Retail is now shaped by algorithms that update pricing, placement, visibility, and shopper discovery around the clock.
That was the central message from Adam Colasanto, Director of GTM for Agentic Products at CommerceIQ, during his CommerceNext Growth Show talk, "AI Across the Ecommerce Funnel: From Discovery to Delivery: Winning the Shopper Journey with Agentic AI."
The tactical takeaway was direct: ecommerce teams cannot close the execution gap with more dashboards or disconnected tools. They need an operating model where AI agents connect media, digital shelf, sales, and content signals, then act within human-approved guardrails.
In Colasanto's view, agentic AI is not about replacing the team. It is about giving a lean team the output of a much larger operation by moving routine monitoring, diagnosis, recommendations, and execution into agents that work continuously.
The execution gap is now the ecommerce problem
Most brand teams are managing more SKUs, retailers, and channels than they were three years ago. Few have received proportional headcount. At the same time, retailer algorithms are moving faster, shoppers are using AI assistants more often, and every delay between insight and action creates lost revenue.
The old workflow depends on people noticing problems, opening reports, coordinating across teams, filing tickets, waiting for retailer approvals, and then checking whether the fix worked. That model breaks down when the shelf changes overnight.
Colasanto shared an example from a CommerceIQ customer where one suppressed SKU took 10 days and five teams to correct. The shelf team needed three days to identify and action the issue. The content team could not resubmit until the following week. Retailer approval added another 48 hours. During that delay, retail media spend kept going to a product that could not convert properly.
That is the core execution gap agentic AI is built to close.
From more tools to one operating model
The biggest change is the move from tool-based workflows to outcome-based workflows. Traditional SaaS tools surface alerts and reports. Agentic systems connect context across functions and help execute the next best action.
For large CPG and FMCG brands, that context matters. A content issue is not only a content issue. It can suppress search visibility, waste media spend, trigger sales risk, and create retailer friction. A media decision is not only a bid decision. It should also reflect inventory, organic rank, content readiness, and shopper demand.
Colasanto described CommerceIQ's model as a singular operator with unified context across media, shelf, and sales. The team sets strategy and approves quality. Agents do the work that requires speed, repetition, and scale.
The result is not four people doing more manual work. It is four people reviewing outcomes from an execution layer that behaves more like a team of 15 to 20.
How agentic AI supports the full ecommerce funnel
Agentic AI can help at every stage of the ecommerce funnel because the funnel itself is now connected by data and algorithms. Discovery depends on AI-visible content and search relevance. Consideration depends on product detail pages, pricing, availability, and reviews. Conversion depends on retail media, inventory, and promotional execution. Delivery depends on operational readiness and issue resolution.
When these signals sit in separate tools, teams react slowly. When agents have the full context, they can identify the issue, diagnose the root cause, recommend a fix, and execute or route it for approval.
This is especially important in retail media. Media teams often manage bids and budgets without full visibility into whether the product is in stock, whether the PDP is suppressed, or whether the brand already owns the keyword organically. That can lead to wasted spend on products that cannot convert or keywords that do not need paid support.
With a unified agentic approach, media execution can run continuously against guardrails informed by shelf and sales conditions. The strategy still comes from people. The 24/7 adjustment layer is handled by agents.
Content is another high-impact use case. Colasanto discussed the launch of a content agent that works within brand guidelines, legal rules, retailer requirements, and approved keyword lists. That structure is what keeps AI-generated recommendations relevant and controlled.
Guardrails make automation useful
The lesson is that guardrails cannot be vague. They have to be built from brand-approved language, legal constraints, blacklisted and whitelisted terms, retailer rules, and real shopper behavior. AI needs complete and accurate context. Fragmented data produces fragmented results.
Guardrails were a major theme in the fireside chat. The group discussed hallucinations, AI slop, and the risk of letting agents act without enough review. Colasanto emphasized that the current state of AI still requires humans in the loop, especially for regulated or brand-sensitive content.
That human role is not busywork. It is how teams train the agent. Like a new hire, an agent needs onboarding, feedback, rules, and examples. Over time, the agent can handle more of the repetitive execution while people focus on strategy, judgment, and approval.
The best pilots start small: one brand, one category, or a subset of SKUs. That allows teams to build confidence in the output before expanding automation across a larger catalog.
How to evaluate real AI innovation
The fireside chat also focused on a practical question every ecommerce leader is asking: how do you separate real AI innovation from hype?
Colasanto's answer was methodology. Brands should dig into the data sets behind any AI product, pressure-test how the system makes decisions, involve security and IT teams early, and pilot with a narrow use case before expanding.
He also warned against vendors trying to solve every problem at once. The stronger starting point is often a focused problem where AI can prove value, build trust, and then scale.
The next skill set: managing agents
Looking ahead, Colasanto expects ecommerce teams to manage agents as part of their operating model. A team lead may oversee a smaller human team and a much larger set of AI agents. That will create new skill requirements around AI fluency, prompt and workflow design, quality review, and agent management.
He also pointed to AEO and GEO as near-term priorities. Shoppers are no longer only searching for short keywords like "dry dog food." They are asking AI assistants detailed questions about use cases, dietary needs, household context, and recommendations. Product content needs to be structured for that shift.
The Amazon title compliance change discussed in the session made the point concrete. When marketplaces change requirements quickly, brands with thousands of SKUs cannot manually rewrite, review, and resubmit every title at scale. An agent can convert titles into the new structure, move supporting details into the right fields, and keep humans in control of approval.
What brands should do now
Colasanto closed with three practical actions for brands:
- Make the data complete. AI cannot produce strong results from fragmented inputs.
- Keep teams in control. Humans should set strategy, define guardrails, and approve quality.
- Make AI actionable and automated. Data that cannot be acted on is not intelligence. It is noise.
For ecommerce leaders, the message is clear. Agentic AI is not a separate innovation track. It is becoming the execution layer brands need to compete in algorithmic retail.
The brands that win will not be the ones with the most dashboards. They will be the ones that connect data across the funnel, define clear guardrails, and use AI agents to turn insight into action before the shelf changes again.
