Digital Transformation on Carles Abarca

Stop Crying About AI and Jobs

Wed, 15 Apr 2026 00:00:00 +0000

Stop Crying About AI and Jobs
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The public conversation about artificial intelligence has filled up with a strange mix of fascination, fear, and theatrical panic. Every week brings a new headline claiming that AI is about to destroy human work, empty offices, make entire professions obsolete, and push millions of people into irrelevance. The problem is that when you actually look at the data with even a minimum of rigor, the real story looks very different.

No, AI is not wiping out employment on a massive scale. What it is doing, and this part is genuinely profound, is transforming tasks, redesigning workflows, and changing the skills the market will demand over the coming years.

And that difference matters enormously.

The Wrong Narrative
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There is a reason the catastrophic narrative spreads so easily: it works extremely well as content. “AI is going to take your job” generates immediate attention. It activates fear, outrage, and anxiety. It is easy to consume and even easier to share.

But a good narrative does not always describe reality well.

The most serious studies emerging in 2025 and 2026 point to a fairly consistent pattern. AI is not operating primarily as a force of brute job elimination, but as a technology that reorganizes the content of work. It automates parts, accelerates others, raises the expected standard in many roles, and creates new demand in layers that either did not exist before or were still marginal.

The right question, therefore, is not whether AI will make all employment disappear. The right question is different: which parts of human work become automatable, which ones gain value, and which new capabilities become decisive?

What the Data Actually Says
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The data currently on the table does not support the caricature of total labor collapse.

An MIT study published in April 2026 points precisely in this direction: AI transforms tasks far more than it destroys entire jobs. This fits with an idea labor economists have understood for a long time: most jobs are not made up of a single task, but of combinations of activities. When a technology automates one part of the work, the role does not automatically disappear. In many cases, it gets redefined.

Other signals point in the same direction and should not be ignored.

LinkedIn has highlighted the creation of 1.3 million new jobs linked to AI. We are not talking only about data scientists or prompt engineers. We are talking about specialized recruiting, technology integration, operations, governance, cybersecurity, sales enablement, training, product, automation, and entirely new service layers.

On top of that, infrastructure expansion is creating a major pull effect. More than 600,000 new roles tied to data center infrastructure are expected, from construction and operations to energy, cooling, maintenance, networking, and specialized support. When the digital economy scales, not only do the models grow. The entire system that makes them possible grows with them.

BCG has also been clear: AI will reshape more jobs than it eliminates. In other words, the dominant effect will be redesign, not pure extinction.

That does not mean there will be no disruption. There will be, and a lot of it. What it means is that the disruption looks less like the mass disappearance of work and more like an accelerated reassignment of value.

The Blind Spot: Your Job Title May Survive, Your Skills May Not
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This is the nuance many people still do not want to face.

The biggest impact of AI will not necessarily fall on the title of your job. It will fall on the actual content of what you do every day and on the skills you need in order to remain valuable.

It is estimated that 70% of the skills associated with many roles will change before 2030. That number should be more unsettling, and more motivating, than any apocalyptic prediction about job destruction.

Because it implies something simple: your role may still exist, but you may no longer be able to do it in the same way.

An analyst will still be an analyst, but with tools that drastically compress analysis time. An executive will still be an executive, but will no longer be able to make decisions without sound judgment about automation, models, data, risk, and augmented productivity. A teacher will still be a teacher, but will need to redesign the learning experience in an environment where knowledge is abundant and judgment becomes the scarce asset. A doctor will still be a doctor, but will work in a context where AI can assist with documentation, diagnostic support, and clinical prioritization.

The continuity of a job title does not guarantee the continuity of professional value.

And that is where the real conversation begins.

What Teams Are Actually Experiencing
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Those of us who have spent years working in technology keep seeing the same thing again and again: teams that integrate AI well do not necessarily reduce headcount as an automatic reflex. What they do, above all, is increase their execution capacity.

They deliver more. They iterate faster. They test more hypotheses. They reduce friction. They reallocate time from the mechanical to the strategic.

That completely changes the competitive standard.

AI does not instantly replace the competent professional. But it does expose, with great clarity, the people who decide to stay still while the environment moves forward. Not because the machine is magical, but because a team that learns to work with AI can, in certain contexts, produce twice as much in half the time.

And when that happens, the problem stops being “AI versus humans.” The problem becomes “humans who evolve versus humans who refuse to evolve.”

The Collapse of Barriers to Creation
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There is another angle that I think matters enormously, and that too often gets buried under all the fear: AI is lowering barriers to create, build, launch, and scale.

Today, one person with judgment, initiative, and the right tools can do things that only a few years ago required much larger teams. Prototyping, analysis, writing, design, automation, research, material preparation, product launches, idea validation, and process operations are now within reach of far more people.

That does not reduce the importance of talent. It multiplies it.

And this is not theory. There are already cases that illustrate the shift extremely well. Peter Steinberger, for example, has shown just how far an entrepreneur with technical judgment and an intense layer of AI agents can operate at a speed that once seemed reserved for full teams. Another widely cited example in the recent conversation around AI-powered solopreneurs is Maor Shlomo with Base44, a project run with an extremely lean structure and supported by AI-assisted development, which scaled rapidly and became a powerful signal of what changes when the cost of building software collapses.

These are examples that help clarify the direction of change: AI is radically expanding the opportunities available to talented individuals with focus and execution capacity.

As LinkedIn’s CEO has put it, AI is lowering barriers to create and build. And that is an enormously powerful signal. Because where some people only see substitution, others are already seeing expansion of capability.

The Real Risk Is Not AI
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The real risk is not that AI exists.

The real risk is responding to this transition with denial, cynicism, or paralysis.

It is confusing comfort with safety. It is assuming the future will respect inertia. It is believing it will be enough to keep doing what you have always done while the rest of the market reconfigures its capabilities.

Resistance to change always disguises itself as prudence. But in deep technological cycles, it often is not prudence. It is delay.

And delay, when the environment accelerates, becomes expensive.

So What Should We Actually Do?
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There is no need to dramatize. There is a need to act.

At least across five fronts:

1. Learn to work with AI, not just talk about AI
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There are too many people expressing opinions about artificial intelligence without having integrated the technology into their real workflows. Useful literacy is not about being able to define an LLM. It is about knowing when to use it, for what, with which limits, and with what judgment.

2. Strengthen judgment
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When intelligence becomes abundant, judgment becomes more valuable. The ability to interpret, decide, prioritize, contextualize, and assume responsibility will matter more and more.

3. Redesign processes
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AI does not produce deep results when it is bolted on as a superficial accessory. Real impact comes when processes, roles, metrics, and forms of collaboration are redesigned.

4. Commit to continuous learning
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This is not a technology you learn once and then finish with. Competitive advantage will come from sustained adaptability.

5. Replace fear with discipline
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Anxiety does not build capability. Disciplined experimentation does.

An Uncomfortable but Useful Conclusion
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AI is not automatically going to take your job.

What can leave you behind is the decision not to evolve while everything around you changes.

That is why I believe it is time to abandon technological victimhood and start speaking more honestly about what is really happening. AI is not destroying the value of human work. It is redefining which human work creates value.

And in that transition, the future will not reward the loudest complainers. It will reward the people who learn faster, combine technology with judgment more effectively, and have the courage to redesign themselves before they are forced to do so.

The question is not whether AI is going to change work.

The question is whether you are going to change with it.

The Era of Cheap AI Is Ending

Tue, 07 Apr 2026 00:00:00 +0000

AI felt cheap. It wasn’t. It was subsidized.

A couple of years ago, if someone had told you that the world’s most advanced AI would be available for $20 a month, you would have laughed. And yet, for a while, that’s exactly what it felt like.

Flat-rate subscriptions. Increasingly capable models. Usage that, in practice, felt nearly unlimited. The prevailing perception was clear: advanced AI was becoming an abundant, accessible resource.

But that perception is starting to crack. Not because of a technical failure — because of something more fundamental: economics.

The Signs Are Already Here
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In recent weeks, several converging signals have pointed in the same direction:

Anthropic, the creator of Claude, has started actively restricting certain usage patterns through its subscriptions. In particular, it has blocked automation tools like OpenClaw from channeling requests through subscription accounts. The reason is straightforward: the inference cost of those intensive usage patterns doesn’t square with the subscription price.

