For mid-market consumer & retail businesses
Your AI pilot is still a pilot.
Almost every mid-market consumer or retail company ran an "AI initiative" in the last year. Most are still demos — impressive in a meeting, untouched in operations. The model was never the problem. Shipping it into the real workflow, and getting people to actually use it, is.
I was VP of AI at Ashley Furniture — $12B, 800+ stores, where I built their agentic commerce platform. I shipped agents into real operations — and watched nine of ten "AI projects" die in pilot. This is the playbook for the tenth: five high-ROI agents, live and in use, in 30 days.
~95%of enterprise GenAI pilots deliver no measurable P&L impact — and MIT found the bottleneck is adoption and integration, not the model.
Why the 90% stall
✕It's a chatbot, not a workflow. A general assistant nobody's required to use. The 5% embed the agent inside an existing job, so using it is the path of least resistance.
✕No success metric. "Explore AI" isn't a goal. Without an hours-saved or error-rate number, there's nothing to defend at budget time.
✕No production-readiness. No evals, no human-in-the-loop on high-stakes steps, no guardrails — so one bad output kills trust and the project.
✕No owner, no adoption plan. It's a science project on the side of someone's desk, not an operational tool with a champion.
The 5 agents worth building first
High-frequency, document-heavy, low-ambiguity workflows — where agents earn trust fast in a retail, commerce, and distribution ops setting.
AGENT 01
Product & catalog intelligence
RAG over product specs, SKUs, pricing, and availability data. "What's the lead time / spec / stock status on X?" answered in seconds instead of an email chain.
Saves: hours/week of merchandising & customer-service lookups
AGENT 02
Order & quote hygiene
Reviews incoming orders/quotes for wrong configs, pricing errors, missing fields — flags them before they ship and become a rework cost.
Cuts: costly downstream errors
AGENT 03
Ops / QBR prep
Pulls from ERP + BI to draft the weekly ops review and flag exceptions — late jobs, margin slips, at-risk orders — so the meeting starts at the answer.
Saves: a day of analyst prep
AGENT 04
Order-status & service triage
Handles "where's my order," tier-1 customer questions, and routes the rest with context — off the CSR's plate, with a human in the loop on anything sensitive.
Deflects: routine ticket volume
AGENT 05
Demand & inventory Q&A
Natural-language over planning/inventory data. "What's at risk of stockout next month? What's overstocked?" — answers without waiting on a report.
Speeds: planning decisions
THE POINT
Pick one, ship it, then repeat
You don't need an "AI strategy." You need one agent live and used by Friday, a number on the board, then the next. Momentum beats roadmaps.
The 30-day path out of pilot
- Scope. Pick one workflow above. Write the success metric (hours saved / errors caught) before any building.
- Build. Wire the data, build the agent, test against real historical cases — not toy prompts.
- Ship with guardrails. Human-in-the-loop on high-stakes steps, evals on real cases, embedded in the actual tool people already use.
- Measure & expand. Track adoption + the metric, fix what drags, then start agent #2. The engine is now repeatable.
What the 5% do that the 95% don't
✓Embed in the workflow — the agent lives where the work already happens, not in a separate app.
✓Evals on real cases — measured accuracy on your actual data before it touches a user.
✓Human-in-the-loop where the cost of a mistake is real — trust is the whole game.
✓One business metric + one owner — defensible at budget time, championed day to day.
✓Ship narrow, then widen — a working agent beats a grand platform every time.
See it on your own workflow — free.
Send me one workflow your team wishes ran itself. I'll build a working agent on it and screen-record the result — so you see exactly what "out of pilot" looks like before deciding anything.
Book a 15-min call →
Jason · ex-VP of AI, Ashley Furniture ($12B, 800+ stores) · AI agents shipped into real retail & commerce ops