How AI agents in eCommerce leaders open doors to scale [Expert insights]

eCommerce used to scale by adding headcount. Now it scales by adding intelligence.

As online shopping becomes faster, more fragmented, and more intent-driven, AI agents are no longer experimental features or customer-facing chat widgets.

They are evolving into autonomous AI agents; systems capable of executing governed decisions across discovery, sales, fulfillment, and operations without constant human prompting.

They are embedded agents operating across multiple systems, discovery, checkout, support, CRM, and supply chain.

But what does that actually mean for growth?

We gathered insightsdirectly from eCommerce leaders across DTC, B2B, retail, services, and manufacturing.

These insights cut through generic AI trends and focus on what eCommerce leaders are actually deploying inside live operations today.

Let's get started.

47 eCommerce leaders share how AI agents are really being used (and where they fail)

From DTC brands to B2B distributors, these insights reveal how AI agents for retail are evolving from support tools into operational decision-makers.

Most conversations around AI in eCommerce focus on what could happen next. This one focuses on what’s already happening, inside live stores, service workflows, warehouses, and operations.

The eCommerce AI agents are now embedded across discovery, checkout, fulfillment, and retention workflows.

The insights that follow come from leaders who are actively deploying AI agents today, navigating real-world constraints such as data quality, brand trust, compliance, and human handoffs.

1. Use AI to remove hesitation without losing brand trust

Anh Ly, Founder & CEO, Mim Concept | LinkedIn

This year, AI agents will become more contextual and brand-aware, especially in eCommerce. The biggest opportunity is personalization at scale.

AI agents that understand a customer’s space, preferences, and past behavior can guide purchases more confidently.

The challenge is restraint. Brands that over-automate risk losing trust. The winners will use AI to enhance clarity and confidence, not replace human judgment.

AI agents have helped us remove friction at key decision points.

In online furniture purchases, hesitation often stems from unanswered questions about scale, materials, or care.

Agents now provide immediate, consistent guidance, which increases confidence and reduces drop-off rates before checkout. We’ve seen higher conversions on product pages where customers engage with the agent.

The biggest challenge has been training the agent to reflect our design philosophy and tone.

Generic responses erode trust quickly in a design-led brand where nuance matters. We invested time in curating prompts and guardrails so the AI feels like an extension of our team, not a scripted bot.

2. Use AI to optimize immersive experiences without breaking the magic

Jonathan Dautrich, Founder, Intrepid Escape Rooms | LinkedIn

I run Intrepid Escape Rooms in Orange County, where our business is built on immersion.

Guests ride a themed train into the experience, interact with live hosts, and rely on human storytelling to feel transported. AI can’t replace that emotional core and shouldn’t try to.

Where AI agents add value is behind the scenes. We use them to analyze gameplay data and identify puzzle sequences where teams consistently stall.

Pattern recognition across hundreds of games is something we’d never catch manually.

We’re testing an agent that recommends optimal hint timing based on team composition and pace.

Early data shows teams receiving hints at minute 32 instead of 18 report 34% higher post-game satisfaction. That level of specificity is powerful for experience design.

By 2026, I expect agents to handle real-time difficulty scaling, adjusting puzzle complexity based on how groups collaborate. The challenge is preserving the magic. Guests shouldn’t feel algorithmically managed during an adventure.

The real test is whether AI can help small creative businesses scale personalized storytelling without losing their human edge. Agents that amplify craft instead of replacing it will win in experiential industries.

3. Use AI to resolve technical uncertainty before it kills the sale

Suresh Babu, Founder, Clads | LinkedIn

After 20+ years in Australia’s cladding industry, I’ve learned our biggest challenge isn’t traffic, it’s decision confidence. Customers abandon carts due to uncertainty, not price.

A homeowner standing in their garage at 8 pm with a tape measure doesn’t want marketing copy.

They want answers like: “How many panels do I need for a 4×3m wall?” or “Will this cladding handle Melbourne’s weather?”

We lose sales because buyers can’t confirm installation details, whether a 2.7m acoustic panel needs special mounting or if the material fades in direct sunlight.

An AI agent resolving those questions in context would directly reduce our 60%+ cart abandonment rate.

By 2026, I expect agents to handle technical and installation questions that flood our inbox daily. The real opportunity is AI that understands building codes, climate, and compliance, not just specs.

My concern is AI becoming a crutch. Cladding isn’t t-shirts. Incorrect advice can cause water damage or fire risks.

AI must escalate structural or compliance decisions to human agents who understand local codes and liability risks.

4. Let AI handle operations, not creative judgment

Alex Staatz, Founder, Rival Ink Design Co. | LinkedIn

At Rival Ink, we’ve tested AI for customer design consultations.

It still can’t match rider-focused humans for creative decisions. It excels at repetitive work, resolving high-volume customer inquiries like order updates, tracking, and basic fit questions, which once clogged our inbox at 3 AM Brisbane time.

The biggest win has been pre-sorting custom design requests before they reach our team. AI flags incomplete orders, missing bike models, and overlooked add-ons. That reduced design revisions by 15–20% by prompting the right details upfront.

By 2026, agents may better understand visual preferences, suggesting graphics kits based on gear photos. That’s still human-led today, but pattern recognition is improving.

The challenge is preserving the personal touch when customers trust us with how their $10k bike looks on race day.

Winning brands will use AI for operational friction while keeping humans in creative and relationship-driven roles. No one wants a robot designing graphics, but everyone wants instant shipping answers.

