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

Description: In this episode from Google Cloud Next 2025, we dive deep into Best Buy's AI transformation with Ashley Daniels, VP of Product Management. Discover how one of America's largest retailers approached AI implementation strategically, moving from 93 contact center applications to a unified solution.

Ashley shares the real story behind Best Buy's AI journey - the quick wins, unexpected challenges, and why your foundation matters more than the technology itself. From gift finder tools to revolutionizing customer care, learn practical strategies for implementing AI that actually drives business outcomes.

Key insights covered:

• Why treating AI as a "tool in the toolbox" leads to better results
• The importance of starting with customer experience, not technology
• How to build strategic partnerships for AI implementation
• Why domain expertise becomes more critical in an AI world
• Real timeline: Getting AI summarization live in 6-8 weeks

Whether you're in retail, customer service, or leading digital transformation initiatives, this conversation offers actionable insights for your AI strategy.

Hosted by Maribel Lopez, founder and principal analyst at Lopez Research who interviewed Ashley Daniels, the VP of Product Management at Best Buy.

You can follow Ashley here https://www.linkedin.com/in/ashley-daniels1219/ and Maribel here https://www.linkedin.com/in/maribellopez/

Guest Profile

Liz Centoni brings over 25 years of experience at Cisco where she currently leads a team of 20,000+ people dedicated to helping customers maximize the value of their technology investments. She also serves on the boards of Mercedes-Benz and Workday.

Episode Highlights

Cisco's Unique Position in the AI Landscape

• Liz outlines Cisco's three-pillar approach to AI:
  1. Investment in back-end AI networks with hyperscalers
  2. Enterprise deployment of secure AI use cases
  3. Meeting increased capacity requirements for both private and public front-end cloud networks
• Recent partnership with NVIDIA to accelerate AI adoption and simplify building AI-ready data centers

Transforming Customer Experience

• Vision for customer experience: personalized, proactive, and predictive
• Goal: Make every customer "feel like they are our only customer"
• Leveraging data across tech stacks to break down silos and deliver proactive experiences
• Using AI to reduce cognitive load and workplace friction for employees

AI Renewals Agent: A Case Study in Predictive AI

• Jointly developed with Mistral AI and announced in February 2025
• Consolidates data from 50+ signals and sources (both structured and unstructured)
• Provides real-time sentiment analysis by incorporating customer support data
• Expected to reduce time spent on renewal proposals from 40% to less than 5%

The Future of Agentic AI

• Moving from AI as a tool to AI as a teammate
• Current focus on assisting and augmenting tasks, not replacing roles
• Human oversight remains critical for complex customer networks
• Evolution from reactive to proactive customer care

Impact on Jobs and Work

• Expectation that everyone needs baseline AI skills
• Historical pattern of rebalancing versus complete replacement
• Focus on using AI to eliminate busy work and reduce cognitive load
• Importance of emotional intelligence and empathy in areas where AI still falls short

Closing Thoughts

Liz's definition of success: "Customers walk up and say, 'You really know me better than I know myself'... and they feel they can't live without three things: Cisco's security, Cisco's networking portfolio, and Cisco services."

Summary
In this conversation, Maribel Lopez and Jeetu Patel discuss the transformative potential of AI in business, the challenges organizations face in adopting AI, and the importance of security in AI applications. They explore the need for visibility, validation, and guardrails in securing AI, the rise of specialized AI models, and the future of AI agents in automating workflows. Patel emphasizes Cisco's commitment to innovation and the urgency for companies to embrace AI to remain relevant in a rapidly evolving landscape.

Takeaways

• AI is transforming business strategies across industries.
• CEOs are optimistic about AI but feel unprepared.
• Security practitioners face significant staffing shortages.
• AI can both complicate and simplify security challenges.
• Organizations must secure AI models and use AI for defense.
• Visibility, validation, and guardrails are essential for AI security.
• Specialized AI models can be more effective and cost-efficient.
• AI agents will enhance productivity and workflow automation.
• Cisco is innovating rapidly and operating like a startup.
• Companies must embrace AI to thrive in the future.

Chapters

00:00
The Exciting Intersection of AI and Business

02:47
Challenges in AI Adoption and Security

06:34
Securing AI: Visibility, Validation, and Guardrails

12:47
The Rise of Specialized AI Models

18:00
The Future of AI Agents and Automation

25:31
Cisco's Transformation and Innovation

31:10
Embracing AI: A Call to Action

Follow us at:

Jeetu Patel https://www.linkedin.com/in/jeetupatel/

Maribel Lopez https://www.linkedin.com/in/maribellopez/

In this episode, Maribel speaks with Shub Bhowmick, the CEO and Co-founder of Tredence on how its using AI internally and externally. Bhowmick also provides advice on what's important for enterprise buyers looking to leverage AI.

