We recently traveled to Saint Louis for the Connected Manufacturing Forum, where manufacturing leaders gathered to tackle the industry's most pressing digital transformation challenges. What we found was a set of organizations that have collectively passed the digital transformation inflection point. We repeatedly heard stories where the promise of AI, digital twins, and automation is finally being fulfilled by pragmatic companies large and small.
The conversations weren't just about shiny new technologies. They centered on fundamental questions that keep manufacturing executives up at night: How do we capture decades of tribal knowledge before our workforce retires? How do we turn overwhelming data streams into actionable insights? And perhaps most importantly, how do we transform our operations without disrupting the very processes that keep us profitable today?
Here's what emerged from two days of panels, demonstrations, and candid conversations with industry peers.
The why driving change
Every digital transformation story begins with a problem that can no longer be ignored. At the forum, we heard manufacturing leaders describe challenges that have moved from "nice to solve" to "business critical."
Addressing core industrial challenges
The pandemic didn't create operational challenges—it exposed them. What we're seeing now is a deliberate response to hurdles that have been building for years.
Supply chain resilience: Post-pandemic, 70% of manufacturers have implemented stronger continuity planning, pushing for more connected and resilient supply networks. We met with both the 70% and the 30% at the CMF. The leaders from the other 30% self describe as in reactive mode for 5 years." Constant demand fluctuations and uneven supplier performance have eroded customer trust in delivery promises. These organizations aren't just looking for efficiency gains; they're fighting for survival.
Knowledge retention and workforce gaps: The skilled labor crisis took center stage in nearly every conversation. Ron Norris, recently retired from Georgia Pacific, shared a stark reality check: In 2015 they realized 45% of their workforce was projected to retire within eight years. That's not just a staffing problem—it's a knowledge apocalypse. When new employees struggle to stay (and experienced workers are stretched thin training them), you're not just losing people; you're losing institutional intelligence that took decades to build.
This is where AI and knowledge management systems stop being nice-to-haves and become strategic imperatives. Organizations can't afford to let decades of expertise walk out the door without a plan to capture, codify, and transfer that knowledge.
Complexity and data overload: We spoke with multiple attendees who described the same frustrating paradox: drowning in data while starving for insights. From equipment operators to C-suite executives, everyone understands the potential value of connecting information across systems. The challenge isn't collecting data—it's transforming that data into something useful.
Disconnected systems, manual integrations, and a lack of focus on moving from raw data to actionable wisdom is paralyzing many organizations. As one plant manager told us, "We have sensors on everything, dashboards everywhere, but when something goes wrong, we're still making decisions based on gut feel."
Downtime and efficiency: The promise of connected equipment is compelling: up to 50% reduction in unplanned downtime. But we heard story after story of organizations investing heavily in sensors and connectivity, only to find themselves overwhelmed by the resulting data streams. The ROI only materializes when raw sensor data becomes intelligent data products that operators and managers can actually use to make better decisions.
Our perspective: It's all about knowledge
When you step back and look at these challenges, they're fundamentally about knowledge and information flow. The most successful organizations we work with frame these obstacles as opportunities:
How do we make sense of disparate data sources?
How do we retain knowledge embedded in our current team?
How do we manage new data sets we've never worked with before?
This is where our DIKW framework becomes essential. We've seen that organizations with clear intent about their desired outcomes—not just "getting data in order"—achieve much higher ROI and longer-lasting success. Starting with wisdom (what decisions do we need to make?) and working backward to data requirements creates a much more strategic foundation for transformation.
The imperative for innovation and competitiveness
Beyond solving operational problems, several speakers focused on leveraging digital transformation to drive innovation and competitive advantage.
Accelerating time-to-market: Lockheed Martin's focus on "accelerating factory-to-field delivery for warfighters" exemplifies the drive for rapid product delivery. As a defense contractor, speed isn't just a competitive advantage—it's a national security imperative. They're using operational technology to fundamentally change the pace of development. Bayer echoed similar goals with their continuous pursuit of "rapid implementation of market solutions."
Economic and national security: Manufacturing is "essential for economic and national security," with increasing focus on domestic capability and flexible regional supply networks. Former Boeing CEO Dennis Muilenburg, who now runs an Advanced Manufacturing institute in St. Louis, highlighted that with current workforce gaps, technology and innovation are the only paths to creating shorter, more resilient supply chains.
The connected workforce
One of the forum's strongest themes was that successful digital transformation happens through people, not despite them. By connecting workers with robust digital infrastructure, real-time data, AI-powered tools, and a culture of knowledge sharing, organizations can overcome traditional bottlenecks and accelerate innovation.
Enhanced data access and real-time insights: Real-time system oversight and AI-assisted troubleshooting reduce information overload while enabling faster decision-making. When operators can see problems developing before they become critical, the entire operation becomes more proactive and strategic.
