When the First Question Breaks the Enterprise

Finance followed. The problem, they said, was margin pressure and a cost structure that no longer fit what the market would pay. Inflation had eaten the cushion. Energy and logistics had moved against you. The company was running out of easy levers. Commercial leaned in. The problem was mix. Too many low margin orders, too many promises made before capacity was confirmed, too much discounting to keep volume flowing. HR pointed at turnover and the long runway to get new people competent. Digital talked about fragmented systems, bad data, and yet another generation of tools that did not quite talk to one another. Safety talked about incident rates. Supply chain talked about lead times and service failures. Within fifteen minutes the table held a small nation state of problems, each with its own language and statistics. Everyone was telling the truth from where they sat. Everyone was convinced that if their problem were solved first the company would be in a different place. You adjourned ninety minutes later with action items, charters, and steering teams. Six months on, the slides were sharper and the vocabulary more refined. The reality in the plants and the warehouses had not changed nearly as much as the PowerPoint. People were tired. Middle managers were stretched thin. The productivity curve was stubborn. The failure was not because you had the wrong people in the room or because they were not trying. The failure began the moment the first question was asked. “What problem are we solving” sounds like a discipline. In practice it gives every leader permission to shrink the world down to the slice they own. That is the fracture. Operations hears that question and feels machines that will not stay up, schedules that will not hold, labor they cannot fully staff. Finance hears it and sees earnings calls, debt covenants, and analysts who have lost patience for stories without numbers underneath. HR hears burned out supervisors, recruiting battles, and the slow erosion of trust. Digital hears technical debt and integrations that never quite finish. Each view is honest. Each is incomplete. The firm is not the sum of these local pains. It is a system that turns time, capital, and talent into value for customers and returns for owners. When that system weakens, every function feels it differently. If you let those feelings define the problem, you condemn the enterprise to work on symptoms at ten different altitudes while the core continues to drift. There is a different way to think. It starts by admitting that the first twenty percent of performance is simply choosing what counts as real.

You already know the rough breakdown. About twenty percent of outcomes comes from doing the right things. Another twenty from doing those things right. The remaining sixty from whether the organization can stay focused long enough for any of it to matter. If the first twenty is wrong, the rest becomes theater. In a modern manufacturing business the right thing is almost never a single functional problem. The right thing is the health of productivity itself. Not as a slogan about “doing more with less” but as a very specific promise. Over time your system must produce more value per hour, per dollar of capital, and per unit of human effort, without trading away safety, quality, or trust. Safety is not separate from this. Safety incidents are what you get when the system is overloaded, badly designed, or distracted. Quality issues are the same. So are late shipments, rising costs, and attrition spikes. They are not independent problems. They are how a failing architecture shows up in different corners of the firm. So the first move is to lift the question up one level. Instead of asking “What problem are we solving,” you start by naming the outcome that sits above the fragments. “Our job is to restore and compound productivity across this enterprise in a way that does not burn out people or hollow out the future.” Once that is on the table, the conversation changes. You are no longer refereeing a competition of pain. You are asking a different question entirely. “Given that outcome, what in the way we have designed and managed this company makes it impossible to achieve.” That question does not ask for a list of grievances. It asks for structural constraints. It invites people to think not in incidents or projects, but in patterns. Very quickly the same themes begin to surface. Information from the edge arrives too late and too filtered at the center. Decisions climb and descend through layers that were drawn for a slower age. No one is quite sure who has the final say when a trade off crosses functions. Coordinators and schedulers are drowning in handoffs. Supervisors spend more time feeding systems than coaching people. New tools are dropped on top of old ones, so the weight of navigation rises faster than the value of insight. You do not fix those with harder work or a new slogan. You fix them with design. Here is where the second twenty percent lives. For most of the last fifty years, productivity improvements came from better equipment, tighter processes, and smarter planning, supported by people who could absorb complexity because the pace of change was manageable. The firm was built around that reality. Data moved in batches.

Decisions were reviewed in weekly or monthly forums. Expertise sat at the top of the pyramid and the job of the rest of the organization was to execute with discipline. Today the visibility you have into your own operations dwarfs anything your predecessors enjoyed. Machines, lines, and plants can tell you what they are doing in real time. Your supply chain is lit up with status signals. Customer behavior can be tracked with a granularity that would have seemed intrusive a decade ago. Yet the design of most enterprises still assumes that insight lives in meetings. So information races forward while decisions lumber behind. People at the edge are surrounded by screens that tell them everything except what to do next. Executives are briefed with backward looking summaries that smooth over the very spikes and anomalies that matter. The firm has more data than it can digest and less clarity than it needs. Doing the right things right in this environment means accepting that the old architecture is no longer neutral. It is now an active drag. You cannot simply install another layer of analytics on top of that structure and expect a miracle. You have to redesign how decisions are made, who makes them, and what help they receive. That is where edge reasoning and agent like systems enter the story, not as a technology story but as a leadership choice. Imagine that instead of treating software as a reader of history, you treat it as a junior scientist stationed at the edge of the business. Its job is to watch what is happening line by line, order by order, compare that reality to patterns it has seen before, and suggest or carry out the next sensible move. When a machine starts to drift, it does not just flag an alarm. It proposes adjustments. When a product mix threatens to choke capacity, it does not just light up a dashboard. It proposes sequence changes, overtime choices, or pricing responses, and it learns from the outcome. This is not about replacing people. It is about removing the constant burden of stitching together half connected systems and rules in their heads. It is about letting human attention move to where it has the highest return. Coaching, problem solving, innovation, and the rare decisions where values and judgment truly dominate. For that to work you need an architecture of permission. You decide which decisions the system is allowed to make, which it can only recommend, and which stay entirely human. You make those boundaries explicit. You monitor them. You adjust them as trust grows. The deeper point is that this is not a “digital initiative.” It is a decision about how your enterprise thinks. If you make that choice seriously, the projects you fund start to look very different. You stop pouring money into one off tools that automate a sliver of a process while leaving the rest of the

