Category Archives: Digitalization

Your AI Is Real-Time. Your Data Operating Model Isn’t (Yet).

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Let’s be honest: many of us are trying to run 2025 AI ambitions on 2010 data habits. Nightly batches, opaque KPIs and committee-driven governance don’t survive contact with agents, RAG and copilots.

The more I work with transformation leads, the more I see two patterns emerge again and again:
1 Real-time velocity and semantically-rich data are no longer optional.
2 Federated production + centralized semantics is the only model that really scales.

This forces a redesign of the Data Operating Model (DOM):

  • Instead of “we have a data lake, we’re fine”, we need an event driven + streaming + semantics fabric.
  • Events, not just ETL.
  • A semantic layer where metrics, dimensions and policies live once and are reused everywhere.
  • RAG and agents consuming governed semantics and live APIs, not random tables.

And the “data mesh vs central model” wars? They’re a distraction. Data mesh delivers measurable outcomes.

What actually works is:

  • Federated production: domains own their data/real-time data products.
  • Centralized semantics: a small central team owns the shared language of the business, metrics and the policies around it.
  • Governance becomes computational: contracts, lineage and rules in code, not PDFs nobody reads.
  • Semantic layers are becoming the governance firewall, resolving data chaos. The semantic layer emerges as the critical “universal translator” between raw data and analytical/AI systems.
  • Data/AI/Analytics Architecture Convergence on Six Pillars: (1) Ingest/Stream, (2) Prepare/Transform, (3) Define/Model (semantic layer), (4) Store/Persist, (5) Integrate/Orchestrate, (6) Deliver/Share. The “Define/Model” stage—semantic layers + metadata management—is the control point for AI governance.

If I had to prioritise the next 12–18 months in a DOM, I’d push for three moves:
– Stand up 3–5 domain teams with clear P&L-linked data products.
– Create a semantic council with the authority to say “no” to broken KPIs and unsafe policies.
– Fund based on outcomes: latency, reliability, AI use-case adoption and reuse of shared semantics.

The hard question is “where do we start federating ownership without losing a single source of truth on meaning and controls”?

I’d love to learn from others here:
Where is your DOM actually stuck today — events, semantics, domain ownership, or governance?

I Just Analyzed the Hyperscalers’ Agent Platforms. Here’s What Shocked Me

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I’ve been deep-diving into what AWS, Azure, and Google are actually building for AI agents—and I’ll be honest, there’s a fundamental shift happening in how we’ll build autonomous systems. The three hyperscalers are making radically different bets on the future of work.

The Reality Check

All three major hyperscalers—AWS, Microsoft Azure, and Google Cloud Platform—provide full-stack solutions encompassing orchestration, deployment, security, observability, and integration capabilities. The choice between platforms increasingly depends on existing enterprise infrastructure, specific framework preferences, required runtime characteristics, and ecosystem integration needs rather than fundamental capability gaps.

When I first examined AWS AgentCore, I thought “this is impressive infrastructure.” The most mature marketplace ecosystem. The deepest tooling, built on 15+ years of cloud services. Eight-hour runtimes, isolated microVMs, seven integrated services. Then I realized—they’re not just building faster AI. They’re building systems that can actually think and act over extended periods.

Azure took a different path. They’re saying: “Most enterprises live in the Microsoft 365 ecosystem. Let’s embed agents there with built-in identity management and multi-agent orchestration.” Smart. Pragmatic. Different.

Google’s playing an interesting game. They’ve released the Agent Development Kit with persistent memory and an open protocol called A2A. Translation? They’re betting that agent interoperability and a framework-agnostic approach are the real competitive advantages.

Why This Matters for You

The marketplace is projected to hit $163 billion by 2030, with agents representing $24.4 billion. But numbers don’t tell the real story.

What matters is this: companies building internal agent marketplaces—treating agents as managed products with governance frameworks—are quietly pulling ahead. They’re not running scattered pilots anymore. They’re deploying reusable agents as organizational assets.

Most enterprises? Still stuck asking “should we build an agent?” Meanwhile, forward-thinking organizations are asking “how do we govern and orchestrate dozens of them?”

The Three Philosophies

AWS is betting on depth and runtime longevity. Azure is betting on ecosystem integration. Google is betting on openness and protocol standardization.

None of them are wrong. Your choice depends on where your organization lives today—and where you want it to be in 2027.

Here’s My Honest Take

The adoption of agentic AI—evidenced by marketplace growth projections and partner ecosystem expansion—signals that enterprises are moving from experimentation to production deployment.

Fifteen months ago, the conversation was “generative AI or not.” Today, it’s “which hyperscaler’s agent architecture aligns with our strategy?” That’s progress. But it also means the clock is ticking.

