Category: enterprise AI

  • The New AI Math: Time-to-Token and Cost-per-Token Gets Highlighted at Dell Technologies World

    The New AI Math: Time-to-Token and Cost-per-Token Gets Highlighted at Dell Technologies World

    At Dell Technologies World this morning, Michael Dell introduced new metrics for measuring whether enterprise AI infrastructure is actually delivering. The AI infrastructure conversation has been dominated by GPU counts, cloud-versus-on-premises debates, and model benchmarks. Dell’s opening keynote added two measures that tie those inputs to outcomes. The first is  how quickly your infrastructure generates tokens, and the second is at what cost.

    Uptime, GPU capacity, and model advances are all foundational. GPUs are what get you to tokens in the first place. But they are not enough on their own to make infrastructure decisions. Organizations are now facing a more challenging optimization problem. Companies face a seemingly endless demand for AI compute and the energy required to support it. Businesses must also balance the performance of these evolving AI workloads within budgetary and location constraints.

     Time-to-token and cost per token are the metrics that tie these competing priorities together. They measure how quickly and how cheaply your infrastructure converts data and compute into intelligence that agents and models can act on. Jensen Huang reinforced this on stage alongside Dell, and OpenAI’s Greg Brockman made the same point independently on X today: “tokens are rapidly becoming the universal input for solving problems.” When the infrastructure providers and the model providers converge on the same metrics within weeks of each other, that is a directional signal worth acting on.

    What Michael Dell described was a two-year refinement of the Dell AI Factory with NVIDIA, informed by 5,000 enterprise customers now running production AI workloads on it. The announcements were substantial. What they mean for enterprise buyers is worth unpacking.

    The Data Bottleneck Is the Real Constraint

    Michael Dell said something on stage that every CIO needs to hear: “If your data is siloed, your agents are blind.” That is a concise description of why so many enterprise AI programs stall after the pilot. It also echoes what Irfan Khan and Muhammed Alam described as the need for business context at SAP’s Sapphire and in an online event about business data. 

    Most organizations are simultaneously trying to prepare data for AI and reengineer the data infrastructure needed to support it. Those are two hard problems happening at the same time, on top of each other. Dell’s announcements around the Dell AI Data Platform addressed both directly.

    One of the things that has changed is the data orchestration engine, the intelligent control center within the Dell AI Data Platform that turns raw, fragmented enterprise data into production-ready AI fuel. It indexes billions of unstructured files of all types, builds governed data pipelines, connects them to the models and agents that need them, and delivers structured outputs at speeds that make agentic workflows viable. Dell claims the platform now delivers 12 times faster vector indexing, 6 times faster data querying, and 19 times faster time to first token than prior generations. While the claims still need to be verified, the direction is exactly what enterprise buyers need.

    Why does this matter? AI agents need business context to be useful. An agent that can reason brilliantly but cannot reliably access your CRM, internal knowledge bases, operational systems, or proprietary data is not doing useful work. The data orchestration engine is what connects the model to the context. Without it, you have a powerful system with nothing meaningful to act on.

    Dell’s approach integrates orchestration, search, and governed pipelines natively into the platform. Getting that platform connected to your actual data sources still requires integration work. Budget for it before you buy the hardware.

    AI Infrastructure Is a Team Sport

    The broader lesson from the Dell Technologies keynote is not about any specific product. It is about what has changed in two years.

    When Dell announced the Dell AI Factory with NVIDIA in 2024, it was largely a hardware and partnership story. Today, with 5,000 enterprise customers running production workloads on it, the conversation has shifted to execution. How do you get from pilot to production? How do you keep agents from being blind to your actual business data? How do you manage cost curves as token consumption scales? How do you maintain security and governance when agents are operating autonomously at machine speed?

    Part of Dell’s answer is its ecosystem. The new Dell AI Ecosystem Program gives AI software providers a validated path to certify solutions on Dell infrastructure. For enterprise buyers, this speeds AI deployments by reducing integration some of the integration burden. Rather than assembling a custom stack from scratch, you get pre-validated blueprints that automate the deployment of software, services, and models together. That automation is a direct lever for reducing time-to-token at the program level. Dell claims it can deliver hundreds of AI racks a week to a given customer and have them generating outcomes within hours. Part of this is also achieved with ecosystem partner blueprints for automation. 

