AI has quickly become one of the most talked-about tools in manufacturing, retail, and consumer goods, but as MetaExperts CEO Ron Crabtree explains, the real question is not whether organizations should use AI. The better question is whether they are ready to use it well. In his conversation with Christine Babington on Decision Loop, Ron discusses what actually makes AI work inside organizations, why technology should never be the starting point, and how leaders can prepare their teams for meaningful digital transformation.
Manufacturers are facing intense pressure from every direction. Ron points to three ongoing challenges that continue to shape the industry: the forever labor shortage, the need for digitization and digital transformation, and the pressure to do more with less. AI has captured attention in this environment because it feels accessible, scalable, and powerful. Unlike some advanced technologies requiring major infrastructure or specialized teams, AI tools are now available to almost anyone, which creates both opportunity and risk.
As Ron explains, AI is not entirely new, despite its recent popularity. Companies have been using forms of artificial intelligence for years, and what has changed is just the level of access. Today, AI is in the hands of employees, leaders, and competitors at every level. This means organizations that learn to use AI strategically may gain a competitive advantage, while those that treat it as a passing trend could quickly fall behind.
One of Ron’s strongest points is that leaders make a major mistake when they start with technology first. AI should not be treated as a blanket solution for every business problem. Ron compares this mindset to having only a hammer and seeing every problem as a nail. AI is one tool in a much larger digital transformation toolbox, and it works best when applied surgically to specific challenges.
Before investing time, money, and energy into AI adoption, leaders need to ask:
Without this clarity, AI initiatives risk becoming expensive experiments with little measurable impact.
Ron emphasizes that successful digital transformation starts with people, process, and technology, in that order. Technology comes last because it should support the way the business needs to operate, not dictate it.
A major theme of the conversation is the human side of AI adoption. Ron notes that many digital transformation and AI projects fail because of poor change management, lack of leadership alignment, data silos, and the mistake of treating transformation as a technology upgrade rather than a new way of operating.
Training everyone on AI and hoping the organization improves is not a strategy. It may create activity, but it does not guarantee meaningful change. Instead, leaders need to communicate the “why” behind the transformation. If the organization needs to reduce product development timelines from six months to three months, employees need to understand this goal, how their work connects to it, and what the change means for them. People are more likely to support change when they are engaged in the process, not simply told what is happening from the top down.
Ron also shares a practical approach for preparing an organization for AI: understand the process first. He recommends tools like value stream mapping to visualize how work actually gets done, but he stresses that mapping alone is not enough. Leaders should also attach data to the process, including cost, time, delays, quality issues, information sources, and system touchpoints.
In one example, Ron describes working with an automotive supplier evaluating a major investment in product lifecycle management automation. By mapping the process and analyzing the data, the team discovered that employees were often moving between multiple systems just to complete one task. Some steps required interaction with three systems, while others required seven or eight. This kind of visibility helps leaders identify where AI or automation could make a real difference.
The goal is not to map everything at once. Ron advises starting with one meaningful process, one product line, one transaction type, or one area where friction is already visible. From there, leaders can identify the 20% of steps creating 80% of the opportunity.
An AI-enabled team is not simply a team that has access to AI tools. Each member of the team should understand where AI fits into the workflow, when to use it, and how to measure whether it is working.
For Ron, measurement is essential. If AI does not show up in performance metrics, leaders should question why they are using it in the first place. The most effective AI initiatives are tied to specific, measurable outcomes and solve real problems, like improving speed, cost, quality, customer experience, or growth. Most importantly, they are built around the way people and processes actually function.
AI can create real value, but only when organizations approach it with discipline. To learn more, listen to the full episode of Decision Loop on Apple, Spotify, or YouTube. For additional expertise and insights, connect with Ron on LinkedIn or visit MetaExperts.com.
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