5. Use AI to educate and surface hidden buying friction

Renee Kemper, Digital Marketing Leader, ModernMom & Molly’s Suds | LinkedIn

I run digital marketing for eCommerce brands and a restaurant, so I see AI from both sides.

At Molly’s Suds, we’re testing agents that handle education, explaining ingredient differences, and recommending starter products based on concerns like sensitive skin or eco-impact.

The agent doesn’t close; it builds confidence. When a human follows up, conversions are 40% higher.

The biggest surprise isn’t efficiency, it’s insight.

AI revealed that 60% of cart abandoners weren’t price-sensitive; they were confused about HE vs standard washers. We built that into the flow and recovered revenue we didn’t realize we were losing.

By 2026, agents must understand cross-channel context. If someone reads a stain-removal blog, visits product pages, then returns days later, that shouldn’t feel like three separate sessions.

Context-aware agents will determine whether brands feel helpful or intrusive.

The restaurant taught me this: automation works when it preserves what customers value. No one wants a robot recommending brisket; they want it handling wait times so staff can perfect the food.

6. Use AI to scale content and personalization without losing brand soul

David Vail, Founder, One Love Apparel | LinkedIn

I’ve spent 20+ years in business development across retail and tech, and now run One Love Apparel.

AI agents are most useful for us in content generation. We publish regularly on mental health and social causes, and AI helps maintain that cadence without burning out our small team.

The biggest win is segmentation and email personalization. We donate to rotating charities, and AI matches customers with causes they’ve engaged with.

Open rates increased 31% when we shifted from blanket emails to cause-specific updates.

Here’s what many overlook: AI is only as good as your brand voice documentation. We spent two months feeding blog content and guidelines into our system before it stopped sounding robotic.

Most small brands lack this foundation, which is why adoption feels overwhelming.

By 2026, AI will handle routine sizing and shipping questions. But conversations about why someone supports a cause? Those stay human.

Winning brands will use AI to create space for meaningful dialogue, not replace it.

7. Win high-intent moments with response-time, not automation

Jennifer Bagley, Founder & CEO, CI Web Group | LinkedIn

At CI Web Group, we serve home service contractors. We use AI agents at the exact moment a homeowner’s AC dies at 9 PM, and they’re comparing companies.

AI doesn’t replace the sale, it protects it. Clients were losing jobs because competitors replied faster.

Now an agent responds in 30 seconds, qualifies the lead, and books the appointment while intent is high. One plumber increased after-hours conversion from 12% to 48% in 90 days.

By 2026, the advantage won’t be smarter agents; it’ll be knowing when they stop.

AI handles speed and triage; humans handle trust and closing. We design systems where agents collect context: “furnace is 14 years old, clicking, house is 62 degrees,” so humans don’t start cold.

Ecommerce can learn this: AI isn’t just a cost-saver. It’s a response-time weapon. Winning brands will feel present at 11 PM, not automated.

8. Shift from reactive support to predictive care

Archie L, Founder, PetMeDaily | LinkedIn

At PetMeDaily, AI enhances the pet-owner bond for our 500,000+ members. We use early-stage AI to analyze behavior data and personalize health content. This moves us beyond transaction support into predictive care.

Our focus is proactive intervention. AI alerts around nutrition or behavior reduce preventable vet visits by up to 20%. We measure engagement with predictive content, not just conversions.

By 2026, AI agents will act as digital wellness companions, anticipating joint discomfort or health risks before symptoms escalate. This shifts the model from transaction to companionship.

The real challenge is trust. Pet parents must engage repeatedly before sharing wellness data.

Brands focused only on ticket automation will miss the data foundation required for predictive AI. The value is in the ecosystem, not the immediate sale.

9. Build proactive relationships on top of unified customer data

Belize Hans Polloso, Vice President, Dog Academic | LinkedIn

At Dog Academic, we use data to build genuine bonds, not just transactions. Early AI tests show personalized care advice and recommendations can lift repeat interaction by 25%.

By 2026, AI’s value shifts from reactive support to proactive relationship management.

We’re building models that predict future needs based on breed, age, and behavior. We expect this to increase lifetime value by 15% and reduce mismatched returns by 20%.

The real challenge isn’t conversation, it’s unified data.

Brands that fail to combine purchase history, behavioral signals, and wellness data into one profile will see AI initiatives fail. By 2026, the agent will simply be the interface for a deeper predictive engine driving loyalty.

10. Use AI to elevate premium outcomes, not automate conversations

Koen Geron, Founder, Hovalo | LinkedIn

At Hovalo, we treat our service as a high-stakes transaction in a client’s personal life.

We use AI for asset creation, enhancing photos, and optimizing profiles, resulting in a 40% increase in qualified matches. This isn’t about ticket deflection; it’s about managing complex client journeys.

Many leaders use AI to cut support costs. That’s low leverage. We use AI to augment experts, generating bespoke profile assets and screening matches against 15 criteria.

This reduced manual workload by over 80%, allowing strategists to focus on coaching and decision-making.

By 2026, markets will split between commodity and premium services. Commodity brands automate interactions.

Premium brands use AI to enhance human expertise and deliver outcomes worth five figures. The real value isn’t replacing small tasks; it’s scaling personalization, once impossible to deliver.

11. Protect high-consideration sales with human-led curation

Carla Nina Pornelos, General Manager, Wardnasse | LinkedIn

As General Manager at Wardnasse, we operate in a model built on subjective value, not transactional efficiency.

