Monday, October 12, 2026

Enterprise Network Modernization, Part 1: Draw the Map Before You Buy the Boxes




Ask five people in the same company what “network modernization” means and you will get five answers, each one shaped by the last vendor presentation that person sat through. One will say SD-WAN. Another will say SASE, or zero trust. The data centre folks will talk about AI fabrics and 800G optics. The cloud team will tell you the network is somebody else's problem now, it lives inside a VPC. And someone from finance will ask why all this costs so much and whether we can just rent it.

None of them is wrong, which is exactly the problem. Everybody is describing one piece and nobody is holding the whole thing. So the roadmap turns into a shopping list, the shopping list turns into a set of projects, and a few years later nobody can quite say who asked for what, why, or whether anything actually got better.

I wrote about the same itch for security a while back (The Enterprise Security Meta Model). Same spaghetti, different kitchen. This post is the networking version, and then a second part on something that bothers me even more: once you have the map, where do you get the evidence to decide anything?

If you are an executive, the map and the evidence section are the parts to read, and you can skim the technical asides. If you run or design networks, the asides are for you, and I would like to hear where you disagree.

Why I start with a map

Without a shared map, three things tend to happen.

First, overlap. Two teams quietly buy the same capability under different names. The firewall team and the WAN team both end up with a cloud security service, each with its own policy engine, each convinced theirs is the strategic one.

Second, gaps. Something important has no owner, and it is usually the thing that sits between two teams. Who owns the path from a branch office to a SaaS application? Ask three people, get three answers, and a fourth who says “it depends”.

Third, sequencing mistakes. Agentic operations get funded before anyone trusts the telemetry. Segmentation projects start before anyone knows what talks to what. You can guess how those go.

There is a second reason, and it is less obvious. Categories keep moving. Gartner published a Magic Quadrant for Single-Vendor SASE in mid-2024. A year later the same space was being evaluated as SASE platforms, and the 2026 edition is dated July. The technology did not change overnight. The label did. If your strategy is organized around somebody else's category names, it ages with their marketing calendar. I would rather own the map and then lay the analyst categories on top of it.

The map


Two questions organize the whole thing. Where does the traffic live? And what governs it? The first gives four domains. The second gives three planes. Under both sits a physical foundation that nobody wants to talk about and everybody depends on. 


That is 24 topics plus the foundation. It is not a magic number, it is just where I ended up after moving things around many times.

The one rule that makes it work: every topic has exactly one home, and the home is decided by who owns the buying decision, not by what the product brochure says. Consultants call this mutually exclusive and collectively exhaustive. I just call it not double-counting.

So SASE platforms sit under WAN, because that is where the traffic path gets bought, even though half of what they do is security. Quantum-safe appears once, under security. Segmentation appears once, under security, even though it gets enforced in campus switches, in DC fabrics and in cloud constructs. The moment you let a topic appear in two boxes, the map turns into a pile of overlapping boxes, and a pile is not a map.

Where the traffic lives

Data center. This is where the money and the heat are. Enterprise DC fabric is the familiar leaf-spine, EVPN-VXLAN world that carries the applications you already run. AI fabric is a different animal: the back-end network that ties GPUs together, with a few enormous, synchronized, loss-sensitive flows instead of lots of small ones, and the Ethernet or InfiniBand debate that comes with it. My view, and it is only a view, is that most enterprises are not building GPU clusters. They are renting capacity or calling APIs, and for them the AI question is less about 800G switches and more about connectivity, data movement and security around that rented capacity. Whether you need the AI fabric topic as a priority depends on which of those two worlds you actually live in. Edge or micro-DC is compute pushed closer to where data gets created, such as stores, plants and branches. The network question there is mostly about how many of them you will end up with and who is going to run them.

Campus and branch. The least glamorous domain and the one most of your users actually touch. Wired and wireless LAN are where Wi-Fi 7 and its predecessors live. Private 5G is for the cases where Wi-Fi is the wrong tool, such as large sites, mobility, or industrial areas. IoT and OT networking covers the devices that cannot run an agent, cannot be patched on your schedule, and were sometimes bought by a department that never told IT.

