Stop Blaming the Wave

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Written By Jeff Winter

I believe we are entering a period where the biggest competitive gap between companies will not be technology access. It will be organizational readiness.

That sounds simple, but I do not think most leadership teams are being honest about it. Every company can buy access to cloud platforms, automation tools, AI copilots, analytics platforms, modern infrastructure, and consulting support. The menu of technologies has never been larger. The problem is not that companies lack options. The problem is that many companies have built organizations that are too slow, too fragmented, too culturally cautious, and too technically burdened to take advantage of those options when they matter.

Technology has become the easiest scapegoat in business. AI is too risky. Cloud is too complex. Automation is too disruptive. Digital transformation is too expensive. The new platform is too hard to adopt. The old system is too important to touch. Some of that is true, of course. New technology does create real risk. Integration is hard. Change is expensive. Poorly governed AI is dangerous. Legacy systems often support critical operations.

But I think those explanations often hide a more uncomfortable truth: many companies are not waiting because the technology is immature. They are waiting because they are.

The Past Usually Makes the Stronger Argument

In most companies, the past has a very persuasive case. It has history on its side. It has budget already spent. It has processes people know how to survive. It has systems that are painful, but familiar. It has leaders who want innovation, provided it does not disturb the operating model that made them successful.

That is why old ways of working are so hard to dislodge. They are rarely defended with foolish arguments. They are defended with reasonable ones.

“We need to protect the business.”
“We cannot disrupt operations.”
“We need more alignment first.”
“We need a stronger business case.”
“We should wait until the technology matures.”
“We already have too many priorities.”

Individually, these statements may be valid. Collectively, they can become a very polished way of doing nothing. This is where leadership teams need to be more honest with themselves. At some point, caution becomes delay. Delay becomes habit. Habit becomes identity. And once an organization starts to see slowness as responsibility, transformation gets very difficult.

One useful antidote is the Playing to Win strategy framework. It forces leaders to answer five deceptively simple questions: What is our winning aspiration? Where will we play? How will we win? What capabilities must we build? What management systems are required? The reason I like this framework is that it exposes whether a company has made actual choices or simply collected ambitions. Roger Martin’s own page frames the work as making strategy simpler and more effective, and the HBR toolkit describes it as a framework for making the right choices to stay ahead of competition.

A lot of technology conversations fail because they start at the wrong level. They begin with “Should we use AI?” or “Should we move to the cloud?” instead of “What kind of company are we trying to become, and what capabilities will that require?” Playing to Win helps move the discussion from tool selection to strategic intent. It makes it harder for legacy arguments to hide behind vague language like “innovation,” “efficiency,” or “modernization.”

Another helpful tool is a simple modernize, optimize, transform portfolio classification model. Modernize means improving the foundation. Optimize means making the current business better. Transform means changing how value is created, delivered, or captured. The categories matter because too many companies confuse foundational cleanup with transformation. Replacing old infrastructure may be necessary, but it is not automatically strategic. Likewise, launching an AI pilot may look transformational, but if it only automates a bad process, it may simply make dysfunction faster.

This kind of portfolio discipline helps leaders stop putting every initiative into the same bucket. It also helps expose the real tradeoffs. If everything is transformational, nothing is. If everything is urgent, nothing is prioritized.

What Technology Really Threatens

A lot of companies do not resist technology because they misunderstand the future. They resist it because they understand exactly what the future threatens.

It threatens the workaround economy. Every organization has one. It is the informal system of manual fixes, shadow processes, spreadsheet reconciliations, side conversations, tribal knowledge, and local heroics that allow the business to function despite its formal systems. People become very good at navigating dysfunction. In some cases, their perceived value inside the company is tied to their ability to make broken things work.

New technology threatens that.

It also threatens local kingdoms. Better data, connected systems, and transparent workflows expose how decisions are made, where bottlenecks live, where accountability is missing, and where value is actually created. That can be uncomfortable. A fragmented organization gives people room to protect turf. A connected organization makes that harder. And perhaps most importantly, technology threatens the comforting idea that “we are being thoughtful” when everyone knows the same conversation has been happening for five years.

That is the part many companies do not want to admit. They are not always studying the future. Sometimes they are negotiating with the past.

This is where Kotter’s 8-Step Change Model can be very helpful as a diagnostic tool. Kotter’s model starts with creating urgency, building a guiding coalition, and forming a strategic vision. Those sound basic until you realize how many transformation programs skip them. They announce the project, fund the technology, assign the implementation team, and assume the organization will eventually catch up. Kotter’s official methodology page explains that the 8-Step Process came from Dr. John Kotter’s observations of leaders and organizations trying to transform or execute strategy. Kotter is helpful because it reminds leaders that change is political, social, emotional, and operational. People do not resist change only because they are stubborn. They resist when the case is unclear, the incentives are misaligned, the risks feel personal, or the people asking for change do not appear committed themselves.

The Prosci ADKAR Model is another useful lens because it brings change down to the individual level: Awareness, Desire, Knowledge, Ability, and Reinforcement. I like ADKAR because it prevents leaders from treating adoption as a communications problem. Sending the announcement is not awareness. Awareness is when people understand why the change matters. Training is not ability. Ability is when people can perform differently in the real operating environment. Go-live is not reinforcement. Reinforcement is when the organization rewards the new behavior enough that people do not drift back to the old one. Prosci describes ADKAR as a model focused on individual change, based on the idea that organizational change only happens when individuals change.

