Author: maribellopez

  • NemoClaw Gives Enterprise AI Agents The Security Layer They’ve Been Missing

    NemoClaw Gives Enterprise AI Agents The Security Layer They’ve Been Missing

    The NVIDIA GTC announcement of the NemoClaw stack addresses one of the real reasons enterprises haven’t deployed AI agents at scale — and one vendor’s alternative to address those concerns.

    OpenClaw became the fastest-growing open-source project in history while enterprise buyers watched from the sidelines.

    Not because the technology wasn’t interesting. It is. Not because employees weren’t already using it. They were — quietly, on personal machines, sometimes on corporate devices. Enterprises held back because an autonomous AI agent that can access file systems, execute code, log into corporate systems, and communicate externally is not something you hand to 10,000 employees without a security framework underneath it.

    NVIDIA ‘s NemoClaw announcement at GTC 2026 offers one solution to address that gap directly. It is not a competitor to OpenClaw. It is a reference stack that adds the infrastructure layer OpenClaw was missing: policy-based security guardrails, a privacy router, a sandboxed runtime called OpenShell, and integration with the security tools enterprises already use, installed in a single command.

    Jensen Huang put it plainly in his GTC keynote:

    “Agentic systems in the corporate network can have access to sensitive information. They can execute code and communicate externally. You could access employee information, access supply chain, access finance information, and send it out. Obviously, this can’t possibly be allowed.”

    For enterprise buyers, this is the right framing. The question was never whether AI agents would be useful. The question was whether they could be deployed safely inside an enterprise environment. NemoClaw is NVIDIA’s answer to that question for OpenClaw.

    Why OpenClaw Alone Wasn’t Enterprise-Ready

    Before discussing what NemoClaw adds, it is worth clarifying the enterprise IT and security leader’s concerns.

    OpenClaw is designed to act like a digital assistant sitting at your computer. It can view your screen, control your browser, open files, execute commands, and log in to websites. If an employee grants OpenClaw access, it operates the computer the same way the employee would. That means if the employee can access corporate email, CRM systems, internal dashboards, customer data, or finance systems, the tool can access them too.

    The issue is not the AI itself. The issue is how much access the software has and where the data goes afterward. Specific risks enterprises were managing before NemoClaw include:

    • Credential exposure. If OpenClaw interacts with browser sessions or system logins, it can gain access to saved passwords, session tokens, and authentication cookies. This could allow the tool — or anything that compromises it — to impersonate the employee in corporate systems.
    • Data leaving the organization. AI automation tools often send information to external services for processing. That can include screenshots, text from documents, browser content, and system commands. If sensitive enterprise data is transmitted outside approved systems, it may violate GDPR, HIPAA, PCI, or data-residency requirements across multiple jurisdictions.
    • Autonomous actions without audit trails. AI agents can operate autonomously, executing workflows, interacting with websites, and performing transactions without logging. If poorly configured, an agent could send emails, download files, update records, or trigger business workflows without IT or compliance teams having visibility.
    • Shadow IT at scale. Meta instructed employees not to use OpenClaw on work computers due to security concerns. Many companies followed suit. Even so, adoption continued. Prohibition alone does not work when the tool is genuinely useful. Enterprises need a sanctioned, controlled alternative or safeguards— not just a policy.

    What NemoClaw Actually Does

    NemoClaw is not a product. It is a reference stack — an open-source configuration of existing and new NVIDIA tools that installs on top of OpenClaw to add the enterprise infrastructure layer. Here is what each component does:

    1. OpenShell. This is the core of the security layer. OpenShell runs OpenClaw agents in an isolated sandbox that limits what files they can access and restricts network connectivity. Enterprises can write policy rules in YAML to define exactly which systems an agent can access, which data it can process, and which actions require human approval. YAML Ain’t Markup Language (YAML) is a human-friendly, plain-text format used to store data, configure software, or move data between systems. Some rules are hot-swappable, allowing them to be changed without restarting the agent.
    2. Privacy Router. When agents need to call cloud-based frontier models, the Privacy Router ensures sensitive enterprise data is not transmitted to those external models. This is the mechanism that makes hybrid local-cloud architectures viable without creating data residency exposure.
    3. Nemotron Models. NVIDIA’s open model family can run locally on dedicated hardware, including NVIDIA RTX PCs and DGX Station. Running inference locally means sensitive enterprise data stays on-premises for tasks that require it. This also reduces cloud inference costs. NVIDIA reports that its hybrid architecture — using Nemotron for research tasks and frontier models for orchestration — can cut query costs by more than 50 percent.
    4. Single-command installation. This matters more than it sounds. One practical barrier to enterprise AI deployment is configuration complexity. A stack that requires days of professional services to stand up will not achieve broad adoption. NemoClaw installs the entire reference configuration in one command.

    Kari Briski, NVIDIA’s VP of Generative AI Software, described it this way at GTC: OpenShell “provides the missing infrastructure layer beneath claws to give them the access they need to be productive, while enforcing policy-based security, network, and privacy guardrails.”

    That framing is accurate. The productivity value of OpenClaw was never in question. What was missing was the control layer.

    The SaaS Ecosystem Is Already Moving

    For enterprise buyers, the breadth of the partner ecosystem matters as much as the technology itself. You are not just evaluating a security sandbox. You are evaluating whether the tools your teams already use will work within that sandbox.

    The list of software companies integrating with NVIDIA Agent Toolkit and OpenShell includes Adobe, Atlassian, Box, Cisco, CrowdStrike, Red Hat, Salesforce, SAP, ServiceNow, and Siemens. A few specific integrations are worth noting:

    • Cisco‘s AI Defense will provide AI security protection for OpenShell, adding controls and guardrails to govern agent actions. For enterprises already running Cisco security infrastructure, this is a direct integration path.
    • CrowdStrike unveiled a Secure-by-Design AI Blueprint that embeds Falcon platform protection directly into NVIDIA AI agent architectures. Enterprises with CrowdStrike endpoint protection can extend that coverage to AI agents.
    • Salesforce is working with Agent Toolkit to enable customers to build and deploy AI agents through Agentforce for service, sales, and marketing tasks, using Slack as the conversational interface and orchestration layer. This matters because Salesforce and Slack are already inside most enterprises.
    • ServiceNow ’s Autonomous Workforce of AI Specialists is built on Agent Toolkit and includes NVIDIA Nemotron models. ServiceNow is the workflow backbone for IT operations in many large enterprises, which means agentic AI is now moving into ITSM and operational workflows directly.
    • SAP is using Agent Toolkit to enable AI agents through Joule Studio on SAP Business Technology Platform, allowing customers to design agents tailored to specific business processes. For enterprises running SAP as their ERP backbone, this is a direct integration with operational data.

    The pattern here is important. These are not experimental integrations. Salesforce, ServiceNow, and SAP together represent the core of enterprise application infrastructure. When your existing SaaS platforms build their agentic strategies on the same security and identity stack, it significantly reduces the interoperability problem. You are not assembling a security architecture from scratch for every new AI agent. You are extending an existing framework.

    It is also worth noting that NVIDIA is collaborating with Microsoft Security, Google , CrowdStrike, and TrendAI to build OpenShell compatibility with their security tools. The intent is to make OpenShell work within security stacks enterprises already have, not replace them.

    What This Means for CIOs and Enterprise Buyers

    NemoClaw does not eliminate the work required to deploy AI agents safely. It reduces the barrier significantly. Before evaluating whether NemoClaw belongs in your environment, three questions are worth resolving:

    Are employees already using OpenClaw? If the answer is yes — even informally — then the risk exists today. NemoClaw gives you a sanctioned path to bring that activity under enterprise controls rather than competing with shadow IT through prohibition.

