Demystifying AI in Manufacturing: A Pragmatic Approach to Digital Transformation
In today’s rapidly evolving industrial landscape, artificial intelligence (AI) promises groundbreaking changes. Yet for many manufacturers, the challenge isn’t just about embracing AI—it’s about knowing where to start and how to proceed. During a recent discussion, Michael Hoagland and Dean Sperry from Hoagland Management & Consulting (HMC) joined John Galbraith and Brock Rowlands from The Data Integrators to share actionable insights for leveraging AI in manufacturing.
Their conversation sheds light on a practical framework, emphasizing that AI is not a panacea but a tool that, when strategically applied, can revolutionize manufacturing operations. Below, we explore key takeaways from their dialogue, offering a roadmap for manufacturers ready to embrace digital transformation.
The Data Dilemma: From Data Takers to Data Makers
Historically, manufacturers have been “Data Takers,” constrained by enterprise software and reactive data ecosystems. The AI experts propose a transformative shift: becoming “Data Makers,” where data strategy becomes a proactive enabler of business value.
Key Insights:
Data strategy isn’t just about amassing data but identifying underperforming use cases and extracting actionable insights.
Key elements of a robust data strategy:
Identifying critical data and insights: Focus on what questions are not being answered repeatably for the business? OR what questions take too many resources to answer?
Making insights usable and timely: Data must be accessible when and where it’s needed.
Facilitating data sharing: Collaborate across internal teams and supply chains.
Organizations need to invest in data operations: To perform better than the industry average, creating value through data understanding is essential.
John Galbraith captured this shift, saying, "I think folks can get lost in the AI narrative when much of the work needed falls within digitization and automation. We should be talking about Data Operations holistically and investing in efficiency.".
By treating data as a strategic asset rather than an application-dependent sinkhole, manufacturers can unlock new levels of performance.
AI is Not a Magic Bullet, But a Collaborative Tool
One of the strongest themes in the conversation was managing expectations. AI isn’t a magical solution; it’s a tool that works best when paired with human expertise.
Critical Observations:
AI excels when humans provide strategic oversight. Critical thinking must remain human-driven.
The primary value of AI lies in:
Breaking through analysis paralysis.
Offering strategic starting points.
Automating repetitive tasks.
Creating intuitive user interfaces, such as voice prompts replacing rigid IT systems.
Sharing tacit knowledge, making it accessible across teams.
Brock Rowlands emphasized the importance of balance: "AI is another voice at the table—a collaborator, not a replacement for human expertise. Its strength lies in amplifying our strategic decisions, not making them for us."
By reframing AI as a collaborator, manufacturers can focus on augmenting processes rather than replacing people or fitting the process to another off-the-shelf software.
Practical Implementation: Small Wins and Strategic Approach
Rather than embarking on large-scale, resource-intensive transformations, the experts advocate for incremental progress through targeted Use Case implementations.
Recommended Strategy:
Identify high-value, underperforming use cases.
Focus on specific data sources and actionable insights needed to support these use cases
Implement manageable automation solutions to address these use cases.
Build feedback loops to continuously learn and iterate.
HMC’s Michael Hoagland reflected on this measured approach, stating, "The biggest mistake I see is companies biting off more than they can chew. Start with small, strategic wins—use cases where you can prove value quickly—and let those successes guide your broader AI journey."
This approach not only reduces risk but also delivers tangible results that build organizational confidence in AI initiatives.
Brock Rowlands offered tangible use case examples:
AI can be chained together to go from question to action.
Example: “What customers do we have that have ordered in the past 12 months but haven’t ordered in the last 3 months?" and then draft an email to reach out to this sub-set of our active customers.
AI can create opportunities to expand interactive training.
Example: Take the manual and create 20 knowledge-based questions and present five questions in a quiz at each of the four training sessions
AI can identify problems.
Example: Using the standards framework, look at the implementation plan and identify any anomalies or issues with material deliveries or timelines that could cause problems.
AI can see patterns.
Example: What are the three “common” characteristics of our highest volume customers and what do their orders have in common across geographies and order cycles.
