AI in Retail: How Voice and Conversational AI Are Driving Innovation

 AI in retail is changing the growth math now. Margins, revenue, and labor efficiency are moving together for the first time in a decade. According to McKinsey (2024), generative AI alone can unlock $240–$390B in value for retail, translating into roughly 1.2–1.9 percentage points of EBIT margin expansion. 

As Dasha’s CEO, I help companies in the retail space navigate what can be done today with this tech to build ultra-realistic, omnichannel voice agents that do real work in retail stacks. This guide lays out where value shows up, how to deploy safely, and why voice-first automation is the shortest path from “AI hype” to P&L impact. If you’re deciding where to start, this is the operating manual I would hand to my own team.

Why is retail embracing AI right now?

Three converging forces: conversational interfaces, tech maturity and deployment speed. 

On conversational interfaces, AI now influences discovery and deal-hunting in a way SEO once did. Adobe’s 2025 holiday outlook forecasts $253.4B in U.S. online sales, with AI-generated referrals set to surge about 520% year over year. That turns AI surfaces into a primary traffic source, not a novelty widget. According to Adobe and Barron’s reporting, Cyber Week alone could drive $43.7B in spend from traffic influenced by LLMs.

In terms of tech maturity, voice quality jumped to the point where barge-in, interruptions, and accents feel natural. Agentic systems also matured: AI doesn’t just answer questions anymore: it acts in your OMS, WMS, CRM, and carrier APIs. Together with innovations in generative AI and chat interfaces have created a level of adoption where users are way more open to interact with a non-human agent, as long as the experience is really there.

Finally, the platform shift reduces time to value. Foundation models, agentic patterns, and retail-ready cloud stacks have matured. In practice, this means retailers can pilot guided selling, catalog enrichment, and agent assist with reference architectures rather than bespoke builds. Google Cloud, AWS, and Microsoft now publish retail accelerators and blueprints that standardize grounding, evaluation, and safe actions, shortening the distance from LLM to ROI.

Together, that combination turns AI from a content generator into an operations engine, cutting handoffs and repetition, speeding resolution, and lowering cost to serve for teams while giving customers a first-time-right experience. The competitive reality is clear: the retailers that operationalize this first will reset expectations for everyone else.

That said, be weary of flashy plug-and-play tools that create MVPs in record time but have no real way to integrate with your tech stack, processes and client needs. Vertical maturity also means that there’s a wide range of options, and the ones that overpromise on no-code freedom might end with an idea that cannot work at scale.

Who’s already using voice AI in support? What can we learn from them?

Look at big-box customer care to see the pattern: fewer handoffs, more completed tasks, and agentic tools acting under policy instead of free-text promises. The shift is away from “deflect” and toward “finish the job.”

Quick-serve deployments are a stress test for frontline voice: noisy environments, accents, high variance, and no patience for lag. Wendy’s is scaling a drive-thru voice agent across hundreds of stores to compress time-to-order and reduce errors while keeping clean human fallbacks. White Castle moved beyond pilots after proving accuracy and escalation etiquette under real-world noise. These are relevant lessons for any retail contact flow that must move quickly and handle variance.

Treat Taco Bell as the cautionary tale. Broad rollout surfaced lane noise, policy edge cases, and trolling that slipped through abuse controls. The lesson for retail care is simple: choose the right environments, design escalation on purpose, harden abuse filters, and tune policies in production before you scale. McDonald’s ending one pilot after accuracy issues didn’t end its voice ambitions; it raised the bar for governance, retraining loops, and human-in-the-loop rules. Reality matters more than the demo reel.

How is voice AI used in retail today?

Voice commerce. Reorder tasks, add-to-cart lists, and routine grocery workflows fit voice perfectly. Walmart’s Voice Order lets customers pair accounts with smart speakers and phones to restock by speaking. The job-to-be-done is low-friction capture of intent in moments when hands are busy.

Call deflection and order status. Voice bots handle FAQs, store hours, delivery ETAs, returns policy, and basic account lookups before escalating to humans. This cuts handle time and raises CSAT when designed with smart escalation and real-time order data. Case libraries from LivePerson show measurable improvements in how AI is being used for 

In-aisle and backroom voice assistants. Associates wearing headsets can ask for inventory, bin locations, substitutions, or planogram steps without leaving the task with solutions like SoundHound’s “Employee Assist”.

Kiosks and embedded voice. Touchless voice interfaces at counters, kiosks, and in-app lower friction and improve accessibility. As buyers adopt multimodal interactions, you’ll see voice combined with on-screen confirmation to prevent errors and improve trust. Feedonomics notes the rising share of AI-guided voice search and shopping behavior in 2025. feedonomics.com

Where Dasha fits. You can use Dasha when you need enterprise-grade conversational voice with low latency and natural prosody. Dasha’s programmable dialog manager and telephony integration allow retailers to spin up voice ordering, store-hours lines, or post-purchase status bots that hand off to agents with full transcript context. In our deployments, we’ve seen Dasha-powered flows lift containment while preserving brand tone in every spoken response.

What is the ROI of automating retail operations with AI?

Speed converts. When shoppers find the right item faster, conversion rises and return risk falls. This is the oldest truth in commerce, now amplified by conversation that narrows from intent to purchase with fewer dead ends.