WAN and secure access. SD-WAN is the overlay that steers traffic across whatever transport you have. SASE platforms bundle that with cloud-delivered security, and the interesting argument inside most organizations is whether the network team or the security team owns the decision. Carrier WAN services are the underlay: the ISPs and telcos that carry it all, and the contracts that make switching hard. Optical or photonic transport sits below that, and it matters when you need a lot of bandwidth or predictable latency between a handful of sites. The conversation moves down the stack, and the people you need in the room change.

Multi-cloud and hybrid. Native cloud networking is every cloud's own constructs (VPCs, VNets, transit gateways), each slightly different. Multi-cloud platforms try to give you one policy and one view across them. Cloud connect, or SDCI, is the software-defined private on-ramp to those clouds. Service mesh and PaaS overlays are application-level networking inside platforms like Kubernetes, and they are usually owned by platform teams, not network teams, which is where a lot of quiet friction comes from.

What governs it

Operations and intelligence. Three items, and the order matters. Observability and digital experience is how you see. Automation and orchestration is how you act, deterministically, from a specification. Agentic NetOps is how you let software decide, and that is a different thing from automation, not a bigger version of it. Automation is governed by the spec: it either does what was written or it does not. A learned system is governed by measuring its behavior over time, with confidence levels and drift. Treat them as the same thing and you will govern one of them badly. You cannot automate what you cannot see, and you should not let an agent act on what you cannot verify.

Security and trust. Zero trust and segmentation is deciding who may talk to whom, and it is hard because it is mostly an organizational problem wearing a technical costume. Hybrid mesh firewall is the idea of consistent firewalling across on-prem, cloud and branch under one management plane. Quantum-safe is about cryptography that survives a future quantum computer, which matters now because some of what you send today will still be sensitive then. Network security basics is the unglamorous baseline controls that still need to be in place before anything fancier is worth discussing.

Consumption and sourcing. NaaS is a commercial model, not a technology. What you are really buying is a contract, so read the exit terms before you read the brochure. Managed and co-managed services answer a different question: who runs it, and who carries the pager.

Foundation. Fibre, cabling and optics. Twenty-year assets, slow to change, and the mistakes here are the most expensive to reverse.

What the map does not cover, and where it is weak

Satellite and LEO connectivity, DNS and IP address management, voice and collaboration, and whatever 6G turns out to be are not on it. Add them when they matter. The structure should make it obvious where they would go.

It is also not perfect. SASE straddles two worlds and any placement is a compromise. Edge or micro-DC could just as easily live under campus and branch. And a map is for deciding what to talk about, not for designing anything. A design still needs requirements, constraints and trade-offs, and no box on a slide replaces that.

What to do with it

Twenty-four topics cannot all be priorities, and if they are, nothing is. Pick a few. For an exercise like this, four to six is plenty.

For each candidate, five questions, in plain language:

  1. What business outcome is pulling on this? If you cannot name one, stop here.
  2. Where are we today, honestly?
  3. What breaks, or gets expensive, if we do nothing until 2030?
  4. What does it depend on? SASE depends on identity being in reasonable shape. Agentic operations depend on observability and automation. Segmentation depends on knowing what talks to what.
  5. What evidence do we have, and how good is it?

The fifth one is where most plans quietly fall apart, so the rest of this post is about that.

What comes next

The map tells you what to talk about. It does not tell you what to believe. In Part 2 I cover where I look for evidence before any of these topics gets a budget, how I read analyst reports without being fooled by the top right corner, and why I rate the evidence separately from the technology.  

HTH...

A Tech Artist 🎨

Wednesday, October 7, 2026

From Pass-Through to Proof: Overcoming the Gartner & IDC Blind Spot in Enterprise Advisory

 



What Traditional Analysts Miss

Traditional industry analysts prioritize market presence, revenue trajectory, and execution scale over technical depth. By evaluating products primarily through commercial readiness, logo adoption, and high-level feature checkboxes, legacy reports overlook underlying architectural mechanics. This creates a critical blind spot: market "Leaders" are often recommended based on brand stability rather than integration feasibility, frequently leading enterprises to adopt bloated, overprovisioned platforms that introduce technical debt and policy fragmentation.