The gap between experimentation and real deployment is widening. Organizations that figure out governance, lifecycle management, and internal marketplaces now won’t just be faster—they’ll build moats their competitors can’t cross.

Where’s your organization in this journey? Are you still in pilot mode, or have you started thinking about what production-scale agent governance actually looks like?

I’m genuinely curious. Drop a comment.

From MLOps to LLMOps to AgentOps: Building the Bridge to Autonomy

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We didn’t just upgrade models—we changed the discipline. What used to be “model lifecycle management” is now autonomy lifecycle management. And with that, enterprises are facing a truth most haven’t yet operationalized: we now live in three overlapping worlds—Traditional AI, GenAI, and Agentic AI—each with its own workflow logic, tooling, and governance.

In traditional MLOps, workflows were deterministic: data in, prediction out. Pipelines were clean, measurable, and managed through platforms like MLflow, Kubeflow, BentoML, or Evidently AI. We focused on reproducibility, accuracy, and drift detection—predictable systems built for static decisions.

Then came LLMOps, and the equation broke. We moved to unstructured data, prompts, RAG, and safety filters. Non-deterministic outputs meant no two runs were ever the same. Suddenly, we were tracking token costs, hallucination rates, latency SLOs, and human feedback loops in real time—using stacks like LangChain, LlamaIndex, PromptLayer, Weights & Biases, and Credo AI.

Now we’re entering AgentOps—the autonomy layer. Systems act, reason, and collaborate through orchestrators like LangGraph, CrewAI, or AutoGen. AWS is already positioning AgentCore (on Bedrock) as the enterprise runtime—agents with persistent memory, context, and real-time observability. But the architecture shift isn’t just technical; it’s organizational. The winning model is “federated”: specialized teams with unified observability across all three layers—AI, GenAI, and Agentic AI.

When I sit with exec teams, I see the same pattern: most can build great models, but few can run parallel operational capabilities at once. And that’s the new muscle—keeping deterministic, generative, and agentic systems aligned under one governance fabric.

What makes the difference isn’t the flashiest demo; it’s boring excellence—clear SLOs, version control, cost discipline, and behavioral guardrails. That’s how we turn agents into trusted co-workers, not expensive chaos engines.

So here’s the question I leave leaders with: If your org had to strengthen just one layer this quarter—MLOps predictability, LLMOps safety, or AgentOps autonomy—where would you start, and how ready is your team to run all three in parallel?

Data Mesh was step one. 2026 belongs to agent ecosystems.

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I used to think “more catalogs, better lakes” would get us there. Then I watched agents start acting—not just assisting—and realized our data products weren’t ready for that responsibility.

Here’s the simple truth I’m seeing with executive teams: bad data becomes bad decisions at scale. If our contracts, SLOs, lineage, and internal marketplaces are weak, agents will scale the wrong thing—errors—at machine speed. That’s a board-level conversation, not an IT complaint.

What changes in practice?
We evolve the data operating model from “publish & pray” to agent-grade: data products with p95 latency targets, explicit access scopes, and traceable provenance. Hyperscalers are now shipping real agent runtimes (memory, identity, observability—and billing), which means the economics and accountability just got very real.

How I’m approaching it with leaders:

  • Certify data products for agents. Each product has an owner, SLOs (latency/freshness), and mandatory provenance. If it can’t meet its SLOs, it doesn’t feed agents—full stop.
  • Enforce least privilege by skill. Approvals are tied to the actions an agent can perform, not just the datasets it can see.
  • Make observability a product. Trace every call (inputs, tools, sources, cost, outcome). No trace, no production.

Practical next steps:
Start by mapping your top 10 data products to target agent skills and auditing them. Set SLOs. Assign owners. Then pick one product—implement policy-aware access and lineage capture, record evaluation traces for every agent call, and scale it. Afterwards, launch an internal Agent Marketplace that connects certified skills and certified data products, with change gates based on risk tier.

KPIs I push for:

  • % of agent invocations served by certified data products meeting SLOs (with recorded lineage)
  • $/successful agent task at target quality and latency
  • Incident rate per 1,000 runs (blocked vs executed)

Behind the scenes, the shift that surprised me most wasn’t technical—it was managerial. The winning teams treat this as work redesign: new ownership, new runbooks, new kill criteria. When we do that, agents unlock speed and resilience. When we don’t, they magnify our mess.

If you had to fix just one weak link this quarter—SLOs, provenance, or access controls—which would it be, and why?

EU AI Act´s General-Purpose AI Models (GPAI) Rules Are Live: How to prove Compliance next months.