    The ecosystem also extends to the agent layer itself. Jensen Huang described on stage how agents do not run directly on the large language model. They run on a harness. The harness sits in a secure, governed container called a sandbox. It manages the agent’s reasoning loop, handles tool use, controls what data and systems the agent can access, and determines when to call the larger model and when to use a smaller local model instead. NVIDIA’s OpenShell is the open-source sandbox now supported across the entire Dell AI Factory. For enterprise buyers evaluating AI infrastructure, the harness is not a detail. It is a primary evaluation criterion. An infrastructure stack that does not clearly define how agent harnesses are deployed, secured, and governed is not production-ready.

    The ecosystem partners Dell named today include Google, Hugging Face, OpenAI, Palantir, ServiceNow, and SpaceXAI, among others. AI is not a solo deployment. The strength of the ecosystem around the infrastructure determines how fast you can actually move.

    Michael Dell put the security dimension plainly: “You can’t protect what you can’t see, and you can’t manage what you can’t see.” That applies to agents as much as it applies to data. Agents have credentials, memory, and access to systems. When they operate autonomously at machine speed, the blast radius of a security failure is no longer contained to one system. It can propagate across workflows and infrastructure. There are also tech tools such as X that help with confidential computing. 

    The Token Economics of Hybrid AI

    Sixty-seven percent of AI workloads already run outside the public cloud, on-premises, at the edge, or in co-location environments, according to Dell’s own survey data. Eighty-eight percent of organizations are running at least one AI workload on-premises. But, that does not mean cloud is going away. It means the real question for enterprise infrastructure leaders is not cloud versus on-premises. It is how to run both well.

    Hybrid AI is not a compromise. It is the architectural reality for most large enterprises. Some workloads need the cloud, which offers speed, training, high capacity, and flexibility. Others belong on-premises because it may access sensitive data that a company doesn’t want in the cloud or regulations require to be in a certain place. There may also be high-volume, continuous inference, where unpredictable cloud token costs create real budget exposure. The strategic challenge is matching the workload to the right environment and doing it consistently at scale.

    Jensen Huang described on stage why the compute requirements have shifted so dramatically. Agentic systems require 100x to 1,000x more computation than responding to a simple query because the agent has to reason, plan, use tools, evaluate results, and iterate. At that scale of consumption, every infrastructure decision has a direct cost consequence.

    Dell’s answer for high-volume on-premises workloads is what it calls “unmetered intelligence.” The idea is that owning infrastructure converts variable cloud API spend into a fixed infrastructure cost. Dell claims organizations can break even on API costs compared to the public cloud in as little as 3 months with desk-side agentic AI configurations.

    Balancing this correctly requires thinking about four variables simultaneously. Latency measures the time it takes for a system to process a request and return a response. Performance refers to the overall capability, accuracy, and capacity of the AI model to handle complex tasks. Cost is what you pay to get those outputs at the required latency and performance level. Energy is the fourth variable, and it is no longer theoretical. A single rack of NVIDIA Rubin GPUs can draw over 130 kilowatts.

    Granted, the average enterprise won’t be running a rack of Vera Rubin’s, but energy availability is becoming a real constraint regardless of sustainability goals. And if you’re using the cloud, you pay one way or the other for that energy. The right infrastructure solution varies by workload type. The time to token and the cost per token let you compare options on the same terms.

    Sovereign AI Is Becoming a Procurement Reality

    Two years ago, sovereign AI was a concept mostly discussed in European regulatory contexts and by a small number of governments building national AI infrastructure. Today, it shows up in enterprise procurement conversations across regulated and unregulated industries.

    Sovereign AI means the ability to independently develop, deploy, and govern AI systems entirely within an organization’s strategic, legal, and jurisdictional boundaries. For enterprises, this means your data does not leave your environment, your model choices are not constrained by a hyperscaler’s catalog, and your AI outputs are not subject to external policy changes.

    The ecosystem Dell announced is designed to both speed AI deployments and address sovereign AI requirements. Google’s Gemini 3 Flash models running on-premises via Google Distributed Cloud on Dell PowerEdge servers. OpenAI’s Codex is connected to the Dell AI Data Platform for agentic workflows on enterprise data. Palantir’s Foundry and AIP platform is deployed on-premises with Dell ObjectScale and PowerFlex as the data layer. SpaceXAI’s Grok is available in on-premises or hybrid enterprise deployments. Reflection’s open-source frontier models for regulated industries and sovereign entities.

