AI Agents Are Now Deciding What Your Customers Buy

Your Weekly DTC Industry Roundup

Another week in ecommerce, and the gap between what customers expect and what most operations can actually deliver keeps getting wider.

Most DTC brands are discovering that omnichannel logistics is way more expensive and operationally complex than anyone anticipated. Meanwhile, AI shopping agents are quietly repositioning themselves as the new gatekeepers between your brand and discovery.

And if your January forecast is already trending south? You're not alone.

Here's what happened this week:

• Omnichannel logistics is costing retailers $95 billion annually in handover inefficiencies alone, with last-mile accounting for 53% of total shipping costs
• Store fulfillment is now table stakes, but most retail locations weren't designed to function as mini-warehouses
• Returns are eating 15.8% of sales ($849.9 billion), with 9% attributed to fraud and abuse
• AI recommendation engines are becoming decision-makers, not just product finders, and visual discovery is outperforming traditional search by 30+ percentage points
• CFOs at 7-9 figure brands are gathering to figure out how to salvage busted Q1 forecasts before it's too late
• Luke Austin explains the Creative Demand Model and reveals exactly how many ads you need to hit your forecast (and most brands are guessing wrong)

Let's dig in.

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Supply Chain Reality Check

Why Omnichannel Logistics Is Bleeding Money

BOPIS was supposed to save money. Ship-from-store was supposed to increase flexibility. Returns-anywhere was supposed to delight customers.

Instead, most brands are discovering that omnichannel logistics is way more expensive and operationally complex than anyone anticipated.

The numbers are brutal. Up to 19% of logistics costs stem from inefficient handover interactions between warehouses, carriers, and customers (amounting to $95 billion in annual losses in the US alone, according to McKinsey). Last-mile delivery now accounts for 53% of total shipping costs, up from 41% in 2018.

Returns are eating another massive chunk. Retailers estimated 15.8% of sales would be returned in 2025, totaling approximately $849.9 billion. Returns abuse and fraud account for roughly 9% of that volume.

The root problem? Most retail operations weren't designed for omnichannel. Stores were built for browsing, not fulfillment. Distribution centers were optimized for bulk shipping, not individual orders. Inventory systems were never meant to pool stock across channels in real time.

Now customers expect to buy online and pick up in-store within hours. They want same-day curbside delivery. They want to return online orders at any retail location. And they want accurate inventory availability shown in real time across every channel.

Delivering on those expectations requires store associates to pick orders while serving customers, distribution centers to handle complex routing logic, and inventory systems that maintain accuracy down to the last unit across dozens of locations.

Half of US adults used store pickup for some portion of their online orders in the prior three months, per a 2024 Forrester survey. This isn't an edge case anymore. It's core to how people shop.

The brands that figure out how to operationalize this without destroying margins will have a significant competitive advantage. The ones that don't will keep bleeding cash on fulfillment while wondering why their unit economics don't work.

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AI Commerce

AI Agents Are the New Shelf Space

AI recommendation engines aren't just helping people find products anymore. They're deciding what people buy.

Pinterest's proprietary multimodal visual search model now outperforms leading off-the-shelf models by over 30 percentage points on recommendation relevance. Their "taste graph" has grown more than 70% in the last two years, meaning the platform is getting dramatically better at connecting visual inspiration directly to purchase intent.

This isn't just a technical milestone. It's a fundamental shift in how discovery works.

Traditional search required you to know what you wanted and type it in. Visual discovery starts with inspiration (a swipe, a screenshot, a vague aesthetic) and AI translates that into specific product recommendations. You're not searching for "minimalist coffee table." You're showing the AI what you like, and it's finding products that match.

But here's what actually matters for DTC brands: AI agents are becoming the intermediaries between your product and the customer. They're not just surfacing your listing. They're making cognitive decisions about whether to recommend you at all.

