Google Cloud Brings Shopping and Support Together with Gemini Enterprise for CX
At NRF 2026, Google Cloud introduced Gemini Enterprise for Customer Experience (CX) - a single interface that connects shopping and customer service. The promise is simple: fewer handoffs, fewer repeats, and agents that actually take action.
If you lead customer support, this matters. It means one system can keep context across web, app, phone, and store while solving real problems in real time - from product discovery to post-purchase fixes.
What it means for support teams
- Unified context across channels: No more asking customers to repeat themselves as they move from chat to phone to store.
- Active problem solving: Agents can execute multi-step tasks (refunds, replacements, order fixes) across internal systems with consent.
- Multimodal help: Support understands text, voice, images, and video in 40+ languages.
- Human-in-the-loop: Live agents get real-time guidance, and customers can escalate seamlessly.
New Shopping agent: beyond chat
The Shopping agent goes past "here's a link." It reasons, acts, and keeps the thread intact.
- Complex reasoning: Filters for specs, constraints, and budget automatically, based on customer input and consent.
- Multimodal interactions: A photo of a handwritten recipe becomes a cart with ingredients and member discounts applied.
- Consented actions: Adds items to cart, checks local availability, and handles checkout after approval.
Support capabilities you can use now
- Customer Experience Agent Studio: Build, test, and deploy multimodal support agents that stay in sync with shopping context.
- AI-builds-AI: Turn existing chat transcripts and policy docs into functioning agents.
- Visual canvas: Drag-and-drop workflows that launch in days, not months.
- Active workflows: Shade-match a beauty product, trigger a local replacement, and issue a goodwill credit in one flow.
- Real-time QA: Ask, "What's causing longer handle times for billing?" and get answers. Auto-score conversations with conditional scorecards.
- Human assist: Live reps get contextual prompts and simulations for faster onboarding and better resolutions.
Who's already moving
- Kroger: Making meal planning and weekly shopping feel personal while helping customers compare and save time.
- Lowe's: Enhancing its Mylow advisor to guide projects based on each home and location.
- Woolworths: Evolving Olive to anticipate needs, plan meals, and highlight relevant specials that fit a budget.
- Papa Johns: Deploying an omnichannel Food Ordering agent across app, web, phone, kiosks, and in-car systems.
Food Ordering agent for restaurants
- Handles orders across channels and languages with natural conversations.
- Suggests items based on menu context while finding the best deal for the customer.
- Acts like a business analyst: surfaces performance insights and simplifies updates to menus and pricing across locations.
How to put this to work in 30-60 days
- Map your top 10 intents by effort and impact (order status, returns, replacements, billing, stock checks).
- Define consent and guardrails: what the agent can do autonomously vs. with approval; log every action.
- Connect systems: order management, inventory, billing, CRM, identity, and knowledge base.
- Start with one end-to-end workflow (e.g., damaged item → photo intake → local replacement → refund/credit).
- Build in Agent Studio: use transcripts to draft flows, then refine with real calls and chats.
- Design escalation rules: confidence thresholds, sensitive intents, and supervisor reviews.
- Launch a limited pilot, then expand by intent and channel.
Quality and performance metrics to watch
- First contact resolution and repeat contacts by intent
- Average handle time (AHT) and time-to-resolution
- Deflection rate with outcome quality (refund accuracy, replacement success)
- CSAT after AI-only and AI-to-human interactions
- Compliance adherence and policy exceptions
Why this changes your operating model
Support is no longer just answers and links. With agents that execute tasks, your team shifts from case handling to exception handling.
That means fewer tickets for humans, tighter control on policy adherence, and better customer outcomes without the back-and-forth.
Safety, privacy, and control
- Customer data is not used to train base models.
- Built-in policy controls keep agents within brand and legal requirements.
- Consent-driven actions with clear logs and reversibility.
Practical build checklist
- Data: Up-to-date product catalog, policies, knowledge base, and clean action APIs.
- Policy: Refund, replacement, and credit thresholds with clear approval paths.
- Design: Conversation flows with edge cases and fallback prompts.
- Training: Short simulations for agents; customer-facing guidance for new flows.
- Monitoring: Real-time dashboards, auto-scoring, and weekly call reviews.
Resources
Bottom line: tie shopping and support into one experience, let agents take action with consent, and keep humans for the edge cases. That's how you reduce effort for customers and lift loyalty without inflating headcount.
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