Over 70% of high-value acquisitions require direct human dialogue because AI cannot replicate the curatorial trust collectors expect.

AI works for transactional support but fails in high-value art sales.

At Wardnasse, any sale over $15,000 requires an average of three human conversations to establish trust and narrative. Agents attempting this nuance have a near 100% failure because they cannot convey cultural significance.

By 2026, AI will remain operational for logistics, not strategic sales in subjective-value commerce.

The opportunity is augmentation, not replacement. We’re testing AI for items under $5,000, freeing 15–20 hours weekly for specialists.

This allows them to focus on dialogues, driving 80% of revenue. AI must evolve into a research assistant synthesizing artist histories before it can impact our core model.

12. Turn AI agents into digital experts, not support bots

Miriam Lawson, Head Editor, Best Hardware Supply

As Head Editor at Best Hardware Supply, I lead a team translating complex technical data into practical guides.

We began testing AI 18 months ago, not for support automation, but to dynamically generate how-to content based on a user’s project and skill level.

Engagement increased 20% because it delivers real expertise, not article links.

By 2026, valuable agents will function as digital trade experts, guiding projects step by step. We’re building agents that generate plans based on tool inventory and skill level.

Early models suggest a 30% lift in project completion and a 15% drop in returns from incorrect purchases due to hyper-personalized advice.

The shift is from efficiency to trust. The real challenge isn’t AI, it’s curating proprietary data and editorial expertise to train it. Brands will win with deep, trustworthy knowledge, not flashy chatbots.

13. Connect AI agents to operations, not just customer touchpoints

Delbert Baron Lee, President, Wynbert Soapmasters Inc. | LinkedIn

I scaled Wynbert Soapmasters to nine-digit revenue by prioritizing operational excellence.

Our AI use case connects B2C sales data directly to production.

Agents analyze sales velocity to automate raw material orders and adjust schedules in real time, projected to reduce inventory costs by 15% in year one.

By 2026, advantage won’t come from chatbots but from AI managing the supply chain.

We’re building agents that negotiate with suppliers based on real-time cost shifts and projected demand, cutting procurement cycle time by 40% and material costs by 5–7% annually.

Brands focused only on front-end AI will fall behind.

Next is full integration with factory automation, agents that order materials when prices drop and adjust production instantly.

eCommerce businesses that fail to connect sales data to physical production risk obsolescence.

Also read: AI agents in action: Best use cases for businesses in 2026.

14. Beyond automation, AI agents are becoming operational decision-makers

Mircea Dima, CEO, CTO & Founder, AlgoCademy

In eCommerce, AI agents are moving from novelty to necessity. Brands already use them for support automation, behavior forecasting, and personalization, driving measurable gains.

The challenge is reliability. Agents must be context-aware to avoid frustrating customers. By 2026, AI will function like junior managers, making decisions across marketing, inventory, and customer interactions while learning from real-time data.

The shift is from reactive automation to predictive intelligence. Winning agents won’t just respond—they’ll anticipate needs, decide, and execute with domain-aware confidence.

15. Scale personalization without sacrificing accuracy or human touch

Cristian-Ovidiu Marin, CEO, OnlineGames.io | LinkedIn

At OnlineGames.io, AI agents are core to improving user interactions and automating routine tasks. They personalize experiences, answer queries instantly, and reduce friction, lifting conversions.

The challenge is balance. Accuracy and human tone matter, especially at scale. Poorly trained agents can damage trust faster than they create efficiency.

By 2026, AI will be deeply integrated across operations, handling complex interactions and powering smarter recommendations. Winning brands will treat AI as an experience layer, not just automation.

16. Use AI agents as invisible infrastructure, not the brand itself

Riley Westbrook, Co-Owner, Valor Coffee | LinkedIn

At Valor Coffee, we use AI agents to protect what matters most: human-first time.

We run high-volume cafés and a global wholesale program, and AI removes the logistical friction that drains creative energy.

By 2026, AI will function as an invisible back office, handling subscriptions, rerouting shipments, and resolving edge cases autonomously. Removing these data-heavy tasks allows us to scale wholesale without compromising the transparency our customers expect.

Longer term, AI will evolve into predictive supply chain support, anticipating harvest impacts before inventory shifts.

The challenge for eCommerce leaders is ensuring AI automates the process, not the soul of the brand. Human connection must remain central.

17. Response speed is a conversion advantage in service commerce

Tetiana Rakhmanska, Co-Founder, Wow Now Cleaning | LinkedIn

At Wow Now Cleaning, AI agents help service businesses operate systematically. They automate bookings and initial requests, reducing response time and operational load.

In service commerce, speed drives conversion. Customers want instant clarity on pricing and availability. AI makes that experience predictable, building trust and increasing completed orders.

By 2026, responsiveness will be standard. The challenge is training AI for real-world, non-standard scenarios.

Service businesses require clear rules and human oversight. AI should assist, not replace. Balance sustains trust.

18. Balance efficiency with authenticity to protect brand trust

Seun Osho, Founder & CEO, Unyield | LinkedIn

At Unyield, we see AI agents becoming embedded in brand–customer interactions. They improve speed and surface insights that help teams make better decisions.

The risk is losing the human touch that drives loyalty. Customers recognize robotic interactions instantly.

Brands that succeed balance efficiency with authenticity, using AI to enhance, not flatten, experience.

By 2026, AI will shift from a reactive tool to a predictive partner, anticipating needs and freeing teams for higher-value work.