    Is your identity framework ready for AI agents? NemoClaw provides the guardrails, but agents still need registered identities and role-based access controls. Most enterprise identity frameworks were designed for people. Before deploying agents at scale, confirm that your identity infrastructure can assign, manage, and revoke agent credentials separately from human credentials.

    Do you have the observability to know if agents are working correctly? OpenShell provides audit logging and network guardrails. That is a starting point, not a complete observability stack. You need monitoring in place to detect when agents are drifting, making errors, or accessing systems they should not.

    The Caveats That Curb My Enthusiasm

    The broader context here matters. Jensen Huang framed the OpenClaw moment as equivalent to the arrival of Linux, HTML, and Kubernetes — foundational infrastructure that reorganized entire industries. That may or may not prove accurate.

    What is accurate is that agentic AI as a category, not just OpenClaw, will fundamentally change enterprise workflow platforms. The companies that define their governance architecture now will be better positioned than those that build it under pressure later.

    It is also worth being realistic about what NemoClaw does not solve. Cross-vendor agent orchestration remains complex. An agent working in Salesforce does not automatically collaborate with an agent in SAP without significant integration work. Governance frameworks for multi-agent systems are still nascent. The tools are improving faster than the enterprise readiness in most organizations.

    Other things to consider. NemoClaw and OpenShell are open source. Anyone can download them. However, to run Nemotron models locally (the privacy-preserving option that keeps data on-premises), you need NVIDIA GPU hardware — an RTX PC, DGX Spark, or DGX Station.

    YAML policy configuration is an IT burden. Enterprises customize OpenShell by writing YAML rules. That’s a developer-friendly approach, not an enterprise admin-friendly one. Large organizations with thousands of use cases will need tooling and staffing to manage that policy layer. This is not point-and-click governance.

    The partner ecosystem is announced, not proven. The list of SaaS partners — Salesforce, SAP, ServiceNow, etc. — represents intent and roadmap, not certified integrations. Most of these are “working with NVIDIA” statements. Before betting enterprise deployments on them, buyers should ask specifically what is shipping, when, and what certification or testing has been completed.

    Start with Governance, Not the Agent

    NemoClaw is genuinely useful for enterprise buyers. It addresses the right problem in the right way — by adding enterprise security controls to an open-source agent platform that employees are already adopting, rather than building a competing proprietary stack.

    The partner ecosystem — particularly Salesforce, ServiceNow, SAP, Cisco, and CrowdStrike — means NemoClaw is not a greenfield deployment for most enterprises. It fits into the existing infrastructure.

    But the technology being ready does not mean you are ready. Before deploying agents in any production environment, define what they are allowed to do, what data they can access, and how you will know when something goes wrong. That work is not optional — and no reference stack does it for you.

    Governance first. Agents second. That is the sequence that works.

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

  • Why Deploying More AI Tools Won’t Fix Your Workflows: Lessons Learned From Cisco

    Why Deploying More AI Tools Won’t Fix Your Workflows: Lessons Learned From Cisco

    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.

  • SaaS Isn’t Dead — But the “Dead” Narrative Is Leading Enterprise Buyers Astray

    SaaS Isn’t Dead — But the “Dead” Narrative Is Leading Enterprise Buyers Astray

    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:

    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

  • Is SaaS Dead? AI Agents and Coding Tools Are Changing the Model

    Is SaaS Dead? AI Agents and Coding Tools Are Changing the Model

    The “SaaS is dead” narrative is getting louder.

    AI coding tools can now generate functional applications in minutes. Agentic AI can complete workflows across systems without human intervention. Investors are watching revenue growth slow and drawing conclusions.

    Two arguments are driving this conversation. Both deserve attention. Neither supports the headline.

    What’s happening isn’t extinction. It’s economic pressure and interface transformation. And enterprise buyers should be focused on very different questions.

    The First Argument: AI Coding Tools Make SaaS Obsolete

    Tools like Claude Code and Codex have changed the economics of software development. They compress timelines. They lower the skill floor. They make it plausible to build usable applications far faster than even two years ago.

    That matters.

    But here’s the part the narrative skips: enterprise development capacity is limited. The strategic question isn’t whether AI can build a CRM or ERP. It’s whether rebuilding commoditized infrastructure is the right use of the engineering capacity AI unlocks.

    In most cases, it isn’t.

    AI coding tools create leverage when they’re applied to differentiated workflows — the integration no marketplace covers, the process unique to how your business operates, the capability that reflects your competitive advantage.

    Rebuilding Salesforce from scratch doesn’t create advantage. It recreates infrastructure.

    And the total cost of ownership rarely shows up in the demo.

    An AI-generated application still requires:

    • Hosting and monitoring
    • Security reviews
    • Compliance certifications (SOC 2, HIPAA, FedRAMP)
    • Identity and access management
    • Audit logging
    • Disaster recovery
    • Long-term ownership

    SaaS vendors absorb these responsibilities at scale. Custom-built applications start at zero. Enterprises have seen this movie before. It was called shadow IT.

    AI coding tools are an accelerant for custom development. They are not a procurement strategy for replacing mature systems of record.

    The Second Argument: Agents Reduce the Need for SaaS Seats

    This argument is more serious.

    Agentic AI introduces automation at the workflow level. An agent can complete an expense report, resolve a service case, trigger procurement actions, or update a CRM pipeline without human intervention.

    Today, agents sit on top of SaaS platforms. They read and write into systems of record.

    They consume SaaS. They don’t replace it.

    But here’s the real investor concern: if agents complete end-to-end workflows, how many human seats are displaced? And if pricing is per-seat, what happens to revenue?

    That’s a legitimate question.

    The pressure here is economic, not structural. Systems of record don’t disappear. Monetization models may evolve.

    Vendors that rely entirely on per-seat pricing for task execution will feel compression. Vendors that move toward hybrid pricing — consumption, workflow-based, or outcome-linked — have a path forward.

    The disruption is about revenue architecture.

    It is not about eliminating the operational backbone of enterprise software.

    Where SaaS Is Actually Vulnerable

    Not all SaaS vendors are equally positioned.

    Higher risk categories include:

    • Single-function tools with minimal integration depth
    • Applications without proprietary datasets
    • Products lacking compliance or regulatory infrastructure
    • Software that can be recreated easily with generative tools

    If a product can be rebuilt with a prompt, defensibility becomes questionable.

    Lower risk categories include:

    • Deep systems of record
    • Platforms with extensive integration ecosystems
    • Industry-specific regulatory workflows
    • Vendors with cross-customer benchmarking data

    In these cases, AI often increases platform value rather than replacing it.

    What Would Actually Have to Change for SaaS to Decline?

    For SaaS to become structurally less relevant, several conditions would need to materialize:

    1. AI-generated code becomes reliably maintainable at enterprise scale — not just generatable.
    2. Compliance infrastructure becomes automated and commoditized.
    3. Integration becomes dynamically AI-negotiated across counterparties.
    4. Enterprise observability and governance wrap automatically around new AI applications.
    5. Legal liability for AI-built systems becomes predictable and manageable for enterprises.

    These conditions are evolving. They are not yet universal.

    Until they are, the “SaaS is dead” narrative runs ahead of the evidence.

    What Enterprise Buyers Should Do Instead

    Rather than debating extinction, enterprise leaders should focus on allocation and evaluation.

    First: Where should AI coding capacity be applied?
    Point it toward differentiated capability — not recreating commodity infrastructure.

    Second: Which vendors are genuinely building for an agentic world?
    Evaluate them on:

    • Production-ready agentic functionality
    • Pricing flexibility beyond per-seat
    • Ability to convert historical data into operational intelligence

    Avoid vendors that simply layer conversational interfaces onto legacy systems without meaningful workflow automation.

    Demand proof of measurable business impact.

    The Bottom Line

    SaaS revenue models are under pressure. That pressure is real.

    But pressure is not death.