This isn’t an isolated case. OpenAI has been adjusting the real usage limits of its subscription plans for months, progressively reducing the number of interactions with their most powerful models before downgrading users to a lesser model. What were once generous, fuzzy limits are becoming explicit, harder caps.

And there’s something more unsettling: even with these adjustments, most major AI operators are still not profitable. Anthropic, OpenAI, and virtually every frontier lab are burning capital at a pace that would make any traditional CFO faint.

The obvious question is: if users are already feeling restrictions, how is it possible that providers are still losing money?

The answer reveals something important about the real economic structure of this industry.

AI Was Never Cheap. It Was Subsidized.
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What we’ve experienced over the past two years was not the real cost of AI. It was an adoption strategy.

The major labs needed to do three things simultaneously:

Demonstrate capability to justify valuations in the tens (or hundreds) of billions.
Drive massive adoption to create network effects, lock-in, and usage data.
Capture developers and enterprises before the competition did.

To achieve this, they offered access to frontier models at prices that didn’t reflect the real cost of operating them. The $20/month subscriptions were, in practice, a subsidy funded by venture capital.

And it worked. Adoption soared. Millions of people began using Claude, ChatGPT, and other models daily. Companies of every size started integrating AI into their workflows.

But now we’re entering the next phase. And in this phase, the numbers have to start adding up.

Frontier Models Don’t Follow Traditional Software Economics
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There’s a widespread misunderstanding that needs to be addressed head-on: many people assume that AI follows the same economic logic as conventional software. That is: you develop it once, distribute it at near-zero marginal cost, and margins improve with scale.

But generative AI — especially frontier models — doesn’t work like that. Not at all.

Every interaction with a large model involves:

Real-time computation on very expensive hardware (state-of-the-art GPUs).
Significant energy consumption per request.
Infrastructure costs that don’t disappear with scale; in many cases, they grow with it.
Latency and availability requirements that demand reserved capacity, not just peak capacity.

And the problem is amplified by the most recent usage trends. AI agents — which execute multiple chained calls to complete complex tasks — multiply inference costs dramatically. A single agent session solving a programming problem can involve dozens of model calls, each with long contexts and active tools.

More capability doesn’t automatically mean lower unit cost. In many frontier AI cases, it means exactly the opposite.

The Flat Rate Was a Commercial Strategy, Not a Sustainable Reality
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Let’s think about what a flat-rate subscription to a frontier model actually meant in practice:

A user paid $20 a month.
They had access to a model whose inference cost, under intensive use, could easily exceed $100 or $200 per month per user.
The provider absorbed the difference, betting that the average user wouldn’t use the model that intensively.

That model works reasonably well when most users are casual: they ask a few questions a day, use the model for simple tasks, and don’t stress the infrastructure. It’s the same principle that makes gyms work: they sell more memberships than the gym can simultaneously accommodate, betting that most people won’t show up every day.

But when tools emerge that channel intensive, programmatic use through those subscriptions, the model breaks. It’s as if someone found a way to fill the entire gym 24 hours a day. The price no longer covers the cost.

That’s why providers are reacting:

More explicit usage limits per model and per period.
Automatic degradation to less expensive models when certain thresholds are reached.
Contractual restrictions against unanticipated usage patterns.
Clearer separation between consumer, API, and enterprise plans.

It’s not a whim. It’s economic survival.

The Great Paradox: More Restrictions, and Still Not Enough
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Here’s the data point that should give pause to any business leader betting heavily on AI:

Even after introducing all these restrictions, most frontier AI operators are still losing money.

Anthropic has raised over $10 billion in funding. OpenAI is on a similar trajectory. And neither has yet demonstrated a sustainable economic model at scale without continuous external capital injection.

This doesn’t mean the business is unviable. There’s probably an economic equilibrium somewhere, with the right combination of pricing, inference efficiency, enterprise volume, and hardware optimization. But that equilibrium clearly hasn’t been reached yet.

And meanwhile, the industry keeps advancing toward bigger, more capable models with wider context windows, more integrated tools, and more agentic capabilities. All of those improvements are fantastic for users. But each one increases inference cost, not reduces it.

What This Means for Enterprises
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If you’re a digital transformation leader or a CTO designing your organization’s AI strategy, the message is clear:

Don’t build your AI architecture assuming that today’s cost is tomorrow’s cost. And certainly not assuming it will go down.

In practice, this means several things:

1. Design for Multiple Model Tiers
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Not everything needs a frontier model. Many tasks — classification, data extraction, routine summaries, basic assistance — can be handled by smaller, significantly cheaper models. Reserve the most powerful models for tasks that truly require them.

2. Implement Intelligent Routing
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Mature AI architectures don’t send everything to the same model. They route each request to the most efficient model that can resolve it at the required quality level. This can reduce inference costs by 60-80% without degrading the end-user experience.

3. Measure Cost Per Use Case
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If you don’t know how much each AI interaction costs, broken down by task type, model used, and outcome achieved, you’re flying blind. AI cost observability should be as standard as it is in cloud infrastructure.

4. Think of AI as Infrastructure, Not Just Software
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Generative AI has a variable cost component that resembles electricity or cloud compute more than a software license. Plan accordingly: with capacity reserves, variable budgets, and consumption governance.

5. Don’t Depend on a Single Provider
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Concentration on a single model provider exposes you directly to their pricing decisions, policy changes, and usage restrictions. A multi-model architecture gives you the flexibility to adapt when — not if — conditions change.

The End of an Era, Not the End of AI
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I want to be clear about something: nothing I’ve described here is negative for the future of AI. It is simply the end of a phase.

The phase that’s ending is one of illusory abundance: frontier models at promotional prices, apparently unlimited usage, and a widespread feeling that advanced AI was becoming a commodity.

The phase that’s beginning is more honest. It’s the phase where:

Prices more faithfully reflect real costs.
Providers find sustainable business models.
Enterprises learn to use AI with economic discipline.
And the market matures, as cloud, SaaS, and every other technology infrastructure that started with promotional pricing has matured before it.

In a sense, it’s good news. Because an indefinitely subsidized market is a fragile market. And a market that finds its economic equilibrium is a market that can last.

Conclusion
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AI isn’t suddenly becoming more expensive. We’re simply stopping the pretense that it was cheap.

And the question that should be on every boardroom table is no longer just “What can AI do for us?” It’s something more uncomfortable and more necessary:

How much is it really going to cost us, and are we designing for sustainability?

Whoever has a good answer to that question will have a real competitive advantage. Whoever doesn’t will discover that the most expensive AI isn’t the one with the best model — it’s the one that was deployed without thinking about the economics.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey. He writes about AI, digital strategy, and the future of technology in organizations.

From SEO to AEO: How AI Agents Are Redefining Digital Transformation in Higher Education

Sat, 28 Mar 2026 00:00:00 +0000

Two weeks ago, Jensen Huang took the stage at GTC 2026 and declared that Artificial General Intelligence has arrived. “We have reached the level of artificial general intelligence,” he said, in his trademark leather jacket and the tone of someone who knows they’re dropping a bomb. His definition, however, is telling: for him, AGI is the ability to create billion-dollar businesses. A purely capitalist definition.

But in universities, the question isn’t whether AGI can generate billions. The question is: what changes when our students, faculty, and institutions interact daily with systems that reason, plan, and execute autonomously?

The answer is: everything.

The Age of Agents
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Forget chatbots. In 2026, the dominant trend in AI isn’t text generation — it’s Agentic AI: autonomous systems that receive a goal, create a plan, use tools (your email, your CRM, your spreadsheets) and execute tasks without constant human intervention.

The data backs this up. According to Deloitte’s State of AI in the Enterprise report, 3 out of 4 companies plan to deploy AI agents within the next two years. Harvard Business Review dedicates its cover this month to: “To Scale AI Agents Successfully, Think of Them Like Team Members.” The message is clear: agents aren’t software you install — they’re changes in how work gets done.

But here’s the number that concerns me: only 1 in 5 organizations has a mature governance model for these agents. We’re about to deploy autonomous systems at scale without knowing who’s responsible when things go wrong.

This isn’t a technical problem. It’s an institutional one. And universities should be the first to solve it.

