Forget Prediction. Build Readiness

My Role Isn’t to Predict the Future, It’s to Make It Less Surprising

I get asked the same question more times than I can count: What’s coming next?
What’s the next big shift in manufacturing? What’s going to happen with AI, with MES, with automation, with the workforce of the future?

And I understand why people ask. Because of my role as a thought leader and influencer in this space, I have access to more data, more conversations, and more of the people shaping the industry than most. I see what is being tested in labs, discussed in boardrooms, and piloted quietly behind factory walls.

But let me be clear: I am not an economist or a futurist. I am not here to predict the next revolution or make bold declarations about what year everything changes. My role is different.

I am someone who has a pulse on the industry and enjoys guiding manufacturers through their digital transformation journeys. I study the signals, connect the dots, and help leaders make sense of where technology meets practicality. My job is to translate what’s possible into what’s actionable.

Predicting the future is overrated. What matters is making it less surprising.

Stop Chasing Predictions, Build Readiness

Prediction feels decisive, but readiness is what creates control. You cannot control when the next disruption hits, but you can control how ready you are to respond.

Readiness means you have built the architecture, governance, and culture to absorb new technology at a sustainable pace. It allows you to run focused experiments, extract insight, and scale what works. It enables faster, data-driven decisions without waiting for perfect information. In short, readiness builds resilience, and resilience compounds.

Here are a few simple ideas that anchor readiness in practice:

• Shorten the loop between sensing and deciding.
  Apply the OODA loop (Observe, Orient, Decide, Act) as a management cadence, not just a strategy model. Map how data moves from the factory floor to decision-makers, identify bottlenecks, and reduce the latency between insight and action.

• Use continuous learning frameworks.
  The PDCA cycle (Plan, Do, Check, Act) remains a powerful structure for digital transformation. Every initiative should conclude with tangible learnings, not just deliverables. Lessons learned in one project should make the next one faster and smarter.

• Follow integration standards.
  ISA-95 provides a common language for aligning enterprise, manufacturing, and control systems. It prevents the “integration spaghetti” that traps companies in pilot purgatory and ensures that system boundaries and responsibilities are clear.

• Operationalize responsible AI.
  Use the NIST AI Risk Management Framework as your guide. It offers a practical approach to identifying, measuring, and mitigating AI risk without slowing innovation. Governance does not have to be heavy; it just has to be intentional.

Readiness is not a slogan; it is a habit. The best manufacturers make readiness routine. Everyone knows the quarterly objectives, the decision rights, the data they can trust, and the handful of metrics that matter.

The Playbook: How to Make the Future Less Surprising

Readiness turns strategy into repeatable patterns. Below is a simple playbook that helps manufacturers move from theory to scalable practice.

1. Pick one value stream as your proving ground.
   Focus on a business problem that matters. Define a measurable outcome such as reducing downtime, improving yield, or shortening lead time. Make the goal achievable within 90 days, assign ownership, and fund it adequately.

2. Start with the decision, not the data.
   Many organizations start by collecting everything and hope value appears. Instead, identify a single recurring decision that would materially improve with better visibility or prediction, then design your data flow backward from that decision.

3. Use ISA-95 to avoid integration traps.
   Understand where data originates, where it is contextualized, and where it drives enterprise decisions. Following the levels of ISA-95 (0 through 4) ensures coherent connections and prevents reinventing integrations at every site.

4. Establish lightweight AI governance early.
   A single page can define how models are validated, who approves use cases, and how bias or drift is monitored. Align it to NIST’s AI RMF to maintain credibility with IT, quality, and legal teams while preserving speed.

5. Adopt a 30-60-90 cadence that focuses on decisions, not decks.
   At 30 days, demonstrate a traceable decision: what data was used, how it informed action, and what the result was. At 60 days, show stability and adoption. At 90 days, decide whether to scale, refine, or retire. This keeps transformation rooted in outcomes, not presentations.

6. Measure learning velocity.
   Track how quickly teams turn data into decisions. Metrics like time from signal to action, percentage of decisions made using recommended insights, and data completeness reveal whether the organization is actually becoming smarter.

7. Budget for scale from the start.
   Success should mean more than completing a single pilot. Design your architecture so that the third site can deploy in one-third the time of the first. Reuse data models, integration templates, and training. Scale is not a final step; it is a design choice.

Scaling What Works: From Pilot to Muscle

Most transformations fail not because the ideas are bad but because they do not scale. Readiness at scale requires three things: standard patterns, repeatable adoption, and aligned incentives.

• Standard patterns are the technical and operational templates you deploy consistently. They include data models, integration flows, KPI definitions, and change processes. Standardization reduces friction and creates predictable results.

• Repeatable adoption turns change into habit. Write concise standard work for operators and engineers, but always explain the “why.” Keep training contextual and continuous. When adoption stalls, look for friction such as unclear ownership or unreliable data before assuming resistance.

• Aligned incentives keep the system healthy. If supervisors are measured only on output, they will ignore analytics under pressure. If procurement is measured only on cost, they will undermine the suppliers who create resilience. Measure across functions, reward shared wins, and make learning visible.

The payoff is measurable. Lighthouse manufacturers that institutionalize these patterns report not only productivity and cost improvements but also double-digit gains in energy efficiency, sustainability, and agility.

My Biggest Piece of Advice

Focus less on adding new technology and more on removing what slows you down.

Manufacturers often equate transformation with addition. A new platform. A new system. A new dashboard. But the real breakthroughs happen when you subtract. When you step back and intentionally clear out the processes, reports, and decisions that no longer serve the business. Complexity is the silent killer of readiness. It disguises inefficiency as sophistication and leaves people too overloaded to act with speed or clarity.

Before adding another layer of automation, ask your teams what actually gets in their way. You will hear answers that have nothing to do with technology. Reports that no one reads. Approvals that exist “because they always have.” Systems that collect data but never return insight. These are not small irritations. They are the friction points that quietly erode agility.

Start by simplifying the everyday moments where work stalls. Map the flow of a single decision, from data collection to action. Eliminate unnecessary checkpoints, redundant sign-offs, and duplicated data entry. Consolidate where you can. Automate only what truly matters. Every removed step frees up attention, energy, and time that can be redirected toward improvement.

This approach does more than streamline operations. It builds confidence. When people experience the benefit of simplicity, they begin to trust change again. They see transformation not as disruption, but as relief. The organization becomes more capable of adopting new tools because it finally has the mental and operational space to use them effectively.

Transformation readiness is not about doing more. It is about creating the conditions where progress can move freely. Technology should accelerate decisions, not complicate them. Removing obstacles is often the most strategic act of modernization you can make.

If you want to prepare your organization for the future, start by clearing the path that leads to it.

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