Tag: AI Strategy

  • AI Has A Business Context Problem.

    AI Has A Business Context Problem.

    Data quality and availability must be fixed. But Agentic AI also requires a connection to business context.

    Ask most enterprise technology leaders about their biggest obstacle to AI, and the answer is data. The data isn’t clean enough, accessible enough, or consistent enough to feed AI with confidence. They’re right. Data quality and availability remain the number one challenge Lopez Research hears from enterprises deploying AI.

    But fixing the existing data problem alone won’t get you there. There’s a second data problem that has to be addressed in parallel, and most organizations aren’t thinking about it yet: business context.

    Context is the difference between AI that has data and AI that understands your business. Clean data tells AI what the numbers say. Business context tells AI what they mean — which customers have contractual guarantees, which products are strategic, and what trade-offs are acceptable under pressure. Without it, AI optimizes for the wrong thing, at speed.

    These are not sequential problems. You can’t fix data quality first, then worry about context later. As Irfan Khan, President and Chief Product Officer for SAP Data and Analytics, put it at a recent virtual summit on data and AI strategy: “AI is incredibly good at producing results. It moves fast, but without context, it can’t exercise good judgment, and good judgment is what creates return on investment for the business. Speed without judgment doesn’t help.”

    I attended that summit to understand how enterprise leaders are thinking about both problems — and what changes they decided were required. Three companies shared their experiences: Ericsson, Vodafone, and Google. Their challenges were different. Their conclusions pointed in the same direction.

    Two Problems, One Strategy

    For years, enterprise data strategies focused on a familiar cycle: extract data, land it in a centralized system or dashboard, and run reports. It worked decently, but not great, for analytics. It is not working for AI.

    The first problem — data quality and availability — has always existed. AI makes it more urgent. Models trained or grounded on inaccurate, incomplete, or inconsistent data produce outputs that are inaccurate, incomplete, or inconsistent. Between 60% and 80% of AI budgets go to data preparation, according to various research reports.

    The second problem is less urgent. Traditional data architectures were designed to capture what happened in the past and surface it for human interpretation. AI is different. It acts. An agentic system making autonomous decisions on behalf of the business needs to know more than what the data says. It needs to understand the business’s values, the rules, and the trade-offs. Business context is not something most data architectures were designed to preserve or carry.

    Khan described the stakes with a supply chain example. Two companies both use AI to manage disruptions. The first feeds it raw signals such as inventory levels, lead times, and supplier scores. The second adds context across business processes, policies, and metadata: which products are strategic, which customers need to be prioritized, and what trade-offs are acceptable under pressure. His summary was direct: “Without context, AI doesn’t know this customer is flagged as strategic. It doesn’t know their lifetime value, whether substitutions are allowed, or when to expedite, so it calculates differently, and the decision changes completely. Both systems move very quickly, but only one moves in the right direction.”

    The implication for enterprise leaders is practical. As you work through modernization tasks such as cleaning, consolidating, and governing, data stewards must simultaneously ask how business meaning will travel with that data.

    Ericsson: Built for Analytics, Not for AI

    Malin Persson, CIO and Head of Enterprise IT at Ericsson, described a data architecture that had served the company well for years — and then hit a wall when AI entered the picture.

    Ericsson’s traditional setup had three layers: data creation, data analysis, and analytics. For reporting and dashboards, it worked. For AI, Persson identified three specific points of failure.

    Context was locked inside individual systems. Business meaning — embedded in application-specific models, calculations, and system-specific rules — couldn’t travel across applications. “AI operates across systems,” Persson explained. “When context is trapped inside them, AI can only stitch together partial truths.”

    The architecture was designed to capture the past, not support decisions about the present. AI needs to judge information and take action on the company’s behalf. A system built to explain historical data is not built for that.

    Every AI initiative required rebuilding the same logic from scratch. Without governed data products, each use case started at zero — recreating models, assumptions, and definitions that had already been built elsewhere. “Without data products,” Persson said, “every AI use case required recreating models, logic and assumptions from scratch, which made scaling slow, expensive, and unsustainable.”

    Her summary was blunt: “We did not have a data foundation built for AI.”

