AI & Sustainability in Retail

Mike Robinson brings more than 25 years in digital retail, including operational roles at The Gap and Macy's. Today he leads The Eighth Notch, a venture focused on coordinating retail deliveries to reduce emissions — and he has a clear thesis on where AI fits into the sustainability puzzle.

Coordinated deliveries for a greener future

Most retailers default to speed. Next-day, same-day — the race to the doorstep has become a brand promise. But Robinson argues that this obsession comes at a hidden environmental cost. Trucks making dozens of individual stops, each carrying a single package, are a systemic inefficiency that no amount of EV adoption will fully fix.

The Eighth Notch's approach is different: coordinate multiple packages from different retailers so they travel together on a shared truck, delivered in a single stop. It sounds simple, but the orchestration — knowing which packages are heading where, in what timeframe, and consolidating routing in real time — is a genuinely hard logistics problem.

"We're changing the nature of when things get shipped to change the downstream impact of when they get delivered."

The last mile: a crucial piece of the sustainability puzzle

Robinson points out that retailers have made meaningful progress on sustainability in areas they can see and measure — renewable energy for warehouses, sustainable sourcing, reduced packaging. But delivery remains a blind spot.

Brands negotiate delivery partnerships primarily on speed and price. The carbon footprint of those partnerships rarely enters the conversation. "What they've missed is the untapped potential lying within their delivery partnerships," Robinson observes. "It's that final piece in the puzzle, the last mile, where the real opportunity for innovation and impact resides."

This gap matters because the last mile typically accounts for 30–50% of total logistics costs — and a disproportionate share of emissions, since final-stop deliveries involve the most vehicle movement per package delivered.

AI and optimised deliveries

Where does machine learning actually fit in? Robinson sees its role as predictive intelligence: understanding consumer behaviour at a granular level — what address orders when, in what volumes, with what delivery sensitivity — and using that to schedule smarter consolidation windows.

That intelligence also benefits carriers like FedEx and UPS directly. Fewer stops, better route density, and more predictable volume translates to real operational savings. Robinson frames this as a win-win: retailers reduce their environmental footprint, and carriers improve margin on their most expensive delivery segment.

He is candid about implementation reality, though. AI models in logistics don't spring to life on day one.

"Sometimes, you need six months for the model to train itself and learn anything… the model still hasn't learned anything, because your data is so complex."

This is an honest assessment that many AI vendors skip over in their pitch decks. Complex operational data — irregular order patterns, fragmented carrier data, seasonal spikes — can delay the moment when a model becomes genuinely useful. Planning for that ramp-up period is essential.

AI as a game-changer for personalisation and sustainable consumption

Beyond logistics, Robinson envisions AI enabling a more fundamental shift in how consumers interact with products. Personalised recommendations that are genuinely accurate — not generic "you might also like" carousels — reduce the impulse to over-purchase and then return. If AI can predict that a specific customer will love a particular product based on preference signals, the retailer ships fewer units and takes fewer back.

He goes further: AI-enabled custom manufacturing. Rather than producing standard SKUs at scale and hoping demand matches inventory, brands could manufacture to individual specification — the right colour, fit, configuration — reducing overstock and the waste that comes with it. This vision is still emerging, but the direction is clear.

Finding the right solution and building consumer trust

Robinson's view on AI UX is worth noting: the best AI solutions are invisible. They enhance experience without demanding that consumers learn a new interface or surrender obvious control. Personalisation should feel like good service, not surveillance.

Consumer trust is becoming a competitive differentiator. Shoppers — especially younger cohorts — are increasingly choosing brands that align with their values. A retailer that can demonstrate a lower delivery footprint, less waste, and more accurate personalisation has a genuine story to tell, beyond greenwashing.

The future of e-commerce

Robinson predicts a near-term shift towards shopping avatars powered by generative AI — virtual assistants that know your preferences, size, budget, and sustainability values, and surface options accordingly. The implication for retailers is significant: discovery will increasingly be mediated by AI intermediaries rather than direct browsing.

For brands and retailers, the strategic question is not whether to engage with AI — it's which AI touchpoints create genuine value versus which add noise. Robinson's lens: if it makes operations greener and customers more confident, it's worth pursuing. If it's a feature for a press release, it probably isn't.