Skip to main content

The Flow Illusion: Why Transformation Feels Like Theatre 

You’ve invested in the tools. Your teams have dashboards that track cycle time, throughput, and work in progress. You’ve likely even built a sophisticated, probabilistic roadmap. Yet, despite the data, it feels like theatre. The moment the workshop ends, that roadmap becomes a static slide deck, and teams remain paralyzed, waiting for you to update forecasts instead of taking ownership themselves. 

Paul Brown notes that most flow metrics programs fail not because of bad data, but because they build measurement systems rather than measurement capability. We end up with reports that go up the chain instead of insights that change team behavior. If your teams cannot interrogate their own flow data six months after you’ve set it up, you haven’t built capability—you’ve built dependency. 

The Hidden Friction Points 

If measurement theatre is the symptom, the disease often lies in the grey zones between our silos. Sadie B. Okiji has spent over 15 years navigating complex environments like the NHS, and she’s identified a brutal reality: value doesn’t fail because of poor methodology, but because it gets lost in the gaps between governance layers and team hand-offs. Organizations are often busy, but that effort is “expensive motion”—high-volume activity that fails to translate into end-user impact. 

To bridge these gaps, we must stop obsessing over rigid standardization. We need to expose the friction points that kill flow before it starts. The precondition for everything else is clarity of value, defined from the user’s perspective rather than the organization’s reporting structure. 

The Leadership Bottleneck 

Even when teams identify the waste and map the flow, execution often stalls. Alison Geskin argues that this isn’t an operational failure; it is a leadership misalignment. Her Strategy Flywheel™ framework addresses the invisible layer above value stream management (VSM). When leadership alignment breaks, the strategy simply cannot move through the organization. 

For practitioners, the task is to stop just diagnosing the plumbing and start fixing the leadership conditions that govern the pipes. We need to enable VSM practitioners to bring the right conversations to the executive layer by implementing a kill-before-you-launch filter that prevents capacity strain and ensures that, when we do move, we move with intent. 

The Trojan Mouse: A New Bottom-Up Approach 

So, how do we penetrate these command-and-control mandates without getting crushed by the weight of corporate inertia? Ortwin De Witte offers a subversive, highly practical solution. Rather than trying to implement “the board to rule them all,” he uses agentic GenAI tooling to introduce “Trojan Mice”—small, lightweight interventions that fit inside the existing organization. 

By creating Value Streamlets—fragments of a wider network that exist within teams’ existing tools, such as Azure DevOps—we can make real work visible again. The irony is that enterprises are desperate to adopt AI, but they cannot see where AI adds value until they can first see the work. By using AI to build flow visibility at the team level, we spark the exact conversations about flow that otherwise never happen. 

From Theatre to Impact 

The path forward is clear: We must stop building systems of reporting and start engineering systems of capability. This means: 

  • Paul Brown’s teach-to-fish model to ensure teams own their data. 
  • Sadie Okiji’s focus is on bridging the hidden friction points in the delivery lifecycle. 
  • Alison Geskin’s Strategy Flywheel™ to align leadership before launching initiatives. 
  • Ortwin De Witte’s Trojan Mouse approach to surfacing work through AI. 

We are moving away from the theatre of transformation and toward the architecture of actual delivery. 

Join the Conversation at Flowtopia Live 

The theories are only as good as the community that pressure-tests them. Flowtopia Live is where these concepts collide with real-world practice. Whether you are battling leadership misalignment, building measurement capability, or looking to deploy AI to make work visible, this is your forum. 

Join us on June 24th. Let’s stop managing the theatre and start engineering the flow. 

Exclusive Offer: As part of the TechStrong community, get a free LIVE pass. Use Code: TECHSTRONGFLOW Get your Live Pass to Flowtopia Live 2026 here 



from DevOps.com https://ift.tt/Bj90KcY

Comments

Popular posts from this blog

Mistral Moves Coding Agents to the Cloud — and Gets Out of Your Way

For the past year or so, AI coding agents have been tethered to your local machine. You kick off a task, watch the terminal, and babysit every step. It works — but it’s not exactly hands-free. Mistral just changed that. On April 29, the Paris-based AI company announced remote coding agents for its Vibe platform, powered by a new model called Mistral Medium 3.5. The idea is simple: Instead of running coding sessions on your laptop, they now run in the cloud — asynchronously, in parallel, and without you watching over them. What’s Actually New Coding sessions can now work through long tasks while you’re away. Many can run in parallel, and you no longer become the bottleneck at every step the agent takes. That’s the core pitch. You start a task from the Mistral Vibe CLI or directly from Le Chat — Mistral’s AI assistant — and the agent handles the rest. When it’s done, it opens a pull request on GitHub and notifies you, so you review the result inste...

Why the Software Development Tools you Choose Directly Affect Your CI/CD Reliability 

Most conversations about CI/CD reliability start in the wrong place. Teams debug flaky pipelines, investigate intermittent failures, tune alerting thresholds and optimize build times. All of that work is legitimate. However, the decisions that most directly determine whether a CI/CD pipeline is reliable or not were made months or years earlier, during tool selection. By the time teams are debugging pipeline reliability, they are usually dealing with the downstream consequences of upstream decisions that seemed reasonable at the time.   The software development tools a team chooses shape their CI/CD pipeline in ways that are not always visible during evaluation. Understanding those connections is the most practical starting point for teams that want reliable pipelines rather than better pipeline firefighting.   The Integration Surface Problem   Every tool in a software development stack creates an integration surface. Integration surface is the set of connections a tool has with oth...

Co-Developing an AI Native Observability Platform  

As AI capabilities continue to evolve, AI is becoming central to managing the growing complexity of distributed, hybrid enterprise environments, enabling more effective analysis, correlation, and automation across interconnected systems.   Traditional infrastructure and specifically network monitoring approaches, often built around siloed tools and static thresholds, struggle to keep pace with the scale, velocity, and interdependencies of modern systems. Further blurring the boundaries between network, application, and infrastructure domains makes it harder to isolate root causes and maintain operational resilience. In this context, AIOps platforms have emerged as one response to the growing need for integrated observability, automation, and data-driven decision-making.   At AI Field Day, Selector AI presented an AIOps platform, which can be considered a foundation for co-creating more adaptive and data-driven network operations. Rather than positioning it purely as a product choice,...