From SEO to AEO: A Metaphor with Real Consequences
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Gartner just published its strategic predictions for 2026, and one caught my attention: traditional SEO and PPC will give way to Agent Engine Optimization (AEO). What does this mean? That products, services, and content will need to be “machine-readable” — legible and interpretable by AI agents, not just by humans searching on Google.

Let’s translate this to higher education.

If AI agents will be the ones recommending academic programs, helping students choose courses, connecting competencies with job opportunities… are our curricula, our competency frameworks, our content ready to be read by machines?

Most of the time, the answer is no. We have PDFs with study plans. Course descriptions written for academic committees. Competencies defined in documents that nobody reads — not humans, and certainly not AI agents.

The shift from SEO to AEO isn’t just a marketing change. For universities, it’s a shift in curriculum design. Competencies need to be structured, semantic, interconnected. Not because of a tech fad, but because the systems that will guide our students need to understand what we offer.

The Tec-UNESCO Observatory: From Principles to Action
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On March 18 — ten days ago — Tecnológico de Monterrey and UNESCO signed an agreement in Santiago, Chile to create the Regional Observatory on Artificial Intelligence in Education for Latin America and the Caribbean. This isn’t an empty press release: it includes a $90,000 USD contribution from Tec, technical teams working on methodological frameworks, digital competencies for educators, AI ethics, and pilot projects in Chile, El Salvador, and Mexico.

As Esther Kuisch Laroche, Director of UNESCO’s Regional Office in Santiago, put it: “The challenge is not only to incorporate new technologies, but to ensure that their use contributes to more inclusive, ethical, and relevant education systems.”

This is exactly what we need. No more grandiose declarations about AGI. More concrete work to understand how AI transforms learning, generate evidence, and formulate public policy based on data, not hype.

The Governance Gap Is Our Opportunity
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Let’s go back to that Deloitte number: 75% of companies will deploy agents, but 80% don’t know how to govern them.

In higher education, the landscape is similar. 86% of university students already use AI regularly. A recent pilot study shows they’re not simply letting AI write for them — they interact, iterate, edit. But our institutional frameworks aren’t designed for this world.

And this is where I see the opportunity. Universities aren’t (or shouldn’t be) mere consumers of technology. We’re the institutions that define ethical frameworks, generate knowledge, and educate the people who will make decisions about AI for decades to come.

If anyone should define how AI agents are governed in educational settings — who’s responsible when an agent gives bad academic guidance, how student privacy is protected, how bias is prevented in recommendation systems — it should be us.

What Comes Next
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Digital transformation is no longer about digitizing processes. I’ve been saying this for a while, but now the meaning runs deeper than ever.

Digital transformation in 2026 means preparing our institutions for a world where AI agents are colleagues, not tools. Where educational content must be as readable by machines as by humans. Where the governance of autonomous systems is as important as their implementation.

Jensen Huang can declare AGI if he wants. I’d rather focus on what truly matters: that technology serves better, fairer, and more relevant education. And that’s not something you declare — it’s something you build.

Carles Abarca is Vice President of Digital Transformation at Tecnológico de Monterrey.

NemoClaw and the Enterprise Awakening: When AI Agents Leave the Garage

Wed, 18 Mar 2026 00:00:00 +0000

Jensen Huang does not use words carelessly. When the CEO of the most valuable company on Earth stands on stage at GTC and says “OpenClaw is the operating system for personal AI,” he is not making a prediction. He is reading a scoreboard.

And then he drops NemoClaw.

What NemoClaw actually is
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Let me cut through the press releases. NemoClaw is not a competitor to OpenClaw. It is an enterprise wrapper around it. A single command — one command — installs NVIDIA’s Nemotron models and the OpenShell runtime on top of any existing OpenClaw instance, adding the three things that kept CIOs awake at night:

Privacy routing. A router that decides which queries go to local models (running on your hardware, never leaving your network) and which can safely reach frontier models in the cloud. This is not a minor feature. This is the difference between “interesting demo” and “approved by Legal.”
Sandboxed execution. OpenShell provides an isolated environment where agents can write code, access files, and execute tasks without touching the host system. The agent gets the access it needs to be productive. Nothing more.
Policy-based guardrails. Configurable security boundaries that define what an agent can and cannot do. Not through hope. Through enforcement.

The message from NVIDIA is unmistakable: OpenClaw is infrastructure. Not a project. Not a trend. Infrastructure.

The enterprise problem was never intelligence
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Here is what most commentary about AI agents gets wrong: the bottleneck was never capability. Claude, GPT, Gemini — they have been capable of executing complex business tasks for over a year. The bottleneck was trust.

No CIO in their right mind would deploy an autonomous agent with access to production systems, employee data, and financial records without three guarantees: that data stays where it should, that the agent operates within defined boundaries, and that every action is auditable. NemoClaw provides exactly those three guarantees.

But NVIDIA is solving a problem that some of us had already started solving from the inside.

AgenTECs: What happens when a university takes agents seriously
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At Tecnológico de Monterrey, we did not wait for NemoClaw. We could not afford to. When you are responsible for the digital infrastructure serving 100,000 students and 30,000 employees, “wait and see” is not a strategy.

We call them AgenTECs — autonomous institutional agents built on OpenClaw, powered by our own TECgpt models, running on our private Azure infrastructure. They are not chatbots. They are not demos. They are digital collaborators with @tec.mx email accounts, Microsoft Teams presence, and defined roles within the organization.

The architecture is deliberately simple:

Layer	Component	Function
Orchestration	OpenClaw	Lifecycle management, plugins, tools, persistent memory
Cognitive	TECgpt (institutional LLMs)	Reasoning, language understanding, decision-making
Infrastructure	Azure private cloud	Isolated, secure, dedicated resources
Channels	Email @tec.mx, Teams, WhatsApp	Communication with users, supervisors, and other agents

Each AgenTEC operates under a governance model with three clearly separated roles: the Administrator (IT — keeps it running), the Supervisor (business area — tells it what to do), and the User (interacts with it as they would with any colleague). This separation is not bureaucracy. It is the difference between an agent that scales and an agent that becomes someone’s science project.

Why this matters now
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Three converging forces make this moment different from every previous enterprise AI wave:

First, the cost equation has flipped. An AgenTEC running 24/7 on a dedicated VPS costs a fraction of a human collaborator performing the same function. Not because the human is replaceable — because repetitive, rule-based work should never have been assigned to humans in the first place.

Second, the framework matured before the enterprise noticed. OpenClaw’s skill system, memory persistence, multi-channel communication, and tool integration were built for personal use. But personal AI turns out to be harder than enterprise AI. If an agent can manage someone’s email, calendar, finances, and home automation simultaneously, it can certainly process support tickets.

Third, NVIDIA just removed the last excuse. Every objection I have heard from CISOs and compliance officers — data privacy, execution isolation, audit trails — has a technical answer in NemoClaw. The conversation shifts from “can we?” to “when do we start?”

The playbook
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For CIOs reading this, here is the practical path:

Start with the mundane. The first AgenTEC we are designing handles TecServices requests — the thousands of monthly queries about credentials, procedures, and administrative processes. Not glamorous. High volume. Perfect for an agent that follows procedures without deviation and escalates anything unusual.

Keep humans in the loop, but redefine the loop. The supervisor does not review every action. They review patterns, exceptions, and escalations. Weekly, not hourly. The agent handles the routine. The human handles the judgment.

Build on open infrastructure. OpenClaw is open source. TECgpt runs on our own cloud. NemoClaw adds NVIDIA’s models locally. At no point does institutional data leave our control. This is not negotiable for a university handling student records, research data, and financial information.

Measure outcomes, not activity. An AgenTEC’s value is not measured by how many messages it processes. It is measured by how many human hours it liberates for higher-value work, how many response times it reduces, and how many errors it eliminates.

The uncomfortable truth
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Here is what I tell my peers at every CIO forum: the question is not whether AI agents will transform your operations. That is settled. The question is whether you will build your own or rent someone else’s.

NemoClaw makes the “build” option dramatically more accessible. OpenClaw provides the foundation. NVIDIA provides the guardrails. Your institutional knowledge — your processes, your data, your culture — provides the differentiation that no vendor can replicate.

At Tec de Monterrey, we chose to build. We call them AgenTECs. And every month, the gap between what they can do and what we imagined widens — in our favor.

