AI for Manufacturers in Western Pennsylvania: Where to Start

A western Pennsylvania manufacturer should start with AI at the point its growth will strain first. For most plants that is one of four places: securing reliable materials, keeping the supply chain responsive, finding and deploying people, or keeping teams coordinated as volume climbs. Pick the one that would cost you most if it broke this year, tie the first AI project to a number you already track, and build from there. That sequence keeps new growth profitable.

On July 30, GE Vernova announced a $138 million expansion of its plant in Charleroi, on the Monongahela River in Washington County. The plant makes high-voltage circuit breakers, switchgear, and instrument transformers, the equipment utilities need to connect growing energy demand, including data centers, to the grid more quickly and reliably.¹

It is growing into a tight market. Wood Mackenzie expected U.S. supply of power transformers to fall 30 percent short of demand in 2025, after demand had more than doubled since 2019.² In a market like that, winning the order is the easy part. Delivering it on time and at a healthy margin depends on materials arriving, lead times holding, the right people on the floor, and every team working from the same plan.

I started my career as a chemical engineer at Air Products, then spent years at FreeMarkets and SmartOps, two Pittsburgh software companies that sold to manufacturers. None of that work involved AI as we know it today. It taught me how manufacturers adopt new technology, and I think that lesson carries into AI.

Is manufacturing growing again in western Pennsylvania?

Manufacturing in western Pennsylvania is growing through new investment, with several of the region's manufacturers announcing major expansions to supply the energy and technology buildout.

GE Vernova's Charleroi project brings its western Pennsylvania commitment to $166 million.¹ In Cheswick, Curtiss-Wright is investing $80 million in two new buildings to expand manufacturing and testing for naval defense and commercial nuclear customers.³ Mitsubishi Electric Power Products announced an $86 million investment in a new western Pennsylvania switchgear factory and upgrades in Warrendale.⁴

Employment is steadier than the headlines. Manufacturing jobs in the Pittsburgh metro stood at 88,600 in August 2026, slightly above a year earlier.⁵ The technology base is growing alongside it, with more than 11,300 jobs across the region's robotics and AI companies by the Pittsburgh Robotics Network's count,⁶ an ecosystem I described in how Pittsburgh businesses can grow with the region's AI economy.

Winning the order is the easy part. Delivering it on time and at a healthy margin is where growth pays.

How can AI help a manufacturer secure reliable materials?

AI helps a manufacturer secure reliable materials by widening the search for qualified suppliers and spotting supply risk early enough to act on it.

The need is sharpest when inputs are concentrated. Cleveland-Cliffs, which makes electrical steel at its Butler Works mill an hour north of Pittsburgh, describes itself as the only U.S. producer of the grain-oriented electrical steel used in transformers.⁷ When a material has that few sources, one disruption can stop a line.

Among the uses I see in the market, AI can speed the search for qualified second sources and the first screen of each one. It can monitor news, financial, and logistics signals across a supplier base, including the suppliers behind your suppliers. It can draft requests for quotation and flag unusual contract terms, giving buyers more time for the relationships that keep material flowing when supply gets tight. Dalinea, a Pittsburgh-area company, uses AI to provide cost intelligence that breaks a product's cost into materials, labor, energy, and market inputs, so buyers can see when global conditions should move what they pay.⁸

Deloitte's 2025 survey of more than 250 chief procurement officers found that the most digitally advanced teams reported three times greater returns on their generative AI investments than their peers.⁹

At FreeMarkets, I watched industrial buyers in this region change how they sourced with the advent of the internet. The companies that gained the most changed their process and prepared their people along with adopting the new tool. I expect the same with AI. Reliable materials protect both the margin and the promise date.

How does AI make a manufacturer's supply chain more responsive?

AI makes a manufacturer's supply chain more responsive by showing planners, early and often, what a change in demand or supply will do to their promise dates, so they can act before it becomes a late order.