    Ericsson’s response was to redesign its data strategy around three shifts: preserving business meaning in a knowledge core, scaling it through governed data products, and connecting it across an open architecture. The approach allows data to stay where it lives, across SAP and non-SAP systems, while business context is managed centrally. Persson described the principle: you define what revenue means, how hierarchies roll up across markets, which rules apply — once, centrally — and that context stays consistent as data moves across platforms.

    The data and context problems were addressed together. That was a deliberate choice.

    Vodafone: Available Data, Inconsistent Meaning

    Ricard Rovira, Head of Corporate IT Platforms at Vodafone, described a common challenge in large enterprises that have grown through mergers and acquisitions: plenty of data, but no shared understanding of what it means.

    As Vodafone expanded across markets, each acquisition brought its own systems, local processes, regulatory requirements, and KPI definitions. The data existed. The problem was that the same business concept was defined differently depending on which system or market you asked.

    The practical consequence was one most enterprise technology leaders will recognize. Teams spent disproportionate time reconciling numbers and explaining why two reports produced different figures, rather than acting on the data. Time to insight stretched. Confidence in the output eroded. End users stopped trusting the systems and started downloading data to build their own versions of the truth.

    The loyalty section of the My Vodafone app made the problem concrete. The same capability was running across five markets with five separate data models, five dashboards, and 40 report pages. Each market had its own reality. There was no shared one.

    The fix wasn’t just cleaner data. It was consistent meaning. Vodafone built a unified semantic layer, which is a single place where business definitions are established once and consistently carried across processes, platforms, and use cases. Rovira described the goal plainly: “Governance is not about control. It’s about preserving the business meaning so it can be reused safely across the enterprise.”

    The company consolidated onto SAP Datasphere and Business Data Cloud, reducing its data footprint by 80 percent on its first Business Warehouse instance. The shift moved teams from reassembling data repeatedly to drawing from governed data products that already carry the right definitions and constraints. Time to insight improved. So did confidence in the output.

    Vodafone’s case illustrates a version of the data problem that often goes undiagnosed. The data is available. The data is not inaccurate in the traditional sense. The data’s meaning is inconsistent, which, in itself, is a form of bad data. For AI systems that act on it autonomously, the consequences are worse than a wrong number in a report. Google faced the same core problem. The cause was different.

    Google: The Culture Problem Underneath the Data Problem

    Jannie Affeld, VP of Finance Systems and ERP at Google, described the same symptom Vodafone experienced — the same data carrying different meanings across the organization — but traced it to a different root cause. At Vodafone, fragmented meaning arose from mergers and acquisitions, with incompatible systems layered over time. At Google, it came from its innovation culture.

    Google operates across more than 200 data centers and offices on six continents, structured by product areas each large enough to function as a standalone global enterprise. The culture has long rewarded individual autonomy within product areas. Teams built their own solutions and defined the same business data differently. The result looked familiar: ten definitions of headcount, multiple approaches to foreign exchange transactions, no single version of the truth that anyone trusted.

    “Technology alone isn’t enough for AI to truly scale,” Affeld said. “Culture must be a significant part of that equation.”

    The data consequence was proliferating definitions. Ten definitions of headcount. Multiple approaches to foreign exchange transactions. When definitions multiply, you don’t have a data quality problem in the traditional sense. You have an alignment problem. And alignment problems do not get fixed by better pipelines or more sophisticated models.

    Affeld described what the fix actually requires: “We actually have to take away some of the access or freedom to create solutions. We’re trying to segregate what is business data from product innovation. We shouldn’t have 10 definitions of headcount in the organization.” Getting there requires sponsorship from both the business side and finance partners — not just a mandate from IT.

    This is a point most data modernization programs underestimate. The data governance conversation tends to focus on architecture and tooling. Google’s experience suggests that the harder work is getting the business to agree on what the data should mean, and then holding that line as teams accustomed to building independently push back.

    For AI, the stakes of this organizational work are higher than they were for analytics. A dashboard with an inconsistent definition of headcount produces a wrong number that a human might catch. An AI agent making workforce decisions based on ten competing definitions produces confidently wrong autonomous actions that are harder to detect and harder to reverse.