AI can draft complex results.
Example: Take the listed availability of employees and create a draft schedule that ensures full time work for every individual, ensures the necessary machine capabilities and places it in a ranked order by seniority. (This would be the starting point for managing shift scheduling)
AI can expedite root cause analysis.
Example: The cookies we are producing are all of a sudden coming off the line with air bubbles, making them brittle and prone to breakage, where should we be looking?
AI can standardize decision making.
Example: Machine X has gone down, here are your three best alternate routes with updated job completion times.
The Human-AI Partnership: Democratizing Data Access
One of the most exciting aspects of AI tools is their ability to democratize access to data. No longer confined to IT departments, insights are now accessible across organizational levels, fostering a culture of curiosity and experimentation.
Empowerment Highlights:
AI tools enable direct querying and retrieval of data.
Reduced reliance on development teams accelerates decision-making.
Employees gain faster, more intuitive access to actionable information.
Dean Sperry highlighted the transformative power of democratization: "The beauty of today’s AI tools is how they empower employees at all levels. People no longer need to wait on IT teams to pull data—they can ask questions directly and get answers that drive decisions in real time."
By removing barriers to data access, AI empowers every employee to contribute to operational improvements, fostering a more agile and informed workforce.
Data as a Competitive Advantage
At the heart of the discussion is a clear message: data is the new competitive currency. Companies that leverage their data effectively will outperform those that don’t.
Future-Facing Principles:
Have a clear understanding of the underlying business processes and look at points of friction to create strategic wins.
Treat data operations as a profit center, not a cost.
Shift from reactive to proactive data operations and strategies.
Recognize that fluid information flow is essential for sound decision-making.
View AI as a strategic capability, not a standalone solution.
By aligning data operations with business goals, manufacturers can create sustainable competitive advantages.
Cautionary Notes and Critical Thinking
While the potential of AI is immense, the experts caution against uncritical adoption. AI is a tool, not an end, and its outputs must always be evaluated within a broader context.
Key Considerations:
Human oversight is critical; AI outputs require subject matter expert validation.
Traditional automation can often be more effective than AI for certain challenges.
Technology adoption should focus on solving business problems, not chasing trends.
Dean Sperry summarized this well: "The goal isn’t to implement AI for AI’s sake. It’s about solving real business problems. Sometimes, the right answer isn’t AI—it’s simpler, traditional automation."
These cautionary notes ensure that manufacturers stay grounded while exploring the transformative potential of AI.
Conclusion: Your Data, Your Destiny
The journey toward AI adoption is not about adopting the latest technology—it’s about understanding your organization’s unique needs and crafting a strategy that addresses them. The transition from “Data Takers” to “Data Makers” requires a commitment to learning, experimentation, and collaboration.
By focusing on high-value use cases, building a proactive data strategy, and embracing AI as a collaborative tool, manufacturers can unlock new levels of operational efficiency and innovation.
Final Thoughts: The future belongs to manufacturers who see AI not as a magic wand but as an integral part of their broader strategic toolkit. With the right approach, the potential to transform operations, empower teams, and outpace competitors is well within reach.
Do you have a Use Case that would significantly impact your business but not sure if AI and Data Operations can help solve it? Please contact Hoagland Management & Consulting and we will be happy to provide a free 15-minute Zoom review call.
For manufacturing executives interested in exploring AI implementation, Hoagland Management & Consulting LLC (www.hoaglandmgt.com) offers comprehensive guidance and support through their partnership with Data Integrators (www.thedataintegrators.com) and CohesivAI (www.cohesivapp.com)
About the Author
With over 30 years of experience in B2B product management, finance, and marketing, Dean specializes in driving innovation and uncovering market opportunities. From startups to Fortune 100 companies across industries like healthcare, technology, and manufacturing, he brings expertise in navigating uncertainty and accelerating growth. As the founder of In-Starting LLC, Dean developed the Lean Product Management (LPM) Framework, now utilized at Hoagland Management & Consulting to help enterprises quickly and strategically advance innovations for a competitive edge.
Read Dean’s full bio to learn more about his background and expertise.