At 77-year-old promotional merchandise firm Gold Bond Inc., CIO Matt Worth knew generative AI adoption wouldn’t come from rolling out a chatbot. Staff wanted AI embedded into the work they already hated doing: messy ERP consumption, doc processing, and name follow-ups.
As a substitute of pitching benchmarks, Worth constructed a small group of “super-users” to floor Gold Bond–particular examples and prepare the remainder of the org. They then wired Gemini and different fashions into high-friction workflows, backed by sandbox testing, guardrails, and human evaluation for something public-facing.
The payoff confirmed up as conduct change, not hype: Each day AI utilization rose from 20% to 71%, and 43% of workers reported saving as much as two hours a day. “I wanted to bring everybody on the journey,” Worth advised VentureBeat. “After we reset some expectations, people started leaning towards it. Our adoption has taken off.”
ERP streamlining, product visualizations
Gold Bond, Inc. — to not be mistaken with the skincare firm — is without doubt one of the largest suppliers within the $20.5 billion promotional merchandise business, producing customized swag and company items for 8,500 lively clients.
Orders, quotes, and pattern requests arrive by way of the web site, e mail, fax, and extra — in each format possible. “So it gets very messy,” Worth stated.
AI proved a pure match. Beforehand, workers manually keyed order particulars into the ERP. Now, Google Cloud ingests incoming paperwork and normalizes them, whereas Gemini and OpenAI extract and construction the fields earlier than pushing a accomplished buy order into the system, Worth stated.
From there, Gold Bond expanded into a practical multi-model strategy: Gemini inside Workspace, ChatGPT for backend automation, Claude for QA/reasoning checks, and smaller fashions for edge experiments.
"We’re pretty agnostic on utilizing AI technology,” Price said. Gold Bond is largely set up as a Google shop, with implementation and change management led by Google premier partner Promevo.
Early wins included phone call summaries, email drafting, and contract review. A more advanced use case is AI-assisted “virtual mockups” of branded products; teams use Recraft to iterate on sample visuals before sending previews to customers, Price said.
Employees also use AI to generate Google Sheets formulas (including Excel-style XLOOKUP logic), while NotebookLM helps build an internal knowledge base for procedures and training.
Other ways Gold Bond uses AI internally:
Presentations: Work that took four hours now takes about 30 minutes, Price said.
Code auditing: Developers run NetSuite scripts, then use two models to review them before moving to testing.
Research: Tracking importer trends and tactics in response to tariffs.
AI also compresses early-stage planning. “We go back and forth with AI and come up with a high level project that we can then build out for execution,” Price explained. “We get to concepts a lot quicker. We have a lot fewer meetings, which is great.”
To quantify impact, Price’s team runs Kaizen events — short workshops that document baseline workflows and compare them with AI- and automation-assisted versions.
To validate multi-LLM workflows, Gold Bond tests changes in a sandbox environment and runs QA scenarios before rollout. “Our technical team, along with the subject matter experts, sign off prior to shipping the changes or integrating to production,” Price said.
Change management is a must
Adoption wasn’t automatic — at a legacy company, change management was the work. “It's just apprehension a little bit, it's something different,” Price said.
Most users start with Gemini because it’s built into Workspace, then move to ChatGPT, Claude, or Mistral when they need different capabilities — or a second opinion.
Price relies on a “small cool group” of about eight early adopters to test bleeding-edge tools; once they land a use case, they train the rest of the team.
“You can't just look at something like a new piece of software," famous Promevo CTO John Pettit. "You actually have to vary folks's ideas and behaviors round it.”
However at the same time as Worth's crew is selling widespread use, blind belief will not be an choice, he emphasised.
Gold Bond added insurance policies, DLP controls, and identification layers to cut back shadow AI use. It additionally makes use of LibreChat to centralize entry to accredited instruments, implement paid/accredited utilization, and block sure fashions when wanted.
Human-in-the-loop is obligatory: Public-facing content material goes by approval, and outputs should be verified. “You have to set the right temperature of trust, but verify,” he stated. Even with robust prompts, outputs nonetheless require verification. “You get the data back, you can't just blatantly take it and use it.”
For example, he’ll ask for sources and reasoning — “Give me all the work cited, where you are grabbing this data from” — and treats that verification step as a part of the workflow, he stated.
Worth additionally cautioned in opposition to overreach. “Agentic solutions can only go so far — there still need to be humans in the loop,” he stated. “Some people have bigger visions than what the tech is capable of.”
His recommendation for different enterprises: Don’t overwhelm your self with the hype. Begin easy. Begin fundamental. “Provide detailed prompting, test it, play around with it.”
