Promega Shows What Real AI Adoption Looks Like

Promega didn’t “try AI.” It operationalized it.

When a U.S.-based life sciences manufacturer goes from curiosity to 80% employee usage, builds 1,400+ custom GPTs, and starts measuring time savings in hundreds of hours per year, that’s not a novelty project—it’s a blueprint. For companies across the United States trying to modernize their technology and digital services, Promega’s story is a practical example of how AI becomes infrastructure, not a side tool.

This matters right now because late December is when teams feel the pain of the year: backlogged process improvements, budget planning, and a fresh slate of 2026 goals. Promega’s approach—top-down sponsorship plus bottom-up experimentation—maps cleanly to what many U.S. organizations want: faster operations, stronger customer communication, and measurable productivity gains without tearing everything down.

The real lesson: AI adoption is a leadership decision

AI adoption succeeds when leaders treat it like a core capability, not an optional perk.

Promega’s CEO didn’t wait for a perfect roadmap. He saw that ChatGPT could help a company juggling thousands of products and 60,000+ accounts manage complexity at speed. That framing is the difference between “AI as a chatbot” and AI as an organizational multiplier.

Here’s what most companies get wrong: they buy a tool, run a few trainings, and hope usage spreads. Promega did the opposite. Leadership buy-in created permission to experiment, and permission created momentum.

What “top-down” actually means in practice

Top-down doesn’t mean forcing everyone to use the same prompts. It means establishing the conditions where safe, measurable AI usage can grow.

Promega ran a pilot to identify use cases, then scaled what worked. The company also organized AI governance through an AI Advisory Council—a practical structure for any mid-market or enterprise team that needs cross-department alignment.

If you’re building an AI strategy for technology and digital services in the United States—whether you’re in SaaS, professional services, healthcare, manufacturing, or government—the transferable idea is simple:

• Make AI usage visible at the leadership level (leaders use it regularly)
• Fund access first (licenses aren’t the bottleneck you want)
• Instrument usage and outcomes (what’s used, what saves time, what improves quality)
• Scale the winners (double down on a few high-impact workflows)

AI in manufacturing: planning beats heroics

AI creates the most value in manufacturing when it reduces uncertainty.

Promega manufactures roughly 4,000 products and deals with customer-specific requirements. In that environment, teams don’t just need speed—they need better forecasting, faster knowledge retrieval, and fewer bottlenecks in quality workflows.

Promega’s example shows how AI supports manufacturing operations in a way that fits how plants actually run: lots of planning, lots of documentation, lots of coordination.

Forecasting equipment replacement (and making assumptions explicit)

One manufacturing leader at Promega uses ChatGPT to forecast equipment replacement timelines and costs, with the model outlining assumptions that can be edited. That detail—assumptions spelled out—is the underrated feature. It makes AI outputs easier to validate, challenge, and socialize with finance.

If you’re doing capital planning in 2026, that’s a strong pattern:

1. Ask AI for a draft forecast with stated assumptions
2. Replace assumptions with your real constraints (supplier lead times, utilization rates, downtime risk)
3. Export the logic into a doc your stakeholders can debate

AI doesn’t replace your judgment; it reduces the cost of producing a first pass that’s structured enough to improve.

Turning scattered scientific knowledge into usable answers

Promega scientists built custom GPTs that can call public APIs to gather protein specs and related data quickly. The broader business point isn’t “APIs are cool.” It’s that AI can sit on top of fragmented systems and compress research time.

In many U.S. businesses, the same pain exists outside life sciences:

• Customer support teams chase answers across knowledge bases
• Sales engineers hunt for the right spec sheet version
• Operations teams triangulate between ERP, spreadsheets, and emails

A custom GPT that retrieves and summarizes information—while linking back to source documents internally—becomes a practical digital service layer.

Quality assurance automation that produces measurable savings

Promega’s Quality Assurance team built a custom GPT integrated with Power Automate to handle customer requests and responses, including quality surveys. The results are the kind executives actually care about:

• 250+ quality surveys per year handled more efficiently
• 600+ hours saved annually
• Faster delivery of certifications and quality policies to customers

If you want AI ROI that survives scrutiny, use this template: pick a workflow with predictable volume, repetitive documentation, and clear accuracy requirements. Then automate the assembly and drafting—not the final sign-off.