Companies won’t replace people; they’ll amplify human connection. Convenience creates access, but connection builds loyalty.

19. Treat AI agents as orchestration layers, not isolated tools

Chris M. Walker, Founder, Legiit | Las Vegas, Nevada | LinkedIn

AI agents have evolved from simple automation to operational partners. At Legiit, we use them to personalize journeys, anticipate support needs, and optimize conversions in real time.

The challenge is transparency. Customers must know when they’re interacting with AI versus a human. Without clarity, efficiency gains erode trust.

By 2026, AI will act as an orchestration layer, coordinating marketing, logistics, and service across the business. The opportunity is enormous, but success depends on trust and human oversight in decision-making.

20. Let AI agents optimize merchandising and pricing, not just support

Lori Appleman, Founder, Redline Minds | LinkedIn

After two years of watching AI evolve, the ROI gap is closing. At Redline Minds, the strongest gains come from merchandising, reordering collections based on real-time conversion data.

One retailer saw a 31% lift in add-to-cart after letting AI detect micro-patterns humans missed.

The common mistake is deploying AI in support first. Revenue impact lies in pricing and inventory. One client adjusted recommendations using abandonment behavior and local weather data, lifting AOV by $47.

By 2026, agents will autonomously run A/B tests and execute low-risk changes. The bottleneck isn’t technology, it’s workflows built for human decision speed.

Winning brands redesign approval processes, assuming the agent is the primary operator, not just an assistant.

21. Apply AI agents where customers feel friction, not where hype lives

Ben Read, Co-Founder & CEO, Mercha | LinkedIn

At Mercha, we’ve used AI since 2022, but not in chatbots.

Our biggest win is backend artwork processing. When clients upload a logo, AI handles placement, scaling, and previews instantly. Previously, every mockup required manual review.

We deliberately avoided marketing ourselves as “AI-powered.” The tech simply enables our promise: merch designed in minutes, not days.

Customers don’t care how it works; they care that they can preview 50 products before morning coffee.

By 2026, AI will crack operational complexity, routing orders based on supplier inventory, decoration compatibility, and shipping timelines.

Today, that requires judgment across 15+ variables per order. Winning brands remove real friction, not ship features no one asked for.

The bottleneck isn’t AI, it’s data readiness. We spent 18 months standardizing supplier data before agents could operate reliably. Clean operational data enables agentic scale.

That data foundation directly improves operational efficiency across suppliers, decoration workflows, and fulfillment routing at scale.

22. Use AI agents to accelerate B2B quoting and cross-border complexity

Wagner Erich Hebert, Managing Director, BI-Trade GmbH

At BI-Trade GmbH, we’ve tested AI agents for catalog management and multilingual inquiries since late 2024. A major win is real-time inventory sync across 400+ SKUs, work that took 6–8 hours weekly now runs automatically.

We also use AI for initial B2B quote requests in German, English, and Hungarian. It pulls pricing, checks MOQs, and suggests complementary products. Sales now focus on closing, not repetitive pricing questions.

By 2026, agents may negotiate payment terms and Incoterms based on creditworthiness, fraud detection signals, and shipping routes.

The limitation is nuance; securing long-term distributors matters more than margin optimization.

The real opportunity is predictive pricing. We lost a €47K deal during a 3% currency swing.

An AI agent monitoring forex in real time could have adjusted terms before the buyer walked. That’s where advantage compounds.

23. Eliminate pre-checkout friction by turning expertise into instant guidance

Haiko de Poel, Jr., eCommerce Leader, Lowcountry Ace Hardware | LinkedIn

Across multiple eCommerce turnarounds, including a surety bond business reaching $600M in six months, I’ve learned AI doesn’t replace humans; it removes friction before checkout.

At Lowcountry Ace Hardware, we’re testing agents for paint consultations and product matching.

Customers upload a kitchen photo, and the agent recommends compatible colors and calculates quantities.

Early results show 31% faster decisions and larger baskets because customers stop second-guessing.

By 2026, agents will reduce “browse then call” behavior in hardware retail. Instead of overwhelming shoppers, AI applies context, location, climate, and project type in real time.

The opportunity isn’t automation, it’s scaling local expertise.

When trained on hyperlocal knowledge like humidity or regional weather, agents deliver expert guidance at 3 am, when DIY planning actually happens.

24. Match on-site experiences to visitor intent, not static pages

Hooman Bahrani, Founder, Birchstream Digital | LinkedIn

AI agents handle repetitive decisions well but struggle with nuanced trust signals.

At Birchstream Digital, we use them for visitor analysis and content personalization, deciding whether someone sees testimonials, specs, or education first. These choices once required weeks of A/B testing.

The missed opportunity for 2026 is failing to connect search intent to on-site experience. A visitor searching “best construction management software for small teams” has a different mindset than a branded search, yet most sites treat them the same.

When AI adjusts messaging and CTAs based on pre-site behavior, we’ve seen 40–60% conversion lifts.

Winning brands will make websites feel like they understand why visitors arrived.

The challenge isn’t technology, it’s shifting from page-based optimization to situation-based thinking. AI handles intent matching; humans define what understanding means.

Learn: 15 eCommerce tasks you should hand off to AI agents right now.

25. Use AI agents to reduce pre-purchase anxiety and know when to stay silent

Louie Rosciglione, Founder, Wispen.Shop | LinkedIn

Running Wispen. Shopping across fashion, baby products, and electronics taught me AI excels at managing pre-purchase anxiety.

