At Gold Bond Inc., a 77-year-old promotional products company, CIO Matt Price knew that generic AI adoption wouldn’t come from introducing a chatbot. Employees needed AI to take on the work they already hated doing: messy ERP intake, document processing, and call follow-up.
Instead of introducing benchmarks, Price created a small group of “super-users” to come up with gold bond-specific examples and train the rest of the organization. Then they wired Gemini and other models into a high-friction workflow backed by sandbox testing, guardrails, and human review for anything public-facing.
The benefits showed up in the form of behavior change, not hype: Daily AI use increased from 20% to 71%, and 43% of employees reported saving up to two hours per day. “I wanted to bring everyone on the journey,” Price told VentureBeat. “Once we set some expectations, people started leaning toward it. Our acceptance has gone up.”
ERP Streamlining, Product Visualization
Gold Bond, Inc. – Don’t go wrong with a skin care company – one of the largest suppliers in the $20.5 billion promotional products industry, producing custom swag and corporate gifts for 8,500 active customers.
Orders, quotes and sample requests come in via website, email, fax and other means – in every format imaginable. “So it gets very messy,” Price said.
AI proved to be a natural fit. Earlier, employees used to manually enter order details into the ERP. Now, Google Cloud ingests incoming documents and normalizes them, while Gemini and OpenAI extract and structure the fields before pushing the completed purchase order into the system, Price said.
From there, Gold Bond expanded into a practical multi-model approach: Gemini inside Workspaces, ChatGPT for backend automation, the cloud for QA/logic testing, and smaller models for edge experiments.
"“We are very agnostic about the use of AI technology,” Price said. Gold Bond has been set up largely as a Google Shop, with implementation and change management led by google premiere partner Promevo,
Early wins included phone call summarization, email drafting, and contract review. A more advanced use case is AI-assisted “virtual mockups” of branded products; Teams use Recraft to iterate sample scenes before sending previews to clients, Price said.
Employees use AI to generate Google Sheets formulas (including Excel-style XLOOKUP logic), while NotebookLM helps build an internal knowledge base for processes and training.
Other ways in which Gold Bond uses AI internally:
- Presentations: A job that used to take four hours now takes about 30 minutes, Price said.
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Code Auditing: Developers run NetSuite scripts, then use the two models to review them before moving on to testing.
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Research: Tracking importer trends and strategies in response to tariffs.
AI also compresses early stage planning. “We go back and forth with the AI and come up with a high-level project that we can prepare for execution,” Price explained. “We arrive at concepts very quickly. We have very few meetings, which is great.”
To measure impact, Price’s team runs Kaizen events – small workshops that document baseline workflows and compare them with AI- and automation-assisted versions.
To validate the multi-LLM workflow, Gold Bond tests changes in a sandbox environment and runs QA scenarios before rollout. “Our technical team, along with subject matter experts, sign off on changes before shipping or integrating them into production,” Price said.
Change management is essential
Adoption was not automatic – in a legacy company, change was the job of management. “It’s just a little apprehension, it’s something different,” Price said.
Most users start with Gemini because it’s built into Workspace, then move to ChatGPT, Cloud, or Mistral when they need different capabilities – or a second opinion.
The price depends on a “small nice group” of about eight early adopters to test the bleeding-edge tool; Once they find the use case, they train the rest of the team.
“You can’t look at something new like software," Famous Promevo CTO John Pettit. "You really have to change people’s thoughts and behavior.”
But even though Price’s team is promoting widespread use, blind trust is not an option, he stressed.
Gold Bond added policies, DLP controls, and identity layers to reduce the use of shadow AI. It also uses LibreChat to centralize access to approved tools, enforce paid/approved use, and block certain models when needed.
Human-in-the-loop is essential: Public-facing content undergoes approval, and output must be verified. “You have to set the right temperature of trust, but verify,” he said. Even with strong signals, the output still requires verification. “You get the data back, you just can’t take it and use it.”
For example, he’ll ask for sources and rationale — “Tell me all the works cited, where you’re getting this data from” — and treat that validation step as part of the workflow, he said.
Price also cautioned against redundancies. “Agent solutions can only go so far – it still needs to involve humans,” he said. “Some people have bigger visions than the technology can handle.”
His advice for other ventures: Don’t overwhelm yourself with promotion. Start simple. Start with the basics. “Provide detailed hints, test it, play with it.”
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