The operating system for AI agents is here. The enterprise wrappers are shipping. The only scarce resource now is the courage to start.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey, where he leads the development of TECgpt and the AgenTECs program. Previously CTO of Banco Sabadell and CIO of TSB Bank.

My AI Agent Is 2 Weeks Old. 72,000 Lines of Code. 5 Projects Shipped.

Wed, 25 Feb 2026 00:00:00 +0000

I haven’t written a line of code in weeks. And yet, my repositories keep growing.

72,563 lines added. 43 commits. 5 projects deployed to production.

All in 14 days. I didn’t write any of it. My AI agent did.

A Clarification First
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Let me be clear about something: my job as VP of Digital Transformation at Tecnológico de Monterrey is not to write code. I lead teams that do that at scale, for one of Latin America’s largest universities.

But I’ve been writing code for 40 years. From assembler to C, Fortran, Visual Basic, Pascal, COBOL, C++, Java, JavaScript, TypeScript, Rust, Python — I’ve touched them all. First because it was my job. Then because it became a habit I never wanted to break.

Why? Because staying close to the code keeps me close to reality. It helps me make better decisions. It lets me have real conversations with my technical teams — not as a manager who reads reports, but as someone who understands what they’re building.

The projects I’m about to describe are personal. Some are proofs of concept that later evolve into institutional initiatives — like TECgpt Desktop, which started as an experiment on my laptop before becoming an official tool at the university. Others are simply things I want to build.

This matters for what comes next.

Meet My Digital Apprentice
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His name is JarvisX. He runs on a Mac Mini in my home office. He’s been “alive” for exactly two weeks.

But here’s the key: I decide the architecture. I choose the tech stack. I define the structure, the patterns, the conventions. JarvisX executes — brilliantly, tirelessly, and autonomously — but the technical vision is mine.

He’s not replacing my judgment. He’s amplifying it.

When I say “build this feature,” I’ve already decided how it should be built. JarvisX handles the implementation: writing the code, deploying to production, opening a browser to verify it looks right, and committing the changes. All without asking me to approve each step.

The Numbers
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In two weeks, JarvisX has:

Added 72,563 lines of code across multiple repositories
Made 43 commits
Shipped 5 complete projects to production
Configured deployments, authentication systems, and cloud infrastructure
Validated his own work visually — taking screenshots, iterating until it’s right

Let me be honest: not all 72,000 lines are hand-crafted code. Some are frameworks, themes, and data files. But every commit, every deploy, every design decision was autonomous.

How This Article Got Here
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Want to see what working with an AI agent actually looks like? Here’s the conversation that published this very article:

Me: JarvisX, I left a new article for my blog in our shared Dropbox folder.

JarvisX: Read it! I see it’s in English. Do you want me to generate the Spanish version?

Me: Yes. I trust your translations by now, so no need for me to review. Upload both versions directly.

JarvisX: Thanks for the trust! Uploading now. Should I add a featured image?

Me: Yes. Something that evokes an orchestra conductor surrounded by AI agents.

JarvisX: Got it. Do you want to review it first?

Me: No. Just upload it.

That’s it. No back-and-forth. No micromanagement. Just trust built over two weeks of working together.

By the time you’re reading this, JarvisX has already translated it, generated the image, and deployed both versions to production. I didn’t check his work. I didn’t need to.

The Real Insight
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A year ago, I wrote an article called The End of the Developer. My thesis: the junior developer role would disappear. The future would belong to “directors of orchestra” who conduct AI agents rather than write code themselves.

I’m now living that prediction.

But here’s what I’ve learned: You can’t direct what you don’t understand.

The “new developer” — the one who orchestrates AI agents — still needs deep technical knowledge. Not to write every line, but to:

Define architecture that makes sense
Recognize good code when the agent produces it
Catch mistakes before they reach production
Make tradeoffs that require experience to evaluate

My 40 years of coding didn’t become obsolete when I started working with JarvisX. They became essential. I can direct him effectively precisely because I know what good looks like.

What This Means
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If you’re a senior developer or architect, this is your moment. Your experience is more valuable than ever — not for typing code, but for directing those who do.

If you’re a junior developer, the path forward isn’t to compete with AI at writing code. It’s to accelerate your learning so you can direct AI sooner. Use these tools to learn faster, not to avoid learning.

If you’re a CIO or CTO, stop asking “how do we adopt AI agents?” Start asking “do our people have the technical depth to direct them well?”

The Bottom Line
#

72,000 lines. 5 projects. 2 weeks. Zero lines written by me.

But every architectural decision? Mine. Every technology choice? Mine. Every quality standard? Mine.

The future of software development isn’t about writing less code. It’s about directing more of it — with the wisdom that only experience can provide.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey and former CTO of Banco Sabadell. He has been writing code for 40 years and plans to never stop — even if he’s no longer the one typing it.

The CIO's Guide to AI Agents: Before You Buy, Ask These 5 Questions

Mon, 23 Feb 2026 00:00:00 +0000

The AI agent market will reach $236 billion by 2034. Every vendor wants your budget. Here’s how to separate hype from value.

Every enterprise software vendor now sells “AI agents.” Salesforce has Agentforce. ServiceNow acquired Moveworks for $2.85 billion. Microsoft promises an “agentic enterprise” through Copilot. Startups like Sierra have hit $100 million ARR in under two years.

The pressure to act is real. 89% of CIOs consider agent-based AI a strategic priority. 51% of large enterprises have already deployed agentic AI. Your board is asking questions. Your competitors are moving.

But here’s what nobody tells you: most AI agent purchases fail to deliver expected value. Not because the technology doesn’t work — but because organizations buy the wrong solution for their specific situation.

After two decades leading technology transformation at a major European bank and now driving digital innovation at one of Latin America’s largest universities, I’ve developed a framework for evaluating AI agents that cuts through vendor hype. Before you sign that contract, ask these five questions.

The AI Agent Landscape in 2026
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First, let’s understand what you’re buying. The market has three distinct categories:

1. Embedded Agents from SaaS Vendors

Your existing vendors are adding agents to their platforms:

Salesforce Agentforce: $0.10 per action or $125-550/user/month
ServiceNow AI Agents: Full orchestration with AI Control Tower
Microsoft Copilot Studio: Included with M365, plus add-ons
Zendesk AI Agents: $1.50 per autonomous resolution

The pitch: “You already use our platform. Now it’s smarter.”

2. Pure-Play Agent Startups

Companies building agents from the ground up:

Sierra (Bret Taylor, ex-Salesforce): $10B valuation, focused on customer service
Adept: Targeting workflow automation
Imbue, Reflection AI: Research-driven approaches

The pitch: “We’re not constrained by legacy architecture.”

3. Foundation Model Providers

The companies building the AI itself:

Anthropic: Claude with Computer Use and MCP (Model Context Protocol)
OpenAI: GPT-4 with Operator
Google: Gemini with Agentspace

The pitch: “Build custom agents on our infrastructure.”

Each category has trade-offs. Your job is to understand which trade-offs matter for your organization.

Question 1: What Problem Are You Actually Solving?
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This sounds obvious. It isn’t.

“We need AI agents” is not a problem statement. Neither is “we need to reduce costs” or “we need to be more innovative.”

A proper problem statement looks like this:

“Our customer service team handles 50,000 tickets per month. 60% are password resets and order status checks. Average handling time is 8 minutes. We need to reduce that to under 2 minutes for routine inquiries.”
“Our compliance team manually reviews 10,000 transactions daily for AML screening. False positive rate is 95%. We need to reduce false positives while maintaining regulatory coverage.”

Notice the difference? Specific process. Measurable baseline. Clear target.

The trap: Vendors will happily sell you a “general-purpose agent platform” that can theoretically do anything. In practice, these platforms do nothing well. Start with one high-value, well-defined use case. Prove it works. Then expand.

Red flag: If you can’t articulate the specific process, current metrics, and target improvement, you’re not ready to buy.

Question 2: Build, Buy, or Extend?
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You have three paths. Let me illustrate each with real scenarios.

EXTEND: Add Agent Capabilities to Your Existing Platforms
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What it means: You already use Salesforce, ServiceNow, or Microsoft 365. You activate their built-in agent features.

