Gopal Chandrasekaran

I've been in B2B and B2C ecommerce implementation for over a decade — BigCommerce, Shopify, enterprise migrations. The clients I work with aren't asking "Should we use AI?" They're asking: → Is our product feed machine-readable? → Does our catalog structure make sense to an LLM? → Can an AI agent execute a purchase through our checkout? → Are we listed in the discovery channels that matter in 2026? Those are architecture questions, not marketing questions. Curious what questions your teams are asking. What's the conversation at your organization?

Two competing open protocols — OpenAI's #ACP, built with Stripe, and Google's UCP, co-built with Shopify — are now defining how AI agents find and buy from merchants. Amazon is building its own proprietary layer. The standards war is real, and your commerce stack's readiness for either protocol will determine whether AI agents can transact through you at all.

Something shifted in commerce in 2025, and I don't think most leaders have fully processed it yet. AI didn't just improve the shopping experience. It inserted itself between the buyer and the brand.

B2B digital commerce in this industry isn't a future problem. It's a present gap — and the window to lead is still open. What's the biggest friction point you're seeing in how fasteners get bought and sold today?

The fastener industry has deep, hard-won expertise. The opportunity is to stop keeping that expertise locked in human conversations — and start building it into the way customers buy. The companies that do this won't just grow. They'll become structurally harder to compete with.

The fastener industry moves $92 billion a year. A surprising amount of it still moves through PDFs, phone calls, and spreadsheets. The B2B buyer has changed. The distribution model hasn't caught up.

The conversation at every eCommerce event this year should be shifting from "how do we adopt AI?" to "how do we govern autonomous agents?" The agents are already running. The question is, who owns the outcome?

3. Agentic AI in eCommerce fundamentally changes the risk profile. When an AI agent can reason, act, and transact autonomously — adjusting prices, processing refunds, modifying inventory — the blast radius of a misconfiguration isn't just a bad recommendation. It's revenue loss, brand damage, or data exposure.

Every eCommerce platform is now building and offering AI. The technology has arrived. The governance hasn't. We see many examples of AI-powered chatbots offering incorrect pricing, making poor recommendations, and other issues. Here, the AI didn't fail; the workflow around it did.

Spinning up an agent is a weekend project. Building the governance layer that makes it trustworthy at scale? That's the actual work. The leaders who win this wave won't be the fastest to deploy agents. They'll be the ones whose agents are still running — and still trusted — 12 months from now.

This is the pattern I keep seeing in our own client implementations: The bottleneck is never the agent. It's the absence of a system around the agent — clear ownership, escalation logic, monitoring, and a human who's accountable when the loop breaks.

Two months ago, I wrote about Outcome Engineering — the shift from prompting AI to governing it.