    The pattern is consistent: bring the model to the data rather than the data to the model. For organizations in healthcare, financial services, defense, and government, this is not a preference. It is often a compliance requirement.

    Planning for Hybrid AI

    Today’s AI question is how to architect a hybrid AI solution that aligns with our organization’s specific workloads, data environment, cost constraints, and governance requirements. Some of that runs on-premises. Some runs in the cloud. The mix differs across organizations and will shift as workloads evolve and model costs change.

    The questions worth asking now: What is your time to first token across your most important workloads? What is your cost per token at scale? Does your data orchestration layer connect your proprietary data to the models that need it? How are your agent harnesses deployed and governed? And do you have the security architecture in place before your agents start making autonomous decisions?

    It’s not easy but nothing worthwhile ever is. 

     

  • Why Enterprises Need an AI Operating Model | IBM Think 2026

    Why Enterprises Need an AI Operating Model | IBM Think 2026

    Last year produced several sobering research findings on AI’s business value. The McKinsey “State of AI in 2025: Agents, innovation, and transformation” survey found that only 6 percent of organizations are seeing a significant financial impact from AI. Deloitte’s research puts typical payback timelines at two to four years. While the exact numbers vary by study and sector, the pattern is consistent: most enterprises are managing something genuinely difficult. AI is a technology that changes every few months, costs more than projected, and carries real governance and security risks that regulators, boards, and risk teams take seriously. Eighty-eight percent of organizations now use AI in at least one business function. The McKinsey study found that approximately one-third have begun to scale it across the enterprise. No matter what research report you review, including those from Lopez Research, you’ll find the number is less than 50%. The gap between “using AI” and “running the business on AI” is real, and it exists for legitimate reasons — fragmented data, evolving tools, integration complexity, and compliance requirements that don’t pause while the technology moves fast. IBM’s Think 2026 announcements are aimed squarely at that gap. The company is calling its approach an AI Operating Model, comprising four connected systems: agents, data, automation, and hybrid infrastructure. Arvind Krishna, IBM’s Chairman and CEO, framed the urgency directly in the Think 2026 keynote: “The enterprises pulling ahead are not deploying more AI — they’re redesigning how their business operates. Running AI in the enterprise requires a new operating model, and IBM is enabling organizations to manage AI-driven systems with the same rigor, governance, and scale as their most critical infrastructure.” IBM argues that these problems are connected, and solving them one at a time, such as buying a point solution here, running a pilot there, is why many AI programs stall. Yet, companies have told Lopez Research they can’t wait for a complete AI strategy before acting. That tension is real, and IBM is trying to help enterprises navigate it.Here’s what it means for organizations in that position. 

    The Real Reason AI Projects Stay in Pilot Mode

    The standard explanation for slow AI scaling is that organizations aren’t moving fast enough. That framing isn’t fair.Deloitte’s 2025 AI survey of 1,854 executives found that 85 percent of organizations increased AI investment over the past year, and 91 percent plan to increase it again. These are not organizations standing still. They are organizations that have invested heavily and are still waiting for returns that take longer than expected. Deloitte found that most executives expect significant AI payback to take 2 to 4 years — far longer than the 7- to 12-month payback period they expect from traditional technology investments. The reasons are structural, not motivational. Lopez Research discussions found that: 