Takeaways

• The shift from proof of concept to proof of value is crucial for businesses.
• AI is enabling organizations to achieve more with fewer resources.
• Agentic solutions are becoming increasingly relevant in various industries.
• Internal innovations at Treatance are focused on developing interconnected AI agents.
• Organizations must prepare for a future where they need to do more with less.
• Crawl, walk, and run is a practical approach to AI implementation.
• Creating a robust monitoring and operations foundation is essential.
• Small language models can be more effective and cost-efficient than larger models.
• AI can significantly enhance productivity and creativity in the workplace.
• Health and personal well-being are important considerations in a fast-paced professional environment.

Sound Bites

• "Proof of value is the new proof of concept."
• "AI is enabling you to do more with less."
• "Agents are like smart interns, very analytical."
• "The speed of AI is moving much faster."
• "AI can 10x your productivity."
• "Crawl, walk, and run with AI implementation."
• "Small language models are the new thing."

Chapters

00:00
Introduction to Treatance and AI Trends

07:28
Emerging Use Cases in AI

11:46
Real-World Applications of AI in Business

18:33
Internal Innovations at Treatance

30:33
Advice for Organizations on AI Implementation

Summary

In this conversation, Maribel Lopez speaks with Markus Nispel about the integration of AI in networking solutions, particularly at Extreme Networks. They discuss the evolution of AI capabilities, the importance of data governance, and the role of AI in enhancing operational efficiency and security. Markus emphasizes the need for trust in AI systems and the potential of agentic AI to transform networking operations. The discussion also touches on the challenges of skill development and the future of AI in the industry.

Extreme Networks, trusted by tens of thousands of customers globally, delivers AI-native cloud networking solutions that seamlessly connect people, applications, data, and devices.

Info on Extreme Networks Platform One: https://www.extremenetworks.com/platform-one and an explainer video https://vimeo.com/1036922077/58472f1411?ts=0&share=copy.

Takeaways

• AI has been integrated into networking solutions for measurable business value.
• Data quality is crucial for effective AI implementation.
• Generative AI can significantly reduce the time for knowledge acquisition.
• Agentic AI combines various capabilities for enhanced networking solutions.
• Trust and transparency are essential for AI adoption in enterprises.
• AI can optimize security policy configurations and reduce attack surfaces.
• The orchestration of agents is vital for achieving automation in networking.
• AI's role in skill development is critical for new employees.
• The future of AI in networking will involve more autonomous systems.
• Continuous feedback loops enhance trust in AI systems.

Sound Bites

• "AI allows for a consistent support experience."
• "Data governance is critical for AI systems."
• "The orchestration of agents is key to automation."
• "Trust is essential for AI adoption in enterprises."
• "The future is dynamic with AI advancements."

Chapters

00:00 Introduction to AI in Networking

03:40 Evolution of AI Integration in Networking Solutions

06:54 Understanding AI's Unique Positioning in Networking

10:18 AI's Role in Skill Development and Knowledge Acquisition

13:02 Defining Agentic AI and Its Current Capabilities

16:54 The Importance of Orchestration in AI Systems

19:45 Addressing Trust and Resistance in AI Adoption

23:19 Demonstrating ROI from AI Implementations

25:29 Future of AI: The Rise of Agentic Systems

Show Notes:

In this episode of "AI with Maribel Lopez," host Maribel Lopez sits down with Vijay Sundaram, Chief Strategy Officer at Zoho, at Zoho Day 25 in Austin, Texas. They discuss Zoho's strategic evolution and approach to AI.

Key Highlights:

• Zoho's Market Evolution: Vijay explains how Zoho has expanded from primarily serving small and medium businesses to increasingly being adopted by larger enterprise customers worldwide. This evolution has happened naturally as their products became more sophisticated and larger customers discovered them.
• Enterprise Adaptation Challenges: To serve enterprise customers, Zoho had to make changes in three areas:
  1. Technology (their strength as a product-driven company)
  2. Operations (building expertise in account management, solutions consulting, etc.)
  3. Transitioning from an inbound to outbound business model
• AI Implementation Strategy: Vijay clarifies that while generative AI has recently captured public attention, Zoho has been implementing various AI technologies (machine learning, NLP, video recognition) for over a decade. Much of this AI has been "headless" - working behind the scenes in applications rather than through conversational interfaces.
• Three Levels of AI: Zoho approaches AI implementation through:
  1. Contextual AI within business applications
  2. Interactive AI for specific purposes
  3. Expert-level insights that enable non-experts to gain valuable business intelligence
• Platform Approach: By integrating applications and creating a comprehensive platform, Zoho can leverage data across domains (finance, sales, HR, operations) to provide more valuable AI-driven insights.
• AI Market Shift: Vijay predicts that AI differentiation will increasingly move from foundational models to the application layer, where companies like Zoho can add value through their access to business data across domains.
• Privacy and Security: Zoho maintains a strong stance on privacy (no trackers on their websites) and has built a "trust layer" into their platform to ensure proper data access controls for AI interactions.