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