Real example — Global Retailer with Salesforce: A retail company with 200 customer service reps was already running Service Cloud. They activated Agentforce Service Agent in three weeks. Configuration, not development. The agent now handles 40% of incoming inquiries (order status, return policies, store hours) without human intervention. Cost: $0.10 per agent action. No new vendor relationship. No integration project.

Real example — Insurance Company with ServiceNow: An insurer using ServiceNow ITSM enabled AI Agents for incident categorization and routing. The agent reads incoming tickets, identifies the affected system, assigns priority, and routes to the right team. Implementation: 6 weeks. Result: 60% reduction in misrouted tickets, 25% faster resolution times.

When to Extend:

You’re already paying for the platform
Your use case is common (customer service, IT helpdesk, HR inquiries)
You need results in weeks, not months
You don’t have AI engineering talent

The limitation: You’re constrained by what your vendor offers. If Salesforce Agentforce doesn’t support your specific workflow, you’re stuck waiting for their roadmap.

BUY: Purchase a Specialized Agent Solution
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What it means: You bring in a startup or specialized vendor that does one thing exceptionally well.

Real example — E-commerce Brand with Sierra: A direct-to-consumer brand with $500M revenue wasn’t satisfied with their existing chatbot. They deployed Sierra for customer service. Sierra’s agents handle complex conversations: processing returns while suggesting alternatives, managing subscription modifications, resolving billing disputes. The agents access their Shopify backend, payment processor, and shipping systems. Implementation: 4 months. Result: 70% of conversations resolved without human escalation. Customer satisfaction increased 15 points.

Real example — Enterprise IT with Moveworks (now ServiceNow): Before the acquisition, companies bought Moveworks specifically for IT helpdesk automation. The agent could reset passwords, provision software, troubleshoot VPN issues, and answer policy questions — all through natural conversation in Slack or Teams. It understood context: “I can’t access the server” triggered different workflows than “I need Photoshop installed.”

When to Buy:

Your existing vendor’s agents aren’t good enough for a strategic use case
A startup has proven traction in your specific domain
You can afford 3-6 months implementation
The use case is important enough to justify a new vendor relationship

The risk: Startup viability. What happens if Sierra gets acquired? If the startup pivots? You’re dependent on a company that may not exist in five years. Mitigate this by ensuring data portability and avoiding deep customizations that lock you in.

BUILD: Create Custom Agents Using Foundation Models
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What it means: You use Claude, GPT-4, or Gemini APIs to build agents tailored to your unique processes.

Real example — Investment Bank’s Deal Analysis Agent: A bulge-bracket bank built a custom agent for M&A analysts. The agent ingests SEC filings, earnings transcripts, news articles, and internal research. Analysts ask natural language questions: “What are the key risks in Acme Corp’s debt structure?” or “Compare the margin profile of these three acquisition targets.” The agent synthesizes information that would take an analyst hours to compile manually. Built on Claude with custom RAG (retrieval-augmented generation) over proprietary databases. Development: 8 months. Team: 6 engineers, 2 ML specialists. The output is proprietary competitive advantage — no vendor offers this.

Real example — Pharmaceutical Company’s Clinical Trial Agent: A pharma company built an agent to monitor clinical trial data in real-time. The agent identifies adverse event patterns, flags protocol deviations, and generates regulatory-ready reports. This isn’t a use case any vendor serves — the domain knowledge is too specialized, the regulatory requirements too specific. Built on GPT-4 with extensive fine-tuning and custom safety guardrails. Development: 12 months.

Real example — University’s Custom Academic Ecosystem (TecGPT): At Tecnológico de Monterrey, we built TecGPT — our academic AI ecosystem, integrated with our LMS, assisting both professors and students, and aligned with our academic regulations. No vendor could offer what we needed out-of-the-box.

When to Build:

Your process is genuinely unique and defines competitive advantage
No vendor solution exists for your domain
You have AI/ML engineering talent (or can acquire it)
You can invest 6-12 months before seeing production value
The long-term value justifies the ongoing maintenance cost

The reality check: Building is expensive. A custom agent isn’t a one-time project — it’s an ongoing product. You need engineers to maintain it, improve it, and adapt it as foundation models evolve. Budget for 2-3 FTEs indefinitely, not just the initial build.

The Decision Matrix
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Factor	Extend	Buy	Build
Time to value	Weeks	3-6 months	6-12 months
Customization	Low	Medium	Unlimited
Vendor risk	Low	High	Medium
Talent required	Admins	Integrators	Engineers + ML
Total cost (3 years)	$$	$$$	$-$$$$
Competitive advantage	None	Low	High

My Recommendation
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Start with Extend for your first agent deployment. Even if it’s not perfect, you’ll learn what works, what users actually need, and where the gaps are. That learning is invaluable before you commit to Buy or Build.

Move to Buy when you’ve proven agent value and need capabilities your platform vendor doesn’t offer. Choose startups with strong traction, clear use case focus, and enterprise customers who can serve as references.

Build only when you’ve exhausted Extend and Buy options, or when the process is so unique that it genuinely differentiates your business. If you’re building agents for commodity processes (customer service, IT helpdesk), you’re wasting engineering talent that could create actual competitive advantage.

Question 3: How Will You Measure Success?
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Before deployment, define:

Efficiency metrics:

Time saved per task
Tasks handled without human intervention
Error rate reduction

Quality metrics:

Customer satisfaction (for customer-facing agents)
Compliance accuracy (for regulatory processes)
Decision quality (measured against human expert baseline)

Business metrics:

Cost per transaction
Revenue impact (if applicable)
Employee satisfaction (agents should help workers, not threaten them)

The 90-day rule: If you can’t demonstrate measurable improvement within 90 days, something is wrong. Either the use case was poorly chosen, the implementation was flawed, or the vendor oversold capabilities. Don’t extend pilots indefinitely hoping for results.

Hidden metric: Adoption. The most sophisticated agent is worthless if your team doesn’t use it. Track actual usage, not just availability.

Question 4: What’s Your Human-in-the-Loop Strategy?
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In 2026, no responsible organization deploys fully autonomous agents for consequential decisions. The question is where to place human oversight.

The spectrum:

Human-initiated: Human starts task, agent assists, human approves result
Agent-initiated with approval: Agent identifies opportunity, proposes action, human approves
Agent-executed with audit: Agent acts autonomously, human reviews after the fact
Fully autonomous: Agent acts without oversight (appropriate only for low-risk, reversible actions)

My framework for choosing:

Risk Level	Reversibility	Recommended Approach
Low	Easy to reverse	Fully autonomous
Low	Hard to reverse	Agent-executed with audit
High	Easy to reverse	Agent-initiated with approval
High	Hard to reverse	Human-initiated only

Examples:

Password reset → Fully autonomous (low risk, reversible)
Customer refund under $50 → Agent-executed with audit
Credit decision → Agent-initiated with approval
Regulatory filing → Human-initiated only

The regulatory reality: Financial services, healthcare, and other regulated industries will require human oversight for most consequential decisions for the foreseeable future. Plan for this. Agents that “recommend and explain” are more valuable than agents that “decide and act” in these contexts.

Question 5: What’s Your Data Strategy?
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AI agents are only as good as the data they can access. Before buying, audit:

Data availability:

Can the agent access all systems needed for the use case?
Are APIs available, or will you need custom integrations?
What’s the latency? Agents that wait 30 seconds for data lookups frustrate users.

Data quality:

Is your data accurate and up-to-date?
Are there known data quality issues that will cause agent errors?
Who’s responsible for data hygiene?

Data governance:

What data can the agent access? What’s off-limits?
How do you prevent agents from exposing sensitive information?
What audit trail exists for agent data access?

The integration tax: Most enterprises underestimate integration effort by 3-5x. If the vendor says “we integrate with everything,” ask for customer references running your specific system combination. Generic claims mean nothing.

MCP and the future: Anthropic’s Model Context Protocol (MCP) is emerging as a standard for agent-to-system communication. Consider whether your chosen platform supports open standards or locks you into proprietary integrations.

The Decision Framework
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After answering these five questions, you should be able to complete this statement:

“We will deploy [specific agent type] to solve [specific problem] in [specific process]. We expect to achieve [specific metric improvement] within [timeframe]. Our human oversight model is [approach]. We have confirmed data access to [systems] and defined governance policies for [sensitive data types].”

