Most enterprises are layering AI tools on top of broken processes and wondering why ROI never materializes. In this solo episode, Maribel breaks down Cisco’s systematic approach to workflow redesign, why visibility into how work actually gets done is the missing first step, and what enterprise leaders need to change about their leadership culture and talent systems before AI adoption will deliver real results.

Key Topics Covered

• Why AI tool adoption without workflow redesign fails to deliver ROI

• How Cisco’s Atlas AI agent system maps work across the enterprise

• The digital workflow canvas that lets leaders redesign processes systematically

• Results from Cisco’s pilot: 60% of activities AI-augmentable, 28 transformational use cases

• Why framing AI as augmentation rather than headcount reduction drives adoption

• The leadership and talent system changes most companies miss

Key Takeaway
The technology exists. The use cases are proven. What’s missing is the organizational discipline to redesign workflows before deploying more tools. Start with your data and your processes, not your tools.

Resources & Links

Blog post: Why AI Tool Adoption Without Workflow Redesign Is a Waste of Money [Lopez Research]

Related: Five Steps to Follow for Successful AI Deployments [Lopez Research]

Related: Three Shifts in AI-Driven Labor That CIOs and CEOs Can’t Ignore [Lopez Research]

Subscribe to AI with Maribel Lopez on your channel of choice here.

Episode Summary: The "SaaS is dead" narrative is generating real confusion for enterprise buyers trying to make procurement decisions right now. In this solo episode, Maribel Lopez breaks down the two legitimate arguments driving the disruption narrative — AI coding tools and agentic AI — separates what's real from what's overstated, and gives enterprise technology leaders the two questions that actually matter for evaluating their SaaS stack in an AI-first world.

What You'll Learn:

• Why AI coding tools like Claude Code and Codex are not a SaaS replacement strategy — and what they should be used for instead
• Where agentic AI creates genuine revenue model pressure for SaaS vendors, and which vendors are already responding
• The specific conditions that would have to be true for SaaS to decline significantly — and which are not yet met
• How to evaluate your SaaS vendors' agentic AI readiness beyond roadmap promises
• Why the liability and compliance math still heavily favors established SaaS platforms for most enterprise use cases

Key Takeaways:

• Rebuilding mature systems of record with AI coding tools is not a competitive advantage — it's a distraction from building software that reflects your actual differentiation
• The per-seat revenue model is under real pressure, but vendors moving on agentic capabilities are finding new revenue: Salesforce is generating $540M ARR from AgentForce; Intercom crossed $200M from its AI-first pivot
• Commodity SaaS with no data moat or compliance depth faces the hardest disruption; platforms with systems of record have a path forward
• The right test for any SaaS vendor right now: what can they show you working in production — not a roadmap, not a demo

Companies and Examples Referenced:

• Salesforce / AgentForce: $540M ARR from agentic capabilities
• Intercom: $200M ARR from AI-first product pivot
• Workday: Certified connector ecosystem as an example of integration moats that can't be replicated quickly
• SAP: Proactive procurement optimization as an example of SaaS becoming more valuable, not less

Resources:

• Read the full article: SaaS Isn't Dead. But Its Revenue Model Is Under Pressure — Lopez Research
• Referenced: Cathay Capital on agentic AI and B2B software
• Connect with Maribel on LinkedIn

Subscribe to AI with Maribel Lopez on your podcast channel of choice — links at lopezresearch.com.

SEO Keywords: enterprise AI adoption, SaaS revenue model, agentic AI enterprise, AI agents B2B software, enterprise software evaluation, AI coding tools enterprise, SaaS disruption, enterprise AI strategy

Keywords
AI, agentic AI, Work Fusion, RPA, intelligent automation, compliance, machine learning, LLMs, automation, enterprise technology

Episode Summary
Agentic AI dominated industry conversation in 2025. But in 2026, enterprise leaders are asking a harder question: How do we deploy AI agents safely, accurately, and in production environments?
In this episode, Maribel Lopez speaks with Peter Cousins, CTO of WorkFusion a UiPath company, about how AI agents evolved from RPA and intelligent automation into production-ready “digital workers.” The discussion focuses on regulated industries, where explainability, auditability, and risk controls matter as much as automation gains.
Rather than hype, this conversation explores what it takes to operationalize AI agents: governance frameworks, confidence thresholds, human oversight, and model risk management.

Sound Bites

• "2025 was the big agentic AI year."
• "You can't just throw it in and it's good to go."
• "It's been great talking to you."