What Architectural Analysts Bring

Emerging technical analysts address this deficit by evaluating solutions from the code and primitive level up. Rather than scoring marketing messaging or corporate viability, they test how an architecture actually translates into real-world capabilities—such as securing AI agents or fitting cleanly into multi-cloud environments. These briefings provide advisory teams with an objective, low-risk environment to stress-test technical trade-offs, evaluate niche versus platform strategies, and verify compatibility before making enterprise commitments.

Where Architectural Analysts Fall Short

Conversely, purely architectural evaluations lack the macro-level risk management that enterprise boards require. They do not measure financial solvency, global support infrastructure, long-term vendor viability, or broad peer-deployment history. A solution may possess an elegant architecture, but if the vendor lacks the scale or financial runway to survive an enterprise procurement cycle, recommending it introduces operational and commercial risk.

The Symbiotic Dual-Lens Framework

Neither evaluation model is sufficient on its own; true advisory leadership requires both. Traditional analysts validate market viability and governance compliance (answering “Will this vendor exist in five years?”), while architectural analysts validate technical fit and stack efficiency (answering “Will this actually work in our environment?”). Combining both frameworks bridges the gap between commercial safety and technical reality, enabling technology advisors to deliver strategies that satisfy both risk-conscious procurement teams and speed-driven technical architects.


HTH...

A Tech Artist 🎨

Friday, March 6, 2026

Problem Framing vs. Solving

 


A quick google search about "Best Books - Problem Solving" will come with 10s if not 100s of recommendations and best sellers list such as: https://www.amazon.com/Best-Sellers-Decision-Making-Problem-Solving/zgbs/books/2679

Now if you search for "Best Books - Problem Framing", you with either see the problem under the category of problem solving, maybe in few cases the books on "Systems Thinking" will appear in the search.


So while 99.99% books will teach you "problem solving", there are just handful and not well known books out there on "problem framing". You will likely come across recommendations in that category from your friends in consulting circle.


If you are ever go to one of those popular consulting schools, they taught me that brilliant people fail when they answer the wrong question.

Don’t just answer questions. Frame them. Because a brilliant answer to the wrong question is still wrong. Ask, “How do we make customer support more efficient?” and everyone races to cut headcount or automate.

You might save dollars and bleed trust. Try this instead: “What service approach builds loyalty while balancing cost?” Now you are designing for humans, not just a spreadsheet. How you frame a question shapes what you notice, what you measure and what you ship. Daniel Kahneman and Amos Tversky called this the framing effect. It’s one of the most underrated leadership skills.

I learnt the value of spending time on framing the question during my time at consulting schools.


At first it felt forced. But projects (case studies) where we invested serious time up front to define the question led to sharper insights, faster decisions and happier teams & clients.

When we didn’t take the time, chaos reigned. Put it into practice this week: 1. Question the question. ↳ What assumptions are baked in? What if you flipped it on its head? 2. Start at the finish line. ↳ Define outcome or experience you want, then trace back the decisions and actions that create it. 3. Make space for the devil’s advocate. ↳ Assign someone to challenge whether you’re even solving the right problem. If you work with data or roll out new tech, your analysis is already shaping outcomes. Make sure you’re shaping the right ones. Have you ever felt like you’ve missed the mark on the question you’re answering?
What's one question your team has been wrestling with that might need a reframe?

Further Readings:






HTH...

A Tech Artist ðŸŽ¨

Friday, October 3, 2025

A Short No Nonsense Guide To Succeed With The Agentic AI - To Drive Business ROI & Maintain Sanity

 







If you're diving into the world of Agentic AI, you're probably hearing a lot of hype. These are AI systems that act autonomously—like little digital agents handling tasks, making decisions, and even chaining actions together to solve complex problems. Think of them as supercharged assistants that don't just respond to prompts but actually pursue goals on their own. Sounds revolutionary, right? But let's cut through the noise: Agentic AI isn't some magical wand that's going to transform your business overnight. It's a tool that demands smart planning, execution, and a healthy dose of realism to deliver real value and ROI.

