Gap Turns On AI Agents Across All Brands: What Support Teams Can Use Today
Gap Inc. has flipped the switch on AI agents across Gap, Banana Republic, Athleta, and Old Navy right as holiday volume spikes. The agents handle order tracking, returns, and gift cards across web, mobile, and voice. The goal is simple: move routine traffic to automation so human reps can focus on higher-value conversations.
This isn't just cost control. It's a capacity strategy. Handle 10 requests or a million without scrambling for seasonal staffing, and do it without tanking quality.
Why this matters for support leaders
Early data from retail adopters shows service can get faster and more consistent at scale. Tubi increased CSAT by seven points after deploying an agent, cutting response times from hours to minutes. Minted held a 95% CSAT on its biggest sales day on record.
Customers don't need a human for transactional issues; they need accurate answers fast. Save your team for the edge cases that create loyalty.
The KPI set that actually moves the needle
- Containment and resolution rate (by intent) - track solved without handoff
- Time to first meaningful response - not just first reply
- CSAT on automated threads vs. human threads
- Escalation rate and reasons - feed training and policy updates
- Conversion lift and revenue per chat/session for sales intents
- Deflection from phone to digital channels
- Refund/return prevention where policy allows (save rate)
What strong teams are doing right now
- Scope the agent's job: order status, returns, exchanges, gift cards, promo code help, shipping issues, store hours, loyalty points
- Stand up all channels at once: web widget, mobile in-app, voice IVR handoff, SMS, email assist
- Build a clean escalation matrix: when to hand off, to whom, and with what context
- Wire up systems: OMS, CRM, payment processor, loyalty, knowledge base, fraud rules
- Use policies as code: return windows, exceptions, price adjustments, out-of-policy responses
- Instrument everything: dashboards by intent, channel, geography, and time of day
Revenue and ops upside you can point to
Sun & Ski Sports saw 3x higher conversion among customers who engaged with their agent. Madison Reed reported a 30x increase in chat interaction after launching "Madi."
The hidden edge: agents double as real-time monitors. During peak, Minted's agent surfaced a broken promo code within hours, preventing a wider mess. Your helpdesk becomes an early-warning system for ops.
Holiday playbook: 7-day sprint
- Day 1: Map top 20 intents by volume and revenue impact; write success criteria for each
- Day 2: Connect order lookup, returns/exchanges, and gift card balance checks end-to-end
- Day 3: Draft response templates for policy-heavy cases; include friendly refusals and save offers
- Day 4: Set escalation rules for refunds, damaged items, split shipments, and partial returns
- Day 5: Launch canary in one brand/channel; live-monitor transcripts for drift and gaps
- Day 6: Add proactive prompts: "Want to track this order?" in high-traffic pages and IVR
- Day 7: Review metrics; expand coverage, trim failure paths, refresh knowledge base
Guardrails that keep you out of trouble
- Strict identity checks before order access; redact PII in logs where possible
- Quote policy verbatim for denials; require human approval for goodwill credits over threshold
- Intent fallbacks with one retry; escalate after two misses to avoid loops
- Block free-text refunds and coupon generation; use API-driven, logged actions only
- Daily transcript sampling: spot policy drift, tone issues, and incorrect concessions
Make the human role bigger, not smaller
As Adam Leubbers of CLEAR puts it, the target is augmentation, not elimination. Let agents clear the queue; let people handle empathy, judgment calls, and relationship work. That's where loyalty is built.
Implementation checklist
- Channels: web, mobile, voice; consistent intents and tone across all
- Systems: OMS, CRM, payment, loyalty, inventory, promotions engine
- Compliance: consent banners, data retention rules, PII controls per region
- Ops: transcript to ticket sync, disposition codes, tag by intent and outcome
- QA: golden test cases, weekly calibration with Support and Legal
- Reporting: CSAT, resolution, escalation, revenue lift, cost per resolution
Playbook tips for frontline managers
- Assign owners by intent, not by channel; they maintain answers and policies
- Publish "known issues" and scripted responses for live agents and the AI at the same time
- Run side-by-side: agent suggestion mode for new agents, full autonomy for mature intents
- Set SLAs for fixes when the agent flags bugs (e.g., promo codes, inventory mismatches)
What to tell your exec team
- Capacity: instant scale without seasonal hiring lag
- Quality: faster replies with steady or higher CSAT
- Revenue: higher conversion on assisted sessions and saved returns
- Intelligence: live detection of errors in promotions, pricing, and logistics
Level up your team's AI skills
If you're standing this up or tightening what you have, train agents to write better intents, policy prompts, and escalation notes. The craft is operational design, not just "chat."
For structured learning by role, see these resources for customer support teams: AI courses by job.
Bottom line
Gap's full-scale rollout signals where retail support is headed: automation for speed and consistency, humans for nuance and trust. If you're preparing for peak, this playbook will help you ship fast without giving up service quality.
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