    • Data hygiene issues persist. Most enterprise data is siloed, static, and inconsistent. AI systems — especially agentic ones that take actions on your behalf — need real-time, reliable data to make decisions you can trust. Lopez Research’s 2025 Enterprise AI Benchmark found that 85 percent of companies were struggling to find AI ROI, with data quality problems the most consistently cited cause. McKinsey’s research reaches the same conclusion: fragmented data and legacy architecture are the most persistent blockers to AI scaling, across industries and company sizes.
    • Governance that hasn’t caught up. Deloitte’s 2026 State of AI report found that only one in five companies has a mature model for governing autonomous AI agents. The Cloud Security Alliance (CSA) puts a finer point on why that matters. In its December 2025 CSA study, only 1 quarter of organizations reported having comprehensive AI security governance in place. Companies with governance policies were twice as confident in their ability to protect AI systems.  Governance isn’t just a compliance exercise. It’s the factor most strongly correlated with successful AI adoption.
    • Security exposure is growing faster than most organizations realize. The Cloud Security Alliance’s April 2026 survey of 418 IT and security professionals found that 82 percent of organizations have unknown AI agents running in their IT infrastructure — agents deployed by employees or teams without central visibility. Sixty-five percent have experienced an AI agent-related security incident in the past twelve months, with consequences including data exposure (61%), operational disruption (43%), and financial losses (35%). This isn’t a theoretical risk. It’s happening now, in organizations that believe they have reasonable visibility into their AI deployments.
    • Integration debt continues to escalate. McKinsey projects that IT infrastructure costs will increase by two to three times by 2030 as AI workloads expand, while budgets remain flat. Organizations are being asked to adopt new tools on top of existing tools that were already expensive to operate and difficult to connect.

    These are structural constraints, not capability gaps. IBM’s AI Operating Model is designed around them. 

    What IBM Announced and What It Solves

    IBM defines the AI Operating Model as the system by which enterprises move from fragmented AI experimentation to AI that runs the business. Where most technology vendors frame AI adoption as a capability question — which models, which tools, which platforms — IBM frames it as an operational question: how do you connect intelligence, action, operations, and trust into a system that functions at enterprise scale? The four pillars are designed to be sequential. Intelligence comes first, because AI amplifies whatever data foundation it sits on — fragmented data produces fragmented decisions, faster. Actions follow, because insight without the ability to act is observation, not transformation. Operations addresses the scale problem: individual AI actions become valuable only when they can run across thousands of systems and decisions simultaneously. Trust closes the loop, allowing an action to be audited and explained. Rob Thomas, IBM’s Senior Vice President, Software and Chief Commercial Officer, frames the progression not as a linear journey but as a zigzag — organizations will move two steps forward and one step back — with the meaningful milestone being the transition from isolated pilots, to connected projects, to a program where AI is embedded in daily operations. IBM is addressing multiple specific problems that enterprise buyers have told it matter most. 

    Giving developers an AI partner that understands enterprise constraints, not just code.

    IBM Bob, now generally available, is an agentic development partner designed to work across the full software development lifecycle — from requirements gathering and architecture planning through code generation, testing, security scanning, and documentation. Bob is not only a code autocomplete tool. Coding assistants can respond to individual prompts, but IBM’s Bob also functions as a persistent team member that reads your organization’s standards, follows your approval gates, and meets the same compliance requirements as any other developer on the team.For organizations running core workloads on mainframes, IBM Bob Premium Package for Z extends these capabilities to IBM Z environments—a detail worth noting for financial services, insurance, and government agencies, where Z infrastructure handles the most sensitive transaction processing. IBM reports that 80,000 of its developers are using Bob, with an average productivity improvement of 45 percent. Those are IBM’s own numbers, but the Dublin development team featured in the keynote reported independently: a 70 percent reduction in onboarding time, 40 percent faster feature implementation, and sprint forecast variance tightening from 35 percent to 15 percent.

    Managing agents you didn’t all build yourself. — and the ones you didn’t know you had.

    IBM watsonx Orchestrate is IBM’s answer to a problem every scaling enterprise will face. AI agents are proliferating faster than the ability to govern them. In most large organizations, agents are already being created across different teams, tools, and frameworks — some built in-house, some embedded in vendor applications, some procured as point solutions. What has been missing is a way to manage that reality without forcing teams to rebuild what they already have. Orchestrate addresses this through an agentic control plane that brings agents into a single operational layer regardless of how they were built or where they run. Today, that includes IBM native agents, LangGraph agents, Langflow agents, and agents built on the open A2A protocol, with broader interoperability planned. Beyond connectivity, Orchestrate adds observability and tracing across agent interactions, build-time, and runtime evaluation. It adds a governed catalog, which is a centralized registry of agents and tools with performance metrics, certification workflows, and lifecycle management. The catalog allows organizations to see which agents exist and assess their trustworthiness. The positioning is deliberate. IBM is not leading with agent-building tooling. The argument is that enterprises don’t need another way to build agents — they need a way to operationalize the ones they’ve already built. As organizations scale from a handful of AI agents to dozens, hundreds, or thousands, built by different teams on different platforms, the governance problem grows faster than the deployment problem. Orchestrate is designed to enforce policy, log actions, and provide accountability across agents regardless of which vendor produced them. This matters because the agent governance gap is already visible in breach data, not just survey responses. Having a place to see what agents are doing, what they’re accessing, and whether they’re operating within sanctioned boundaries is a reasonable requirement before deploying them in consequential workflows. The 82 percent unknown-agent figure from CSA is the risk case for why this capability exists.