    The enterprise stack is shifting toward agent-driven interfaces and automation. Systems of record remain foundational. Compliance infrastructure still matters. Integration ecosystems still matter. Accountability still matters.

    The question isn’t whether SaaS survives.

    The question is which vendors adapt quickly enough to remain relevant — and which enterprises deploy AI capacity where it actually creates advantage.

    That’s the strategic lens buyers should use.

    A version of this Is SaaS Dead was also posted in my LinkedIn newsletter. You can subscribe to the AI Decoded newsletter here.

  • Agentic AI Beyond the Hype: How Banks Are Actually Deploying It

    Agentic AI Beyond the Hype: How Banks Are Actually Deploying It

    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

  • Three Shifts in AI-Driven Labor That CIOs and CEOs Can’t Ignore

    Three Shifts in AI-Driven Labor That CIOs and CEOs Can’t Ignore

    What Sam Altman’s Vision at the Cisco AI Summit Means for Enterprise Workforce Strategy

    By Maribel Lopez, Lopez Research  |  February 2026

    Cisco builds over 70% of its AI software products using AI. Not on a roadmap. Not as a pilot. Today, in production, through its partnership with OpenAI’s Codex platform. When Jeetu Patel, Cisco’s Chief Product Officer, shared this at the Cisco AI Summit alongside OpenAI CEO Sam Altman, the audience heard more than a product update. They heard a preview of how labor itself is about to be restructured.

    For CIOs and CEOs rethinking workforce strategy, three shifts from this conversation demand immediate attention: AI that acts on your behalf, the transformation of software development roles, and the emergence of AI-only companies as a new category of outsourced labor.

    Shift 1: AI That Acts on Your Behalf

    AI is crossing from finding information to acting on it. For years, the promise centered on surfacing insights, answering questions, connecting dots across silos. What Altman described goes further: always-on AI that accesses your computer, browses the web, edits your documents, and executes tasks without waiting for human approval.

    This shift is already underway. Consumers use OpenClaw’s Clawdbot as a personal assistant, granting it access to everything (risky, but the usefulness is undeniable). On the enterprise side, SaaS vendors are embedding agents into customer service platforms, IT operations workflows, and sales processes where AI doesn’t just recommend an action but completes it. These deployments remain narrow: an agent that resolves a tier-one support ticket, triages security alerts, or drafts and sends a follow-up email after a sales call. But they mark the beginning of a fundamental change in knowledge work. The AI no longer waits for you to act on its suggestion. It acts.

    Altman described giving Codex full access to his computer and lasting only two hours before he couldn’t go back. He acknowledged the real challenges this creates around security, data access, and permissioning. Existing software, hardware, and even legal frameworks weren’t designed for always-on AI that watches what you do and takes action on your behalf.

    For enterprise buyers, this reinforces a message I’ve been sharing for some time: the AI infrastructure conversation extends well beyond models and compute. Identity frameworks, governance stacks, observability, and security architectures all need rethinking, because they were designed for people, not AI agents. The good news is that organizations already investing in these foundational capabilities will absorb AI labor more safely and more quickly. But it requires treating security and governance as enablers of AI adoption, not obstacles to it.

    Shift 2: Software Development Roles Are Being Redefined, Not Eliminated

    Patel described how Cisco works with OpenAI and Codex to fundamentally change how it develops software. AI Defense, a security product Cisco launched last year, will have nearly 100% of its code written by Codex within weeks. This reflects a pattern that will spread across the enterprise.

    Developers aren’t going away, but their job is evolving. The core competency shifts from writing code to constructing precise software requirements, evaluating whether AI output meets those requirements, and articulating what needs to change when it doesn’t. Running tests, writing documentation, producing boilerplate? AI handles that. Defining what the software should accomplish and judging whether it got there? Still human.

    Altman described Codex as feeling less like a tool and more like a teammate: “The Codex app is the first time, to me, it has truly felt like interacting with a teammate.” That distinction matters. When AI shifts from tool to collaborator, the human role shifts from operator to supervisor. CIOs should already be rethinking team composition, performance evaluation, and career development within their engineering organizations.

    Even with AI doing the heavy lifting, design still matters enormously. As Altman noted: “There’s so much value in how you package it, how you have users interact with it, how easy you can make it.” Better models alone don’t guarantee better outcomes. The interface, the workflow, the experience determine whether adoption accelerates or stalls.

    A deeper shift sits underneath this conversation: the future of software requires designing it to work equally well whether a human or an AI operates it. That’s not how software works today. Most software isn’t even easy for humans to use, let alone optimized for AI agents. Altman illustrated this with a telling example: his AI agent used Slack on his behalf, marked everything as read, and broke his workflows. Software built for one type of user doesn’t automatically serve another. This is a design problem as much as a technology problem, and product teams and CIOs need to tackle it now.

    Shift 3: AI-Only Companies and the New Workforce Marketplace

    The third shift is the most speculative but potentially the most disruptive. Altman described a future with “full AI companies”: a coding model creates a complete, complex piece of software and also interacts with the real world to build a company around it.

    Consider what that implies. Not AI-assisted companies. AI-only companies: entities with no human employees, just AI systems performing the work. You would hire them the same way you hire a consulting firm or a staffing agency today.

    The concept follows a natural progression from agentic AI. Today, leading enterprises build a variety agents with the aim of having the agents collaborate to accomplish specific goals. Agents perform a task here, an automated workflow there. As agents grow more sophisticated, more of a given role consolidates into a single agentic entity. That entity can then be sold as a digital employee, just as you would hire a temporary worker from an agency or outsourcing firm.

    I can see a marketplace emerging where enterprise buyers source these agents. Today, you acquire them from software vendors and hyperscalers. But nothing prevents a person from building an entirely new AI workforce company. Envision your company hiring a cybersecurity agent from an AI agent company to build new security playbooks. The technology to support this is coming together now.

    But CEOs and CIOs need to understand something: the existence of an agent marketplace doesn’t mean you can just show up and shop. It will be like facing a thousand choices in the cereal aisle. You need to know whether you want hot or cold cereal before you walk into the store, and that’s just the first filter. Cold cereal? Sweet like Fruity Pebbles or plain like Rice Krispies? True success requires knowing exactly what talents your organization lacks and targeting AI to fill those specific gaps. Skip the hard work of defining the skills and roles you actually need, and you’ll end up overwhelmed by options or acquiring agents that don’t solve your real problems. The companies that benefit most from this marketplace will be the ones that mapped their talent gaps first.

    The implications run deep. Outsourcing firms that provide human labor for repeatable tasks face a direct competitive threat and must learn to integrate AI faster and better than their customers do. Companies struggling with persistent talent gaps in cybersecurity, data engineering, or compliance could discover a genuinely new category of solution. But it also raises hard questions about governance, accountability, and quality assurance when the “worker” is an AI system contracted from a third party.

    The Real Shift Is in Software Itself

    All three of these changes point to the same underlying transformation. The question isn’t whether AI will change your workforce. It already has. The question is whether your software, your infrastructure, and your design thinking are ready for a world where AI isn’t just a tool your employees use but a co-worker that uses your software right alongside them.

    Software is changing not just in how it gets developed but in who the user is. When the cloud emerged, companies had to rethink applications for a new delivery model. When mobile took off, they had to redesign for a second screen. AI demands something bigger: a new UX paradigm where humans and AI agents work within the same systems, using the same data, without breaking each other’s workflows. The companies that design for that world will be the ones that capture the value from everything Altman described. Start there.

  • Agentic Commerce QuickTake: Should Anyone Care?

    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

  • Five Steps to Follow for Successful AI Deployments

    Five Steps to Follow for Successful AI Deployments

    The paradox facing enterprise AI today is stark: organizations report an eightfold increase in AI spending over three years, yet studies show 95% of generative AI use cases never reach production, and 75% fail outright. This disconnect reveals that most organizations are making fundamental execution errors—not that AI lacks business value.