    What All Three Companies Have in Common

    Ericsson, Vodafone, and Google came to the AI-readiness problem from different starting points. Ericsson’s challenge was architectural: context locked inside systems, no reusable data products, a foundation built for the past. Vodafone’s challenge was semantic. It had data available everywhere, meaning consistent nowhere. Google’s challenge was cultural: an organization that rewarded independence, producing data that couldn’t be shared with confidence.

    But all three arrived at the same conclusion: fixing the data problem and establishing business context are not sequential steps. They are parallel workstreams.

    You cannot finish cleaning and governing your data and then turn to context. By the time the data is clean, AI deployments are already in motion. Context has to be built into the architecture from the beginning, into how data products are defined, how semantics are governed, and how meaning is preserved as data moves across systems.

    That is a meaningful shift from how most enterprises have approached data modernization. The question is no longer only “how do we make our data more accurate and accessible?” It is “how do we make sure our data carries the business understanding AI needs to act on our behalf?”

    Where to Start

    If you are in the middle of a data modernization program or about to start one, three questions are worth adding to the agenda.

    Are you addressing both problems at once? Data quality and business context are separate challenges that require parallel effort. A data modernization program that focuses only on cleanliness and availability will produce better-quality inputs for AI, but it still lacks the judgment to use them well.

    Where is business meaning living today, and can it travel? In most organizations, meaning is embedded in individual applications. It doesn’t survive when data moves. Identify the definitions, policies, and semantic rules that matter most for AI decision-making and decide how they will be captured and carried consistently. Some firms are calling this a context library.

    Is this a leadership issue or an IT issue? Google’s experience suggests it has to be both. IT can build the architecture. Business leaders have to agree on what the data means and defend those definitions against teams accustomed to building their own. That requires sponsorship, not just tooling.

    More than a decade ago, in my book Right-Time Experiences, I wrote about the importance of context in creating experiences that are adaptive, predictive, and prescriptive. It wasn’t a new concept, but mobility was the catalyst to drive that change. Companies made significant progress, especially as we moved into the early days of machine learning. But very few organizations today can say business context flows coherently across the systems that run their operations.

    Agentic AI has made that gap consequential in a new way. When a human reads a dashboard with missing context, they can compensate for it. When an AI agent acts on data without context, the error compounds automatically, at scale, without a flag.

    Fix the data. Build the context. Do both at the same time.

    Subscribe to my AI Decoded Newsletter here and share with a friend. You can also find the AI with Maribel Lopez podcast on your channel of choice by clicking here.

  • Four Enterprise AI Spending Pitfalls and How to Avoid Them

    Four Enterprise AI Spending Pitfalls and How to Avoid Them

    By Maribel Lopez, Lopez Research

    A few practical thoughts on where AI spending goes wrong — and what separates the organizations getting it right. Most organizations aren’t failing at AI because the technology doesn’t work. They’re failing because of decisions made before a single model was deployed. Decisions such as how to scope and fund an initiative, what success was supposed to look like, and whether anyone was measuring whether they got there. I recently joined Tom McHale, CFO and VP of Business Operations at SunStream Business Services and Apptio, an IBM company, for a webinar conversation about where spend management goes wrong.   McHale shared how he has navigated technology trade-off decisions as a CFO for years. Our observations converge on the same patterns. Here are the four pitfalls McHale and I spoke about during the session — and what organizations can do about them. 

    Pitfall 1: The Board Issues an AI Mandate Without Funding the Foundation

    Seventy-two percent of companies Lopez Research surveyed had received a directive from their board or senior management to implement AI last year. Most of those mandates arrived without acknowledging the trade-offs required to fulfill them. The pressure is real, and organizations that don’t leverage AI within their apps and services will fall behind. The problem is fixating on the technology without resourcing the operational requirements underneath it. To move into AI effectively, you need data quality, governance, and a clear plan for budgeting for ongoing costs. Boards often ask for AI outcomes without understanding the foundational work it takes to deliver them. There is also a funding gap that sneaks up on organizations. Many companies attempted to fund AI by reallocating from existing cloud or operations budgets. That worked at the margins. It does not work at scale. Internal capital reallocation as the primary AI funding source jumped from 50% to 67% in a single year in Apptio’s 2026 Technology Investment Management Report. At some point, there is not enough money in the couch cushions to do what is being asked. McHale also shared that most management teams expect first-class technology at bargain-basement prices. He brought up the reality many organizations face when he shared an example: you can’t always make trade-offs between technologies, such as funding a batch scheduler in a mainframe environment or investing in AI. You need both. What to do: Before responding to an AI mandate, attempt to map the real cost. That means data preparation, governance infrastructure, security review, and ongoing model costs — not just tool licenses.  Bring that full picture to leadership. The conversation about tradeoffs is easier to have before you start spending than after you have run out of budget. 