"Agents will increasingly handle the cognitive drudgery of comparing products, reviews, prices, and specs," says Griffin Smith, director of behavioral science at Ogilvy. "Brands will be competing not just for human attention but for a place inside the agent's recommendation logic."

Product content, attributes, and digital assets now need to be optimized for machine interpretation, not just human eyes. If your product data isn't structured for AI consumption, you're invisible to the fastest-growing discovery channel.

The challenge? "An agent can optimize for features and price, but it can't (yet) get at who we truly are or what moves us," Smith adds. Brand storytelling and emotional resonance still matter, but only after you clear the technical hurdle of being discoverable in the first place.

See more here

Finance & Forecasting

CFOs Are Already Fixing Busted January Forecasts

It's Week 2 of January and a lot of finance leaders are already staring at forecasts trending in the wrong direction.

CTC is hosting a live CFO summit on January 21st specifically for 7-9 figure brand finance leaders trying to figure out how to salvage Q1 before it's too late.

The core question: Your January forecast is off. Now what?

Most teams waste weeks diagnosing why performance is down, then more time debating what to do about it, and by the time they take action it's February and the damage is done.

The webinar promises to show how to identify why performance is off in under an hour, isolate the signal that matters most, and take immediate corrective action. The focus is on connecting forecasting, measurement, and creative output to accelerate decisions.

This matters because mid-month misses don't have to become mid-quarter disasters. But only if you have systems that surface problems fast and workflows that translate insight into action.

Finance leaders who can move from "we're down" to "here's exactly why and here's what we're doing about it" within 48 hours will separate themselves this year.

Reserve your spot

Data Deep Dive

Most Brands Are Guessing How Many Ads They Actually Need

How many ads do you actually need to hit your 2026 forecast?

If you're like most brands, you're just … making things up.

Producing creative based on vibes. Or what your media buyer "feels like" they need. Or whatever number your competitor mentioned on a podcast.

Luke Austin just dropped a framework that answers this question with actual data.

It's called the Creative Demand Model.

And it analyzes five specific creative metrics from the past six months to calculate a brand-specific "Creative Score."

That score tells you whether you need to produce MORE ads this year to compensate for declining efficiency … or whether you can actually produce FEWER ads and still hit your targets.

Most brands fall into one of two camps:

They're over-producing creative (burning budget on ads that never get meaningful spend) …

Or they're under-producing (running the same fatigued ads into the ground because they don't have fresh alternatives).

Both are expensive mistakes.

The brands that nail creative volume relative to their actual efficiency metrics spend less on production while getting better performance.

Everyone else is flying blind.

Luke breaks down all five metrics, how they interact, and what your Creative Score actually means for your 2026 production calendar.

Worth a read if you're tired of guessing.

See more here

Final Thoughts

What This Means for Founders

Omnichannel logistics is expensive and complicated, but it's also what customers expect now. The brands figuring out how to operationalize BOPIS, ship-from-store, and seamless returns without destroying margins will have a real advantage. The ones treating it as a "nice to have" feature will keep bleeding money on fulfillment.

Returns are a profit lever, not just a cost center. The brands treating reverse logistics as a designed flow (with clear grading, efficient processing, and strategic policies) will recover significantly more value than those treating it as an afterthought.

AI-powered discovery is moving fast, but it's not replacing human decision-making yet. The platforms getting visual search right are showing massive gains in relevance, which means your product content needs to work harder. Images, attributes, and context all feed the algorithms that decide whether you get recommended or ignored.

January forecast misses don't have to become Q1 disasters. But only if you have systems that surface problems within days, not weeks, and workflows that translate insight into action fast enough to actually matter.

Creative volume relative to efficiency matters more than absolute creative output. Producing 100 ads means nothing if 80 of them get zero spend. The brands measuring creative performance against Luke's five specific metrics will spend less on production while getting better results.

None of this is easy. But the operational gaps separating high-performing brands from everyone else are becoming more obvious. Speed of diagnosis and speed of response will determine who recovers from rough starts and who spends the year playing catch-up.