If you can’t complete this statement with confidence, you’re not ready to buy.

My Recommendations for 2026
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For most enterprises: Start with your existing vendor’s agent capabilities. Salesforce Agentforce, ServiceNow AI Agents, or Microsoft Copilot Studio will handle 80% of common use cases. The integration is already done. The governance frameworks exist. The risk is manageable.

For customer service: Sierra has proven traction. If customer experience is strategic and your existing vendor’s agents aren’t cutting it, evaluate Sierra seriously. Their $100M ARR in 21 months signals real value delivery.

For unique processes: Build on Anthropic Claude or OpenAI, but only if you have the engineering talent to maintain custom solutions. The foundation models are extraordinary. The engineering required to productionize them is not trivial.

For everyone: Start small. One use case. 90-day proof of value. Then expand. The vendors want you to buy platforms. You should buy solutions to specific problems.

The Bottom Line
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The AI agent market is real. The value is real. But so is the hype.

62% of companies investing in agentic AI expect more than 100% ROI. Some will achieve it. Many won’t — not because the technology failed, but because they bought the wrong solution for the wrong problem with the wrong implementation approach.

Don’t be a statistic. Ask the five questions. Complete the decision framework. Then — and only then — sign the contract.

The agents are ready. Make sure you are too.

Banking Will Never Be the Same: An Insider's View on AI Agents

Sun, 22 Feb 2026 00:00:00 +0000

I led digital transformation at a €200B European bank. AI agents will change everything I thought I knew.

Between 2001 and 2023, I spent over two decades at Banco Sabadell, one of Spain’s largest financial institutions, rising from technology roles to CIO and ultimately CTO. I oversaw the technology integration of TSB in the UK, navigated one of the most public banking IT crises in recent memory, and led a multi-year digital transformation that touched every corner of a 25,000-employee organization.

Back then, we believed APIs would revolutionize banking. We were thinking too small.

The AI agent revolution will make our API transformation look like a warm-up exercise.

Why Banking Is Perfect for AI Agents
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Banks are unique. They operate in an environment that seems hostile to automation — heavy regulation, risk aversion, legacy systems — but is actually ideal for AI agents.

Repetitive processes with clear rules. KYC verification. Transaction monitoring. Dispute resolution. Compliance reporting. These processes follow explicit rules that humans apply thousands of times per day. Rules that can be encoded, executed, and audited by agents.

Structured data everywhere. Unlike creative industries, banking runs on structured data: transactions, contracts, balances, rates. This is exactly what agents excel at processing.

Regulation as an advantage. This sounds counterintuitive. But consider: agents can follow rules more consistently than humans. They don’t get tired. They don’t take shortcuts. They don’t forget steps. In a world where compliance failures cost billions, agents that provably follow every regulation become assets, not risks.

Economics demand it. European banks operate on razor-thin margins. Cost-to-income ratios above 60% are common. The math is brutal: reduce operational costs or die. Agents offer a path that doesn’t require another round of layoffs — they offer a path to do more with the same people.

The Agent Transformation Map
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Here is how I see AI agents transforming core banking operations:

Function	Today	With Agents
KYC/Onboarding	3-5 days, multiple handoffs	15 minutes, agent + human final approval
Fraud Detection	Alerts generate queues → humans investigate	Agent investigates, escalates only genuine concerns
Customer Service	Limited chatbots, frustrated transfers	Agent with full context, handles 80% end-to-end
Compliance Monitoring	Armies of reviewers sampling transactions	Continuous agent monitoring, humans handle exceptions
Credit Decisions	Score + human judgment + committee	Agent analysis + recommendation + human approval
Regulatory Reporting	Manual data gathering, reconciliation hell	Agent assembles, validates, humans verify and submit

The pattern is consistent: agents handle the volume, humans handle the judgment. The ratio shifts from 90% human work / 10% oversight to 10% human work / 90% oversight.

The Real Challenges (From the Inside)
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I would be lying if I said this transformation will be easy. Having lived inside a large bank, I know the obstacles are formidable.

Legacy systems are not going anywhere. COBOL is still running critical systems at most major banks. Mainframes process millions of transactions daily. These systems work. They are paid off. No CEO will approve ripping them out for an AI experiment. The winning strategy is not replacement — it is an agent layer that interfaces with legacy systems through existing APIs and screen scraping where necessary.

Regulators are conservative — but evolving. Banking regulators move slowly by design. They remember 2008. But they are not blind to AI’s potential. The key is engaging them early, demonstrating auditability, and framing agents as risk reduction tools, not risk introduction.

Culture resists change. Banks are risk-averse institutions filled with risk-averse people. “We’ve always done it this way” is not a cliché — it is a deeply held belief. Change management matters more than technology selection.

The talent gap is real. Bankers do not understand AI. AI engineers do not understand banking. Finding people who speak both languages is nearly impossible. Building that bridge — through training, hiring, and partnerships — is essential.

Data silos persist. The customer data you need for intelligent agents is scattered across dozens of systems that do not talk to each other. Data unification projects have failed for decades. Agents will not magically solve this — but they can work around it better than traditional integration approaches.

What I Would Do Differently Today
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If I were back in that CTO chair today, knowing what I know about AI agents, here is what I would change:

Stop buying “AI-powered” SaaS. Every vendor now claims AI capabilities. Most are wrappers around the same foundation models with limited customization. Instead: build an agent layer on top of your existing systems. Own the intelligence, rent the infrastructure.

Start with back-office, not customer-facing. Regulators scrutinize customer-facing AI. Back-office operations have more freedom. Prove value internally — reconciliation, reporting, internal fraud investigation — then expand to customer touchpoints with evidence and confidence.

Adopt a hybrid approval model. For the next 3-5 years, the winning pattern is: agent proposes, human approves. This satisfies regulators, builds trust, and creates training data for future full automation. Do not try to remove humans too fast.

Invest in observability. Agents must be auditable. Every decision, every data access, every recommendation must be logged and explainable. This is not optional — it is the foundation of regulatory acceptance.

Predictions for 2027-2030
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Based on what I am seeing in the market and my experience inside banking:

50% reduction in back-office headcount. Not through layoffs, but through attrition and redeployment. The work will not disappear — it will transform. Banks will need fewer processors and more agent supervisors.

“Agent-first” banks will emerge. New entrants — likely from Asia and Latin America — will build banks designed around agents from day one. They will operate with 1/10th the staff of traditional banks. This is the real competitive threat.

Regulators will create AI agent certification. Just as we have software audits today, we will have agent audits. Banks that achieve certification early will have a competitive advantage.

M&A will accelerate. The investment required to build agent capabilities favors scale. Smaller banks that cannot afford the transformation will merge or be acquired. The industry will consolidate further.

The “AI banker” role will emerge. A new job category: professionals who understand both banking operations and AI agent orchestration. They will be the most sought-after talent in financial services.

The Choice Ahead
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Every banking executive faces the same question today: invest heavily in AI agents now, or wait and see.

Waiting feels safe. It is not.

The banks that move first will reduce costs, improve compliance, and — crucially — attract the talent needed to keep improving. The banks that wait will find themselves competing against institutions that operate at half their cost structure.

I have seen banking transform before — from branches to ATMs, from paper to digital, from monoliths to APIs. Each wave rewarded the early movers and punished the laggards.

This wave will be faster and more decisive.

The question is not whether your bank will use AI agents. The question is whether you will be the one deploying them — or the one being replaced by a bank that did.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey. He spent 22 years at Banco Sabadell, including roles as CIO and CTO, leading the bank’s digital transformation and the technology integration of TSB. He writes about AI, digital transformation, and the future of enterprise technology at carlesabarca.com.

The Agent Economy: From Selling Software to Selling Artificial Employees

Wed, 18 Feb 2026 00:00:00 +0000

The SaaSpocalypse of February was not an accident. It was a warning. The market saw what many CIOs still do not want to see: the business logic that justifies billions in licensing fees fits in a text file that an AI agent can execute.

But the interesting question is not what gets destroyed. It is what emerges.