    Pitfall 2: Failing to Define Problems and Measurable Outcomes

    In the early days of AI adoption, running experiments made sense. Organizations needed to learn what the technology could do. That phase is over. In 2026, no one should be running an AI proof of concept without a production path and a timeline. In research Lopez Research conducted in mid-2025, 85% of companies said they were struggling to find AI ROI. When we looked at why, three causes kept surfacing. First, there was a data quality problem. Second, the use case was too vague to measure. Third, there were no metrics, monitoring, or observability in place to gauge whether the initiative was working. The fourth issue is that fewer than half of the organizations had a governance strategy, which tends to create downstream compliance and legal exposure. All these solutions are foundational solutions that require time and money. And we didn’t even discuss the cybersecurity concerns, which is always one of the top three technology spending categories. Selecting AI technology solutions before defining what you are trying to accomplish is like buying a full set of hammers, screwdrivers, and impact drivers before knowing what you are building. The tools are not the strategy. What to do: Understand what specific organizational strategic goal or KPI you’re trying to achieve before you start. “Improve customer experience” is too vague. Whereas something specific enough to measure, like reducing billing errors by 80% to improve customer satisfaction, or improving deployable software development velocity by 15%, allows you to understand the impact and the metrics, and provides a set of requirements for AI tool selection. If you cannot define success before you deploy, you are not ready to deploy. Note: I am researching the merits and detriments of an “AI use cases” versus “creating reusable AI skills/capabilities with AI agents”. See the March Newsletter on Yumm Brands for more on this. Given that I don’t yet have solid guidance on how to build and scale reusable AI skills, I maintain that you need to understand which real business problems you need to apply AI to, which helps winnow the platform selection. 

    Pitfall 3: Assuming the Budget You Can See Is the Actual Spend

    Shadow AI is this year’s shadow IT. Every technology wave produces a version of this problem. Employees find tools that help them work faster, stand them up without IT involvement, and pay for them however they can — personal credit cards, discretionary budget lines, expense reports. It adds up quickly and never shows up in the official budget. McHale shared a real example from a prior role. After conducting a full audit of actual spend at a Fortune 500 organization, the actual IT budget was double the official number. Shadow IT had been absorbing that difference for years. With AI tools accessible to anyone with a credit card and a browser, the same dynamic is accelerating. The financial risk is significant. An employee can spend $20 to $300 per month on AI tools, such as ChatGPT and Claude Code. Untracked AI spend scales fast across an organization. But the non-financial risk may be more serious. Unvetted tools accessing company data, unapproved models processing sensitive customer or employee information, and no audit trail if something goes wrong. The governance and security risks posed by shadow AI are not hypothetical. McHale put it well: defining clear objectives at the start, having someone accountable for documenting them, and treating governance as an ongoing discipline rather than a one-time checkbox is what separates organizations that can scale AI from those that cannot. Organizations that lack centralized visibility into AI spend will discover this the hard way. When it comes time to request a budget increase for next year, leadership will ask why more money is needed, given that things seemed to work fine with what was available. The answer — that it was all going on personal credit cards — is not a conversation anyone wants to have. What to do: Treat AI spend tracking as an urgent priority, not a future initiative. Establish a process for centralizing AI tool procurement now, or at least provide guardrails for AI spending. This is not about restricting what employees can use. It is about knowing what is being used, what it costs, and what data it can access. Shadow AI that stays invisible today becomes a budget and compliance problem tomorrow. 

