AI adoption stalls at major USD 20.5B promo products supplier serving 8,500 clients
Why does a $20.5 billion promotional‑products powerhouse still wrestle with AI? The firm, which outfits corporations with custom swag and gifts, handles a torrent of orders, quotes and sample requests that flow in from a website, email, fax and a handful of other channels. With 8,500 active customers demanding fast turn‑arounds, its sales pipeline resembles a bustling highway rather than a quiet back‑office.
Yet the promise of generative AI has barely shifted that traffic. While many vendors tout seamless chatbot integrations, this company’s experience shows a different picture: AI tools sit on the periphery, waiting for the IT department to stitch them into existing workflows. The result?
Stalled adoption, fragmented processes, and a missed opportunity to turn data into speed. The upcoming quote underscores exactly how the business—often confused with a skin‑care brand—fits into a $20.5 billion industry, and why its AI journey matters.
-- not to be mistaken with the skin care company -- is 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 arrive via the website, email, fax, and more -- in every format imaginable. Previously, employees manually keyed 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 a completed purchase order into the system, Price said. From there, Gold Bond expanded into a pragmatic multi-model approach: Gemini inside Workspace, ChatGPT for backend automation, Claude for QA/reasoning checks, and smaller models 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.
Will AI truly lift Gold Bond’s daily grind? The company’s experience suggests that merely adding a chatbot won’t change anything. By embedding Gemini‑type models into the ERP intake, document handling and call‑back steps, the firm hopes to turn tedious tasks into smoother processes.
CIO Matt Price’s “super‑user” squad surfaced use‑cases that mattered to the shop floor, then taught the broader workforce how to tap the new tools. Still, adoption hinges on whether those integrations survive the company’s patchwork of order channels—website, email, fax and more. Early feedback points to faster quote generation, yet the impact on overall efficiency remains unclear.
Without IT‑driven workflow redesign, the AI layer could become another silo. Gold Bond’s 8,500 customers will likely notice any shift in turnaround time, but the internal metrics that matter most have yet to be published. As the pilot rolls out, the true test will be whether the technology eases the “messy” processes it was built to address, or simply adds another layer of complexity.
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Common Questions Answered
Why has AI adoption stalled at the $20.5 billion promotional‑products supplier serving 8,500 active customers?
Despite the massive order volume, simply adding a chatbot has not altered workflows because most processes still rely on manual data entry. The company finds that AI must be deeply integrated into ERP intake and document handling to deliver real efficiency gains.
How does Google Cloud, Gemini, and OpenAI work together to process incoming order documents?
Google Cloud first ingests documents from the website, email, fax and other channels, then normalizes the data. Gemini and OpenAI models extract relevant order details, reducing the need for employees to manually key information into the ERP system.
What role does CIO Matt Price’s “super‑user” squad play in the AI rollout?
The squad identified high‑impact use cases on the shop floor and created training for the broader workforce on how to use the new AI tools. Their hands‑on approach aims to ensure the integrations are adopted and remain functional over time.
What specific processes does the company hope to improve by embedding Gemini‑type models into its ERP?
Embedding Gemini‑type models targets the intake of orders, the handling of varied document formats, and the call‑back steps that currently consume staff time. By automating these steps, the firm expects smoother, faster processing and reduced manual effort.