We deployed an agent to answer international shipping, duties, and delivery questions before checkout. Cart abandonment from non-US shoppers dropped 34% as uncertainty disappeared early.

The real breakthrough won’t be smarter agents; it’s knowing when to stay silent. An overeager bot reduced conversion by 12%.

Electronics shoppers often don’t want help; parents comparing baby carriers do. Winning brands train agents to read buying signals and appear only when guidance matters.

Looking ahead, agents will double as discovery engines.

When we launched digital downloads, conversations revealed demand for interview prep checklists, products we hadn’t planned.

Brands mining agent conversations for unmet needs and emerging market trends will outperform those using AI only for support deflection.

26. Use AI agents to enforce compatibility, not just push upsells

Lenny Valdberg, Executive Leader, VIGO Industries | LinkedIn

At VIGO, we use AI for product spec matching and cross-sell recommendations.

When someone views a kitchen faucet, the agent analyzes cart contents, browsing behavior, and installation requirements to suggest compatible sinks or accessories, never random upsells.

Basket size increased 41% because recommendations prevent compatibility mistakes that customers would otherwise discover during installation.

The biggest operational win is backend support. Our agent processes warranty registrations by cross-referencing product codes and issue descriptions against a 20-year database.

What took 2–3 days now resolves in under 4 hours, lifting customer satisfaction from 72% to 89%.

By 2026, AI will manage international compliance as we expand.

Launching in new markets currently requires weeks of researching plumbing standards and certifications. An agent tracking regulatory changes across 15+ markets could cut time-to-market by months.

The hardest lesson was teaching design intent. One agent paired an industrial faucet with a porcelain sink, technically correct, visually wrong.

Now humans approve aesthetic pairings. Agents ensure correctness; humans protect taste.

27. Use AI agents to prevent bad purchases, not just enable fast ones

Brett Henrichsen, Owner, Posterprintshop | LinkedIn

After running Posterprintshop for 20 years, the most valuable use of AI agents hasn’t been chat; it’s file quality validation before customers waste money.

We built an agent that instantly flags issues like “your iPhone photo won’t look good at 48×72 inches” before purchase. That single change reduced customer service volume by 30% and cut returns in half.

The real breakthrough coming in 2026 isn’t chatbots, it’s agents that understand creative intent.

Instead of just saying “resolution too low,” future agents will recommend better print sizes, textures that hide pixelation, or optional AI upscaling. That’s solving the actual problem, not just blocking the order.

One of the hardest challenges is teaching agents operational reality. We offer same-day printing only if orders arrive by noon Pacific.

An overeager agent promising “ships today” to an East Coast customer at 2 pm creates frustration and costly shipping comps. Agents must understand business constraints, not just customer desire.

By 2026, the winning eCommerce brands will be those whose AI agents know when to stop and escalate to a human.

When a customer uploads their deceased grandmother’s only photo, that’s not an automated workflow; it’s a moment that deserves human care. Agents should protect trust, not process everything blindly.

28. Use AI agents to personalize messaging at scale, without diluting brand voice

Ross Plumer, Founder, RJP.design | LinkedIn

After integrating AI into client websites since early 2024, the most underrated use case I’ve seen isn’t chatbots or lead gen, it’s content personalization at scale.

We use AI agents to dynamically adjust homepage messaging, service descriptions, and imagery based on referral source, time of day, customer history, and prior behavior.

One medical practice saw appointment bookings jump 47% when AI delivered different messaging to new versus returning visitors.

The psychological edge is massive. AI agents can A/B test messaging in real time across thousands of micro-segments simultaneously, something no human team could coordinate quickly.

We’ve seen 6 PM traffic convert 3× better with urgent language, while morning visitors respond to educational framing. AI identified those patterns in weeks, not months.

By 2026, AI will increasingly manage the full content lifecycle, writing, publishing, monitoring performance, and iterating automatically.

The real risk is brand voice dilution. When AI generates most of the content, differentiation becomes fragile. The brands that win will use AI for execution speed while keeping strategic creative direction firmly human.

29. Use AI agents to scale emotional personalization, without crossing the line

Suchi Jain Saxena, Founder & CEO, CustomCuff | LinkedIn

After scaling CustomCuff from a small online shop to a multi-million-dollar business selling personalized jewelry in 70+ countries, the biggest opportunity I see for AI agents is in handling customization.

We’re testing agents that help customers choose between coordinate engravings, handwriting jewelry, or star maps based on their story, not just the product.

The most immediate win has been during peak gifting seasons. We used to get flooded with “what should I engrave?” questions around Mother’s Day and graduations.

AI agents now handle these consultation conversations, suggesting ideas based on occasion and relationship, which frees our team to focus on production quality and complex requests that require real human judgment.

By 2026, AI agents will finally crack personalization at scale for DTC brands, but the real challenge is emotional context.

A customer buying a handwriting necklace from a late grandmother’s recipe card needs a very different experience than someone engraving coordinates from their engagement. The agents that can sense that nuance without being creepy will win.

The ultimate test is brand authenticity. We didn’t grow because checkout was fast; we grew because customers felt their jewelry captured real emotion.

I believe AI agents should enhance that human touch, not replace it, by handling logistics and guidance so teams can focus on the stories behind each piece.

30. Pre-qualify complex purchases so humans enter when decisions matter

Eryk Piatkowski, Owner, K&B Direct | LinkedIn

At K&B Direct, we began testing AI chat agents to help homeowners navigate cabinet selection and get instant design guidance, something that previously required scheduling consultations days in advance.

