On Thursday, June 19th, 2025, the new Sip Club, hosted by Expandable Software, MIE Solutions, and Mirador Software Group, gathered nearly 50 leaders from across the industry for a real-talk look at how AI is being used today—on the floor, in the field, and beyond. From Hype to Hands-On: How AI is Changing Manufacturing featured Jaime Portocarrero and his endless energy and enthusiasm for the topic.
A Quick Survey
The session began with a quick survey of the participants on their current level of understanding or experience with AI, from “None: I’m a deer in the headlights” to “Experienced: I’m using AI successfully in many areas.” The good news is that while 58 percent responded that their experience to date was limited, 33 percent responded that they were either planning or developing an AI strategy and assessing tools—or adopting and using AI tools in a few key areas … thus validating that the AI evolution/revolution is here!
The State of AI in Manufacturing
The simple definition of AI is when machines can learn, think, make decisions, and perform tasks autonomously, as well as—or often better than—humans. This exists at several levels:
- Narrow/Weak AI: Performs only the tasks it was programmed or trained for. Limited to a specific purpose or domain. (Where we are today)
- General/Strong AI: Would be able to reason, solve problems, make judgments under uncertainty, plan, learn, and communicate in natural language. Multi-domain capable. (5 years out, ~ 2025 – 2040)
- Super Intelligent AI (SAI): Would outperform the best human brains in practically every field. (Anyone’s guess 2045 – 2050)
RPA (Robotic Process Automation) and AI (Artificial Intelligence) are NOT the same
In general, RPA is a fixed (pre-defined) rule-based automation based on lookup tables: If, Then, Do. There is no learning or adaptation; rulesresponses don’t change. RPA lacks cognitive capabilities. It does not use an AI engine, it just follows the rules. Examples of this would include automating invoice entry or form completion.
On the contrary, Narrow or Weak AI, at today’s level, is AI that performs a specific intelligent task on the fly based on a given context. It can learn from data (e.g., Machine-Learning [ML] models). Responses can change and adapt to change. It mimics human-like decision-making and uses an AI engine for pattern matching. ChatGPT, MS CoPilot, facial recognition, and spam filters are examples of this technology.
A Massive Amount of Money and Effort is Being Invested in AI
$1.4 trillion has been invested in AI, with more to come. As of 2025, there are approximately 70,000 AI companies—companies offering Machine Learning, Natural Language Processing, Computer Vision, predictive analytics, and others across all markets—worldwide. According to Stanford’s 2024 AI Index Report, there were approximately 10,095 AI startups across the top ten countries leading in artificial intelligence innovation.
McKinsey and Company reports that for many technical capabilities, Generative AI (GAI) will perform at a median level of human performance by the end of this decade (2030). They project its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. In some cases, that’s 40 years faster than experts previously thought!
But AI is not replacing ERP; a system of record is and will always be required.
AI enhances ERP automation, predictive analytics, and data-driven decision-making, making enterprise systems more intelligent, responsive and productive.
The 2024 Gartner Hype Cycle for ERP sees these key themes for AI:
- Generative AI in ERP – Automating workflows, enhancing decision-making, and improving efficiency.
- Sustainability in ERP – Embedding ESG (Environmental, Social, and Governance) data into ERP systems.
- Extended Planning & Analysis (xP&A) for ERP – Enhancing financial forecasting and business planning.
- Composable ERP Strategies – Building adaptable, modular ERP ecosystems for increased agility.
How are Manufacturers Using AI Today
The use of AI is rapidly proceeding in leading major manufacturers today. These include mega-manufacturers like Boeng, GM, Ford, Tesla, Intel, GE, Proctor & Gamble, 3M, Lockheed Martin and John Deere, along with many others. Their applications include:
- Predictive Maintenance: AI is used to monitor machinery to predict failures before they happen.
- Robotic Process Optimization: AI is used to adjust robotic arms in real time to improve efficiency.
- Quality Assurance (QA): AI is used to inspect products for defects using high-speed cameras.
- Supply Chain Optimization: AI is used to predict demand and adjust logistics in real time.
- Generative AI for Documentation: AI is used to draft Standard Operating Procedures (SOPs), maintenance logs, and part descriptions.
- Product Innovation & Simulation: AI is used to simulate new materials or part designs before prototyping.
What’s Our Call to Action?
Using a crawl, walk, run approach is widely considered to be a smart and effective strategy, especially for organizations that are early in their AI journey. We’re not changing “the what” we do, but “the how” and “the who.” We need to welcome the age of AI agents.
Data accuracy and quality are critical. Success is data quality dependent—and a disaster without it. But remember, it’s not that complicated! Adoption will take a bit of up-front hard work (which is temporary) but once organized and mobilized, it gets easier.
How Do We Start?
Every journey requires that first step—that leap of faith. But it always helps to have a roadmap, a plan for how to get there.
- Secure Executive Ownership & Accountability: This secures “skin-in-the-game,” no “opting-out,” and “failure is not an option.” Drive for strategic alignment and resource allocation—play to win!
- Define Clear Business Objectives: AI should solve real business problems, not be just a technology experiment.
- Establish Governance and Ethics Framework: Responsible AI use builds trust and ensures compliance.
- Assess Data Readiness: AI thrives on quality data.
- Build a Cross-Functional AI Team: AI success requires collaboration between domain experts, data scientists, and IT.
- Start Small with Pilot Projects: Small wins build momentum and reduce risk.
- Invest in Scalable AI Infrastructure: AI workloads require robust computing and storage.
- Focus on Change Management and Training: AI adoption is as much about people as it is about technology.
What’s the Bottom Line?
AI is coming. The Grinch couldn’t prevent Christmas from coming, and we can’t stop the driving force of AI. AI is not a strategy, but a tool for business transformation and scale. It needs to be a commitment to forever change the DNA of the business. This is not an “opting-out” situation. You can either be an ostrich and put your head in the sand and hope it goes away or embrace AI and soar with the eagles.
Thanks and credit to Jaime Portocarrero for his contributions and insights for the Sip Club.
Jaime Portocarrero is a self-proclaimed “Silicon Valley kid” with over 25 years of experience helping companies scale their businesses and enabling competitive advantage via frictionless / LEAN process re-engineering and automation. He has strong cross-functional experience across Idea-to-Offer, Quote-to-Cash, Demand-to-Supply and Source-to-Pay. He specializes in ERP, CRM, high-tech sales, finance, operations execution and supply chain optimization. Jamie is a sourcing and procurement expert, contracts negotiator and SRM (Supplier Relationship Management) Program leader. https://www.linkedin.com/in/jaimeportocarrero/
Jeff Osorio is a Consulting CFO with over 30 years of experience in operationally oriented companies ranging from pre-Revenue to $4B with over 40 ERP implementations in his portfolio. He is also an Adjunct Professor in the MBA program of the Leavey School of Business at Santa Clara University.