AI in sales and marketing: scale communication without losing relevance

AI improves sales and marketing when it cuts “prep work” time, not relationship time.

Promega’s sales and marketing teams built custom GPTs that help them navigate a large product portfolio while keeping messaging aligned. This is a major theme in the broader series How AI Is Powering Technology and Digital Services in the United States: organizations aren’t using AI just to create content—they’re using it to scale customer communication without drowning teams in admin work.

Prospect research that saves hours per lead

A custom GPT (“My Prospecting Pal”) helps identify key details about a prospect, suggest relevant Promega offerings, and even find potential common ground for natural outreach. Reported impact:

• 1–4 hours saved per prospect on lead analysis

That time savings isn’t trivial. It changes the economics of outbound.

If you’re running a U.S. sales org, the practical play is to standardize what “good research” looks like and let AI draft the first version:

• Company overview and current initiatives
• Likely needs based on role and industry
• Relevant product/service mapping
• Draft talk track + email opener
• Risk flags (regulated industry, procurement constraints)

Then your reps spend time where humans win: discovery, negotiation, trust.

Email marketing that moves faster (with fewer meetings)

Promega’s “Email Marketing Strategist GPT” reportedly cut the time from content creation to campaign execution by about half, saving 135 hours as hundreds of emails were deployed faster.

The interesting part is the quote: getting time back from aligning on email strategy can be invested into user experience. That’s the shift many marketing teams need.

AI shouldn’t encourage more email volume. It should reduce the internal friction—rewrites, approvals, formatting, audience variants—so the team can spend more time on:

• Segmentation that reflects real customer behavior
• Lifecycle timing (when to message, not just what to say)
• Offer clarity and landing page consistency
• Post-send learning loops

Building a company-wide AI operating system (not a pile of prompts)

The fastest path to sustainable AI value is an internal “AI operating system”: governance, enablement, and measurement.

Promega’s AI Advisory Council approach is a clean model for U.S. companies scaling AI across departments. It’s also a reminder that custom GPTs are a product portfolio, not random experiments.

The adoption playbook you can copy

Promega’s internal learnings translate well outside life sciences:

• Over-provision access early. Give teams enough licenses to experiment. Then invest more deeply where impact is highest.
• Track usage like you track product analytics. Adoption tells you where the value is hiding.
• Make learning social. Workshops and share-outs create internal proof.
• Use data to build confidence. Promega found that even basic prompt users outperformed non-users.
• Leaders set the norm. If leadership doesn’t use AI, the org reads that as “optional.”

Guardrails that keep AI useful (and safe)

If you’re responsible for AI deployment in technology and digital services, you need guardrails that don’t kill momentum. I’ve found these three rules are enough to start in most orgs:

1. Define what can’t be entered (customer PII, sensitive IP, regulated data) and enforce it.
2. Require source-backed outputs for high-risk workflows (QA, compliance, finance).
3. Keep humans on the hook for final approvals, especially anything customer-facing.

These aren’t abstract policy statements. They’re operational constraints that keep adoption high while reducing avoidable risk.

What Promega’s case study says about AI in the U.S. economy

AI is becoming a baseline expectation for competitive U.S. businesses, not a differentiator reserved for tech companies.

Promega’s story is a reminder that American manufacturing and life sciences are now digital service businesses too: they compete on responsiveness, documentation quality, turnaround time, and customer experience. AI strengthens all four—when it’s implemented with leadership support and measurable workflows.

The most useful framing is this: AI proficiency is a workforce capability, like spreadsheets were in the 1990s and cloud tools were in the 2010s. The companies that treat it that way will hire better, move faster, and serve customers with less friction.

If you’re planning your 2026 initiatives, here’s a practical next step: pick one workflow each in operations, customer communication, and internal knowledge retrieval—and run a 30-day build-measure-learn cycle. If you can’t quantify time saved or quality improved after a month, the use case needs to change.

Where do you have the most expensive “busy work” in your organization right now—and what would it mean if AI gave those hours back?