Democratized data products: Mobile and tablet applications are putting previously siloed information into everyone's hands. KPIs like inventory levels, batch quality, and daily production volumes are critical to manage your facility. When everyone can understand the current state, you create a more data-literate workforce capable of continuous improvement. This creates valuable second-order benefits are often more valuable than the immediate operational gains.
AI augmentation and automated assistance: AI systems provide strong support across multiple use cases, with many team members adding "manager of AI agents" to their job descriptions. One compelling example involved AI agents that connect information traditionally held at various organizational layers to support planning cycles (SIOP, S&OP, IBP) and accelerate decision-making.
Improved knowledge management and coordination: Equipping the next generation with decades of accumulated experience allows them to become productive significantly faster. This lets CFOs and COOs continue expecting productivity improvements even as the workforce loses its institutional memory. The emphasis was on human-AI complementary approaches where operators retain final decision authority while being empowered with better information.
Key technological enablers and their applications
The forum showcased a range of advanced technologies forming the backbone of manufacturing's digital transformation.
Artificial intelligence and machine learning
AI is moving beyond basic automation toward predictive and prescriptive capabilities. We heard compelling stories supporting the current buzz around agentic AI, device intelligence, and optimized operations.
Stefanini's AI troubleshooting approach utilizes a four-agent system architecture with real-time data monitoring and PLC/SCADA interface integration for immediate responses. Georgia Pacific's advanced AI agent systems combine theoretical datasets with proprietary knowledge, enabling agents to learn, predict, reason, decide, and explain their recommendations.
Lenovo's Device Intelligence (LDI) provides a fascinating example of AI in action. They use AI to monitor their connected products in the field and predict future failures, enabling them to make device upgrade recommendations that have saved IT departments money by extending the life of machines performing above expectations.
Automation and robotics
Automation, often enhanced by AI, is reshaping physical processes while reducing space requirements and enhancing real estate productivity. Lockheed Martin compressed a 30,000 square foot operation to 8,000 square feet through holistic automation, achieving over 100 hours of lights-out machining with no human intervention.
Other organizations highlighted similar transformation: one reduced their Annual Product Quality Review process from 6-8 weeks to 2-3 weeks. Georgia Pacific achieved 90%+ touchless order processing, reducing cycle times from days to seconds.
Digital twins, extended reality, and scanning technology
These technologies enhance visualization, training, data capture, and simulation. Bayer uses iPad integration for shop floor data access and 3D model overlay capabilities for design verification. Lockheed Martin is developing comprehensive factory digital twin models that connect tools, equipment, asset tracking, personnel, and product data for process line optimization and shop floor layout improvements.
The scanning and LiDAR integration capabilities mentioned by several speakers collect millions of data points for real-time 3D model integration—creating living, breathing digital representations of physical operations.
Data management and connectivity
Successful digital transformation hinges on robust data infrastructure that delivers information in understandable, valuable formats. Many organizations discussed unified data approaches and opportunities to bring IT expertise to help manage OT (operational technology).
The concept of "digital thread"—information throughlines spanning entire product lifecycles from design to operation—expands traditional PLM and MES systems by incorporating equipment data and downstream product performance to improve design and reduce reliance on error-prone manual processes.
Implementation challenges and success strategies
Successfully implementing digital transformation requires more than technology adoption; it demands strategic planning, robust change management, and relentless focus on people.
The primacy of foundational elements
A critical warning emerged from multiple speakers: prioritize fundamentals before diving into advanced technology.
Foundations first: As one presenter put it, "Don't focus on AI (the roof) while neglecting foundational elements that support it." This includes people, plant design, maintenance, safety, and digital backbone infrastructure. Without solid foundations, even the most sophisticated AI implementations will struggle.
Operational excellence: Stephen Graham emphasized a framework encompassing Culture (clear vision, growth mindset, treating people as untapped assets), Structure (objectives, metrics, digitalized systems), and Execution (customer value, process compliance, accountability). Technology amplifies existing capabilities—it doesn't create them from nothing.
Effective change management
Here's a sobering statistic: 70% of change initiatives fail to deliver expected results, with manufacturing showing a 72% failure rate. This highlights why deliberate change management isn't optional—it's the difference between transformation and expensive disappointment.
People-centric approach: Technology isn't the primary challenge; people and process changes are. Success hinges on early operator involvement in solution development and focusing on how AI augments rather than replaces work. When people understand they're gaining capabilities rather than losing relevance, adoption accelerates dramatically.
Leadership commitment: Leaders must visibly "walk the talk" and be first to adopt new practices. Top-down leadership buy-in isn't just helpful—it's essential. When executives demonstrate genuine commitment to new approaches, it signals to the entire organization that change is real and important.
Clear communication and value proposition: Define what change will realize, not just what tools to implement. Establish the "why" and "what's in it for us." Clear communication of personal benefits and growth opportunities drives adoption far more effectively than technical specifications or efficiency metrics.