    Giving AI access to data that reflects what’s happening now.

    IBM acquired Confluent and is integrating the company’s Kafka streaming and Flink processing capabilities into watsonx.data. The practical effect: AI systems can act on data that’s current, not data that was accurate several hours ago.Most enterprise AI runs on batch data with snapshots taken at intervals and loaded into a system that the AI then queries. For low-stakes use cases, that’s fine. For AI agents making supply chain decisions, resolving customer service issues, or assessing fraud, the gap between what the data says and what’s actually happening in real time poses real risk. In the keynote, Krishna described the logic directly: “Confluent, which is the data streaming platform used by over 6,500 enterprise clients and 40% of the Fortune 500, brings real-time streaming data into our data foundation. After all, AI agents are only going to be as good as the data they can access.” The Confluent acquisition is recent, so buyers should validate the depth of integration in production scenarios before committing.

    Fixing security vulnerabilities, not just finding them.

    Concert Secure Coder addresses a gap that every enterprise security team lives with. Scanners surface vulnerabilities, and developers don’t always fix them. It’s not because developers don’t care. Developers struggle because fixing a dependency that’s been in production for years requires understanding what else will break. The blast radius of a patch is often unknown, which means the patch doesn’t happen. Concert provides visibility into the dependency chain first, then uses an AI agent to execute the fix, verify the result, and create the pull request. Dinesh Nirmal, SVP of IBM Software, described the Sovereign Core design philosophy in terms that apply equally here: “AI has made sovereignty a runtime requirement, not a policy statement.” The same shift applies to security — from point-in-time scanning to continuous, embedded remediation.

    Giving regulated enterprises a sovereignty foundation they actually control.

    IBM Sovereign Core is a full-stack deployment foundation designed for organizations where data residency, model governance, and operational control are non-negotiable — particularly in Europe, where the EU Sovereignty Framework issued in October 2025 introduced a measurable eight-criteria standard for evaluating sovereignty claims. The distinction from conventional approaches is meaningful: most vendor-sovereignty offerings stop at contracts, data-residency commitments, and policy documentation. Sovereign Core goes further — the control plane, secrets, keys, identity, and access all stay within the customer’s environment boundary, and the customer decides what runs inside it. It ships with 160 compliance frameworks out of the box, continuously monitored and automatically verified, with proof generated at the ready rather than reconstructed at audit time. It is also explicitly architected for replaceability. A customer can retain a credible exit strategy rather than trading one form of dependency for another. For organizations in heavily regulated industries — financial services, healthcare, government — Sovereign Core is positioned as the answer to whether a company can adopt AI at scale without surrendering operational control. But the harder problem is the accountability gap. Governance frameworks from NIST AI RMF and ISO 42001 require explicit accountability lines for AI decisions — specific people with documented oversight responsibilities. In practice, security teams assume compliance owns model monitoring. Compliance assumes security owns it. When an agent makes a consequential error, the question “who approved that decision?” often generates no clear answer. 

    What’s Still Hard

    IBM’s portfolio addresses structural problems, but structural problems don’t resolve quickly. For example: 