    Pitfall 4: Confusing Operational Maturity with Technical Maturity

    This is one of the more subtle pitfalls, and it trips up organizations that are genuinely sophisticated technically. A company can have strong cloud infrastructure, capable engineering teams, and real AI experience — and still be operationally immature in managing AI investment. The gap is most evident in IT financial management. IBM Apptio’s survey data shows that 59% of ITFM professionals are confident their forecasts are highly accurate. The tools and processes many teams rely on to produce those forecasts were not designed for the pace or variability of AI spend. AI costs scale with usage in ways that are difficult to predict. They appear across every function in the organization. They change as models are updated, as usage grows, and as new capabilities are deployed. Managing that with processes built for a slower-moving environment creates real risk, even when the people running those processes are skilled and confident. Yet the potential visibility gap is where budget surprises live. What to do: Audit your financial management practices against the specific demands of AI spend. Variable usage-based costs, multi-cloud workloads, hybrid AI, and distributed AI tools across business units require practices built for that environment. The goal is not to find fault with what you have been doing. The goal is to identify where the current setup leaves gaps that AI spending will widen. 

    The Pattern Behind the Pitfalls

    These pitfalls are not independent. These pitfalls interconnect. An AI mandate without a real budget forces organizations to fund initiatives on the margins, leading to cuts in data, governance, observability, and security. Without visibility into spend, shadow AI accumulates, and real costs stay invisible. Without defined success metrics, there is no way to know whether cutting those corners mattered. The organizations that are getting AI right did not avoid these problems by being smarter. They avoided them by doing the less exciting work first: defining use cases clearly, understanding true costs before committing, building governance before it was required, and measuring outcomes from day one.While the technology changes,  the adoption challenges remain remarkably consistent. Every wave has its version of the couch cushions problem — organizations moving fast on an exciting new capability without the financial and operational discipline to sustain what they are building. Focus on the foundation first. The shiny AI tools can follow. Subscribe to my LinkedIn newsletter here. Also, you can subscribe to the AI with Maribel Lopez podcast on your channel of choice here.

     

  • The New Rules for Scaling AI: What Yum Brands Learned

    The New Rules for Scaling AI: What Yum Brands Learned

    Picking a use case, proving value, and expanding has been the standard starting point for enterprise AI. For organizations early in their AI journey, that advice still holds. But for large enterprises that are past the pilot stage and trying to scale across business units, geographies, and brands, it isn't enough.

    At NVIDIA GTC, Cameron Davies, Chief Data Officer of Yum Brands, shared how his team is thinking about AI differently — and why they had to. With 63,000 restaurant locations, 100 million daily transactions, and 1,500 franchisees across 155 countries, Yum operates at a scale where a single bad AI decision can fail loudly, repeatedly, and fast.

    In this episode, Maribel breaks down Davies' framework and what it means for how enterprise leaders should be thinking about AI in 2026 and beyond.

    —

    **What you'll learn**

    – Why the use case as a unit of AI planning has a structural limitation at enterprise scale
    – What “scalable AI skills” means and why it's different from building agents for specific use cases
    – Why governance has to come before deployment, not after — and what happens when it doesn't
    – How measurement functions as operational discipline, not just a reporting obligation
    – What Yum's AI flywheel looks like and why it only works if measurement is continuous
    – What this framework means for organizations that aren't Yum-sized

    About Cameron Davies

    Cameron Davies is the Chief Data Officer at Yum Brands, the parent company of KFC, Taco Bell, Pizza Hut, and The Habit Burger Grill. He leads the company's corporate data and analytics strategy and oversees the development and adoption of advanced data capabilities. He previously spent seven years as SVP at NBCUniversal and over 18 years at The Walt Disney Company, where he led the Corporate Center of Excellence for AI and machine learning.

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    **Resources and references mentioned**

    -NVIDIA GTC session: “Scaling AI Agents Globally Across Brands, Use Cases, and Restaurants” (S81755) — Cameron Davies, Yum Brands
    – Responsible AI Institute — chaired by Manoj Saxena
    – Trustwise — AI trust startup founded by Manoj Saxena
    – Byte — Yum Brands' proprietary e-commerce, point-of-sale, and menu platform
    – Lopez Research blog: The Rules for Scaling AI Have Changed. Yum Brands Proved It. — [LINK]

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    📢 STAY CONNECTED

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    —

  • Beyond Models and GPUs: Why Enterprise AI Libraries Matter

    Beyond Models and GPUs: Why Enterprise AI Libraries Matter

    By Maribel Lopez

    NVIDIA GTC 2026 Part 2. Everyone is talking about AI hardware and AI models, but companies need more than that to make AI deployable in the enterprise. Let’s talk about enterprise AI libraries.