Avoiding common pitfalls: Don't implement solutions without shop floor input. Avoid chasing "shiny objects" or starting multiple new projects without long-term strategy. One facility showed only 20% adoption versus 80% in others due to poor change management—a costly reminder that technology success depends on human acceptance.
Metrics for realization: Focus on competency to apply knowledge, not just training completion. Track ROI against baseline measurements and monitor user adoption rates as leading indicators of ultimate success.
Workforce development and empowerment
Empowering the workforce emerged as a strategic imperative across all successful implementations.
Capability building: Emphasis on capability building alongside technology deployment proved critical. The dairy industry's graduate rotation programs focusing on career progression paths provide a model for developing internal expertise while demonstrating commitment to employee growth.
Employee engagement: Acknowledge both fear and excitement around AI. Interestingly, frontline workers are three times more likely to use generative AI than C-suite leaders believe—indicating significant untapped potential for bottom-up innovation.
Knowledge management: Cloud, AI, and big data technologies can counter employee turnover and data silos by ensuring valuable information and experience remain accessible. This isn't just about documentation; it's about creating systems that learn and adapt as institutional knowledge evolves.
Making manufacturing aspirational: Initiatives like St. Louis's Advanced Manufacturing Innovation Center aim to make manufacturing jobs aspirational again through apprenticeships and STEM programs. Attracting the next generation requires demonstrating that manufacturing offers sophisticated, technology-enabled career paths.
Integration and scalability
Seamless integration and strategic rollout proved vital for sustained success.
Single platform integration: Ensure single platform integration to avoid vendor conflicts and "application fatigue." Multiple disconnected point solutions create more problems than they solve, fragmenting workflows and creating new silos.
Pilot programs: Select pilot or lighthouse sites and define clear success criteria. Focus on quick wins in specific areas before scaling. This approach builds confidence, demonstrates value, and provides learning opportunities before broader deployment.
IT/OT cooperation: While full IT/OT convergence remains elusive, successful organizations focus on cooperation rather than convergence due to different priorities and requirements. Solutions that can "simplify operations from factory floor to front office" bridge these worlds without forcing unwanted consolidation.
Security: Manufacturing has surpassed the financial sector as the most hacked business globally. Remote access security, vendor control, and compliance requirements are critical considerations that can't be afterthoughts in digital transformation planning.
Measurable outcomes and ROI
Digital transformation initiatives are yielding tangible benefits across industries, providing clear justification for continued investment.
Cost reduction: Organizations are seeing 5-30% reduction in costs. PCC Submarines saved multiple six figures annually through an Epicor database for welder control logs—demonstrating that even seemingly small improvements can generate substantial returns.
Time savings: Okhart Pharma reduced APQR processing time from 6-8 weeks to 2-3 weeks. Freshworks customers see 46% improvement in resolution times and 50% reduction in resolution time. Georgia Pacific reduced product development cycles from 3 months to days.
Productivity gains: 40% boost in agent productivity was reported across multiple implementations, suggesting that well-designed systems can dramatically amplify human capabilities.
Quality improvements: Lockheed Martin achieved coating process improvement from 40% to 95% first-pass yield—the kind of transformation that justifies significant technology investments.
Market share and revenue growth: Baker Hughes's Completions Business saw 5-point market share improvement, with Middle East operations increasing operating income from 7% to 28% alongside 35% revenue growth.
Enhanced customer satisfaction: 96% increase in customer satisfaction scores demonstrates that internal operational improvements translate directly into external value creation.
Moving forward: From data to wisdom
The Connected Manufacturing Forum reinforced something we see consistently in our work with mid-market manufacturers: success isn't about having the most advanced technology—it's about thoughtfully connecting people, processes, and systems to create sustainable competitive advantages.
The current landscape of manufacturing digital transformation is characterized by a dual focus: harnessing advanced technologies like AI, digital twins, and automation to drive efficiency and innovation, while simultaneously investing in foundational elements such as workforce development, strategic change management, and robust IT/OT integration.
The organizations that will thrive are those that understand transformation is fundamentally about people and the decisions they make. Technology should augment human capabilities, not replace human judgment. The goal isn't to collect more data—it's to create systems that help people make better decisions faster.
As we continue working with leaders navigating these challenges, we're reminded that the most successful transformations start with clear business outcomes and work backward to technology requirements. When you know what decisions you need to make better, the path to wisdom becomes much clearer.
The future belongs to companies who can balance technological sophistication with organizational wisdom—creating operations that are not just more efficient, but more resilient, adaptable, and human-centered. That's the real promise of connected manufacturing, and it's closer than many realize.
About the author:
Adam Schanfield is a Principal Strategist at TXI. He works with clients to envision, plan, and govern their efforts to differentiate with digital, data, and AI. He is based in TXI’s Chicago headquarters.