    After years of advising enterprise buyers on AI deployments, several critical success principles have emerged. Organizations that follow these principles consistently deliver measurable ROI. Those that don’t contribute to the failure statistics.

    1. Start with Business Outcomes, Not Technology Platforms

    The most common AI failure pattern mirrors mistakes from mobile and IoT adoption: organizations build “AI platforms” before identifying specific use cases that deliver measurable business value.

    The problem: Selecting tools (LLMs, vector databases, orchestration frameworks) before defining what you’re trying to accomplish is equivalent to buying hammers, screwdrivers, and impact drivers before knowing what you’re building.

    The solution: Begin every AI initiative by identifying specific use cases tied directly to organizational KPIs. Examples of scoped, high-value use cases include:

    • Reducing containment rates for specific support call types by 90%
    • Deploying task-specific LLMs customized on proprietary data for financial services advisors
    • Achieving 15-20% improvement in software development velocity (not the 40% often marketed, but still significant ROI)

    The use case must be specific enough to measure, valuable enough to justify investment, and scoped tightly enough to complete successfully. “Transforming customer experience” is not a use case. “Reducing password reset support calls by 85% using a custom chatbot” is.

    2. Fix Your Data Foundation First

    Bad data produces bad AI outcomes. This principle has not changed despite advances in model capabilities, and it remains the biggest obstacle after poor use case selection.

    Many AI vendors assume enterprise data is ready for production use. It rarely is. Organizations must prioritize:

    • Data quality: Ensuring accuracy, completeness, and consistency across sources
    • Data availability: Making relevant data accessible to AI systems without introducing security or governance gaps
    • Data architecture modernization: Restructuring how data is stored, indexed, and retrieved to support AI workloads

    Infrastructure strategies established five to seven years ago did not account for generative AI as the dominant workload. A strategic pause to reassess data architecture is not optional—it’s a prerequisite for success. This includes evaluating hybrid and distributed infrastructure models, as the industry has conclusively moved past the “public vs. private vs. hybrid” debate: AI will be hybrid and distributed.

    3. Implement Observability and Metrics from Day One

    Approximately half of organizations implementing AI fail to establish proper governance and observability frameworks at the outset. This virtually guarantees failure.

    Why this matters: Without metrics, you cannot determine if an AI system is delivering value. Without observability, you cannot determine if it’s functioning correctly or degrading over time.

    What this requires:

    • Establishing baseline measurements before AI deployment
    • Defining success metrics aligned with business KPIs
    • Implementing monitoring systems that track model performance, data drift, and output quality
    • Creating feedback loops that enable continuous improvement

    Organizations that cannot answer “Is this working?” or “Are we winning?” with quantitative data are not ready for production AI deployment.

    4. Narrow Your Focus to Maximize Impact

    The temptation to launch hundreds of AI initiatives simultaneously is strong, particularly when board-level mandates lack specific structure. Resist this temptation.

    The bowling alley principle: Aim for the first pin precisely rather than all ten at once. When you get the first one right, it often knocks down others. This approach matters for two reasons:

    First, fewer projects enable teams to deliver measurable impact rather than spreading resources too thin. Second, and more importantly, each AI initiative requires rethinking the entire technology stack—data architecture, governance structures, security frameworks, infrastructure placement decisions. Working through these complexities across 1,000 applications simultaneously is unmanageable. Solving them for three high-value initiatives is achievable.

    These initial projects serve as “pipe cleaners” that help organizations establish repeatable patterns for data modernization, security implementation, and governance frameworks. Once established, these patterns accelerate subsequent deployments.

    5. Establish Governance and Security Before Deploying Agents

    The current push toward agentic AI—systems that pursue goals autonomously across multiple steps and applications—introduces new risks that many organizations are unprepared to manage.

    The fundamental difference: Chatbots respond to queries. Agents pursue multi-step goals that often span multiple software systems, trigger financial transactions, and make autonomous decisions. An agent onboarding a new employee might schedule calendar invites, order laptops (procurement decisions with financial impact), create email accounts, and establish identity credentials—all without human intervention.

    Critical requirements before deploying agents:

    • Identity management: Every agent must have a registered identity, even those provided by third parties. Treat agents as assets in your CMDB (Configuration Management Database).
    • Authorization frameworks: Define what each agent can do, when it escalates to humans, and what systems it can access. Role-based access control applies to agents as it does to humans.
    • Security protocols: Establish standards for agent-to-agent communication, particularly across vendors. This includes adopting emerging protocols (like OAuth for AI and Model Context Protocol) and defining interworking standards.
    • Risk assessment: Before deploying any agent, map out what could go wrong if the agent operates without proper controls. Customer data exposure, unauthorized spending, and incorrect decisions all carry material risk.

    Most organizations should pause agentic deployments until they have clear answers to these questions. The upside: we can use a mix of new tools and  existing identity, authorization, and security tools with modest modifications. You don’t have to throw out your entire security stack but you do need to modernize it for agents. The industry does not need entirely new protocol stacks.

    Key Takeaways from Lopez Research

    Organizations achieving strong AI ROI share common execution patterns: they select narrow, high-value use cases; they modernize data architecture before deployment; they establish observability frameworks from the start; they resist the temptation to scale before validating their approach; and they implement governance and security commensurate with the risk agents introduce.

    The failure rates cited at the beginning of this article reflect poor execution, not technological limitations. When AI is deployed with discipline, precision, and appropriate governance, it delivers measurable business impact. The question is not whether your organization should pursue AI—board mandates have settled that question. The question is whether you will execute with the rigor required to avoid becoming another failure statistic.

    Most of the technology exists. The use cases are proven. The only remaining variable is execution discipline.

  • AI, CX, and the Shift from Automation to Action with Jarrod Johnson of TaskUs

    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 Quick Take Part 1: Julie Ask of Ask Advisory

    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. 

  • From AI Chaos to Production: Why 2026 is the Year Enterprise AI Gets Real

    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

  • AI Search Helps SAP Improve Customer Support With Coveo

    AI Search Helps SAP Improve Customer Support With Coveo

    Most companies struggle to prove AI ROI, but enterprise software provider SAP found an approach that works


    The artificial intelligence (AI) market is expanding rapidly. Enterprise AI spending surged eight-fold in 2024 according to the Menlo Ventures. This year is expected to be a banner year for AI adoption. However, many AI initiatives are not delivering measurable returns.


    Recent AI studies paint a stark picture. MIT reports that 95% of generative AI pilots fail to reach production, while RAND found that 46% of companies scrapped their AI projects before launch. Perhaps most telling, Boston Consulting Group discovered that only 24% of firms have developed the capabilities needed to move AI beyond proof-of-concepts into tangible business value. While we could debate what the actual percentages are, one thing is clear. Companies are spending money on AI but aren’t getting the returns they expected.

    What separates the AI strategy winners from the also-rans? It appears the key to AI success is selecting a set of narrowly scoped use cases with a quantifiable cost that will benefit from using AI. As Best Buy said, we don’t have AI projects, we have projects that use AI. SAP’s journey offers one success story with crucial lessons for any organization struggling to prove AI’s worth.


    Some of the best AI success stories start by addressing a simple problem. For example, Cisco discussed how leveraging AI to detect network configuration issues solved a key customer support challenge.

    The Million-Case Support Crisis That Sparked SAP’s Internal AI Journey


    For SAP, the path to AI success began with a simple but expensive problem: customer support for its over 300 million cloud subscribers. Like many companies, the German enterprise software vendor was experiencing significant outflow of money on avoidable service requests. The goal was not to remove employees, but to eliminate unnecessary inquiries.

