Most online stores already run some automation: an email that fires the moment a cart sits idle, a rule that reorders stock once it drops below a set number. An ecommerce AI workflow works differently.
It reasons through a situation and picks its own next step, instead of following one path a person wrote in advance for every case.
IBM's explainer on agentic workflows makes the difference concrete with an IT support example. A rule-based assistant facing an unfamiliar network problem just escalates it to a person.
An agentic one gathers information, runs diagnostics, tries a fix, checks whether it worked, and logs the outcome for next time. Traditional automation, in IBM's own framing, follows fixed rules and static patterns built for repetitive, standardized tasks. An AI workflow adapts to real-time data instead.
What Separates an AI Workflow From Basic Automation
That's the same question worth asking before scoping any AI and automation work: does this task genuinely need judgment, or would a fixed rule handle it just as well?
A lot of what gets called "AI" in ecommerce is still the second kind. There's nothing wrong with that. It just isn't the same thing.
The distinction changes what you're buying. A rule reorders stock at a threshold you set.
An agentic system decides whether to reorder at all, weighing demand, lead time and a dozen other signals that shift week to week.
Where an Ecommerce AI Workflow Pays Off First
BigCommerce's rundown of ecommerce automation points to a handful of places this already works well.
- Cart-abandonment sequences that follow up automatically after someone leaves without buying
- Inventory rules that reorder stock once it drops below a set threshold
- Chatbots that resolve routine questions and hand off anything unusual to a person
- Recommendation engines that read browsing and purchase history to decide what to show next
We've written before about keeping that kind of follow-up automation feeling personal rather than robotic.
The advice on where to start lines up too: pick one focused use case, get it working, and only then expand into forecasting, pricing or broader marketing. Trying to automate everything at once is how a promising pilot turns into a mess nobody trusts.
The Adoption Numbers Hide a Maturity Gap
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Nearly every retailer has touched this already. A Capital One Shopping review of eTail Insights survey data puts AI use, full or experimental, at 96% of online retailers.
What that figure hides is how little of it runs deep. Stord's 2026 State of AI in Ecommerce report found 88% of organizations now use AI regularly in at least one part of the business, up from 78% in 2024.
Fewer than one in ten have gotten it embedded enough to call fully scaled.
| Stage | Share of organizations | What it looks like |
|---|---|---|
| Fully scaled | 7% | Deep integration driving material benefits |
| Scaling | 31% | Moving past initial tests into standardized departmental use |
| Piloting | 30% | Testing isolated use cases |
| Experimenting | 32% | Still working with basic tools |
The businesses that push past piloting see it in revenue. Stord's report found early adopters leading on AI-driven personalization out-earn non-adopters by 40%.
Getting there usually runs into a plainer problem than the AI itself: whether the underlying store can expose clean data in the first place.
If your platform can't tell a workflow what's in stock or who's browsing what in real time, the workflow stalls before it starts. That's often as much a web and ecommerce platform question as an AI one.
Start with the one workflow that would save the most staff time this month, prove it, and keep a person reviewing the calls it gets wrong before handing it more.
Cover photo by AS Photography on Pexels
Sources
- What are Agentic Workflows? — IBM
- Ecommerce AI Automation in 2026 — BigCommerce