chain untouched. You stop approving dashboards that cannot shorten a single meeting. You start asking, every time, whether a proposal will change the speed, quality, or location of decisions in a way that compounds productivity across years, not just across the next quarter. That brings you to the last and largest piece of the model. The sixty percent that is all about focus. Even with the right outcome and a better architecture, the enterprise can still quietly slide back into old reflexes. The reason is simple. Reality keeps throwing new shocks at you. A bad safety event. A customer who walks. A raw material crisis. A cyber incident. A shift in regulation. Each event arrives wrapped in urgency. Each carries its own fear. Boards and investors rarely ask whether you are still on the right ten year path. They ask what you are doing about this quarter’s surprise. It is very easy to answer that pressure with a new task force, a fresh metric, and yet another initiative. It is much harder to hold your ground and route the shock through the architecture you have already chosen. Focus does not mean ignoring the new problem. It means refusing to let the event redefine the problem every time. When a serious incident occurs in a plant you are trying to redesign around better decision support and lower mental load, you treat the incident as data about how far you still are from that design. You look at whether the tools were confusing, whether the procedures were mismatched to reality, whether fatigue and distraction were baked into the role. You strengthen the long term fix instead of bolting on extra compliance for the same exhausted supervisors. When a quarter is soft while you are investing in a new way of running the system, you may adjust pacing and spending. You do not cannibalize the very work that could pull you out of the pattern of lurching from one short term fix to another. You remember that productivity decline is rarely visible in one quarter and almost always obvious in ten years. This is where leadership earns its name. Many teams can describe the right outcome. Many consultants can sketch a better architecture. Very few organizations can resist the temptation to abandon both at the first hard shock. You do not need inspirational posters to hold that line. You need a few simple habits that you practice until they become part of the culture. You insist that every major decision, every new project, every reaction to a crisis be explained in terms of your chosen outcome and your chosen design. People must be able to say how this spend or this change will affect the way the company thinks and decides, not just which metric it moves in the next report. You refuse to sign off on work that cannot make that connection.

You shrink the portfolio of initiatives so that people can actually feel progress. A smaller number of deep changes beats a crowded field of half finished ones. You repeatedly explain to your own board that the firm is not just changing tools but changing the way it produces productivity. You show them how each element fits into that story. You report setbacks honestly and do not allow the narrative to drift back into a string of disconnected projects. Over time, if you hold to this, the feel of the enterprise begins to shift. Meetings that once revolved around slides of what went wrong start with a clear statement of the outcome you are all here to protect. Conversations that used to dissolve into negotiations between perspectives start to focus on the design of the system itself. Supervisors who spent their days chasing screens and filling reports start to have time for their people. They see that when a problem repeats, the question is not who to blame but where the architecture is weak. Engineers and analysts who were trapped in endless production of charts begin to work on models that both understand and shape reality at the edge. They see that their craft is no longer about describing history in more detail but about changing the probability of good outcomes tomorrow. People at the front line feel that the enterprise is slowly taking weight off their shoulders instead of quietly adding to it. They are still busy. Manufacturing will never be calm. But the noise begins to separate from the signal. The constant sense of barely keeping up starts to ease. From the outside, competitors may not see any of this unfolding. They will see only that your productivity line bends differently from theirs. That you can absorb shocks they cannot. That you turn visibility into advantage rather than anxiety. Inside, you will know that it began with something very simple. You stopped treating “What problem are we solving” as the first question. You replaced it with a harder one. “What outcome are we truly responsible for, what about the design of this firm makes that outcome impossible today, and are we willing to stay with that work long enough for the system to learn a new way to think.” Everything else flows from that. References: This article builds on the author’s original 20.20.60 performance model, developed in his work on manufacturing productivity and leadership learning rate, where outcomes are driven by doing the right things, doing those things right, and protecting focus so that improvement compounds over time. It also extends the author’s causal architectures for industry. including the system maps of stagnation and reduced productivity, the Accumulated Advantage and Decision Velocity frameworks, and the application of agentic AI as an “automated scientist”

operating at the edge of the enterprise to remove decision latency and cognitive overload. These original models are informed and pressure tested through the author’s work with LNS Research. Particularly the Productivity Pathfinders, IPI program and The World’s Most Productive Companies research, and through the LNS’s The COO Council, which serves as a living laboratory of global operations leaders confronting the realities described here. The ideas sit in conversation with the broader tradition of systems thinking and operations research in industry, including Deming’s quality and learning loop work and Judea Pearl’s causal reasoning.