The goal wasn’t automation for its own sake; it was helping customers arrive informed instead of overwhelmed.

The biggest win has been project pre-qualification. When a homeowner says, “I want to redo my kitchen for under $15K,” the agent immediately narrows options to appropriate cabinet lines, captures budget and style preferences, and books them with our design team.

As a result, we’ve cut initial consultation time in half because customers arrive educated and ready to decide.

By 2026, I expect AI agents to handle the entire inspiration-to-install flow for straightforward projects.

A customer uploads a photo, the agent suggests styles that match the home’s era, shows similar past projects, checks availability, and generates a preliminary quote before a human steps in.

The real risk is losing the personal touch that differentiates us from big-box stores. Customers come back because we remember their kids’ names and the odd 18-inch cabinet gap in their kitchen.

AI should handle logistics and education, so our team has more time for those relationships, not replace them.

31. Treat AI-powered responsiveness as the baseline, not the differentiator

Scott Purcell, Co-Founder, Man of Many | LinkedIn

AI agents have reshaped eCommerce engagement. Instant support, smart recommendations, and tailored content are no longer advantages; they’re expectations.

By 2026, winning brands won’t simply automate faster. They’ll use AI to deliver true personalization and rapid responsiveness while preserving trust and authenticity. Efficiency without empathy doesn’t build loyalty.

The challenge is balance. Brands must scale AI-driven experiences without losing transparent, trust-building communication. Those delivering real value, clearly and responsibly, will see gains in both loyalty and conversions.

32. Move from AI-assisted tasks to autonomous, governed workflows

Garrett Yamasaki, CEO, WeLoveDoodles, LinkedIn

AI agents represent the biggest operational shift since cloud computing. We’re moving from prompt-based tools to autonomous systems that manage workflows end to end.

At WeLoveDoodles, we’re piloting an agent that autonomously runs personalized win-back campaigns, analyzing lapse behavior, generating creative, and executing outreach. The result is true one-to-one engagement instead of broad customer segmentation.

The defining challenge is governance. The best agents won’t just be autonomous, they’ll be transparent and auditable.

Brands need frameworks for how decisions are made, especially when managing budgets, ad spend, and customer relationships.

For smaller brands, this is transformative. Well-governed agents enable personalization and service levels once possible only with large teams, leveling the field through execution, not headcount.

33. Prepare for agentic commerce and machine-readable brands

Lexi Petersen, Founder & Chief Creative Officer, Cords Club | LinkedIn

AI agents are transforming discovery and support in DTC retail. We’re moving beyond chatbots toward autonomous shopping concierges.

When a customer asks for earrings for sensitive ears to match a summer wedding in Barcelona, the agent considers history, seasonality, material sensitivities, and style guides instantly.

This context-aware support lifted our conversion rate by 0.9 percentage points and cut pre-purchase support time nearly in half. The value isn’t speed, it’s relevance at intent.

By 2026, agentic commerce will intensify competition. Agents will compare, negotiate, and transact across thousands of storefronts within the broader digital marketplace in milliseconds.

Brands must become machine-readable or risk being bypassed.

The challenge is preserving trust when transactions are agent-driven. Winning brands will adapt their data architecture while reinforcing loyalty beyond surface-level design.

34. Optimize for AI-led shopping, not just human browsing

Eric Gantz, Co-Founder, Verena Street Coffee | LinkedIn

By late 2026, AI agents will handle more shopping on customers’ behalf, choosing where and how purchases happen.

Shopify’s move toward agentic storefronts signals this shift, allowing products to surface and transact inside AI-driven experiences while still flowing through brand stores.

The opportunity is becoming understandable to AI, not just search engines. Clean product data, clear positioning, and strong trust signals will determine which brands agents recommend.

The challenge is ownership. As agents mediate purchases, brands must ensure convenience doesn’t erode the relationship. Winners will adapt storefronts and data for AI-led shopping while preserving trust and differentiation.

35. Use AI to accelerate sales workflows without compromising accuracy or trust

Eric Turney, Sales & Marketing Director and President, The Monterey Company | LinkedIn

At The Monterey Company, AI agents support, not replace, our sales process. They gather lead details, summarize quote requests, suggest product options, and draft replies for human review.

That speed improves customer experience. Buyers get faster clarity on timelines and artwork requirements while we maintain accuracy.

Operationally, agents keep our CRM clean and reduce repetitive admin work. But adoption requires guardrails, clear tone guidelines, responsible data use, and mandatory human review at key moments.

By 2026, agents will manage more end-to-end workflows, from quote-to-proof handoffs to reorder prompts.

Winning brands won’t automate the most; they’ll treat accuracy and trust as the product, backed by strong QA and clear escalation paths.

36. Let autonomous agents own operational micro-decisions at scale

Elvin Zhang, Founder, PodPartner | LinkedIn

In global dropshipping, autonomous agents are essential for managing nonstop micro-decisions that keep international commerce running 24/7.

They coordinate orders, logistics, and customer interactions across time zones without human bottlenecks.

We see this most in support. About 85% of routine tickets for high-growth fashion startups are now routed through agents, freeing creative teams to focus on product development while operations run continuously.

By 2026, the shift will be from reactive fixes to predictive logistics. Anticipating demand before orders are placed could cut lead times by up to 40% through proactive inventory positioning and smarter ways to manage inventory globally.