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EU obligations for general-purpose AI kicked in on 2 Aug 2025. Models already on the market before 2 Aug 2024, must be fully compliant by 2 Aug 2027 – but boards won’t wait that long.

Over the past few weeks I’ve sat with product, legal, and model teams that felt “compliance-ready” … until we opened the evidence drawer. That’s where most programs stall. The good news: the playbook is clear now. GPAI Code of Practice (10 Jul 2025) gives a pragmatic path, and the Guidelines for GPAI Providers (31 Jul 2025) remove a lot of scope ambiguity. Voluntary? Yes. But it’s the fastest way to show your house is in order while standards mature.

Here’s how I’d tackle this —no drama, just discipline. First, align on who you are in the Act (provider vs. deployer). Then make one leader accountable per model and wire compliance into your release process.

My advice, Companies should:

  • Gap-assess every in-scope model against the Code. Do you have a copyright policy, a training-data summary, documented evals, and a working view of downstream disclosures? If any of those are fuzzy, you’re not ready.
  • Stand up model cards and incident logs; add release gates that block launch without evidence. Map risks to your cyber program using CSF 2.0 so Security and Audit can speak the same language.
  • Run an internal GPAI evidence audit. Publish an exec dashboard with: % of models with complete technical files and disclosures, incident MTTD/MTTR, and time-to-close regulator/customer info requests.

A quick reality check: big providers are splitting—some signalling they’ll sign the Code, others not. That’s strategy. Your advantage (especially if you’re an SME) is disciplined documentation that turns “we promise” into procurement-ready proof.

My rule of the thumb: if the CEO can’t see weekly movements on documentation completeness and incident handling, you are in pilot land – no matter how advanced the model sounds.

What would you put on a one-page dashboard to convince your CFO – and your largest EU customer – that your GPAI program in truly under control?

Agentic Operating Models: from Pilots to P&L

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We’re past the demo phase. Boards are asking a harder question: how do human-plus-agent workflows show up in cash flow—this quarter? There is a clear answer: The winners don’t “add an agent”; they redesign the work. That means owners, SLAs, guardrails, and value tracking—weekly. Not glamorous, just effective.

Here’s the short playbook I’d bring to the next ExCo:

  • Make Agents products. Name a product owner, publish SLAs (latency, accuracy, human-override rate), and set chargeback so value—and cost—land in the P&L.
  • Design human+agent flow, end-to-end. Pilots fail for organizational reasons. Tie every pilot to a customer metric and a service level from day one.
  • Build guardrails you can audit. Map risks to NIST’s Cyber AI Profile; log decisions, provenance, and incidents. “Trust” that isn’t evidenced will stall at Legal.

Does it pay?  Signals are real but uneven. A European bank modernization program cut 35-70% cycle time with reusable “agent components.” In KYC/AML, agent “factories” show 200-2000% productivity potential when humans supervise at scale. Klarna’s AI assistant handles  ~1.3M monthly interactions (~800 FTEs) with CSAT parity. Yet BCG says only ~5% are truly at value-at-scale, and Gartner warns ~40% of agentic projects could be scrapped by 2027. Operating model discipline determines who wins.

If I had 90 days:

  • 30: Inventory top 5 agent candidates; assign owners; baseline SLAs and override rates.
  • 60: Stand up an Agent Review Board (CIO/CDO/GC/CISO); add release gates and rollback.
  • 90: Ship two agents to production; publish a value dashboard (savings, cycle time, SLA hit rate) and decide scale/retire.

A candid note on risk: labor anxiety and model drift will erase ROI if we skip change management and runtime oversight. Bring HR and the 2nd line in early, and rehearse incidents like you would a cyber tabletop.

If we can’t show weekly value, SLA adherence, and audit-ready evidence, we’re still in pilot land—no matter how advanced the model sounds.

What would make your CFO believe – tomorrow – that an agent belongs on the P&L?

Agentic Mesh or Just Another Buzzword? Cutting Through the Hype

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Let’s be honest: most of us have sat through AI demos that looked impressive… and then quietly died in the pilot graveyard. Why? Because smarter models alone don’t create enterprise value. The real shift is moving from shiny pilots to system-level architectures—what McKinsey calls the Agentic Mesh.

I’ve seen this firsthand. When teams focus only on “better models,” they often miss the harder (and less glamorous) work: wiring agents together, defining guardrails, and making sure actions are auditable. That’s where scale either happens—or fails.

What are we learning as an industry?