Chapters

00:00
Introduction to Agentic AI and Work Fusion

02:00
Transitioning from RPA to AI Agents

04:38
Operationalizing AI Agents in Business

09:21
Navigating the Hype of Agentic AI

12:04
The Role of LLMs in Regulated Environments

14:47
Multi-Agent Orchestration and Collaboration

17:21
Improving AI Agents through Learning

21:01
The Importance of Non-Human Identity in AI

24:06
Closing Thoughts on Adopting Agentic AI

The National Retail Federation Show highlighted that Agentic Commerce is the new buzzword for 2026. But before you rewrite your roadmap, let's talk reality.
Julie Ask and Maribel Lopez are discussing:

What actually has to happen before agents can buy things for consumers
Why 85% of retail is still offline (and what that means for AI commerce)
The payments protocol wars: Google/Shopify vs. OpenAI/Stripe/PayPal
Where to actually invest your AI budget in customer experience

Spoiler: The "auto-magic" future isn't here yet. But the opportunities in between?
#AgenticAI #RetailInnovation #CommerceAI #NRF2026

Agentic AI is emerging as the next evolution of artificial intelligence in customer experience (CX), moving beyond chatbots to systems that can take real action on behalf of customers. In this episode of AI with Maribel Lopez, Maribel Lopez speaks with Jarrod Johnson, Chief Customer Officer at TaskUs, about how enterprises are actually deploying AI in customer experience today. The conversation covers real-world CX use cases, where AI delivers measurable ROI, why data and process design remain the biggest bottlenecks, and how organizations should manage risk, governance, and human handoffs as agentic AI scales. This episode is designed for enterprise leaders evaluating AI strategies for customer experience transformation.

Bio: Jarrod Johnson, Chief Customer Officer, TaskUs
Jarrod Johnson is the Chief Customer Officer of TaskUs. He is responsible for TaskUs' go-to-market strategy and execution across all client-facing and market-facing functions. Jarrod leads the "Client Organization" at TaskUs, including client success, sales, product and service management, and TaskUs’ consulting function, which includes the Agentic AI Consulting Practice. Jarrod is responsible for all aspects of revenue management and growth for TaskUs. He brings over 20 years of experience in enterprise technology-enabled services and business management.

Show notes
00:00 – AI in Customer Experience (CX): What This Episode Covers

01:31 – What a Chief Customer Officer Does in AI-Driven Customer Experience

03:46 – Top Customer Experience (CX) Bottlenecks Blocking AI Adoption

05:56 – Chatbots vs. Agentic AI: What’s the Difference in Customer Experience?

09:31 – How to Start with Agentic AI in Customer Experience (Real ROI Use Cases)

12:46 – When AI Should Hand Off to Humans in Customer Experience

15:41 – AI in Customer Experience: Cost Reduction vs. Revenue Growth

18:21 – Voice AI in Customer Service: Why It Finally Works

22:01 – AI Guardrails, Safety, and Brand Risk in Customer Experience

26:31 – Measuring AI-Driven Customer Experience (CX Metrics That Matter)

29:46 – AI for Customer Experience: Market Fragmentation and Vendor Landscape

33:46 – Agentic AI Pitfalls to Avoid in Customer Experience Transformation

CES 2026 Quick Take: Physical AI, Ambient AI, and the Reality of Adoption

In this episode, Maribel Lopez, founder and principal analyst at Lopez Research, is joined by Julie Ask, founder of Ask Advisory, for a candid, unscripted conversation on what CES 2026 actually revealed about the state of AI.

Rather than focusing on flashy demos or speculative promises, Maribel and Julie examine where AI is delivering real value today—and where expectations are running ahead of reality.

Julie's bio

Julie is a prominent customer experience analyst, technology futurist, and digital product strategist who has advised hundreds of global brands on the impact emerging technologies (e.g., mobile, sensors, extended reality, networks, AI) can and will have on customer experiences. She actively works with enterprises and vendors to understand how technology and consumer trends will impact their business with a deep focus on customer engagement strategies.

For more than 25 years, her work has defined the evolution of consumer digital experiences and inspired brands to take action. Her combined background in engineering and business gives her a unique ability to help business leaders understand what is possible and leverage technology to drive business outcomes. She has appeared frequently on Bloomberg while her research has been cited by the Wall Street Journal, New York Times, Financial Times, and a breadth of marketing publications. She co-authored The Mobile Mind Shift book in 2014. She founded Julie Ask Advisory in 2024 to pursue her passion for helping business leaders understand the impact of AI on experiences.