Brands relying on manual logistics will fall behind. Autonomous systems are becoming the operational foundation that absorbs complexity so teams can focus on creativity and differentiation.

37. Use AI agents as proactive operators, not reactive support tools

Dario Markovic, CEO, Eric Javits | LinkedIn

AI-driven support and personalization reduce friction, helping customers find products faster and improving conversion and satisfaction.

By 2026, agents will evolve from reactive responders into proactive operators, anticipating needs, monitoring demand signals, and equipping sales teams with timely context rather than replacing human judgment.

The opportunity is scaling without losing brand voice. Automation should amplify consistency, not flatten differentiation. Success depends on strong data quality and human oversight, so AI builds trust instead of eroding it.

38. Let AI agents own measurable revenue outcomes, not just assistance

Rafael Sarim Oezdemir, Head of Growth, EZContacts | LinkedIn

At EZContacts, AI powers personalized recommendations and 24/7 chat, lifting conversion by 18% and reducing support tickets by 35%.

The impact goes further.

Inventory prediction reduced stock-outs and optimized stock levels by 22% last quarter, and AI-driven personalization lifted AOV by 12% among repeat buyers in Canada and the U.S., proving agents can drive revenue without replacing human expertise.

By 2026, AI agents will function as autonomous sales closers, managing over 50% of customer journeys while aligning with fast-moving eyewear trends.

Our biggest adoption hurdle was data privacy compliance. Strengthening safeguards reinforced a customer-first approach. Brands that combine autonomy with responsibility will accelerate growth while earning long-term trust.

39. Keep AI agents visible to operators, invisible to customers

Nicky Zhu, Senior AI Product Manager, Dymesty | LinkedIn

AI agents deliver the most value when handling narrow, predictable tasks while escalating complex problem-solving to human teams when nuance is required.

During peak periods, we use them for first-line support, order status, return eligibility, and routine queries, so humans focus on complex or sensitive cases.

Agents also power personalized recommendations, improving repeat conversion.

By 2026, agents will expand into operational decisions, adjusting promotions as inventory tightens or flagging fulfillment risks before delays occur. The advantage is consistent logic applied accurately at scale.

Problems arise when agents become black boxes. The strongest setups keep humans in oversight while agents handle repetitive work quietly. With transparency and control, AI improves efficiency without undermining trust.

40. Let AI agents optimize speed and revenue, without diluting premium brand control

Brad Jackson, Founder, After Action Cigars | LinkedIn

At After Action Cigars, AI handles 78% of shipping and bundle inquiries, reducing response time from four hours to 12 minutes and lifting chat-driven conversions by 14%.

Agents also forecast demand, flag inventory shortages, and identify shipping risks before customers notice, critical for maintaining a premium experience.

By 2026, we plan to deploy AI for dynamic pricing on limited drops and personalized upsells based on real-time demand. We estimate up to 25% additional revenue while improving margins.

The opportunity is autonomous merchandising at speed.

The challenge is governance. Agent decisions must align with brand identity and compliance standards. Winning brands let agents move fast—but never unsupervised.

41. Build full-funnel AI-powered concierges, not isolated support bots

Matthew Kinneman, Founder & CEO, Bully Max | LinkedIn

AI delivers the most value when it spans the full customer journey, not just chat support.

At Bully Max, we’ve seen the biggest gains when AI handles order, shipping, and product questions while also guiding discovery and selection. This connected flow helps customers choose faster, lifting conversion and reducing support load.

The challenge is precision. Agents must be tightly trained on product data and brand voice to maintain trust, especially in health and nutrition categories.

By 2026, AI will evolve into full-funnel concierges blending sales and support from the first question to post-purchase follow-up.

Brands investing early in training, governance, and integration will gain durable advantages in efficiency and experience.

42. Use AI agents as operational partners in high-trust commerce

Eva Goldfarb, Founder, Mod City Madness

In high-value, one-of-a-kind eCommerce, AI works best as an operational partner, not a replacement for judgment.

At Mod City Madness, agents support inquiries, listing optimization, logistics, and forecasting, freeing our team to focus on sourcing, restoration, and relationships.

By 2026, agents will deepen personalization, acting like knowledgeable sales associates who understand taste, space constraints, and budget in real time to guide decisions confidently.

The opportunity is significant, but trust is non-negotiable. For storytelling-driven brands, AI must enhance, not dilute, authenticity.

Winning brands let agents handle operational complexity while humans own narrative and nuance.

43. Ground AI agents in systems of record to prevent costly mis-specs

Stephen Rahavy, President & Owner, Kitchenall | LinkedIn

In commercial kitchen eCommerce, AI already plays a critical pre-sales role.

At Kitchenall, agents verify CFM, amps, BTUs, NSF/ETL compliance, and freight options based on site constraints, preventing downstream errors that would delay installs or kill deals.

The challenge is accuracy at scale.

AI agents must operate with a deep understanding of product specifications, compliance rules, and logistics constraints to enable complex problem solving without creating costly mis-specs.

Hallucinations don’t just confuse; they create costly mis-specs. Our agents are grounded in clean product data, logistics rules, and local codes.

They connect directly to PIM, ERP, warranty databases, and carrier APIs, with decision logs for traceability.

By 2026, agents will assemble full cooklines, generate submittals and MEP checklists, book installers, and price lead times by ZIP code. Conversion will rise, and damage claims will drop, while humans stay involved for edge cases.

The future isn’t unchecked autonomy; it’s auditable, system-aware execution grounded in AI accountability.

