Uber Eats Launches AI Cart Assistant: What It Means for E-commerce Teams

Uber Eats Cart Assistant changes the AI shopping timeline

On February 11, 2026, Uber announced that Uber Eats is launching a new AI-powered Cart Assistant in beta for grocery shopping. The announcement matters because it moves AI shopping from search-only experiences toward in-cart decision support, where shoppers compare pack sizes, substitutions, and value before checkout. For digital commerce teams, this is exactly where margin and conversion decisions happen.

Uber says the feature is designed to help users discover relevant grocery items and make more confident choices while building an order. That framing is important: this is not just a chat widget bolted onto search results. It is a workflow layer inside the cart, where recommendations can influence basket size, substitution acceptance, and private-label share.

If your team follows AI commerce trends, this launch confirms a broader direction we covered in our post on AI shopping assistants and catalog readiness: assistants are becoming transactional, not informational.

Why this news is bigger than one app feature

A lot of AI commerce headlines over the last two years focused on discovery, content generation, or ad targeting. Cart-level assistance is different for one reason: it directly touches checkout intent. In practical terms, that changes what retailers should measure.

You are no longer only optimizing for click-through on product listing pages. You now need to optimize for whether an assistant can reliably answer cart-time questions such as:

• Is this item truly the cheapest per unit?
• Is this replacement allergen-safe and size-compatible?
• Does this multipack fit the shopper's prior preference?
• Is there a better-value alternative in stock right now?

Those questions require richer product data than many grocery catalogs currently provide. Teams that still rely on shallow titles and inconsistent attributes will struggle to feed high-quality recommendations into this new interface layer.

Product data requirements for AI-guided carts

For most retailers, the technical challenge is not building another chatbot. It is turning raw supplier feeds into assistant-ready product intelligence. Three data blocks become critical:

1. Comparable attributes Your catalog needs normalized units (g, ml, count), consistent brand naming, and structured pack-size logic so an assistant can compare value correctly.

2. Substitution-safe metadata When shoppers swap products, the model needs dietary and functional constraints: allergens, compatibility notes, and clear variant distinctions.

3. Freshness and availability signals Cart guidance degrades fast when stock and price deltas are stale. Near-real-time synchronization becomes a performance requirement, not a nice-to-have.

If this sounds familiar, it is because the same foundations also power stronger feeds and organic discovery. A practical starting point is to benchmark your current quality against a repeatable checklist like this product data quality framework.

At this stage, tools like Lasso features can help automate feed mapping, attribute normalization, and enrichment so your team can ship cleaner data into AI-assisted shopping flows without expanding manual QA headcount.

What e-commerce teams should do in the next 30 days

You do not need to rebuild your stack this quarter. You do need a clear execution plan. A focused 30-day sprint can materially improve your readiness:

1. Audit cart-critical attributes Create a short list of fields that matter for comparison and substitution in your top categories (for example unit size, allergens, dietary tags, material, compatibility).

2. Score completeness by category Measure fill rate and consistency for those fields. Avoid one global score. Category-level gaps are what break assistant quality.

3. Fix title and variant ambiguity Where variants are mixed in titles, split them into structured attributes. This is often the fastest conversion lift lever for assistant-driven recommendations.

4. Strengthen feed governance Define validation rules before publish. We outlined a tactical approach in our product feed optimization guide.

5. Align teams on accountability Assign clear ownership across merchandising, data operations, and performance marketing. AI cart experiences fail when ownership is fragmented.

A useful operating model is to treat AI cart quality as a cross-functional KPI, similar to availability or return rate.

Risks to watch as AI cart assistants scale

This trend is promising, but it is not risk-free. Retail teams should watch three areas closely:

• Recommendation opacity If shoppers cannot understand why an item was suggested, trust drops. Explainability UX will become a competitive differentiator.

• Bias toward promotional inventory Assistant behavior can drift toward paid or overstock outcomes. Governance rules need to protect customer value and long-term retention.

• Data liability and compliance Incorrect dietary or compatibility guidance can create regulatory and reputational exposure. Validation gates must be strict for sensitive categories.

The strategic takeaway: AI carts can increase convenience and basket value, but only if your product data is reliable enough to support high-stakes recommendations.

Another practical risk is weak measurement design. Many teams launch AI assistant pilots and only track session-level engagement, which hides commercial outcomes. For cart assistance, you should define a minimum metric set before rollout: substitution acceptance rate, value-per-unit savings versus baseline basket, gross margin impact after substitutions, and order completion rate for sessions where the assistant was used. Without that structure, teams can misread a higher interaction rate as success even when margin or trust is falling.

It also helps to set category-specific guardrails. Frozen food, baby care, and health-related products do not behave like beverages or snacks. The tolerance for substitution and the cost of wrong recommendations are category-dependent, so decision policies should reflect that. Even simple rules, such as restricting automatic substitutions in high-risk categories or requiring stronger attribute confidence before suggestions are shown, can prevent expensive mistakes during early rollout phases.

What this means for Lasso customers and next steps

The Uber Eats Cart Assistant launch is another sign that AI in e-commerce is moving deeper into transactional moments. Teams that invest now in structured, validated catalogs will be easier to surface and easier to trust when assistants are choosing between alternatives.

If you are planning your 2026 AI commerce roadmap, start with a data-readiness baseline and a pilot category. Then scale only after you can measure improvement in substitution quality, AOV, and checkout conversion.

Lasso can support that rollout with catalog cleaning, enrichment, and scalable content generation workflows built for retail product operations. You can review options on pricing, or talk with the team via contact.