     Enterprise AI spending has grown eightfold over the past three years. The ambition is real. So is the frustration.

    Ask any CIO who has tried to move a pilot to production, and you’ll hear the same story: the technology worked well enough, but everything around it — the data, the skills, the integration work — took far longer and cost far more than expected. The gap between “we want to use AI” and “we have AI delivering measurable ROI” is where most initiatives stall.

    NVIDIA GTC this week was full of announcements — a new LPU architecture, next-generation inference servers, new models. The hardware gets the headlines. But tucked inside the announcements from both NVIDIA and its infrastructure partners was something that matters as much to enterprise buyers right now as the models: the emergence of AI libraries and validated blueprints as a serious category.

    A briefing with Lenovo on its AI factory and AI libraries brought this to light during the GTC announcements. AI libraries aren’t a new concept, but the category is getting more serious.  Jensen Huang made clear at GTC why that matters.

    The Part of the AI Stack Nobody Talks About Enough

    When most enterprise buyers think about AI investment, they think about two things: hardware and models. Which GPU? Which LLM? These are legitimate questions, but they’re not the only questions that determine whether an AI deployment succeeds.

    At GTC, Jensen Huang made the point directly. Speaking about NVIDIA’s CUDA X libraries, which are domain-specific algorithm libraries that sit between the hardware and the application.  He said:

    “The libraries are the crown jewels of our company. It is what makes it possible for that platform, the computing platform, to be activated in service of solving a problem.”

    He’s describing NVIDIA’s own libraries such as cuDNN, cuOpt (real-time logistics and routing optimization), Parabricks (Genomics Analysis), and many more.  Each one purpose-built for a specific problem domain. But the principle extends beyond NVIDIA. The layer between the hardware and the business outcome is where AI either becomes useful or becomes expensive shelfware.

    Enterprise-grade AI libraries serve the same function at a different level of the stack. They translate validated infrastructure configurations into deployable patterns for specific business problems, such as robotic inspection, customer service agents, and supply chain optimization. They don’t entirely replace data science expertise. They compress the amount of it you need to get started.

    This is an underserved part of the conversation. Infrastructure vendors are competing aggressively on compute specs. Model providers are competing on benchmarks. AI libraries that offer the connective tissue between hardware capability and business outcome — get far less attention than they deserve.

    Why AI Libraries Are Important

    Every analyst (including myself) talks about data as the bottleneck for AI. That’s the first hurdle, but it’s not the only one.

    Most enterprises don’t have enough data scientists. The companies that can hire and retain data scientists at scale are a small fraction of the market. For most organizations, standing up even a well-scoped AI use case requires expertise they don’t have in-house such as the expertise to evaluate which models fit the problem, which infrastructure to run them on, and how to configure the full stack from data ingestion to output.

    A curated, validated AI library addresses this directly. It isn’t a data solution. It’s an expertise solution.

    An AI factory offers the infrastructure layer these libraries sit on top of. It provides the compute, orchestration, and resources to run those use cases. The library tells you what to build and how to configure it. Together, they significantly compress the starting problem.

    Lenovo offers its own AI factory solutions. During a briefing with analysts, Dipak Prasad , who leads hybrid cloud and AI solutions at Lenovo, described the AI Library’s purpose this way: “The AI library is a curated collection of use cases and outcomes that are designed to help customers accelerate their enterprise AI journey. It’s something meant to give them a clear and practical starting point with proven patterns for success.”

    Flynn Maloy , Lenovo’s Chief Marketing Officer for its Infrastructure Solutions Group, framed the buyer need plainly: “They don’t just want to buy the parts. They want to see validated designs. They want to see solutions and outcomes.”

    That’s a real need across the market. Infrastructure vendors building in this direction are responding to the same signal.