The truths? When done right, Agentic AI can streamline operations, boost efficiency, and open up new possibilities in areas like customer service, supply chain management, or even creative workflows. The lies? It's not "set it and forget it" tech. A lot of these systems lean on large language models (LLMs) like GPT or similar public ones to handle reasoning and language tasks. But here's the kicker: despite all the buzz, LLMs aren't truly reasoning in a human-like way. They excel at pattern matching and generating plausible outputs based on massive data, but they lack genuine cognition—no real understanding, no common sense beyond what's trained in, and they can hallucinate or falter on novel problems. Studies and real-world tests show they're more like sophisticated parrots than thinkers. So, if you're building Agentic AI, expect to integrate LLMs, but don't rely on them for deep reasoning without safeguards.

The key to success? Treat it like any major project: break it down into phases with clear goals across short-term (quick wins), mid-term (scaling up), and long-term (transformation) horizons. Define your path, measure success not just in tech metrics but in business ROI—like cost savings, revenue growth, or improved user satisfaction. Below, I'll walk you through a practical thinking process to make this happen. This isn't theoretical fluff; it's a roadmap based on how real teams are approaching it.

1. Narrow Down on a Use Case and Get Your Basics Sorted

First things first: don't boil the ocean. Pick a specific use case where Agentic AI can shine—maybe automating report generation in finance or handling inventory predictions in retail. Once you've narrowed it, map out the essentials.

  • Sponsors and Actors: Identify who’s championing this (e.g., C-suite execs for funding) and who’s involved day-to-day (like developers, domain experts, or end-users). Get buy-in early to avoid roadblocks.
  • Scope: Define boundaries—what's in (e.g., handling customer queries) and what's out (e.g., no legal decisions). This keeps things focused.
  • Success Metrics: Set measurable goals. Short-term: Completion rate of tasks. Mid-term: Reduction in manual hours by 30%. Long-term: 20% ROI boost through efficiency gains.
  • Options and Players: Explore what's out there—open-source tools like LangChain for agent frameworks, or vendors like Microsoft’s AutoGen. Assess competitors or similar implementations.
  • Readiness and Assessment Methods: Audit your current setup. Do you have the data infrastructure? Use checklists or pilot audits to gauge gaps, like running a SWOT analysis on your tech stack.

This step is your foundation. Skip it, and you're building on sand.

2. Build vs. Buy vs. Co-Develop: Choose Wisely

Now, decide how to get your Agentic AI off the ground. Building from scratch gives customization but eats time and resources—think months of dev work integrating LLMs with custom logic. Buying off-the-shelf (e.g., pre-built agents from Hugging Face or enterprise solutions) is faster for short-term wins but might not fit perfectly, leading to integration headaches.

Co-developing? That's often the sweet spot—partner with a vendor or open-source community to tweak existing tools. Weigh costs: Building might cost $100K+ in dev salaries, buying could be a subscription at $10K/year, co-dev a hybrid. Factor in your team's skills and timeline. For mid-term goals, aim for something extensible; long-term, ensure it scales without vendor lock-in. Remember, since agents often hook into public LLMs, test for reliability—LLMs can change APIs or behaviors, so have fallbacks.

3. POC and Fine-Tune: Test the Waters

Jump into a Proof of Concept (POC) to validate your idea without going all-in. Start small: Build a prototype agent for your use case, say one that automates email responses using an LLM backbone.

Run experiments—feed it real data, simulate scenarios, and iterate. Fine-tune by adjusting prompts, adding rules-based logic where LLMs fall short (e.g., hard-coded checks for accuracy), or even training on domain-specific data if possible. Measure against your success metrics: Did it handle 80% of tasks correctly? Track failures—LLMs might "reason" wrong on edge cases, so log them.

This phase is short-term focused: Aim for a working POC in weeks, not months. Use it to gather feedback and refine, ensuring the agent isn't just flashy but actually useful.

