Navigating AI Integration: A Case Study in Manufacturing

Understanding the Challenge

This company had been in business for over a decade. They offered a good product and had loyal customers. Their team worked hard. Yet, growth had plateaued. For ten years, revenue had remained roughly flat while costs kept climbing. Hiring had become more expensive, and competitors were beginning to make moves that made the CEO uncomfortable.

The way work got done had not changed much either. Production was still largely manual. Three separate systems were running in parallel, often overlapping in function and rarely communicating with each other. Each department had developed its own methods, and what worked when the company was smaller was now creating friction at every level.

The CEO was not naive about what needed to happen. He understood that technology had a role to play. He recognized that AI was part of the conversation. However, he was unsure where to start, what was worth pursuing, and how to align his leadership team.

They had already attempted to answer these questions. Consultants from well-known firms had been brought in before I arrived. Unfortunately, their work did not deliver what the business needed. The thinking was not entirely wrong, but it never translated into clear decisions that the leadership team could act on.

Three risks were becoming harder to ignore:

1. Budget was being allocated to tools and initiatives without any clear measure of success.

2. The leadership team had differing opinions about priorities, and nobody had forced a resolution.

3. The fear of falling behind competitors was creating pressure to act without the clarity needed to make informed decisions.

   
   

Discovering the Root Causes

I began by spending time with the head of each department. My goal was to understand how work actually got done, where time was being lost, where mistakes were occurring, and where tasks were being duplicated across multiple systems.

A few things became clear quickly:

• The company was running three separate systems when one could have done the job of all three. This created duplication, manual workarounds, and human error that slowed output and added invisible costs.

• There was a real opportunity to make work faster, more accurate, and more consistent—not by removing people, but by alleviating the friction that was hindering their efforts and providing tools that worked together effectively.

  
  

Moreover, there was no shared view among the leadership team regarding how AI could change the economics of the business. Everyone sensed the need for change, but nobody had an agreed-upon plan.

As the CEO reflected afterward, what I surfaced was not entirely new to them. They had been thinking along similar lines for some time. What had been missing was the clarity and structure to move forward.

Crafting a Strategic Plan

I brought the CEO, operations lead, and key department heads into a focused working session. Our task was clear: agree on where AI could create real value in this business and decide on actionable steps for the next 90 days.

We began by mapping how the business generated revenue and identifying areas of operational friction. This grounded our conversation in real economics rather than trends or vendor pitches.

From there, we narrowed our focus. Several areas emerged where AI could plausibly help. We evaluated each opportunity based on business impact, speed to value, and what the organization could realistically support at that moment. Most ideas fell away, but one stood out clearly.

Together, we made a decision, named an owner, defined what success would look like in 90 days, and agreed on what we would not pursue. That last part was just as important as the rest; knowing what to say no to is what keeps a plan honest.

I also examined the three systems the company was using separately and identified a single platform that encompassed the capabilities of all three, including AI functionality they could grow into over time. The case for consolidation became straightforward once the numbers were laid out.

Transformative Outcomes

By the end of a three-week engagement, the leadership team had aligned on one AI initiative directly tied to operational efficiency. They developed a 90-day execution plan with named owners, clear milestones, and a defined way to measure progress. They also made a decision on what not to pursue, which protected the budget from being committed to less impactful initiatives.

Additionally, they replaced three disconnected systems with one integrated platform designed to support the company's future direction.

What struck the CEO most was not just the outcome but how quickly it came together. Previous engagements with larger consulting firms had not yielded the same results, despite significantly more time and resources. In just three weeks, the leadership team gained more clarity and confidence than they had after months of prior work.

The CEO left the engagement with something he had not had before: a clear answer to the AI question, one he could present to his board with confidence.

Measurable Business Impact

Within the first 90 days, the company consolidated from three systems to one integrated platform, with projected cost savings visible in the forecast from the first month.

Teams stopped duplicating work across disconnected tools. Human error in key production processes dropped as manual steps were replaced by faster, more consistent automated ones.

Decision-making regarding new AI opportunities became faster because the leadership team now had a clear framework to evaluate what mattered and what to ignore.

The shift was not merely operational; it was strategic. The leadership team transitioned from reacting to AI pressure to making deliberate, confident decisions about where to invest and where to hold back.

The Real Insight

This company did not need more AI tools. It did not require a team restructuring. What they needed was focus. They needed a leadership team that agreed on one priority, had a plan behind it, and knew how to evaluate everything else that would inevitably come across their desk.

The thinking was already there. What had been missing was someone to facilitate the decision-making process, build the structure around it, and make it executable.

That is what changed. And that is what made everything else possible.

Client details have been kept general to protect commercial privacy.

If you’re dealing with multiple AI ideas, internal misalignment, or pressure to act without a clear plan, the issue isn’t capability. It’s decision clarity.

I run a focused 90-minute AI Decision Briefing to help leadership teams define where to invest and what to stop. If that conversation would be useful, book *time directly