    1. Observability consolidation takes time. Concert’s value proposition — currently in public preview — depends on bringing signals from applications, infrastructure, network, and cost into a single view. Most enterprises already have Datadog, Dynatrace, Splunk, or a combination of these tools handling parts of that job. IBM’s positioning is that Concert doesn’t require replacing those tools. That’s the right framing, but integration depth varies, and the work required to connect existing tooling into a coherent view should be factored into any realistic timeline.
    2. AI infrastructure costs are continuing to rise. Deloitte found that AI is now the fastest-growing line item in corporate technology budgets, with cloud costs rising roughly 19 percent in 2025 for many enterprises. IBM’s GPU-accelerated Presto capability, co-developed with NVIDIA and currently in private preview, aims to reduce data processing costs. IBM cites 83 percent cost savings and a 30x price-performance improvement from a proof-of-concept with Nestlé spanning 186 countries. That reference is credible, but a proof-of-concept in one environment is not a production benchmark. Test against your own data before treating it as a baseline expectation. General availability is targeted for later in 2026.
    3. The governance gap isn’t policy — it’s proof. Many, but not all, enterprises have AI governance strategies. For those with governance in place, many are still missing the audit trail that demonstrates it’s working. The EU AI Act comes into full effect on August 2, 2026. High-risk AI systems, such as those that touch employment decisions, essential services, or critical infrastructure, must have conformity assessments completed, human oversight mechanisms operational, and technical documentation ready for regulatory inspection.

    What the Customer Evidence Actually Shows

    IBM’s strongest proof point is internal. Krishna cited $4.5 billion in annualized productivity gains from applying AI and automation across IBM’s own operations — a reported figure in financial filings, not a projection. Eighty thousand IBM developers use Bob, achieving an average productivity improvement of 45 percent. External customer evidence is early but directional. Aramco, whose partnership with IBM dates to 1947, described moving AI from experiments to field-scale operations across upstream, refining, and corporate functions — generating more than $5.2 billion in value from AI executions, with over 50 percent of that coming from production deployments rather than pilots. Elevance Health described investing approximately $1 billion in AI to simplify the healthcare experience for members, providers, and internal staff — using 500 data points to match members with appropriate providers and deploying AI to help members understand their benefits through a virtual assistant. These are meaningful reference points. They also reflect organizations with substantial data infrastructure, technical teams, and leadership commitment already in place. Neither story starts from zero.

    One Thing to Remember

    In the Think 2026 keynote, Krishna drew a historical parallel worth taking seriously. He traced the arc from computing in the 1960s through the internet era of the 1990s and 2000s. In each case, the organizations that redesigned their business model around the new technology pulled decisively ahead of those that used it only at the margins. His assessment of where most enterprises stand today: “Most enterprises run AI at the margin. The core end-to-end processes — how an enterprise makes money, makes decisions — are largely untouched.” That observation is accurate, and the data support it. The organizations making progress with AI are not the ones with the most tools. They are the ones who have connected the right data to the right processes with clear accountability for what happens when AI gets it wrong. IBM is not the only company providing these types of AI strategies. Different vendors have one or more of these aspects; however, it’s good to see that IBM’s portfolio is built to address real enterprise challenges.

  • AI Success Requires Redesigning the Future of Work

    AI Success Requires Redesigning the Future of Work

    Cisco’s Systematic Approach to Redesigning Processes for the Future of Work Offers a Blueprint for Enterprise Leaders Struggling with AI ROI

    Originally posted in the March 6, 2026 “AI Decoded With Maribel Lopez” LinkedIN newsletter that discusses AI and the future of work for enterprise leaders. 

    Most enterprises are layering AI tools on top of broken processes and wondering why the ROI never materializes. Cisco took a different approach. Instead of adding more tools to existing workflows, Cisco built a methodology for redesigning work from the ground up. The results from its initial pilot suggest that approximately 60% of workflow activities can be AI-augmented. But the more important finding is what had to happen before any of that augmentation delivered value: the work itself had to be re-architected.

    This mirrors a pattern I’ve seen repeat across mobile, cloud, and now AI adoption. Organizations that treat new technology as a layer on top of existing operations get incremental gains at best. Organizations that redesign how work gets done around the technology’s capabilities capture the real value. Cisco’s approach is worth studying because it offers a replicable model for making that shift.

    See the Work Before You Redesign It

    Cisco created Atlas, an AI agent system that analyzes jobs and workflows to build an enterprise-wide map of how work actually gets done. Atlas cataloged approximately 4,000 entities and 25,000 relationships across job roles, activities, and tools. For each activity, the system identifies which AI tools can augment the work and suggests how roles may evolve.

    The most useful insight wasn’t about AI at all. Atlas revealed that roughly 75% of project management tasks are identical across IT, operations, and software development. Without that visibility, each department would redesign independently, duplicating effort and missing opportunities to reuse what works.