  • Models matter, but architecture and process discipline matter more.
  • Standards like MCP and A2A are becoming the “USB-C of AI,” cutting down brittle integrations.
  • Governance isn’t optional anymore—ISO/IEC 42001, NIST AI RMF, and “human-on-the-loop” ops are quickly becoming the baseline.
  • We have to treat agents like digital colleagues: assign roles, permissions, even offboarding procedures.
  • And without proper observability—AgentOps, logs, kill-switches—autonomy can turn into automated chaos.

For executives, here’s what I’d do today if I were scaling this in your shoes:

  1. Name it. Create a platform team that owns the “mesh”—protocols, policy engines, memory hubs, observability.
  2. Start small, but measure big. Choose a few revenue- or cost-linked workflows, run shadow/canary pilots, and track hard KPIs.
  3. Bake in governance early. Build an agent registry, enforce least-privilege access, and red-team agents before production.
  4. Scale with discipline. Treat agent patterns like products—documented, reusable, and measured.

Here’s my takeaway: the winners won’t be those with the smartest model, but those who can turn agents into an integrated, trusted system—a digital workforce that’s secure, observable, and genuinely valuable.

👉 What’s been your biggest blocker moving from pilots to scaled AI systems—technology, governance, or people?

The New Cold War is Digital – and it’s Already Reshaping Global Power

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From artificial intelligence to quantum tech, we’re witnessing a seismic shift in how countries define influence and sovereignty. This isn’t just about innovation anymore — it’s about values, governance, and global positioning.

As someone deeply interested in tech policy and diplomacy, I’ve been reflecting on how major powers are approaching this transformation. Here’s a high-level look at how governments are navigating this new digital landscape:

Key Global Approaches to Tech Governance

  • EU: Leading the way with the AI Act — a bold, risk-based framework grounded in ethics and digital rights.
  • US: Prioritizing innovation with executive orders and a $1.7T bioeconomy push — focused on resilience, competition, and security.
  • China: Driving state-led initiatives like Made in China 2025 and massive quantum R&D for tech sovereignty.
  • UK: Taking a flexible, pro-innovation stance while fostering transatlantic cooperation.

Strategic Tech Domains to Watch:

  • AI Governance: From the EU’s regulatory leadership to China’s national security-driven controls, the race to shape AI norms is on.
  • Cybersecurity & Digital Sovereignty: NIS2 in the EU, new US cyber strategy, and China’s walled cyberspace — all aiming to secure digital autonomy.
  • Biotech & Biosafety: With the US and EU aligning bio-innovation to health and sustainability, we’re seeing biotech become a strategic pillar, not just a science issue.
  • Quantum Tech: Quantum is no longer future talk — the US, EU, and China are investing billions in what could be tomorrow’s defining tech advantage.

Multilateral Moves that Matter:

  • EU–US Trade and Tech Council (TTC) is shaping AI and semiconductor coordination.
  • UNESCO, OECD, G7: Working on global ethical frameworks.
  • Digital Partnerships (EU–Japan, UK–Singapore, etc.) show a real appetite for trusted, cross-border digital cooperation.

What’s the Common Thread?

Despite differences in approach, there’s a shared recognition that the future must be built on:

  • Ethical AI
  • Sustainable innovation
  • Strategic investment
  • Digital rights and accountability

A call to Action:

We need governance models that are collaborative, values-driven, and future-proof. The digital world is too important to be left to chance or rivalry alone.

Let’s build a digital future that supports democracy, development, and dignity!.

Curious to hear your thoughts: How do you see your country, company, or community navigating this new era?

Rethinking Data Operating Models: One Size Doesn’t Fit All

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If your data strategy isn’t delivering business impact, it’s time to rethink the Data Operating Model (DOM) behind it.
We often focus on tools and platforms, but without the right DOM, even the best data strategies struggle to scale, govern, or generate ROI. DOMs align strategy with execution—embedding governance, literacy, and data quality across the enterprise.

Five Proven DOM archetypes:

1.- Descentralized – Domain-led, mesh-style team
 ▪️ Pros: Flat, aligned with lines of business
 ▪️ Cons: Ownership gaps, legal risk
2.- Network – RACI-based structure layered over decentralization
 ▪️ Pros: Clarifies roles, retains flexibility
 ▪️ Cons: Complex to maintain
3.- Centralized – One team owns all
 ▪️ Pros: Speed, control
 ▪️ Cons: Low agility, tough for transformation
4.- Hybrid – CoE leads, domains execute
 ▪️ Pros: Best-practice factory
 ▪️ Cons: Hard to align, costly
5.- Federated – Subsidiaries empowered with central governance
 ▪️ Pros: Works at global scale
 ▪️ Cons: Requires maturity and resources

There’s no perfect model—just the one that best fits your size, culture, regulation, and maturity. DOMs should evolve: start lean, then grow as literacy, tech, and governance mature

Practitioner Takeaways: 
 • Anchor in a problem-back value story
• Publish a one-page DOM charter: integration, funding, accountability
 • Pilot federated or network models before scaling
 • Build trust by staffing CoEs with rotational talent
 • Track both data KPIs (e.g., completeness, timeliness) and business KPIs (e.g., ROI, forecast uplift)

Which model are you using? What’s working—and what’s not? Let’s elevate the conversation.

