I recently had the privilege of joining Presidio’s first sponsorship and exhibitor presence at a manufacturing-focused event: the American Manufacturing Summit in Chicago. It was packed with leaders spanning factory operations, supply chain, and boardroom.
Presidio has invested in the business side of several vertical industries so we can serve customers better, speak their language, and apply our engineering talent to solve real business pain points.
We formally launched our manufacturing practice along with two AI-powered manufacturing accelerators at the Summit, which gave me the chance to speak with many manufacturing leaders. The major takeaway: AI in manufacturing has entered a new phase. The conversation is no longer about potential; it’s about turning investment into impact across the value chain.
Momentum is real, but many organizations are still struggling to turn AI spend into measurable outcomes.
In this article, I’ll share what I heard at the Summit: the execution gap manufacturers are working to close, and nine ways AI is reshaping manufacturing right now.
The Gap Between Investment and Value Is Still Wide
Most manufacturers we spoke with are funding AI, automation, and data initiatives. Budgets aren’t the barrier, but execution is.
Leaders described common breakdowns: ownership is fragmented, initiatives are driven by IT without enough connection to plant-floor realities, and results are inconsistent.
This disconnect is shaping how manufacturers evaluate partners. They want teams that understand operations, can take solutions from pilot to production, and can build AI-enabled software that performs on the factory floor.
Where AI Is Delivering Real Impact Today
Across sessions and conversations, nine themes came through consistently on how AI is reshaping manufacturing today:
1. Predictive maintenance is leading the way
Predictive maintenance stood out as the most trusted and mature AI use case because it directly improves uptime, throughput, safety, and financial performance; when failures can be anticipated and prevented, the value is immediate and visible.
2. AI is moving closer to execution
Manufacturers are shifting from dashboards and insights toward action; reducing variability, accelerating decision-making, and automating responses through process optimization, predictive intelligence, and connected worker experiences that support frontline teams in real time.
3. Quality is the next frontier
Improving quality through data and AI is quickly emerging as a top priority. Leaders are looking for more systematic approaches that reduce defects, improve consistency, and tie directly to customer outcomes.
4. Cloud-connected production is becoming the norm
Manufacturers are connecting production environments to the cloud, so data can move from machines, sensors, and control systems into centralized platforms and back to the field, enabling real-time visibility and AI-driven decision-making at scale.
5. Data foundations are now a prerequisite
There is strong alignment on one point: AI at scale requires a modern data foundation with real-time data, unified access layers, and tight integration between operational and enterprise systems.
6. Sustainability is being embedded into operations
Sustainability is showing up less as a standalone initiative and more as a byproduct of better operations; reduced waste and energy optimization are driving both cost and environmental benefits.
AI is increasingly the lever here; optimizing energy use and process setpoints in real time while improving traceability for product-level sustainability reporting.
7. Talent is a critical differentiator
Manufacturers are investing in cross-functional teams and continuous learning to equip frontline workers with technology that enhances capabilities and supports better decision-making; execution speed and resilience increasingly depend on how well organizations develop and deploy talent.
AI copilots are starting to capture institutional knowledge and guide frontline troubleshooting, helping teams act faster and standardize best practices across shifts and sites.
8. Autonomous workflows are on the horizon
Early use cases for more autonomous and agentic workflows include predictive maintenance, invoice exception management, dynamic inventory management, and operational decision support as manufacturers test how far day-to-day automation can go.
9. A return to fundamentals
Amid all the innovation, leading manufacturers are doubling down on the basics; quality, on-time delivery, productivity, and inventory performance remain the core metrics, and AI/digital technologies are increasingly treated as the operating system that enables improvement.
What This Means for the Market
Manufacturing leaders are ready to invest. That means expectations are higher than ever.
They are prioritizing partners who connect strategy to execution by bridging OT and IT, aligning data with workflows, and delivering outcomes that show up on the factory floor.
There is less tolerance for long pilot phases that do not scale. Speed and measurable impact are becoming the standard.
How Presidio is Leading
The conversations in Chicago reinforced what manufacturers are asking for from partners right now.
Manufacturers are looking for partners who can operate across the full stack; from plant-floor systems to cloud platforms to AI-driven applications, and deliver solutions that connect data, workflows, and people.
This full-stack approach is most impactful where the stakes are highest:
- IT and OT convergence to drive use cases such as predictive maintenance and quality control
- Supply chain and operational optimization to improve resilience and efficiency
- Connected worker solutions that enhance frontline productivity and decision-making
Take the Next Step
AI in manufacturing is shifting from experimentation to operational adoption, and the winners will be the teams that embed AI into everyday workflows with clear owners, clean data, and metrics tied to uptime, quality, and energy.
The fastest path forward is to pick one high-value use case, take it from pilot to production, and scale based on impact.
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