The Tool Desk
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Why Graham is working hands-on with AI
Graham describes himself as a non-engineer who has returned to close collaboration with product and technology colleagues. He said: “I have personally spent many hundreds of hours over the past three months getting my head around AI and the future of commerce – with agentic commerce in mind.” The focus is not simply on generating software: it is on understanding how AI might improve the marketplace and help its brands reach shoppers.
He said the opportunity is to improve “the efficiency and discovery on Wolf & Badger by better understanding our shoppers and our brands and the products they sell.” That points to two related goals: making internal work more effective and helping customers find relevant products in a large, varied catalogue.
What AI work has Wolf & Badger described?
Claude Code and an internal assistant
Graham called Claude Code his “go-to app.” He also described an internal AI “chief of staff” connected to tools through MCPs or APIs. Those are accounts of how he and the company are experimenting; the interview does not independently test his setup or quantify any productivity gain.
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Anthropic describes Claude Code setup in its documentation and explains that the Model Context Protocol (MCP) is supported in Claude Code. Anthropic describes MCP as an open protocol for standardizing how applications provide context to large language models. That technical context helps explain the kind of connections Graham mentions, but does not establish which integrations Wolf & Badger uses.
The interview also names Microsoft Copilot and Cursor among tools the company’s team explored. It does not present a controlled comparison of the products, so it cannot establish which is best for a particular coding or business task.
Prototypes for brand and sustainability assessment
Graham described prototypes for assessing brands and vetting sustainability. The account signals areas where the company is exploring AI support, not a claim that an automated system has replaced human review or that the prototypes have been independently evaluated. The interview does not specify their accuracy, deployment scope, or decision-making safeguards.
Product data and discovery
The marketplace use cases include image recognition, product tagging, richer product attributes, and personalization intended to improve discovery. Better structured descriptions and tags could help connect shoppers’ interests to products, but the interview does not report a technical evaluation of these capabilities or isolate their effect on customer experience.
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What the reported figures do—and do not—show
Computer Weekly reported company figures that give a sense of Wolf & Badger’s scale and the results it associates with its AI initiatives. The figures are attributed to the company, not presented as an independent audit.
| Reported figure | Attribution and period |
|---|---|
| More than $500 million in cumulative sales since inception | Wolf & Badger, in its January 2026 performance update as reported by Computer Weekly |
| Almost 40 million website visits in 2025 | Wolf & Badger, in its January 2026 performance update as reported by Computer Weekly |
| $100 million (£75 million) annual sales in 2024 | Wolf & Badger, as reported by Computer Weekly |
| More than 2,000 brand partners | Wolf & Badger, as reported by Computer Weekly in March 2026 |
| £3.2 million in directly attributable incremental sales from recent AI initiatives | Wolf & Badger’s attribution, as reported by Computer Weekly in March 2026 |
The £3.2 million figure is the company’s attribution of incremental sales to recent AI initiatives. The interview does not provide an independent assessment of the attribution or enough detail to infer that the same result will recur. It should therefore be read as a company-reported outcome, not as a verified estimate of AI’s general effect on sales.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What agentic commerce could mean for independent brands
Agentic commerce refers here to shopping journeys in which AI-powered agents can help people discover products and, as systems develop, take actions in the path to purchase. For a marketplace such as Wolf & Badger, Graham sees this as a prospective channel opportunity: independent brands need their products to be understood and discoverable by AI-led services as well as by conventional search and browsing.
Google describes its Universal Commerce Protocol (UCP) as an open standard intended to support agentic actions across Google AI surfaces. Its merchant guide directs businesses to implementation documentation and a waitlist; Google’s UCP overview presents it as a common language for platforms, agents, and businesses across commerce, from discovery to checkout. This describes Google’s standard and current merchant pathway; it does not establish that Wolf & Badger has implemented UCP.
Claude Code and UCP have different roles: Claude Code is a coding assistant Graham says he uses, while UCP is a commerce protocol Google describes for connecting platforms, agents, and businesses. One is a tool for product and software work; the other is a possible infrastructure layer for future shopping journeys. The interview does not treat them as competing products.
What the interview establishes—and what remains uncertain
- Graham’s activity: he says he has invested hundreds of hours learning about AI and is experimenting with coding assistance and internal tools.
- Company use cases: the story describes AI-related work in brand assessment, sustainability vetting, image recognition, product tagging, product attributes, and personalization.
- Business results: the £3.2 million incremental-sales figure is attributed to Wolf & Badger; the interview does not independently validate it.
- Future channel: agentic commerce is framed as an opportunity to prepare for, not as an established source of sales for Wolf & Badger.
Graham’s approach is summed up by his advice: “You just have to try to stay ahead.” The interview shows how one marketplace executive is exploring AI in product work and commerce; it does not amount to a general endorsement of AI-generated software or proof that every experiment will translate into lasting business gains.
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