Maribel Lopez reports live from AWS re:Invent 2025 in Las Vegas, unpacking why the AI experimentation phase is officially over. With statistics that say 95% of AI projects are failing and enterprise budgets tightening, 2026 demands production-quality AI—not more proof-of-concepts. This episode explores the critical shift from building agents to deploying them safely at scale.

Key Themes

The Reality Check (2025 Recap)

• MIT study reveals 95% AI project failure rate
• McKinsey and BCG document widespread implementation struggles
• Board-level AI initiatives now demand real ROI, not just innovation theater
• The POC gold rush is over—experimentation budgets are drying up

Agentic AI Grows Up The conversation has evolved from "can we build agents?" to "can we trust them in production?" Three critical roadblocks:

• Security & Orchestration: How agents interact without creating vulnerabilities
• Policy & Governance: Preventing rogue agents and establishing guardrails
• Observability: Real-time monitoring to ensure agents perform as intended

AWS re:Invent 2025 Highlights

Agent Core Improvements

• Enhanced policy frameworks defining agent boundaries and permissions
• Human-in-the-loop controls for high-stakes decisions
• Better cross-stack orchestration for multi-agent workflows

The Discoverability Problem

• AWS Marketplace now features natural language search
• Upload requirements documents instead of filling rigid forms
• AI-suggested prompts help non-technical users navigate complex decisions
• Smarter filtering for nuanced needs (performance vs. cost vs. compliance)

The Full-Stack Maturity

• Recognition that AI "takes a village"—no single vendor owns the entire stack
• Growing emphasis on open standards (A2A, MCP) for SaaS integration
• Tools designed for all skill levels, not just data scientists

Key Takeaway

Enterprise AI in 2026 isn't about doing more—it's about doing it right. The winners will be organizations that prioritize governance, observability, and practical deployment over flashy demos.

Host: Maribel Lopez
Recorded: AWS re:Invent, Las Vegas, December 2025
Follow-up: Stay tuned for next week's deep-dive episode with demos and vendor interviews

In this episode of AI with Maribel Lopez, Maribel sits down with Ian Bramson, Vice President of Global Industrial Cybersecurity at Black & Veatch, to explore the growing intersection between artificial intelligence and operational technology (OT) security.

From power grids and oil refineries to manufacturing plants, critical infrastructure systems are becoming increasingly connected—and therefore more vulnerable. Ian shares how Black & Veatch is helping industrial organizations rethink cybersecurity from the ground up, integrating protection early in the design and build process rather than bolting it on later.

Together, Maribel and Ian discuss the evolution of OT threats, the rise of AI in both defense and attack scenarios, and why cybersecurity must be seen as a core business function, not an afterthought.

🧩 Key Discussion Topics

1. The Evolution of Industrial Cybersecurity

• Ian’s unconventional career path—from Coca-Cola to futurist consulting with Alvin Toffler to leading cybersecurity initiatives.
• Why Black & Veatch launched its dedicated industrial cybersecurity practice and how it’s integrated across engineering, procurement, and construction (EPC).

2. IT vs. OT Cybersecurity: What’s the Difference?

• IT focuses on data protection; OT focuses on physical safety and uptime.
• The rising threat of cyber-physical attacks on power, water, and manufacturing systems.
• How the increasing connectivity of devices—from pumps to sensors to AI controllers—creates new risks.

3. Foundational Security: Basics Still Matter

• Start with asset inventory—knowing what you need to protect.
• Identify vulnerabilities and train your “human layer.”
• Build security in from day one instead of bolting it on later.

4. The Expanding Threat Landscape

• Why ransomware is still relevant but no longer the only concern.
• The growing risks of supply chain attacks, remote operations, and super dependencies (as seen in the CrowdStrike outage).
• How attackers are weaponizing AI to accelerate attacks—and how defenders can use AI for faster detection and response.

5. AI and OT: A Double-Edged Sword

• How AI is reshaping the attack surface for industrial systems.
• Why every company is already “in the AI game,” whether they realize it or not.
• The three layers of AI to consider: AI used in cybersecurity, AI inside your operations, and AI in the wild used by partners and adversaries.

6. The Biggest Misconceptions About OT Security

• The “myth of the air gap”—why physical isolation no longer guarantees safety.
• Common organizational blind spots: board confusion between IT and OT, fragmented responsibility, and lack of lifecycle thinking.
• The need for Cyber Asset Lifecycle Management (CALM) to ensure long-term resilience.

7. Building a Resilient Future

• Why early planning and a holistic approach are key to managing future risks.
• The importance of embedding security, governance, and ethics into every new AI or industrial project.