Enterprise on Carles Abarca

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.

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.

Enterprise on Carles Abarca

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 #

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 #