    AI has the ability to create capacity for the existing workforce to build new customers service experiences. Customers were struggling to find answers , leading to unnecessary support tickets that customers could have avoided through self-service.
    “We weren’t hitting our self-service targets,” explains Michelle Lewis-Miller, the Head of Digital Experience and Voice of Customer for Customer Support at SAP. “We couldn’t see what was happening and couldn’t measure anything.”

    Visibility is a key issue across businesses of all sizes. Many companies deploy AI solutions without clear metrics or understanding of where the technology can create the most impact. SAP started with a measurable business problem and worked backward to find the right AI solution.

    The Foundation: Getting Knowledge Quality Right First


    Before implementing any search solution, SAP had already invested heavily in knowledge quality. Lewis-Miller uses a culinary metaphor to explain: “If you’re serving dinner with poor ingredients, it doesn’t matter how great the chef is.”

    Too many organizations rush to implement AI technology without first ensuring their underlying data and content are ready to support it. Knowledge wasn’t the company’s problem. SAP had over 10 million knowledge assets—spanning help documents, SAP Knowledge Base Articles, community posts, and videos- as a foundation for its search efforts. “SAP had already made massive investments to get the knowledge into good shape, and introduced AI-driven Incident Solution Matching recommender for knowledge. The problem was an inferior knowledge discovery experience via search,” Lewis-Miller notes.

    30% Case Reduction: The Pilot Results Nobody Expected


    Rather than launching a company-wide AI initiative, SAP began with a targeted pilot in their Concur travel and expense division. The Concur division was already using Coveo’s AI-powered search platform, making it an ideal testing ground for generative AI capabilities.


    The Coveo AI pilot results exceeded all expectations. Within six months, SAP Concur experienced a 30% drop in support case volume, translating to €8 million in annual cost avoidance. For many customers, this was their first encounter with generative AI—and it delivered immediate, tangible value.


    “When we piloted Relevance Generative Answering in Concur, it went so well it was shocking,” Lewis-Miller recalls. “We saw such a huge decrease in case submission that helped us tremendously in keeping up with business growth.”


    The Coveo-SAP Concur pilot’s success illustrates a key principle: AI ROI comes from solving specific, measurable problems rather than implementing technology for technology’s sake. The SAP Concur team had clear metrics (case volume reduction), a defined user base, and a focused use case.


    The Cultural Shift That Enabled Bold Decisions


    The cultural shift at SAP proved crucial to the initiative’s success. In a discussion with Lewis-Miller, she emphasizes that executive support went beyond budget approval: “Our executives came in and made a very clear statement. They said, You’re not going to be punished for trying.” This created an environment where teams could be decisive rather than seeking consensus from dozens of stakeholders.


    She refers to this as the “tip of the spear approach,” noting that “at an organization of any size you need to find the people who are willing to take risks, and you need to have the executive leadership being unequivocal about supporting that person who’s willing to take the risk.” Without this top-down support for calculated risk-taking, the project likely could have stalled in committee discussions and consensus-building exercises that ultimately may have prevented any meaningful progress.


    Scaling Success: The SAP for Me Challenge


    Emboldened by the Concur results, SAP faced a much larger challenge: implementing AI-powered search across SAP for Me, the central portal serving millions of customer interactions every month. Unlike the focused SAP Concur deployment, this required integrating 14 to 20 different knowledge bases while serving vastly different user types—from certified engineers with 30 years of SAP experience to small business owners with limited technical background.


    The complexity initially threw the team. When they deployed the same generative AI technology on SAP for Me, the results looked worse at first glance—higher click rates and more case submissions seemed to indicate the system wasn’t working.
    “We expected it to address the low-hanging fruit. It wasn’t at all like what we saw with Concur. We asked Coveo if something was broken in their backend,” Lewis-Miller admits.


    The breakthrough came when SAP’s analysts realized they were measuring the wrong things. While simple questions decreased on the Concur platform, SAP for Me users were asking much more complex queries. The AI wasn’t avoiding low-value cases—it was helping resolve sophisticated problems that would have required expensive expert support.


    “We were handling a more valuable subset of cases,” Lewis-Miller explains. “When we changed our perspective and looked at the overall numbers, we saw that submissions had actually gone down.”


    Here’s where many AI projects fail: teams panic when initial metrics don’t match expectations and abandon promising initiatives. SAP took a different approach, partnering with their technology vendor to dig deeper into the analytics. Successful AI implementations require a company to deploy, monitor, analyze and iterate. SAP’s story highlights how theory rarely matches reality.


    The Results


    Since launching in 2023, the AI-powered search has enabled intelligent scaling of the support function to drive business growth. SAP’s AI strategy ensures that support costs grow slower than revenue, thereby expanding profit margins, and allowing its business to absorb significant market expansion. As a result, customer self-service success from search is now at over 80%, and the technology spans 47 different sources and indexes 11.2 million documents across the SAP ecosystem.


    The Technical Foundation That Makes It Work


    SAP’s success stems from choosing the right technical approach for their specific needs. Rather than building custom AI models from scratch, which is labor and time intensive, SAP leveraged a “hybrid search” system that combines multiple AI techniques:


    • Lexical search (traditional keyword matching) finds documents that contain the exact words you type—like searching for “invoice processing” and getting results that include those specific terms.
    • Semantic search (context-aware search) understands what you actually mean, not just the words you use. It recognizes that “payment issues” and “billing problems” refer to similar concepts, even when the exact words don’t match.
    • Behavioral machine learning (learning from user patterns) tracks which search results people actually click and use, then automatically surfaces the most helpful content for similar future searches.
    • Generative AI (AI-powered answer creation) provides direct responses to questions in plain English while showing exactly which company documents or sources the information came from, similar to how ChatGPT works but trained on your organization’s specific knowledge base.


    This hybrid approach addresses common search frustrations: lexical systems miss relevant results when you don’t use the exact right keywords, while pure generative AI can sometimes provide inaccurate information. The combination delivers both precision and intelligence.


    Advice For Building ROI with AI


    SAP’s re-invested cost avoidance didn’t happen by accident. Behind those impressive numbers lies a methodical approach that any organization can follow—if they’re willing to resist the allure of flashy AI demos and focus on fundamentals. Key steps that every organization can take include:


    • Start with measurable problems rather than exciting technology possibilities
    • Ensure your knowledge foundation is solid before implementing AI search solutions—garbage in, garbage out applies to AI just as much as any other technology
    • Pilot in controlled environments where you can isolate variables and measure impact
    • Secure executive support for risk-taking and empower decision-makers to act without requiring consensus from dozens of stakeholders
    • Document rigorously for compliance and architecture
    • Evaluate enterprise-ready AI solutions instead of jumping first into extensive custom AI development
    • Invest in proper analytics and measurement to understand true impact versus surface-level metrics


    While all of these points are important, Lopez Research has seen many companies skip the governance and compliance step. Lewis-Miller identifies meticulous documentation as a critical but often overlooked success factor.

    “We invested a lot of time in finding employees who had the soft skills necessary to interpret the technical detail for the legal people and the legal detail back to the technical detail.” This documentation became essential reference material that prevented the project from stalling at multiple compliance checkpoints. At enterprise scale, having detailed outlines for every architectural and legal decision proves invaluable.


    Beyond Cost Avoidance: Proactive Customer Experience


    SAP isn’t stopping at self-service enablement. The company is now using behavioral analytics to intervene before customers encounter problems. Lewis-Miller describes the vision: “As a user is engaging in one of our support journeys, our goal is to intervene in real time to get ahead of issues before they actually emerge.”


    The approach involves using customer behavior patterns to identify when someone is struggling to find information, then proactively routing them to chat support before frustration sets in. “We’re not trying to give everybody a white glove experience. We’re specifically intervening only in those moments where we can see it’s going down the wrong path,” Lewis-Miller explains.