Why 90% of Companies Fail at Digital Transformation (And How Modular Architecture + AI Fixes It)

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Here’s a hard truth: Most enterprise architectures are built like medieval castles—impressive, rigid, and completely useless when the world changes overnight.

The $900 Billion Problem No One Talks About

While executives throw billions at “digital transformation,” they’re missing the fundamental issue. It’s not about having the latest tech stack or hiring more developers.

It’s about architecture.

Think about it: You wouldn’t build a house without blueprints, yet companies are running multi-billion dollar operations on architectural chaos. The result? They can’t adapt fast enough when markets shift, competitors emerge, or customer needs evolve.

The Four Pillars That Make or Break Your Business

Every successful enterprise runs on four architectural foundations. Get one wrong, and your entire digital strategy crumbles:

1. Business Architecture: Your Money-Making Blueprint

This isn’t corporate fluff—it’s how you actually create value. Your business models, processes, capabilities, and strategies either work together like a Swiss watch, or they’re fighting each other like a dysfunctional family.

Red flag: If you can’t explain how your business creates value in one sentence, your architecture is broken.

2. Data Architecture: Your Digital Nervous System

Data is the new oil, but most companies are drilling with stone-age tools. Your data models, flows, and APIs should work seamlessly together, not require a PhD to understand.

Reality check: If finding the right data takes your team hours instead of seconds, you’re bleeding money.

3. Application Architecture: Your Digital Muscles

Your applications should be lean, mean, and modular. Instead, most companies have Frankenstein systems held together with digital duct tape.

Warning sign: If adding a simple feature requires touching 15 different systems, you’re in trouble.

4. Technology Architecture: Your Foundation

This is your infrastructure, networks, and security. It should be invisible when it works and obvious when it doesn’t.

The test: Can you scale up 10x without your systems catching fire? If not, you’re not ready for growth.

The Million-Dollar Dilemma Every CEO Faces

Here’s where it gets real: Every business faces the same impossible choice—perform today or transform for tomorrow.

  • Focus on core business = make money now, but risk becoming irrelevant
  • Focus on transformation = maybe make money later, but struggle today

Most companies choose wrong. They either become innovation-paralyzed cash cows or transformation-obsessed startups that never turn a profit.

The Game-Changing Solution: Modular Architecture

Smart companies have figured out the cheat code: modularity.

Instead of choosing between today and tomorrow, modular architecture lets you do both. Here’s why it’s pure genius:

✅ Adapt in days, not years when markets shift
✅ Scale individual components without rebuilding everything
✅ Test new ideas without risking core operations
✅ Pivot instantly when opportunities emerge

Real talk: Companies with modular architecture adapt 3x faster than their competitors. While others are still having meetings about change, modular companies are already capturing new markets.

Where AI Becomes Your Secret Weapon

Here’s where it gets exciting. AI isn’t just another tool—it’s the ultimate architecture amplifier. But only if you use it right.

At the Business Level: AI predicts market shifts, mines hidden process insights, and simulates business models before you risk real money.

At the Data Level: AI automatically cleans your data mess, detects anomalies you’d never catch, and creates synthetic data for testing without privacy nightmares.

At the Application Level: AI monitors your systems 24/7, generates code that actually works, creates self-healing applications, and automates testing that would take humans months.

At the Technology Level: AI manages your cloud infrastructure, fights cyber threats in real-time, and optimizes everything automatically.

The Bottom Line (And Why This Matters Right Now)

The companies winning today aren’t the ones with the biggest budgets—they’re the ones with the smartest architecture.

While your competitors are stuck in architectural quicksand, modular architecture + AI gives you superpowers:

  • React to market changes in real-time
  • Launch new products at lightning speed
  • Scale without breaking everything
  • Innovate without sacrificing stability

Your Next Move

The brutal reality: Every day you delay building modular architecture is another day your competitors get further ahead.

The companies that embrace this approach won’t just survive the next market disruption—they’ll be the ones causing it.

The question isn’t whether you should build modular architecture enhanced by AI.

The question is: Can you afford not to?

What’s your biggest architectural challenge right now? Share in the comments.