9 Exclusive Insights from the Baird Global Industrial Conference: How AI is Transforming Manufacturing and Industrial Operations
This year’s Baird Global Industrial Conference in Chicago brought together senior executives, investors, and leaders from the largest industrial companies in the world. In addition to the many strategic and financial company updates, one highlight during the conference was the AI Applications panel, where industrial manufacturing trends and the future of AI in operations were explored in depth. The panelists, including Eyelit Technologies CEO Joe Bellini, discussed how Artificial Intelligence (AI) is reshaping manufacturing, planning, and execution across the entire value chain.
Below is a full breakdown of the key themes shared with investors, incorporating exclusive insights from the panel discussion.
1. A Long-Standing Problem: The Gap Between Planning and Execution
Industrial organizations have historically struggled with a persistent and costly disconnect between planning and execution. This challenge is frequently amplified by the “three Vs” of manufacturing: Volume, Variety, and Value. Balancing labor, materials, assets, and demand across highly configurable products creates ongoing tension, especially when planning is based on forecasts that are never perfect.
Software vendors have focused deeply on solving this issue, closing the execution gap by enabling adaptive manufacturing capabilities that increase asset utilization, lower material costs, improve labor productivity, and increase throughput by optimizing order mix.
A key point emphasized during the discussion:
Most organizations try to improve individual KPIs in isolation, reducing lead time, lowering inventory, or improving utilization, but real success comes from optimizing the trade-offs between them. A successful platform must support manufacturers in determining what to build, where to build, when to build, and how to build with data-driven precision.
2. AI Is Unlocking the Value Hidden in Manufacturing Data
Manufacturing has always generated massive amounts of valuable data, some structured, some trapped on spreadsheets, and some unused entirely. With AI’s rapid evolution, this “hidden goldmine” is finally becoming accessible.
The panel highlighted four types of AI that are actively transforming industrial operations:
- Machine Learning–based AI
- Generative AI
- Agentic AI
- Inference AI
With accurate data representation, improved analytics, and workflows that connect planning and execution, these technologies are now capable of driving a true production revolution.
One analogy shared onstage resonated strongly with the investor audience:
Most operational data sits in a checking account earning no interest. AI moves that same data into an interest-bearing savings account, turning it into an asset that generates measurable value.
3. Agentic AI and Inference AI Have Changed the Conversation in Just One Year
Over the past twelve months, the most transformative advancements have come from Agentic AI and Inference AI:
- Agentic AI governs the workflow that activates prescriptive analytics. The system reschedules operations as manufacturing adapts to variations in demand and supply through both autonomous decision execution and interactive workbenches for planners.
- Inference AI optimizes production outcomes by evaluating scheduling options and providing prescriptions to close the gap between planning and execution. This adaptive manufacturing capability merges planning and execution data, enabling optimal production planning and scheduling without relying on outdated heuristics.
These developments have allowed AI to move beyond simple chatbots and productivity helpers into the realm of real manufacturing decision-making simulation, justification, recommendations, and even autonomous triggers.
4. To Fully Benefit From AI, Manufacturers Must Modernize OT, IT, and ET
A significant portion of the discussion focused on the Engineering Technology (ET), Operational Technology (OT), and Information Technology (IT) systems that must evolve to support an AI-driven future.
Industrial operations move through three primary phases:
- As designed
- As built
- As maintained
Current enterprise systems struggle with the “as built” phase, resulting in both lower revenues and increased costs. Improvements can be delivered in areas such as:
- Safety stocks
- Order lead times
- Premium and expedited freight
- Overtime
- Capacity utilization
- Labor utilization
- Quality management
The panel underscored that improvement begins with data convergence:
AI can only reach its full potential when all three data layers across OT, IT, and ET unify into a common and inference-based data model. Solutions that embrace and extend existing systems, rather than replace them, will lead the future of adaptive manufacturing.
5. Automation Must Include Human Guardrails
Despite rapid advancements in autonomous operations, the panel emphasized that humans must remain integral to the decision-making process.
While autonomous agents can drive tremendous value, planners often possess contextual knowledge not yet known to the system. Therefore, human-in-the-loop design is essential for:
- Compliance
- Ethical decision-making
- Situational awareness
- Safe overrides
- Continuous learning
AI alone cannot replace operational judgment, but it can enhance it significantly.
6. High-Value Use Cases: Volume and Mix Optimization Leads the Pack
When asked about the most impactful AI-driven ROI, it was pointed to volume and mix optimization.
Even with AI, forecast error exists. Manufacturing schedules also require a freeze window to maintain efficiency. With advanced pattern matching, AI can dynamically adjust schedules for both volume and configuration mix at the last possible moment, while still achieving all production KPIs.
- Additional high-value use cases include:
- Predictive KPI forecasting
- Dynamic lead-time prediction
- Real-time order substitution
- Optimized dispatch
- Traceability and advanced process support
With algorithms now running on Graphics Processing Units (GPUs) instead of Central Processing Units (CPUs), real-time mathematical optimization has become not only possible but practical.
7. AI Is Already Transforming Internal Operations
While the panel focused primarily on customer impact, there was also a look inward. Across software engineering and corporate operations, AI has already created measurable gains:
- 20 percent increase in developer productivity
- 30 percent faster onboarding for engineers, QA testers, and support teams
- Time savings across marketing, HR, sales, training, and administrative functions
These efficiencies are being reinvested into new product capabilities and faster innovation cycles.
8. The Future: Convergence, Blockchain, and Evolving Monetization Models
The market is moving toward full convergence across Planning, Scheduling, and Execution. In that context, the combined Sales Inventory and Operations Planning (SIOP), Advanced Planning and Scheduling (APS), and Manufacturing Execution System (MES) platforms benefit from increasing interest in:
- Model-based adaptive manufacturing
- Expanded track-and-trace capabilities
- Blockchain-enabled genealogy for regulated industries
While subscription-based pricing remains the foundation, the panel suggested that usage-based pricing could become relevant as AI deepens its role inside mission-critical operations.
9. Will AI Reduce Switching Costs? A Realistic View
Some software leaders worry that AI will make switching vendors easier, but the panel offered a different perspective:
- AI may lower implementation costs, but best-of-breed capabilities will still win
- It remains rare for an organization to be “so unique” that custom-building solutions is more efficient than buying
- AI will accelerate innovation for incumbents as much as for newcomers
Most importantly, AI will finally make digitization affordable for smaller manufacturers who previously lacked the ROI to upgrade.
Overall, this year’s Baird conference underscored the reality that industrial leaders are now accepting: AI is no longer theoretical. It is operational, measurable, and transformative.
The next era of industrial excellence will be defined by:
- Unified and inference-based data models
- Convergence of planning and execution
- Adaptive manufacturing
- Human-guided autonomous decision-making
- Real-time optimization at plant and network scale
Manufacturers who embrace this shift and who break down the silos between planning, scheduling, and execution will lead the next chapter of industrial innovation.