From SaaS to SaS
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The industry consensus already has a name for this transition: SaS — Services-as-Software. The term, popularized by Foundation Capital in their thesis on the $4.6 trillion agentic AI opportunity, inverts the formula that has dominated enterprise software for two decades. SaaS sold software as a service. SaS sells services as software — autonomous agents that deliver outcomes, not interfaces.

It is the difference between selling a scalpel and selling the surgery.

Others call it WaaS — Workers-as-a-Service. Or simply what it is: the Agent Economy. An economy where companies do not buy software licenses. They hire artificial employees. Employees that do not negotiate salaries, do not request time off, and scale without limit.

The reconversion map
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If the thesis is correct, the question is not whether the major SaaS players will reconvert, but when and how. Here are my concrete predictions:

SAP will stop selling FI/CO licenses for accounting. Instead, it will offer an accounting agent that, based on business events — an invoice issued, a payment received, a period close — autonomously maintains the books in SAP. Humans will stop interacting with SAP’s UI. The agent will interact with SAP’s API. The financial controller will shift from operating the system to supervising the agent.

Salesforce will stop selling CRM per user. It will offer a sales agent that qualifies leads, updates opportunities, schedules follow-ups, and generates forecasts. Salespeople will stop filling in Salesforce fields. The agent will extract information from emails, calls, and meetings, keeping the pipeline updated. The sales manager’s role will shift from chasing their team to update the CRM to reviewing the agent’s decisions.

ServiceNow will stop selling support tickets per seat. It will offer an operations agent that diagnoses incidents, executes runbooks, escalates when necessary, and closes tickets without human intervention. Eighty percent of L1 and L2 support will be invisible to humans.

Workday will stop selling HR modules per employee. It will offer a people management agent that processes payroll, manages absences, generates compliance reports, and executes full onboarding — from provisioning access to scheduling the first week’s agenda.

HubSpot will stop selling a marketing suite. It will offer a growth agent that generates content, optimizes campaigns, segments audiences, and adjusts budgets in real time based on conversions. The CMO will not operate tools. They will conduct an orchestra of agents.

Atlassian will stop selling Jira per developer. It will offer a delivery agent that breaks epics into tasks, assigns work based on capacity, detects blockers, and generates progress reports. The engineering manager will shift from managing a board to managing a strategy.

The pattern
#

In every case, the pattern is the same: the human stops being the software operator and becomes the agent’s supervisor. The value is not in the interface. It is in the intelligence.

And the business model transforms with it. You no longer charge per seat — because there are no seats. You charge per outcome. Per invoice processed. Per lead qualified. Per incident resolved. Per payroll executed.

The companies that understand this first will capture the market. Those that keep selling interfaces with a copilot bolted on top will discover that a markdown file and an agent with access to their APIs do the same job at a fraction of the cost.

The Agent Economy is not coming. It is already here. The question is whether you are hiring agents or still buying licenses.

The SaaSpocalypse: $300 Billion Evaporated in 48 Hours

Tue, 17 Feb 2026 00:00:00 +0000

$300 billion evaporated in 48 hours. Welcome to the SaaSpocalypse.

In October 2024, I wrote about the next wave of AI: agents. In May 2025, I wrote about the end of the developer as we know them. Both articles shared a thesis: AI is not coming to assist. It is coming to execute.

The first week of February proved us right in the most brutal way possible.

Anthropic launched a set of plugins for its Cowork tool — essentially markdown files that encode legal, financial, and sales expertise. Nothing revolutionary on the surface. But the market understood something that many CIOs still have not processed: if Thomson Reuters’ business logic fits in a text file that an AI agent can read and execute, their $200/user/month software has an existential problem.

Thomson Reuters lost 57% from its highs. ServiceNow 48%. Salesforce 43%. The S&P software index had its worst month since October 2008.

But the interesting part is not the decline. It is the thesis behind it.

Satya Nadella said it in December 2024: “SaaS applications are CRUD databases with business logic on top. Agents will absorb that logic.” Foundation Capital put a number on it: $4.6 trillion in opportunity. They called it “Service as Software” — the inversion of the SaaS model. You no longer sell tools for humans to solve problems. You sell outcomes. The agent does the work.

A Palantir client summed it up better than any analyst: “All third-party software must justify its existence. And so far, it has not been able to.”

This does not mean Salesforce or SAP will disappear tomorrow. Switching costs are real. Deep integrations provide protection. But the per-seat pricing model has its days numbered. IDC predicts it will be obsolete by 2028. And companies that do not transition to outcome-based models will discover their moat was narrower than they thought.

I have spent over 20 years leading technology transformation in banking and education. I have seen panic cycles before — cloud, mobile, blockchain. But this time is different. It is not a new layer being added to the stack. It is a layer that absorbs the others.

The question for every CIO today is not “how do I integrate AI into my software.” It is “how much of my software can an agent replace with a markdown file and access to my APIs.”

If the answer makes you uncomfortable, you are probably already late.

Most Companies Don't Have an AI Problem. They Have an Organization Problem

Wed, 14 Jan 2026 00:00:00 +0000

Everyone talks about models. Everyone talks about agents. Everyone talks about copilots.

But when you analyze what actually happens inside companies, an uncomfortable truth emerges: AI is not failing; organizations are.

The data is consistent across multiple studies:

Between 70% and 80% of AI and advanced analytics initiatives fail.
Only 23% of companies derive real, sustained value from AI.
81% struggle to bring AI to production.
70% of digital transformations fail due to culture and organization.
The main blockers for AI are data, skills, and organizational complexity.
Additionally, 63% of companies do not have AI-Ready data, putting their initiatives at risk.

Yet many executives continue to say “the technology is not ready.”

The Five Uncomfortable Truths
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1. Non-Existent Governance
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Models without owners, without policies, without controls, and that do not scale.

2. Data in a Wild State
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Silos, duplicates, poor quality, lack of lineage. AI amplifies disorganization.

3. Invisible or Inconsistent Processes
#

You cannot automate what is not defined or integrate AI into workflows that do not exist.

4. Unbalanced Teams
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Lots of enthusiasm, little engineering. Many pilots, zero operations.

5. Strategies Built Backwards
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Starting with the model instead of the business case. Celebrating the prototype and burying it a month later.

AI is not going to replace those who work well. But it will expose organizations that work poorly.

2026 will be the year when companies must confront their operational maturity: data, processes, governance, and culture.

Because AI works. What does not work is implementing it without organization.

Sources: Harvard Business Review, MIT Sloan Management Review, O’Reilly / VentureBeat, Boston Consulting Group, IBM Global AI Adoption Index, Gartner.

AI: A Giant in Expectations with Feet of Clay

Mon, 15 Dec 2025 00:00:00 +0000

For most organizations, AI is still a giant in expectations with feet of clay in the present reality.

The promise of AI is real: automation, efficiency, margin growth.

But there is something that some prefer not to say out loud. When we talk about “AI strategy,” in many cases it is conceived on top of:

Legacy systems
Fragile data pipelines
Accumulated technical debt
Undocumented code / shadow IT

AI fails to deliver on its promise when we try to build the future on foundations we never reinforced.

Who Will Win
#

The organizations that will win will not be those that “adopt AI faster,” but those with the courage to:

Clean up their architecture
Invest in data before demos
Treat technical debt as a strategic problem, not just a technical one

AI does not replace hard work – it amplifies it. And those of us in technology have a responsibility: prepare the foundations and stop building castles in the air.

AI Won't Replace Teachers, But It Will Transform Their Role

Wed, 02 Jul 2025 00:00:00 +0000

Artificial intelligence won’t replace teachers… but it will profoundly transform the role of the educator of the future.

In my latest column for Expansion, I reflect on how AI is changing education – not to replace teachers, but to empower them, expanding their capabilities and redefining their role as guides, mentors, and designers of meaningful learning experiences.

Year Three of AI: The Age of Agentic AI

Wed, 11 Dec 2024 00:00:00 +0000

And now, on to the challenges of 2025 – Year Three of the new AI era: the year of Agentic AI.

Year I - 2023: the emergence of ChatGPT, the year of prompting where AI was text-based conversation. We launched our TecGPT.

Year II - 2024: AI becomes multimodal and learns to generate image, sound, music, voice, and video content. We launched our SkillStudio and incorporated multimodality into our platform.

Year III - 2025: AI enables the creation of autonomous agents that take actions on our behalf – the year of Agentic AI. We will launch AgentStudio with the essential contribution of Manuel Teran and his incredible team.

