Agentic AI hits checkout: Google's shopping push, Apple-Google Siri pact, and the GEO scramble

Eye on AI for Customer Support: Agentic checkout, Siri's upgrade, Slack's new bot, and the governance gap

Another loaded AI week. Here's what matters for support leaders: Google moved checkout inside Search's AI Mode and the Gemini app, Apple tapped Google's models to upgrade Siri, Meta is racing to lock down massive compute, Salesforce shipped a Claude-powered Slackbot, and researchers used AI to design new gene-editing tools.

Now, the part most teams miss: agentic commerce is blurring the line between shopping and support. If your workflows, data, and governance aren't ready, your queue-and your risk-will swell.

Google brings checkout into AI Search and Gemini: why support should care

Google launched AI-driven checkout directly inside Search's AI Mode and Gemini. Walmart is already in. Under the hood is a new Universal Commerce Protocol, built to make agent-led purchases simpler for retailers. Google Cloud also introduced Gemini Enterprise for Customer Experience-a single stack that spans shopping and support. Home Depot is an early customer.

• Expect more "I bought it right in Search-fix it here" contacts. Your team will inherit post-purchase issues started outside your site.
• Shopping and support data will need to live closer together. Refunds, order edits, warranties, and backorders must be AI-readable and policy-safe.
• Agent handoffs matter. Clear escalation paths and audit trails are no longer optional once AI initiates the sale.

GEO/GAIO: Your trust signals now affect AI answers

A new crop of vendors is selling "generative engine optimization" to influence how AI agents recommend products. Early pattern: agents give extra weight to reputable press coverage and trustworthy review sites. That's fine for product details-but unreliable for governance, certifications, and financial suitability.

Findings from AIVO Standard: leading models consistently describe product features, yet struggle with cybersecurity certifications, governance claims, and financial queries. Worse, they sometimes double down on incorrect answers when asked to verify.

• Publish a clean, public "Trust Center" page: security certifications (e.g., ISO/IEC 27001), compliance scope, data flows, uptime, DPAs, and contacts for enterprise review.
• Make facts machine-readable: product specs, pricing eligibility, returns/SLAs, and certifications. Keep them versioned. Out-of-date PDFs create AI confusion.
• Seed credible signals: accurate listings on review sites, documentation that's clearly dated, and third-party validations your buyers already trust.
• If you sell into regulated industries, align responses with a risk framework such as the NIST AI Risk Management Framework. Also ensure your certs are verifiable against sources like ISO/IEC 27001.

Governance you can't postpone: log prompts, answers, and decisions

Agentic workflows have a moment where AI outputs turn into actions-refunds, cancellations, replacements, eligibility calls. Regulators will want to know what prompt was used, which model responded, what sources were cited, and who approved the final move.

• Prompt logging: store prompts, model IDs, temperatures, timestamps, and user/agent IDs.
• Output capture: save responses, citations/URLs used, confidence scores, and escalation notes.
• Decision traceability: record the exact step where a human approved or overrode the AI.
• Retention policy: define how long logs are kept and how they're secured; restrict access by role.
• Evaluation: routinely test risky topics (certifications, financial suitability, medical disclaimers). Force citation and source display for these categories.
• Fail-safes: block irreversible actions (refunds, account closures, PHI access) without human sign-off.

What moved this week-and what it means for support

• Apple selects Google models to upgrade Siri. Expect a spike in voice-first support expectations and "Siri started this ticket" handoffs. Prepare your IVR and voice assistants to meet that bar.
• Meta's new AI infrastructure push (Meta Compute). More capacity means cheaper inference over time. Translation: higher agent traffic and 24/7 expectations as AI gets baked into more touchpoints.
• Microsoft flags rising adoption of low-cost Chinese open models like DeepSeek in emerging markets. If you support customers across the global south, you'll see more requests for lightweight, local models and stricter cost controls.
• Salesforce rolls out a Claude-powered Slackbot. This can answer questions across Slack, Salesforce, and connected tools. Great for deflection and agent assist-just enforce permissions, source priority, and red-teaming for sensitive topics.
• Anthropic ships Claude for Healthcare. If you handle PHI, push for strict data boundaries and explicit consent flows before experimentation.
• AI-aided gene editing (Eden models) is impressive science. For support teams, treat it as a reminder: model outputs can look factual yet carry high stakes. Keep humans in the loop where risk is real.

90-day action plan for support leaders

• Days 0-30: Map your top 20 intents to agentic flows (orders, returns, shipping, billing). Add guardrails for refunds, replacements, and eligibility. Require citations for anything "trust" related (security, compliance, financials).
• Days 31-60: Stand up prompt and decision logging. Publish a public Trust Center. Update knowledge with structured, current data. Add user-permission checks to any Slack/assistant integrations.
• Days 61-90: Run red-team tests on governance prompts. Measure containment rate, AHT, first-contact resolution, CSAT, and recontact within 7 days. Tie AI deflection to dollar savings you can defend.

Level up your team's AI skills

• Browse practical AI courses by role: Customer Support
• Get sharper prompts and safer automations: Prompt courses and Automation guides

Dates to keep on your radar

• AAAI Conference on Artificial Intelligence, Singapore: Jan. 20-27
• Mobile World Congress, Barcelona: March 2-5
• Nvidia GTC, San Jose: March 16-19

The bar just moved again. If your support org can explain its AI decisions, cite its sources, and close the loop on risky actions, you'll be fine. If not, start with logging, trust signals, and guardrails-this week.