    This includes predicting system issues before they occur and routing insights to product teams to fix confusing features before they generate support cases. By analyzing search patterns and support interactions, SAP can identify friction points in their products and address them proactively—turning support data into product improvement fuel.


    AI is doing more for internal innovation: For the cases that do reach a support engineer, SAP focuses on agent augmentation. AI-based case summarization enables faster handoffs, and intelligent routing connects a case to the correct expert instantly.
    The AI revolution is about implementing the right technology to solve real business challenges. SAP found their formula. Now it’s time for other enterprises to find theirs.

  • AI Meets Cybersecurity: Protecting Critical Infrastructure with Black & Veatch’s Ian Bramson

    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.

  • Why Your Gut Instinct is Costing You Millions a Chat with Verint’s AI Analytics Expert Daniel Ziv

    About This Episode
    Daniel Ziv, Global VP of AI and Analytics at Verint, reveals why experienced executives are making their worst decisions in decades—and how AI analytics is rewriting the rules of business intelligence. Learn the two critical frameworks that separate AI winners from losers, and why the biggest risk isn't picking the wrong technology—it's doing nothing at all.
    Guest Bio
    Daniel Ziv leads AI and analytics product management and go-to-market strategy at Verint, where he helps global enterprises transform customer experience through data-driven decision-making. With two decades in the analytics space, Daniel has witnessed firsthand how AI is fundamentally changing what's possible in customer insights.
    Key Timestamps
    [00:00] – Why change is happening faster than ever before
    [03:04] – The Macro vs. Micro Analytics Framework explained
    [06:19] – Two flawed decision-making patterns destroying value
    [09:20] – Real ROI: $80M saved, $10M found in 48 hours
    [15:32] – Generative AI vs. Agentic AI: What's the difference?
    [21:03] – The hybrid cloud advantage (why on-prem isn't dead)
    [26:35] – Common misconceptions about Verint
    [28:49] – Daniel's advice for making AI decisions today
    [32:17] – Final thoughts: “Ride the dragon”
    Key Takeaways
    The Two Fatal Mistakes:

    Gut-based decisions without data – Your experience is becoming less reliable as change accelerates
    Analysis paralysis – Waiting weeks for insights while competitors move in hours

    The Macro-Micro Framework:

    Macro Analytics: Understand patterns across ALL interactions (the 30,000-foot view)
    Micro Analytics: Apply insights to individual interactions in real-time
    Companies that excel at both create significant competitive advantage

    Real Results:

    Large telecom: $80M saved + 11% sales increase
    Typical deployment: $5-10M in insights found within 1-2 days
    UK financial services: $5M additional revenue from loan process improvements
    Energy supplier: $2M saved through increased agent capacity

    Generative → Agentic Evolution:

    Generative AI responds to prompts (you ask, it answers)
    Agentic AI breaks down goals and executes multi-step workflows autonomously
    Example: Genie Bot evolved from answering questions to analyzing, quantifying, and exporting results automatically

    Action Items for Listeners

    Audit your decision-making speed – Are you making gut calls or waiting too long for data?
    Identify one quick-win AI deployment – What could you turn on this week without changing infrastructure?
    Evaluate your analytics gaps – Do you have macro insights, micro operationalization, or both?
    Test before scaling – Start with 300 users, validate, then scale to 30,000
    Connect with Daniel – Reach out on LinkedIn to discuss your specific use case

    Connect With Daniel Ziv
    LinkedIn: https://www.linkedin.com/in/dziv1/
    About the Host
    Maribel Lopez brings decades of technology industry analysis experience, helping business leaders cut through hype to understand what actually works in AI, cloud, and digital transformation. https://www.linkedin.com/in/maribellopez/
    Subscribe & Follow
    If you found this conversation valuable, subscribe for more deep dives with AI leaders who are actually deploying this technology and seeing real business results.

    Tags: #AI #Analytics #CustomerExperience #GenAI #AgenticAI #BusinessIntelligence #CXAutomation #DataDriven #DigitalTransformation #Verint

  • AI Analytics for Customer Experience: Advice for CX Leaders From Verint

    The promise of artificial intelligence in customer experience has never been clearer—or more urgent. Yet many organizations find themselves paralyzed between the fear of falling behind and uncertainty about how to proceed. According to Daniel Ziv, Global VP of AI and Analytics at Verint, this hesitation itself poses the greatest risk.

    “The pace of change impacts the need for much, much faster insights,” Ziv explains. “We need to analytics that could provide either real time or near real time insights.”

    Two Critical Frameworks for Understanding AI Analytics

    Macro vs. Micro Analytics in Customer Experience 

    Ziv introduces a useful distinction between two complementary types of analysis that organizations need:

    Macro analytics examines patterns across all customer interactions to identify emerging trends and issues at scale. As Ziv notes, “You need a system that can look scalably at the entire volume of stuff and give you a statistically valid sample across that trend.” For instance, one Verint customer discovered they were receiving three times as many calls about tariffs compared to recent periods, with these calls lasting twice as long.

    Micro analytics applies these insights to individual interactions in real-time. “I need to infuse that insight and analyze the call as it’s happening, provide real time guidance or apply this information to my website or to my IVA or IVR,” Ziv explains.

    The gap between these two creates competitive advantage: “Companies that have both have a real advantage,” he observes. Organizations that excel at macro analysis often fail to operationalize insights quickly enough, while those focused on micro  analytics may be optimizing  “on last year’s insights.”

    Generative vs. Agentic AI in Customer Experience

    Understanding the evolution from generative to agentic AI helps organizations plan their investments more strategically.

    Generative AI responds to prompts with content, understanding, and analysis. It excels at analyzing unstructured data like voice conversations and text interactions. As Ziv describes it: “AI can generate content, can understand unstructured conversations, including voice and text and video and images and respond to it.”

    Agentic AI takes this further by breaking down complex goals into multiple steps, executing them autonomously. “I can set a goal and say, hey, I want to increase revenue for my business. Go figure this out. And it’ll itself generate questions. Go to the Web. Go to my database. Go to different places. Come up with answers,” Ziv explains.

    Verint’s evolution of its Genie Bot illustrates this progression. The initial generative version analyzed calls about complaints or churn on request. The agentic version now breaks down questions independently, scales analysis across larger samples, quantifies results, and exports findings to presentation formats—executing entire workflows with minimal human intervention.

    The Flawed Decision-Making Patterns Holding Organizations Back

    Ziv identifies two common failure modes that prevent organizations from capitalizing on AI:

    Pattern 1: Gut-Based Decisions Without Data

    “A lot of executives tend to be in this position because they’ve been in a space for so long, they feel their gut feeling is usually accurate,” Ziv observes. While experience matters, rapid change undermines intuition’s reliability. Leaders are making decisions without data “because I don’t have the data, and I have to make a decision.”

    Pattern 2: Analysis Paralysis

    The opposite problem occurs when leaders recognize they need data but wait too long to get it. “I’m making the decision too late. By the time I make the decision, I’ve missed the opportunity. I’ve lost money and I’m behind compared to the competition,” says Ziv.

    Both patterns stem from the same root cause: analytics systems that can’t deliver insights fast enough to support decision-making at the speed of business change.

    Quantifiable Outcomes: What’s Actually Possible

    When organizations implement both macro and micro analytics effectively, the financial impact is measurable and significant. Verint provided the following examples:

    • A large international telecom saved $80 million and increased sales by 11% using macro insights combined with real-time coaching capabilities
    • Organizations typically find $5-10 million in insights within one to two days of deploying generative AI analysis
    • A UK financial services company generated $5 million in additional revenue by using AI to identify and resolve process inefficiencies in loan applications
    • An energy supplier saved $2 million through increased agent capacity after using AI to identify processes suitable for self-service

    “When we deploy our latest (Verint) Geniebot, we see that within one or two days, we typically find five to ten million dollars of insights just off the bat.” Ziv reports.