4. Scale and Integrate: Make It Part of the Ecosystem

Once the POC shines, ramp up. Scale by deploying to more users or data volumes—move from testing 100 tasks to 10,000. Integrate with existing systems: Hook your agent into CRMs like Salesforce or databases via APIs.

Watch for bottlenecks—LLMs can be slow or costly at scale, so optimize with caching or hybrid models (rules + AI). Mid-term goals here: Seamless integration without disrupting ops, hitting metrics like 99% uptime. Plan for data flows: Ensure secure, compliant connections, especially if using public LLMs that might expose sensitive info.

5. Parallel Run for Ops: Safety Net Mode

Don't flip the switch abruptly. Run your Agentic AI in parallel with human processes—let it shadow real ops, comparing outputs. For example, have the agent suggest inventory orders while humans approve.

This builds confidence and catches issues, like LLM hallucinations leading to bad decisions. Short to mid-term: Monitor discrepancies, refine based on them. Success metrics? Alignment rate with human judgments, say 95%. It's your buffer to ensure reliability before full rollout, minimizing risks in critical areas.

6. Pick on Skills and Get Experience: Level Up Your Team

Agentic AI isn't plug-and-play; it needs skilled hands. Identify gaps—do you need prompt engineers, data scientists, or ethicists? Invest in training: Workshops on agent frameworks or LLM limitations.

Gain experience through hands-on projects. Start with internal hackathons or collaborate on open-source agents. Long-term: Build a center of excellence. This drives ROI by reducing dependency on externals—your team becomes the asset, turning one-off successes into repeatable wins.

7. Strengthen Guardrails and Improve Observability: Keep It Safe and Transparent

Here's where you address the lies head-on. Since LLMs lack true cognition, add guardrails: Rules to prevent harmful actions, bias checks, or human-in-the-loop for high-stakes decisions.

Boost observability with logging tools—track every agent decision, input/output, and LLM call. Use dashboards for real-time monitoring. Mid to long-term: Evolve these as threats emerge, like new LLM vulnerabilities. Metrics? Error rates below 1%, compliance scores. This isn't optional; it's what turns potential disasters into managed risks, ensuring sustainable success.

8. Takeover and Transform to Drive ROI: The Big Payoff

Finally, let the agent take over where it excels, phasing out manual processes. Transform your ops—reallocate humans to creative tasks, unlocking innovation.

Drive ROI by tracking hard numbers: Cost savings from automation, revenue from faster decisions. Long-term: Evolve the agent ecosystem, maybe chaining multiple agents for complex workflows. But stay vigilant—regular audits, updates for LLM advancements. If done right, this isn't just tech; it's a business multiplier.

In wrapping up, Agentic AI is powerful, but only if you ditch the hype and embrace the grind. By following this phased approach, you're not chasing magic—you're engineering success. Plan meticulously, measure relentlessly, and integrate wisely. The result? Real, tangible wins that boost your bottom line. If you're starting out, pick one use case and iterate from there. You've got this!


One-page checklist (print this)

  • Named sponsor & owner, signed success metrics

  • Golden set & evaluator defined

  • Guardrails v1 (policy + filters + approvals)

  • Shadow run results and incident log

  • Integration plan (APIs, data, auth, SLOs)

  • Observability dashboards live

  • Parallel-run exit criteria met

  • ROI model and phase-out of legacy step




HTH...

A Tech Artist ðŸŽ¨

Wednesday, October 1, 2025

Have you tested your strategy lately?


Ten timeless tests can help you kick the tires on your strategy, and kick up the level of strategic dialogue throughout your company.

 

Test 1: Will your strategy beat the market?

Test 2: Does your strategy tap a true source of advantage?

Test 3: Is your strategy granular about where to compete?

Test 4: Does your strategy put you ahead of trends?

Test 5: Does your strategy rest on privileged insights?

Test 6: Does your strategy embrace uncertainty?

Test 7: Does your strategy balance commitment and flexibility?

Test 8: Is your strategy contaminated by bias?

Test 9: Is there conviction to act on your strategy?

Test 10: Have you translated your strategy into an action plan?



HTH...

A Tech Artist ðŸŽ¨