://www.linkedin.com/in/dwsinger/

You can follow Maribel here:

Closing Thoughts

Singer emphasizes two crucial points for organizations embarking on AI initiatives:

1. Avoid spending significant resources on new technology only to use it exactly as you did before
2. Always start with outcomes first - let business goals drive vendor selection, implementation strategy, and change management approaches

In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives, Vijoy Pandey, SVP of Outshift at Cisco and Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco, to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation.

Host: Maribel Lopez
Guests:

• Vijoy Pandey, SVP of Outshift at Cisco
• Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco

Recorded at: Cisco Live

Episode Overview

In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation.

Key Topics Discussed

The Three Waves of AI Infrastructure Evolution

• Wave 1: AI training in public cloud (mostly behind us)
• Wave 2: AI inference moving to enterprise data centers for control, security, and economic reasons
• Wave 3: AI moving to the edge with physical and embodied AI requiring new infrastructure for robots and devices

From Deterministic to Probabilistic Computing

Vijoy explains the fundamental shift happening in computing:

• Traditional computing: deterministic, machine-speed but limited
• Human intelligence: agentic but slow
• New paradigm: AI agents with human-like behavior operating at machine speed and scale

The Internet of Agents

A collaboration platform where AI agents from different vendors can:

• Get discovered and authenticated
• Compose workflows together
• Execute tasks collaboratively
• Be evaluated for performance

Real-world example: Building a sales funnel portal using agentic interfaces from Salesforce, ServiceNow, Microsoft, and Cisco security - all working together without manual UI clicking.

AI and Energy Challenges

• The Problem: By 2028, projected 63 gigawatt shortfall for new data center capacity
• Solutions:
  • Invest in diverse energy sources (nuclear, renewables, battery storage)
  • Build data centers near power sources (e.g., Cisco's Middle East partnerships)
  • Develop more energy-efficient infrastructure
  • Focus on smaller, specialized models instead of racing for maximum parameters

Cisco's Specialized AI Models

• Foundation SAC 8B: 8 billion parameter model specialized for security policy
• Deep Network Model: Expert model trained on network configurations

Outshift: Cisco's Innovation Engine

Cisco's internal incubator tackling problems adjacent to core business in:

• Space: Areas adjacent to networking, security, observability, collaboration
• Time/Risk: Higher-risk ventures that can't enter at Cisco scale initiallyCurrent Big Hairy Audacious Goals (BHAGs):

1. Internet of Agents
2. Quantum Internet - building quantum networks for distributed quantum computing