    Practical Guidance for Getting Started

    Start Small, Move Fast

    Rather than comprehensive transformation initiatives, Ziv advocates for incremental deployment: “What can you turn on without changing anything that will add value to your environment? Similar to your phone. I can download an app and turn on capability that now is transformative.”

    One Verint customer piloted automated call wrap-up notes with 300 agents, validated the impact, then scaled to 30,000 agents. This approach reduces risk while accelerating time-to-value.

    The Hybrid Architecture Advantage

    Organizations with on-premises infrastructure face a false choice between wholesale cloud migration and missing out on AI innovation. “You can have telephony on-premises and we send recordings in a secure way to cloud services with your existing deployment. You get immediate outcomes with very little cost, very little effort, very little risk,” Ziv explains.

    This hybrid approach allows organizations to access cloud-based AI compute power and models while maintaining existing infrastructure, avoiding the complexity and risk of full migration projects.

    Focus on Outcomes, Not Infrastructure

    “Ninety percent of the time and the effort is focused on moving infrastructure but only 10 percent is about outcomes,” Ziv observes about traditional cloud migration approaches. By inverting this ratio—focusing first on turning on AI capabilities that deliver business value—organizations can fund subsequent innovations through realized savings.

    The Strategic Imperative

    Ziv frames AI adoption as fundamentally strategic: “If you think about the biggest companies in the world, they are data companies that have better analytics, whether it’s Amazon or Google.”

    The competitive advantage lies not in which models to use, but in whether organizations can operationalize their data: “Your competitive advantage is the data that you have that your competitors don’t have. More importantly, how well are you using the data that you have access to?”

    His assessment of the current moment is unambiguous: “These are make or break years and down to minutes. Every minute where you’re not taking advantage, learning and embedding AI, it’s either a great opportunity or a huge risk.”

    Conclusion

    The path forward requires organizations to abandon both gut-based decision-making and analysis paralysis in favor of rapid, iterative deployment of AI capabilities. By implementing both macro and micro analytics, organizations can identify opportunities at scale while operationalizing insights in real-time customer interactions.

    The technology has matured to the point where organizations can achieve meaningful business outcomes—measured in millions of dollars—within days of deployment. The question is no longer whether AI analytics can deliver value, but whether organizations can move quickly enough to capitalize on the opportunity before their competitors do.

    As Ziv puts it: “The biggest risk is inaction.”

    You can see the whole interview on Youtube here or subscribe to the podcast at here.

  • Disney, Walmart, And Carnival UK Share Lessons In AI For Customer Experience

    Disney, Walmart, And Carnival UK Share Lessons In AI For Customer Experience

    When it comes to implementing AI for customer experience (CX), the gap between technology promises and enterprise deployment reality can be vast. During the NiCE Interactions conference, customer experience executives from three major companies—Disney, Walmart, and Carnival UK—shared how their companies navigated these challenges with different approaches, offering a glimpse into what works when deploying AI at scale.

    The companies’ collective experience reveals that successful AI implementation requires more than a focus on finding the right technology solution. Instead, CX success demands strategic partnerships, effective organizational change management, and a fundamental shift in how companies approach customer service operations.

    Carnival UK: The Knowledge Management Imperative

    When John Wells inherited customer service operations at Carnival UK, he faced a familiar enterprise challenge: 1.25 million annual guest interactions flowing through disconnected legacy systems that couldn’t even link a customer’s phone call to their email inquiry.

    “We had siloed systems,” Wells, the company’s Contact Center Director, explained. “If guests would phone us or they’d email us, we wouldn’t know one interaction to the next.”

    The cruise line’s transformation over 18 months represented a desire to reimagine the experience. “This wasn’t a technology change program,” Wells emphasized. “This was a business change program underpinned by a technology change.” Carnival UK understood that data quality was key to delivering a successful customer experience. The company spent six months consolidating scattered knowledge from multiple systems, recognizing that AI success depends entirely on information architecture.

    “Knowledge management and structuring your knowledge is as important to your success as AI management,” Wells discovered. “Getting the data and the knowledge in the right place, structured in the right way, enables you to be able to be successful in your development of the AI.”

    AI guardrails are mechanisms and strategies designed to ensure that AI systems, especially generative AI, operate within safe, ethical, and legal boundaries. Carnival UK’s methodical approach to knowledge management enables it to create guardrails that ensure AI responses align with company policies while maintaining the premium service experience that luxury cruise customers expect. 

    Carnival UK’s Key Lesson: Treat AI like a new employee. “You wouldn’t just put a new team member in place and just let them get on with it,” Wells explained. “You have to train and coach them. You have to work with them every day, tweak (the process), and point them in the right direction.”

    Disney: Leadership and Security First as a Foundation for Customer Experience

    Arun Chandra, SVP for Customer Experience at Disney, shared Disney’s vision to build the best CX program globally, serving over 150 million customers in 100 geographies. Disney’s approach uses three foundational principles that address the organizational dynamics often overlooked in AI implementations.

    First, Disney insists on direct senior leadership involvement, recognizing that executives must work alongside their teams to separate genuine AI capabilities from marketing hype. This hands-on approach ensures that AI initiatives align with business objectives rather than getting caught up in technological possibilities.

    Second, Disney places extraordinary emphasis on data privacy, legal requirements, and security challenges. The company focuses on critical questions are data use and management. For example, what data trained the AI models and was it proprietary Disney company data? It also has to evaluare the implications of using models trained on external and potentially inaccurate data sources.

    Disney understands that AI implementations can create new security vulnerabilities if not properly managed. Data governance isn’t just a compliance issue for a company handling millions of customer interactions across theme parks, streaming services, and merchandise operations. It’s essential for securing data and delivering a seamless experience. 

    Third, Disney views change management as the cornerstone of successful AI implementation. The company recognizes that AI transformation affects not just customer-facing agents but the entire organizational workforce.

    “AI impacts everyone across the organization, as everyone ultimately contributes to serving customers and stakeholders,” according to Chandra.

    Disney’s Key Lesson: AI implementation requires comprehensive organizational change rather than isolated departmental deployments. Success depends on addressing security concerns upfront and ensuring executive leadership remains actively involved throughout the process.

    Walmart: Consolidation and Strategic Partnership For Scaling Customer Experience

    Walmart’s journey offers a distinct perspective on enterprise AI adoption, emphasizing the crucial distinction between products and strategic partnerships. Anderson Wilkins from Walmart explains the company’s rationale: “We selected NiCE as that one (contact center) platform, not because it was perfect, but because we found a strategic partner. We created a shared vision to co-innovate together, to scale with a microservice architecture and auto-scaling for on-demand capacity.”

    This approach proved crucial for handling Walmart’s massive scale challenges. “When everybody calls us on Black Friday, many of our brands are unified under one Walmart contact center platform.”

    Walmart shared how it collected stakeholder feedback to manage organizational resistance to change. The key to its success was transparency about the transformation roadmap: “We shared the roadmap of how we would reduce costs, streamline our tech, eliminate friction, and give them a platform where we could deliver changes faster.”, said Wilkins. 

    Walmart’s Key Lesson: Enterprise AI success depends not just on technical capabilities but on organizational acceptance across diverse business units with different priorities and concerns. 

    Common Ground In Customer Experience Transformations

    Despite their different strategic approaches, Disney, Walmart, and Carnival UK encountered remarkably similar obstacles that reveal the universal challenges of enterprise AI implementation in customer experience such as legacy system integration, organizational resistance and data quality and governance concerns.