If a company is trying to adopt AI, connected operations, modern data platforms, or new automation capabilities, ADKAR can quickly reveal where the real failure point sits. Maybe people know what is changing but do not want it. Maybe they want it but do not know how to use it. Maybe they can use it, but their managers still reward the old behavior. That is not a technology problem. That is an operating system problem inside the organization.

Readiness Is Becoming the Real Strategy

The companies that win the next decade will not necessarily be the ones that chase every new tool first. I am not arguing for reckless adoption. In fact, I think the “move fast and break things” mentality is especially dangerous in industries where safety, quality, uptime, compliance, and trust matter.

But I am arguing that readiness is becoming a strategy in itself.

Readiness means your data is usable enough to support better decisions. It means your architecture is not so fragile that every new capability requires a heroic integration effort. It means your operating model can absorb change without treating every improvement as an exception. It means your people understand why the company is changing, not just what technology is being deployed. It means leaders can make tradeoffs without hiding behind endless alignment cycles.

Readiness also means being willing to retire things that still technically work but strategically hold the company back. That is one of the hardest leadership moves to make. Broken things are easy to criticize. Familiar things are harder. The most dangerous systems, processes, and habits are often not the ones failing loudly. They are the ones quietly limiting what the company is capable of becoming.

For industrial companies, this is where maturity models can be extremely helpful. Frameworks like the Smart Industry Readiness Index and the acatech Industrie 4.0 Maturity Index give companies a structured way to understand where they are, not just where they wish they were. The value is not the score itself. The value is the conversation the score forces. INCIT describes SIRI as a framework for assessing Industry 4.0 readiness, benchmarking performance, identifying gaps, and driving digital transformation; acatech describes its maturity index as a six-stage model that analyzes capabilities across resources, information systems, culture, and organizational structure.

A good maturity model makes gaps visible across dimensions such as technology, process, organization, data, integration, and culture. That matters because many companies overestimate their readiness by looking at isolated examples. They see one advanced pilot, one automated line, one dashboard, one data science team, or one impressive vendor demo and convince themselves they are further along than they are. Maturity models help separate pockets of excellence from enterprise capability

For architecture and integration, companies can also benefit from using reference models and standards such as ISA-95 in manufacturing environments. ISA-95 helps define the relationship between enterprise systems and control systems, which is critical when companies are trying to connect business planning, manufacturing execution, operations, and shop floor data. It is not exciting in the way AI is exciting. But that is exactly the point. The exciting things usually depend on the boring things being well understood. ISA describes ISA-95 as an international set of standards for integrating logistics systems with manufacturing control systems.

Similarly, TOGAF or lighter-weight enterprise architecture practices can help companies map current-state systems, target-state architecture, integration patterns, data flows, and capability gaps. The goal is not to create a giant architecture bureaucracy. The goal is to stop treating every new initiative as a one-off construction project. Without architectural discipline, every wave of technology adds another layer of complexity. Eventually, the organization is not transforming. It is accumulating sediment. The Open Group describes TOGAF as a proven enterprise architecture methodology and framework used by leading organizations

The Foundation Is Not Separate from the Future

One mistake I see often is treating foundation work as less strategic than innovation work. Executives want the vision, the AI roadmap, the future-state operating model, the impressive use cases, and the board-level narrative. All of that matters. But if the foundation is weak, the roadmap becomes theater.

This is where DAMA-DMBOK and practical data governance become essential. Companies do not need academic perfection, but they do need clear ownership, definitions, quality expectations, access rules, and accountability for critical data domains. The practical question is: what data must be trusted for the business to make better decisions, automate work, or enable AI? Start there. Define owners. Establish standards. Measure quality. Fix the highest-value data domains first. Data governance fails when it becomes abstract. It works when it is tied to decisions that matter. DAMA International describes its mission around advancing data management and positions well-managed data as foundational to decision-making.

Another useful practice is Value Stream Mapping, borrowed from lean. It helps companies visualize how work actually flows across functions, systems, approvals, delays, and handoffs. This is especially powerful before applying automation or AI because it reveals whether the company is improving the work or just digitizing friction. The Lean Enterprise Institute defines value-stream mapping as diagramming every step in the material and information flows needed to bring a product from order to delivery.

Too many companies automate the process they have rather than redesigning the process they need. Value Stream Mapping forces the organization to look at waiting time, rework, approvals, information gaps, duplicate entry, and unnecessary handoffs. It turns vague complaints about bureaucracy into something visible. And once visible, it becomes harder to defend.

The Waves Are Getting Bigger

The pace of change is not slowing down so companies can catch up. The waves are getting bigger, faster, and closer together.

Cloud was a wave. Automation was a wave. Digital transformation was a wave. AI is a wave. Physical AI, autonomous operations, intelligent infrastructure, and new forms of human-machine collaboration are already forming behind it.

This current wave feels massive, and it is. But I suspect it may look small compared to what is coming next. That should make leaders uncomfortable, but not paralyzed. The answer is not to chase every wave. The answer is to stop pretending the company can keep standing still in the water and call that discipline.

Every leadership team should be asking harder questions right now. Where are we genuinely being prudent, and where are we protecting legacy? Which systems, processes, incentives, and cultural norms make us slower than we admit? What do we keep calling a technology problem that is actually an operating model problem? What decisions are we postponing because the current business still performs well enough to avoid the hard conversation?

The companies that are ready will ride these waves into new advantages. The companies that are not will keep calling the ocean unfair.

References:

Jeff Winter
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