    What You Still Need

    To be clear, AI libraries and validated blueprints are a faster on-ramp, not necessarily a complete solution. Three prerequisites remain that no vendor can hand you:

    • Clean, accessible data. Data readiness is still on you. AI outcomes are only as good as what you feed them. Validated blueprints assume data is available and reasonably structured. If your customer data lives in five different systems with inconsistent schemas, that integration work comes first. We’re starting to see some AI factorie address data prepareness, but it’s not universal. A blueprint will help structure your approach, but won’t fix the underlying problem.

    • A governance framework. Roughly half of organizations still don’t have a formal AI governance policy. Deploying production AI — especially in customer-facing or operational contexts — without one creates legal, compliance, and reputational risk that a blueprint can’t manage.

    • Integration planning. Every blueprint connects to your existing systems at some point. The scope of that integration — to your CRM, your ERP, your identity stack — determines actual deployment cost and timeline. It’s rarely trivial.

    Service partnerships help. For example, Lenovo’s expanded collaboration with IBM Technology Lifecycle Services, which will support the deployment and ongoing management of hybrid AI infrastructure in regulated industries. It’s an example of what filling the services gap looks like. But a services partnership expands your support options; it doesn’t substitute for your own operational readiness.

    Early, But Real Outcomes Exist

    One fair critique of AI libraries and validated blueprints at this stage: the evidence base is thin. Most enterprise AI deployments are less than a few years old. Production-grade results with published ROI are still the exception.

    Lenovo references internal deployments — what they call “Lenovo powers Lenovo,” built first to run their own 36 factories and FIFA as an external example of the knowledge super-agent.

    But ‘early’ doesn’t mean there are no results. If we look at AI deployments across industries over the past several years, we see documented real returns. American Express focused on 70 high-impact use cases and saw a 10% increase in developer productivity and a 40% reduction in IT escalations. Cisco applied AI to network management, reducing the time spent on renewals from 40% to under 5%. Walmart used AI to cut inventory waste by 30%. These outcomes came from the same discipline that AI libraries are designed to replicate: a specific problem, a defined scope, foundations built before deployment, and measurement from day one.

    The AI library concept is a mechanism for replicating that pattern without requiring every enterprise to discover it through trial and error. Expect the evidence base to strengthen over the next 18 to 24 months. Companies that start now with well-scoped use cases will be the ones producing it.

    You Have Choices. Start With Your Current Infrastructure Partner.

    Lenovo is not the only infrastructure vendor building in this direction. Dell, HPE, and the major hyperscalers are all developing versions of AI factories with validated software layers and ecosystem programs.

    Dell Technologies‘ AI Factory with NVIDIA wraps validated reference architectures, partner software, and services around comparable infrastructure breadth. Hewlett Packard Enterprise‘s Private Cloud AI combines GreenLake infrastructure with NVIDIA AI Enterprise software. IBM has its own watsonx AI platform and infrastructure stack. The hyperscalers — Amazon Web Services (AWS) , Microsoft Azure, Google Cloud — offer industry-specific AI solution catalogs, primarily cloud-native rather than hybrid.

    The market is moving quickly. Enterprises will have genuine choices, and those choices will vary by infrastructure philosophy, existing vendor relationships, and the specific use cases being targeted.

    The practical starting point: begin your evaluation with whichever vendor already has the most significant footprint in your data center. Not because that vendor necessarily has the best AI library and factory offering because it  may or may not. If your existing vendor has a solid, but perhaps not the best offering, you will have a baseline for your evaluation. With this baseline, you can decide whether the difference in product offerings is worth the pain of switching.

    Three questions to ask any vendor with an AI factory or library offering:

    • Show me a deployed customer in my industry. Not a reference architecture. Not a proof of concept. A production deployment with measurable outcomes and a customer willing to discuss it. If they can’t provide one, treat the offering as early-stage.

    • What do I need to have in place before this works? Push for specifics on data readiness, integration requirements, and governance prerequisites. Vague answers signal the vendor hasn’t worked through enough real deployments to know what breaks.

    • What does the full engagement cost? Blueprints reduce the expertise required to start, but they don’t eliminate service costs. Get the total cost — hardware, software, implementation, ongoing management, etc.

    The use cases are real. The tools are rapidly evolving. Seek out solutions that minimize the execution variable.

     

    Subscribe to the AI with Maribel Lopez podcast 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

  • 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 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.