    The most pervasive issue was legacy system integration. Each organization discovered that their existing infrastructure created barriers to seamless customer experiences. Carnival UK’s siloed systems prevented agents from connecting a customer’s phone call to their previous email inquiry, while Disney’s complex multi-platform operations spanning theme parks, streaming services, and retail required coordination of multiple data source. Similarly, Walmart’s diverse brand portfolio and large number of stores demanded integration strategies that could unify multiple business units under a single contact center platform without sacrificing each brand’s unique requirements.

    Organizational resistance emerged as another significant hurdle across all three implementations. Each company faced internal pushback when introducing AI-enabled contact center systems, as employees worried about job security and changing workflows. The companies learned that success required more than technical deployment—it demanded proactive change management, transparent communication about transformation goals, and concrete demonstrations of how AI would benefit rather than replace human workers.

    Scale requirements presented unique challenges that tested each organization’s infrastructure decisions. Whether managing cruise guest inquiries during peak booking seasons, handling Disney’s massive theme park operations during holidays, or supporting Walmart’s Black Friday shopping surges, all three companies needed AI solutions capable of dynamic scaling without manual intervention. This requirement influenced their architectural choices and partnership strategies, as they sought platforms that could automatically adjust capacity based on demand rather than requiring constant human oversight.

    Data governance concerns proved equally critical across all implementations. Each organization recognized that AI success fundamentally depends on clean, well-structured data and robust security protocols. This wasn’t merely a technical checkbox but a business imperative that affected everything from customer trust to competitive positioning. The companies discovered that poor data quality could undermine even the most sophisticated AI capabilities, while strong data governance created the foundation for sustained AI success.

    The common thread across all three implementations is patience and methodology. Rather than rushing to deploy the latest AI features, successful companies invest time in organizational preparation, data quality, and strategic partnerships.

    For enterprise leaders embarking on AI initiatives, one lesson is clear. Getting the technology right is only half the battle at most. The real challenge lies in organizational transformation, data governance, and creating the cultural conditions for AI customer experience success. Companies that master these fundamentals position themselves not just for immediate AI benefits, but for long-term competitive advantage in an increasingly AI-driven marketplace.

  • What’s Next for Cognitive ERP and Manufacturing Intelligence with Epicor’s Kerrie Jordan

    Episode Overview

    Host Maribel Lopez sits down with Kerrie Jordan, the newly appointed Chief Marketing Officer at Epicor, to discuss the evolution of ERP systems and the transformative power of cognitive ERP in manufacturing, distribution, and supply chain industries.


    Guest Bio and social links

    Kerrie Jordan – Chief Marketing Officer, Epicor

    Kerrie Jordan, Chief Marketing Officer at Epicor, leads the global go-to-market efforts, bringing together her deep product innovation and strategic marketing experience to drive brand growth and customer engagement across the make, move, and sell industry communities.

    https://www.linkedin.com/in/kerriejordan/

    Key Topics Discussed

    Cognitive ERP: From System of Record to System of Action

    • Definition: Transforming ERP from passive data storage to intelligent, proactive decision-making systems
    • Key capabilities:
      • Sensing signals in data noise
      • Serving up actionable insights when needed
      • Connecting organizations across supply chains
      • Creating intelligent business communities

    Epicor Prism: Agentic AI Technology

    • What it is: Conversational ERP experience launched last year
    • Key features:
      • Natural language interaction (type or speak)
      • Information querying without knowing system screens/reports
      • Automated actions with human approval (semi-autonomous approach)
      • Multiple specialized agents (Knowledge Agent, RFP Agent, Business Communications Agent)

    Real-World Success Stories

    Measuring AI ROI

    • Focus on specific business outcomes, not just AI implementation
    • Apply fundamental business case principles
    • “Nail it before you scale it” approach
    • Baseline analysis and clear success metrics

    Future Vision (Next 1-2 Years)


    Data Platform Evolution

    • Explosion of structured and unstructured data
    • Critical need for data normalization and health
    • Open, secure connections as “good cloud citizens”


    AI Development Trajectory

    • Current: Pre-trained models and agentic AI
    • Future: Self-service pipelines for custom AI model creation
    • Model-agnostic strategy with patented inference pipeline
    • Community-based insights and collaboration


    Quotable Moments

    • “We are an organization that is really focused on our core industries… making, moving, selling the things that we use every day”
    • “It's all about accelerated value… How can we get as close to zero as possible?”
    • “This era that we're in [is] like the modem dial-up era of AI”
    • “Nail it before you scale it

  • Racing Against AI-Powered Fraudsters: How Experian Stays Ahead


    Overview

    Maribel Lopez interviews Kathleen Peters, Experian's Chief Innovation Officer, about AI's evolution in fraud detection, the shift to generative and agentic AI, and balancing innovation with security in financial services.

    Key Topics

    AI Evolution at Experian

    • 15-year AI journey: Using machine learning for fraud detection long before generative AI
    • Democratization shift: Public LLMs like ChatGPT and Claude made AI accessible beyond data scientists
    • Innovation labs: 15-year-old team of PhDs and researchers finding insights in vast datasets

    Responsible AI Implementation

    • Risk Council: Cross-functional team ensuring responsible AI adoption
    • Security-first approach: Enterprise tools with guardrails protecting sensitive credit data
    • Custom AI stack: Proprietary systems maintaining data privacy while leveraging AI

    Agentic AI Applications

    • EVA Experian Virtual Assistant (Consumer Assistant): Evolved from chatbot to personalized agent that can take actions like unlocking credit scores
    • Business Assistant: Democratizes data science, enabling rapid model development through natural language
    • Real-time capabilities: Shifted from batch to real-time fraud detection

    AI-Powered Fraud Threats

    • Fraudster empowerment: Bad actors adopting AI faster than security measures
    • Deep fake risks: Sophisticated impersonation for identity theft and account takeover
    • Agent authentication: Challenge distinguishing legitimate vs. fraudulent AI agents
    • Industry urgency: Can't wait for regulation; must develop solutions proactively


    Key Achievements

    • Fast, safe adoption: Chose innovation over waiting, with proper security guardrails
    • Product success: Launched consumer EVA and business AI assistants
    • Industry leadership: Staying ahead of evolving fraud landscape


    Advice for Organizations

    1. Establish Risk Council: Cross-functional leadership team for AI governance
    2. Define values first: Determine organizational risk tolerance before technical implementation
    3. Support curiosity safely: Enable experimentation within secure boundaries
    4. Don't wait: Move quickly but responsibly – the technology won't slow down

    Key Quote

    “If you set up the infrastructure right, then you can let them hack away. You can let people be very curious.”

    Participants: Maribel Lopez (Host), Kathleen (CIO, Experian)
    Focus: #AI #FraudDetection #GenerativeAI #AgenticAI #FinancialServices #Security

    Kathleen Peters Chief Innovation Officer NA Fraud, Innovation & Commercialization 

    Kathleen Peters leads innovation and strategy for Experian’s Fraud and Identity business in North America, continuously exploring new ways to solve market challenges in identity, risk, and fraud detection. She and her team define business strategies and investment priorities while incubating new products, analyzing industry trends and leveraging the latest technologies to bring ideas to life. Kathleen joined Experian in 2013 to lead business development and global product management for Experian’s newest fraud products. She later served as the Head of the North America Fraud & Identity business, until being named Chief Innovation Officer for Decision Analytics in 2020. Kathleen has twice been named a “Top 100 Influencer in Identity” by One World Identity (now Liminal), an exclusive list that annually recognizes influencers and leaders from across the globe, showcasing a who’s who of people to know in the identity space.For nearly two decades, she has lived in

  • Ford Pro’s Kevin Dunbar Shares How AI Transforms Fleet Management

    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 

  • Verint Executive Reveals: The 3 Best Starting Points for Enterprise Agentic AI Adoption

    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

  • Cisco Live 2025: Jokel and Pandey on Enterprise AI Infrastructure and the Internet of Agents

    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