Beyond Agile: Embracing the Product-Centric Revolution

Tue, 27 Feb 2024 00:00:00 +0000

Classic Agile methodology has been the organizational framework that is perfect for companies seeking efficiency and adaptability within their projects. Yet, the customer has gradually become more complex in demands while more markets tend to mature, and there is a call for change. This leads to an emerging configuration: organizing around product-based structures that promise a more holistic way to center on customers.

Disadvantages of Classic Agile Methodology
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While Agile encourages fast iterations and responsiveness, traditional agile cells also result in silos. In fact, they may go to the extent of creating compartmentalization. When teams work in silos, there is a fragmented perspective toward the bigger picture — efforts become disjointed, resulting in products that do not fully reflect user needs. The lack of a single vision may dilute ownership and accountability for delivering coherent and impactful outcomes.

Advantages of a Product-Based Organization
#

Moving to a product-based structural organization aligns all teams under one product vision and encourages them to deeply collaborate on shared visions of the end-user experience. It makes them accountable and vested, ensuring that at least one if not more functions are directly and solely contributing to delivering value to customers. Resource prioritization becomes more logical when the central entity is product-focused, and organizations can make streamlined decisions in the development of products that truly satisfy market demands.

The Transition
#

From Agile cells to a product-based framework is a journey that needs to be made very thoughtfully and executed with precision. Redefinition of roles, realignment of goals, and inculcating a culture of cross-functionality and customer orientation require much thought and time.

Conclusion
#

The product-based organization offers a realistic and compelling alternative for adapting to an increasingly demanding environment. The orientation toward processes does not always fulfill the task of meeting customer expectations and achieving competitiveness — sometimes it acts in contradiction with them.

This paradigm shift from classic Agile methodologies to a product focus will greatly influence how business is conceptualized and executed.

Digital Transformation on Carles Abarca

Stop Crying About AI and Jobs

Stop Crying About AI and Jobs #

The Wrong Narrative #

What the Data Actually Says #

The Blind Spot: Your Job Title May Survive, Your Skills May Not #

What Teams Are Actually Experiencing #

The Collapse of Barriers to Creation #

The Real Risk Is Not AI #

So What Should We Actually Do? #

1. Learn to work with AI, not just talk about AI #

2. Strengthen judgment #

3. Redesign processes #

4. Commit to continuous learning #

5. Replace fear with discipline #

An Uncomfortable but Useful Conclusion #

The Era of Cheap AI Is Ending

The Signs Are Already Here #

AI Was Never Cheap. It Was Subsidized. #

Frontier Models Don’t Follow Traditional Software Economics #

The Flat Rate Was a Commercial Strategy, Not a Sustainable Reality #

The Great Paradox: More Restrictions, and Still Not Enough #

What This Means for Enterprises #

1. Design for Multiple Model Tiers #

2. Implement Intelligent Routing #

3. Measure Cost Per Use Case #

4. Think of AI as Infrastructure, Not Just Software #

5. Don’t Depend on a Single Provider #

The End of an Era, Not the End of AI #

Conclusion #

From SEO to AEO: How AI Agents Are Redefining Digital Transformation in Higher Education

The Age of Agents #

From SEO to AEO: A Metaphor with Real Consequences #

The Tec-UNESCO Observatory: From Principles to Action #

The Governance Gap Is Our Opportunity #

What Comes Next #

NemoClaw and the Enterprise Awakening: When AI Agents Leave the Garage

What NemoClaw actually is #

The enterprise problem was never intelligence #

AgenTECs: What happens when a university takes agents seriously #

Why this matters now #

The playbook #

The uncomfortable truth #

My AI Agent Is 2 Weeks Old. 72,000 Lines of Code. 5 Projects Shipped.

A Clarification First #

Meet My Digital Apprentice #

The Numbers #

How This Article Got Here #

The Real Insight #

What This Means #

The Bottom Line #

The CIO's Guide to AI Agents: Before You Buy, Ask These 5 Questions

The AI Agent Landscape in 2026 #

Question 1: What Problem Are You Actually Solving? #

Question 2: Build, Buy, or Extend? #

EXTEND: Add Agent Capabilities to Your Existing Platforms #

BUY: Purchase a Specialized Agent Solution #

BUILD: Create Custom Agents Using Foundation Models #

The Decision Matrix #

My Recommendation #

Question 3: How Will You Measure Success? #

Question 4: What’s Your Human-in-the-Loop Strategy? #

Question 5: What’s Your Data Strategy? #

The Decision Framework #

My Recommendations for 2026 #

The Bottom Line #

Banking Will Never Be the Same: An Insider's View on AI Agents

Why Banking Is Perfect for AI Agents #

The Agent Transformation Map #

The Real Challenges (From the Inside) #

What I Would Do Differently Today #

Predictions for 2027-2030 #

The Choice Ahead #

The Agent Economy: From Selling Software to Selling Artificial Employees

From SaaS to SaS #

The reconversion map #

The pattern #

The SaaSpocalypse: $300 Billion Evaporated in 48 Hours

Most Companies Don't Have an AI Problem. They Have an Organization Problem

The Five Uncomfortable Truths #

Stop Crying About AI and Jobs
#

The Wrong Narrative
#

What the Data Actually Says
#

The Blind Spot: Your Job Title May Survive, Your Skills May Not
#

What Teams Are Actually Experiencing
#

The Collapse of Barriers to Creation
#

The Real Risk Is Not AI
#

So What Should We Actually Do?
#

1. Learn to work with AI, not just talk about AI
#

2. Strengthen judgment
#

3. Redesign processes
#

4. Commit to continuous learning
#

5. Replace fear with discipline
#

An Uncomfortable but Useful Conclusion
#

The Signs Are Already Here
#

AI Was Never Cheap. It Was Subsidized.
#

Frontier Models Don’t Follow Traditional Software Economics
#

The Flat Rate Was a Commercial Strategy, Not a Sustainable Reality
#

The Great Paradox: More Restrictions, and Still Not Enough
#

What This Means for Enterprises
#

1. Design for Multiple Model Tiers
#

2. Implement Intelligent Routing
#

3. Measure Cost Per Use Case
#

4. Think of AI as Infrastructure, Not Just Software
#

5. Don’t Depend on a Single Provider
#

The End of an Era, Not the End of AI
#

Conclusion
#

The Age of Agents
#

From SEO to AEO: A Metaphor with Real Consequences
#

The Tec-UNESCO Observatory: From Principles to Action
#

The Governance Gap Is Our Opportunity
#

What Comes Next
#

What NemoClaw actually is
#

The enterprise problem was never intelligence
#

AgenTECs: What happens when a university takes agents seriously
#

Why this matters now
#

The playbook
#

The uncomfortable truth
#

A Clarification First
#

Meet My Digital Apprentice
#

The Numbers
#

How This Article Got Here
#

The Real Insight
#

What This Means
#

The Bottom Line
#

The AI Agent Landscape in 2026
#

Question 1: What Problem Are You Actually Solving?
#

Question 2: Build, Buy, or Extend?
#

EXTEND: Add Agent Capabilities to Your Existing Platforms
#

BUY: Purchase a Specialized Agent Solution
#

BUILD: Create Custom Agents Using Foundation Models
#

The Decision Matrix
#

My Recommendation
#

Question 3: How Will You Measure Success?
#

Question 4: What’s Your Human-in-the-Loop Strategy?
#

Question 5: What’s Your Data Strategy?
#

The Decision Framework
#

My Recommendations for 2026
#

The Bottom Line
#

Why Banking Is Perfect for AI Agents
#

The Agent Transformation Map
#

The Real Challenges (From the Inside)
#

What I Would Do Differently Today
#

Predictions for 2027-2030
#

The Choice Ahead
#

From SaaS to SaS
#

The reconversion map
#

The pattern
#

The Five Uncomfortable Truths
#

1. Non-Existent Governance
#

2. Data in a Wild State
#

3. Invisible or Inconsistent Processes
#

4. Unbalanced Teams
#

5. Strategies Built Backwards
#

Who Will Win
#

Disadvantages of Classic Agile Methodology
#

Advantages of a Product-Based Organization
#

The Transition
#

Conclusion
#