Curtiss-Wright says its Cheswick expansion is meant to improve operational efficiency and reduce lead times, which makes responsiveness part of the growth plan itself.³

In my experience, a slow supply chain hides its cost across the income statement: expedited freight, weekend overtime, a line waiting on a part, extra safety stock held just in case, and the customer who quietly gives the next order to someone else.

AI can help with each. It can check a promise date against real capacity and material before sales commits to it. It can model what happens when a supplier slips and lay out the options while there is still time to choose. It can update the odds on a plan as orders and shipments arrive, instead of waiting for the next monthly cycle.

At SmartOps, putting probability into planning was the hardest part of the work, and running it often was expensive. That barrier has fallen, as I explained in the step change supply chain planning has been waiting for. Responsiveness protects the customer relationships that pay for the expansion.

How can AI help a manufacturer find, hire, and deploy people?

AI helps a manufacturer with people in four ways: wider reach, faster response, better matching, and smarter deployment of skilled people.

Deloitte and The Manufacturing Institute estimate U.S. manufacturing could need up to 3.8 million workers from 2024 to 2033, and that about 1.9 million of those jobs could go unfilled if workforce challenges are not addressed.¹⁰

Neither Proxigee example below comes from a manufacturer. Both lessons carry to one.

Reach comes first. For a greater-Pittsburgh energy and industrial services company, AI-driven publishing widened its reach for new business and new hires. A hiring manager then fielded more than fifty calls on a single job posting, a first in a twenty-year career.

Speed comes next. For a convenience store operator, agents scheduled interviews from inside the HR system recruiters already used, and recruiters went from 10 to 15 interviews a week to 55 to 80, as I described in my piece on integrating AI with existing systems.

Matching turns a flood of applicants into a short list, with AI reading each posting and résumé together and surfacing the candidates worth a call.

Deployment comes last. Once that retailer's staffing moved from emergency to manageable, the next question was how to share people between stores. A plant faces the same question across lines and shifts.

How does AI help a growing plant work as one team?

AI helps a growing plant work as one team by putting the same current information in front of sales, procurement, planning, and the floor, so each group's decisions fit the others.

A common request from manufacturers is to make it easier to find information across systems so that decisions can be made faster and with more confidence.

In my experience, growth strains coordination quietly, and the cost shows up somewhere else. Sales promises a date the plant cannot hit. Procurement buys for last month's plan. A new hire learns the job without the veteran who knows why a machine runs the way it does. A shift handoff loses the one detail that mattered.

AI tools can summarize what changed, answer questions from a company's own documents, and route the right update to the right person. That coordination is what turns the other three supports into one system.

Proxigee is working with companies on sovereign AI platforms designed to give the whole company a secure view of issues needing more than one team, track each one to resolution, and turn what was learned into policy for next time.

How should a manufacturer navigate an AI landscape this new?

A manufacturer should navigate AI one value-tied step at a time, with a partner who tracks the landscape while the plant stays focused on customers.

Much of what I have described is early. The tools change month to month, and a capability that needed a custom build last year may arrive inside your ERP next year. That is a reason to start small and measure every step against margin, delivery, or customer trust. The right order also differs from one company to the next. In two Proxigee engagements, hiring came first in one and second in the other, a pattern I described in why your AI roadmap keeps changing.

That is the role Proxigee plays, and the one I described in how to choose a partner for your AI roadmap: separating what is ready from what is still a demo, choosing the first support worth strengthening, and keeping each project tied to value.

Help is also close to home. Catalyst Connection hosted CATALYZE 2026 at Mill 19 this spring, a manufacturers-only summit covering AI, automation, workforce, and supply chain challenges.¹¹

Proxigee Services is an AI consulting firm based in Wexford, Pennsylvania, that helps small and midsize manufacturers, distributors, and service firms put AI to work inside the systems and teams they already have. See how we work with Pittsburgh-area companies or Terry Lyon's background in engineering and supply chain.

Growth that pays

Western Pennsylvania is building the equipment the next economy runs on, and the orders are coming in. Profiting from them takes materials that arrive, promises that hold, people in place, and teams working from one plan.

Start with the support closest to breaking. Then build the rest.