44. Use AI agents to scale expert recognition without losing curatorial authority

Patricia Curts, Founder & Managing Director, The Mexican Collection | LinkedIn

By 2026, niche retail’s breakthrough will be agentic visual recognition.

At The Mexican Collection, we’re testing AI that helps collectors identify silversmith hallmarks from a smartphone photo, work that currently requires hours of specialist research.

The challenge isn’t technical; it’s preserving curatorial authority. An agent must speak with the credibility of a scholar, not the tone of a generic script. Accuracy alone doesn’t drive trust in heritage commerce; voice does.

The opportunity is scaling artisanal expertise without stripping away the story. Brands that train agents on cultural context, not just data, will amplify craftsmanship rather than flatten it.

45. Treat AI agents as trusted team members, not experimental tools

Amber Taylor, Owner & Creative Director, Pink Cove | LinkedIn

At Pink Cove, AI agents are no longer experimental; they’re part of the team.

They answer real-time questions about fit, sizing, and styling, turning hesitation into purchase. In a margin-sensitive business, that lift in conversion matters.

Behind the scenes, agents reveal which products customers save, which combinations they explore, and how items should be styled together — patterns nearly impossible to analyze manually at scale. These insights directly shape merchandising and creative decisions.

What matters most isn’t the technology, but how customers feel. AI only works if it improves that experience.

This shift reflects the rise of agentic AI, where autonomous systems don’t just respond to prompts but execute governed decisions across eCommerce workflows.

By 2026, agents will evolve into true shopping advisors — less like bots, more like personal stylists. eCommerce will shift from static browsing to guided conversations that build confidence and clarity.

46. Use AI agents to turn post-purchase uncertainty into loyalty

Ender Korkmaz, CEO, HeatAndCool.com | LinkedIn

AI has proven more impactful in post-purchase care than marketing alone.

At HeatAndCool.com, agents track orders, clarify next steps, and guide delivery and installation expectations, reducing cancellations and preventing ticket spikes.

When customers feel guided instead of abandoned, loyalty follows.

By 2026, agents will become proactive coordinators, anticipating delivery issues, missing parts, and scheduling workflows before customers ask.

Support shifts from reactive fixes to continuous reassurance.

The challenge is integration. Most eCommerce, logistics, and service systems don’t share a unified customer timeline.

Brands that build clean connectors will turn post-purchase experience into a competitive advantage.

47. Elevate AI agents from efficiency tools to strategic commerce partners

Yuki Yang, Manager, Kabeier

AI agents have moved beyond efficiency into strategic partnership.

At Kabeier, we use them for customer inquiries, demand forecasting, and personalized B2B communication, freeing teams to focus on creative, high-impact decisions.

By 2026, agents will operate autonomously across sourcing, pricing, and post-purchase engagement. Their value won’t come from doing more tasks, but from making better, faster decisions with consistent logic at scale.

The opportunity lies in balancing autonomy with empathy. As agents become more decision-driven, data quality and human oversight become critical guardrails.

Winning brands will treat AI as a governed strategic collaborator aligned with human intent.

Across 47 leaders, one pattern is clear: the future of eCommerce belongs to governed, autonomous AI agents embedded directly into operational systems to meet rising customer expectations, not surface-level chat layers.

How Skara AI eCommerce agents power sales and support on one unified layer

Skara AI Agents by Salesmate are designed to power custom agents that align with your workflows, brand rules, and operational systems.

Unlike traditional chatbots built for basic automation, Skara functions as multiple specialized AI agents that operate independently across your CRM, catalog, order system, and knowledge base.

With Skara, teams can:

• Guide shoppers to the right products by interpreting customer intent, size, fit, compatibility, and availability in real time.
• Recommend bundles and contextual upsells to increase AOV
• AI abandoned cart recovery across chat, WhatsApp, SMS, and email
• Resolve high-volume support queries using synced knowledge base content
• Handle post-purchase flows like WISMO, returns, and exchanges
• Qualify inbound prospects through dynamic, conversational discovery
• Book meetings and update CRM records instantly

Every interaction updates records, triggers workflows, or moves a buyer closer to purchase or resolution.

AI support agents verify customers, fetch order history, initiate returns, and escalate complex cases with full context, so humans step in informed, not blind.

This isn’t automation layered on top of eCommerce.

It’s a system where AI agents work together to increase conversions, lift AOV, reduce ticket volume, and prevent missed opportunities, without expanding headcount.

As eCommerce moves toward agentic, intent-driven commerce, platforms that connect AI directly to operations become infrastructure, not experiments.

Wrap up

If there’s one pattern across these 47 leaders, it’s this: AI is no longer being tested at the edges of eCommerce. It’s moving into the core.

The leaders preparing for 2026 aren’t asking, “Can AI answer more questions?”

They’re asking:

• Can AI remove hesitation before checkout?
• Can it qualify intent before competitors respond?
• Can it prevent costly errors before fulfillment?
• Can it connect sales data directly to operations?
• Can it escalate trust-sensitive moments without damaging the brand?

That’s the real shift.

AI ecommerce agents are increasingly shaping inventory management, supplier coordination, and demand forecasting, turning sales data into operational action in real time.

The brands that win won’t deploy the most AI. They’ll deploy AI where friction lives. And they’ll build the data, governance, and trust layers that allow those agents to operate intelligently, not recklessly.

2026 won’t be defined by automation. It will be defined by AI commerce driven by governed, autonomous agents.