    This is the step most organizations skip. They deploy AI tools without understanding how work is actually performed across the enterprise. Pattern recognition across functions prevents waste and ensures that successful redesign approaches can be applied broadly. You can’t redesign what you can’t see.

    Give Leaders a Redesign Tool, Not Just an AI Tool

    Cisco paired Atlas with a digital workflow canvas that translates analytical insights into an actionable design interface. Leaders can see their current workflows, drag in available AI tools, reconfigure roles, and deploy new agents or assistants. When a leader saves a redesigned scenario, the system calculates the percentage of work now AI-augmented, estimates efficiency or growth gains, and produces an implementation plan.

    This matters because workflow redesign requires structured methodology, not ad hoc experimentation. A formal design interface forces leaders to make explicit tradeoffs between efficiency, growth, role changes, and tooling investments. Most organizations lack this discipline. They experiment with AI tools in isolation, resulting in tool sprawl rather than transformation.

    Gianpaolo Barozzi, VP and Chief Innovation and Technology Officer for Cisco’s People, Policy & Purpose Organization, put it directly: “Let’s pause and let’s not just keep layering on more AI agents or tools. Let’s actually reengineer the workflow and understand what the use cases that are in flight.”

    That’s a message every enterprise leader needs to hear. Proliferation of AI tools without process redesign creates complexity, not value.

    What Cisco’s Pilot Actually Proved

    Cisco tested the methodology with its People, Policy, and Purpose (3P) organization. The pilot identified 28 transformational use cases and found that approximately 60% of workflow activities could be AI-augmented. Cisco is now expanding the approach to Product, Engineering, and Strategy organizations.

    The deliberate balance between efficiency gains and growth opportunities is worth noting. Cisco explicitly avoided framing AI as a headcount reduction play. Instead, it positioned augmentation as enabling existing teams to expand scope or improve quality. This distinction matters for adoption. Teams that believe AI exists to eliminate their jobs will resist it. Teams that see AI as a way to do more meaningful work will embrace it.

    This is consistent with what I see across enterprise AI deployments. The organizations getting measurable ROI from AI are not the ones deploying the most tools. They are the ones redesigning processes around specific, scoped use cases and measuring outcomes against defined business KPIs.

    The Leadership and Talent Shift Most Companies Miss

    Cisco is repositioning workflow redesign as a core leadership competency, not a delegated HR or IT responsibility. Fran Katsoudas, EVP and Chief People, Policy & Purpose Officer, described the shift: “It’s a leadership and a cultural change that has to happen, not just a talent change. Leaders need to lead change, embrace AI and reinvent their work.”

    On the talent side, Cisco is revising its systems to identify AI explorers, employees demonstrating high AI adoption. AI tool usage data now informs engagement assessments, promotion prioritization, and career acceleration. AI proficiency is being treated not as a secondary skill but as a primary indicator of adaptability and future leadership potential.

    Both moves are significant. Without visible leadership commitment, workflow redesign initiatives get treated as HR programs rather than strategic transformation. And if AI proficiency is strategically important but not formally recognized in performance management, you’re asking employees to change behavior without aligning incentives. That rarely works.

    What Enterprise Leaders Should Do

    Cisco’s approach reinforces a principle that applies to every enterprise AI deployment: tools alone don’t transform work. Redesigned workflows, combined with leadership commitment and updated talent practices, do.

    Three actions to take now:

    • Map your workflows before deploying more AI tools. You can’t redesign what you don’t understand. Build visibility into how work is actually performed across functions and identify where the overlaps and redundancies live.
    • Make workflow redesign a leadership responsibility. If AI transformation is delegated to middle management or treated as an IT project, it will be seen as tactical rather than strategic. Leaders must own the redesign.
    • Align talent systems with AI priorities. If you want employees to adopt AI, recognize and reward the ones who do. Update performance management, promotion criteria, and retention strategies to reflect AI proficiency as a core competency.

    The question for most enterprises isn’t whether to adopt AI. It’s whether they’re willing to do the harder work of redesigning how their organizations operate. The technology exists. The use cases are proven. What’s missing is the organizational discipline to redesign workflows before deploying more tools. Start there.

    Also, don’t forget to subscribe to the AI with Maribel Lopez podcast on your channel of choice here and the LinkedIn newsletter 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.

     

  • 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.

     

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