Pioneering the Future of Manufacturing Execution Systems: Joe Gerstl Talks AI, Change Management & Smart Manufacturing

• Manufacturing execution systems are evolving from rigid systems of record into flexible platforms that support intelligence, guidance, and optimization
• While core MES functions remain consistent, modern platforms enable greater scalability, personalization, and enterprise-wide standardization
• Change management, not technology, is often the biggest barrier to successful MES adoption
• AI is expanding MES capabilities through predictive insights, conversational interfaces, and more proactive decision-making
• Cloud-based MES deployments are accelerating modernization with faster implementation, easier maintenance, and improved scalability

Manufacturing is evolving fast - and so is the technology behind it. On a recent episode of Factory Forward, a podcast by Retrocausal dedicated to the future of industrial innovation, Joe Gerstl, Senior Director of Product Management at Proficy Software, sat down to talk shop with host Zeeshan Zia. With more than 25 years in industrial software, Joe has seen firsthand how manufacturing execution systems (MES) have evolved from paper-based processes to AI-driven, cloud-enabled platforms that empower the next generation of smart factories.

If you're exploring MES software, cloud-based manufacturing solutions, or how artificial intelligence is changing the shop floor, this conversation offers valuable insight and perspective from one of the industry’s most experienced voices. Listen to the full episode on Retrocausal’s website here.

Joe’s journey began at Mountain Systems, a startup focused on MES for the paper industry. From sleeping on the floor to writing code and building teams, Joe wore many hats in the early days. When the company was acquired, he transitioned from startup life to leading MES innovation at a global scale - expanding his impact across multiple industries and customers. The episode offers a rare behind-the-scenes look at how the MES industry matured - and what it still needs to do to keep pace with modern manufacturing demands.

Joe shared how the core principles of manufacturing execution systems - tracking production, managing specs, monitoring quality - have remained consistent, but the execution has become exponentially more powerful and flexible. Today’s MES platforms are no longer rigid systems with out-of-the-box screens and one-size-fits-all logic. Manufacturers can now personalize applications to fit their operations, integrate data across systems, and even embed predictive analytics and AI-powered decision-making.

Yet, despite all this progress, some of the biggest challenges in manufacturing execution systems adoption aren’t technical - they’re human. Joe emphasized that change management is often the hardest part of digital transformation. Teams are naturally resistant to new ways of working, and many prefer to recreate their old processes on a new platform rather than embracing more efficient approaches. Helping operators and supervisors understand how new systems can improve their day-to-day experience is critical to success.

One of the most forward-looking parts of the conversation focused on how artificial intelligence is reshaping the future of MES software. As manufacturers seek new ways to reduce downtime and make smarter decisions faster, AI is opening doors that weren’t possible just a few years ago.

Imagine being able to interact with your MES using natural language - typing or speaking questions like, “What was my worst-performing line last week?” or “How many defects did we see on the morning shift?” Instead of manually pulling reports or digging through dashboards, production teams could get answers in seconds. These types of conversational interfaces are beginning to emerge, reflecting a broader industry shift toward more intuitive, accessible systems.

Joe and Zeeshan also discussed the growing potential of predictive capabilities powered by machine learning. In the near future, MES platforms could use patterns in production data - like shift trends, maintenance schedules, and even ambient temperature - to estimate when a machine is likely to go down. This kind of proactive insight could help manufacturers avoid unplanned downtime altogether, rather than responding to it after the fact.

These are just a few examples of how AI may soon transform manufacturing execution systems from a system of record into a system of intelligence - offering not just visibility, but guidance. While these capabilities are still evolving, it’s clear that the future of manufacturing will be shaped by how well organizations harness both their data and the technologies that bring it to life.

As manufacturing technology continues to evolve, cloud-based MES platforms are becoming a key enabler of agility and innovation. In addition to advancements in AI and analytics, Joe and Zeeshan discussed how cloud deployment models are helping manufacturers modernize faster and more efficiently.

Rather than waiting months for on-prem hardware or dealing with upgrade complexities, SaaS-based MES solutions offer faster implementation, easier maintenance, stronger cybersecurity, and built-in scalability. Centralized cloud environments are often more secure and easier to keep up to date than locally managed systems - especially with dedicated DevOps teams and automated patching. Joe noted that even companies once hesitant to move critical systems to the cloud are now requesting SaaS options in their RFPs - a significant shift from just a few years ago.

Cloud MES adoption also supports broader goals like sustainability. Hyperscale cloud providers such as AWS invest heavily in energy-efficient infrastructure, making cloud deployments not only more convenient but also more environmentally responsible. For manufacturers, this means modernizing operations without expanding their own carbon footprint.

One of the most practical strategies for scaling manufacturing execution systems across a multi-site operation, Joe explained, is standardization. Whether you’re running five plants or fifty, the ability to create a consistent, reusable system architecture can dramatically accelerate digital transformation.

Joe painted a picture of what this might look like in a large-scale operation: imagine a company with 100 manufacturing sites, 10 of which are producing the same product - say, biscuits - on similar equipment and using similar processes. Rather than building a new MES configuration from scratch at each plant, manufacturers can develop a standardized template - a repeatable model that includes not only production logic, but also operator interfaces, workflows, and configurations. Once proven at one site, that template can be rolled out to similar lines across the network, dramatically reducing deployment time and engineering effort.

He also highlighted the importance of no-code/low-code, configurable solutions that minimize the need for custom development. In most cases, teams can implement powerful MES capabilities without writing a single line of code. And when unique requirements arise - such as complex genealogy or specialized reporting - logic can be layered in where needed. This kind of repeatable, scalable approach is key for manufacturers looking to modernize operations efficiently - without reinventing the wheel at every site.

From improving sustainability reporting to enabling more informed decisions, contextualized data is becoming a secret weapon in modern manufacturing. Joe emphasized that MES plays a critical role in connecting data across the factory - giving teams the visibility they need to identify anomalies, uncover inefficiencies, and drive improvement.

He shared an example of a company running the same product on two nearly identical lines - only to discover, with help from MES data, that one line was consuming twice the energy of the other. Without the right context, that kind of issue can go unnoticed. With it, manufacturers can dig deeper to find the root cause and make smarter decisions faster.

Looking ahead, Joe sees generative AI and robotics playing increasingly central roles in factory operations. The real breakthrough, he said, will come from merging AI with the rich, relational data that MES platforms manage - allowing for more intelligent recommendations, better root cause analysis, and automation that’s both smart and situationally aware.

As these technologies mature, manufacturers may be able to use AI not just for reporting or prediction, but for closed-loop decision-making - automatically adjusting operations based on real-time inputs and historical patterns. Combined with robotics and advanced automation, this could unlock entirely new levels of efficiency, precision, and responsiveness on the shop floor. For industry leaders, staying ahead means not only adopting these tools but also investing in the data infrastructure and cross-functional collaboration needed to make them truly effective.

Retrocausal is a leader in AI-powered manufacturing solutions. Their Factory Forward podcast explores the latest trends, challenges, and technologies transforming factory operations - featuring conversations with industry leaders at the cutting edge of innovation. Learn more at retrocausal.ai.

Catch the full episode of Factory Forward featuring Joe Gerstl on Retrocausal’s website:

Factory Forward Podcast – S2E3: Joe Gerstl