Automation compounds. When routine contacts self-resolve, your agents focus on exceptions, styling, and clienteling: the interactions that create loyalty and raise AOV. Post-purchase leakage drops too. Clean returns, proactive delivery nudges, and accurate ETAs reduce reships and churn. These are operational gains that stack week over week, and you’ll see them in dashboards long before they roll up to a quarterly report.

High-volume, rule-bound intents return value first: “Where is my order?” becomes a two-minute voice flow that reschedules drops, fixes addresses, and confirms the outcome without opening a ticket. Returns run on policy-aware logic that grants instant credit when appropriate and schedule pickup, avoiding ping-pong between support and warehouse. Store hours, stock checks, and appointment booking respond in your brand voice rather than a generic IVR. 

From there, loyalty lookups, points redemption, and back-in-stock outreach remove a large share of inbound volume and create visible savings in the first quarter. 

Once service stabilizes, associate onboarding benefits from a voice tutor for SOPs and day-one tasks, which accelerates time to productivity and frees managers to coach instead of repeat.

Which cloud stacks and tooling speed retail AI deployment?

Retail-ready solutions. Google Cloud rolled out retail-specific gen-AI tools including Vertex AI Search for retail and generative solutions for product discovery and contact center. AWS introduced Amazon Q Business for Retail Intelligence to unify enterprise search, insight generation, and BI. Microsoft’s research with IDC highlights the shift from basic copilots to domain-specific, agentic solutions. These moves de-risk common use cases with proven scaffolding

Grounding matters. We anchor responses in your policies, catalogs, and knowledge so the system retrieves facts rather than guessing them. Tool use is permissioned and state-aware, which means the agent can only do what you explicitly allow it to do at that moment. Every action is observable, sensitive fields are redacted or isolated, and audit trails are readable by non-engineers. Security teams sign off because governance is coded, not implied.

Watch out for privacy settings, loss prevention, and capex vs. opex tradeoffs — these will dictate pace. Design for transparency and give shoppers clear receipts and dispute flows. Pair with CV-based shrink controls to protect ROI. Amazon’s engineering notes also show continuous model improvements that raise accuracy ceilings

What does good sound like in voice AI for retail calls?

It sounds like a brand-trained, policy-aware associate who knows your brand and your rules. The agent greets, verifies identity, anticipates likely intent, and proposes the fix, then handles the steps that matter most, like order status, address changes, refunds, or rebooking, and confirms outcomes in plain language you can track to first-contact resolution. If the customer gets impatient, it picks up the pace; if the situation calls for a human (policy, empathy, or preference), it routes with full context and zero friction. The goal isn’t to sound human for style; it’s to deliver human-quality results with machine consistency. That’s the standard we ship against.

Where does a general chatbot like ChatGPT fit and where not?

It’s great for content and quick answers (FAQs, discovery copy or internal enablement) and excellent at the “tell me” tier. When outcomes matter (refunds, reschedules, RMAs, bookings, loyalty actions), you need a production-grade voice agent with tools, policies, observability and guardrails. That’s the line between answering a question and owning the outcome. Answering is cheap, finishing the job is valuable. Build the ecosystem so each system does what it does best.

Retail workers move from task repetition to the creation of value.

AI takes the repetitive tier. People take the relational tier. 

Voice agents handle high-volume, rules-bound tasks (WISMO, address fixes, returns initiation, appointment booking, loyalty lookups) at machine speed and consistency. Associates then focus on exceptions, styling, clienteling, and recovery moments that require judgment and empathy. Fewer tickets. More finished jobs. Your people spend their time where they create margin and loyalty, the machines handle the rest.

This only works with clear routing rules and incentives. Define what the agent must finish, what it may attempt, and what it must hand off. Optimizing everything isn’t a realistic goal for your retail or Ecommerce, and it’s important to have clear expectations on what AI can and cannot do.

Route on sentiment, repetition, risk, and value. Give associates full context at handoff so they start at sentence two, not sentence zero. Retrain teams for higher-order work: objection handling, cross-category styling, post-purchase care, and proactive outreach. Measure differently too: less on handle time alone, more on resolution quality, AOV influence, retention, and NPS. 

Expect role redesign, not reduction.

How do you prove value without betting the brand?

Pick one intent that costs you money and patience. “Where is my order?” is common because it’s constant. Provide policy, sandbox credentials, and sample calls. In two weeks you should hear your brand’s voice complete that task end-to-end with no human glue.

In the UK, IKEA’s customer service assistant “Billie” uses AI to triage order issues in chat, a narrow, low-risk lane that proves the loop before you scale to voice and broader automation.

Then route a small share of real volume. Compare AHT, containment, CSAT, and recontact against baseline. If it clears the bar, expand deliberately. If it doesn’t, fix or stop. No drama. This discipline is how you avoid the “pilot that never ends.”

What should you expect from the next 12–24 months?

Voice will move from IVR replacement to action engine. The useful deployments won’t try to “sound human.” They’ll prove they can authenticate, pull context, execute tools, and finish tasks. Expect retailers to push more post-purchase flows (delivery changes, returns, warranty, loyalty) into voice because those journeys are procedural and measurable.

Agentic patterns will harden. You’ll see clear verbs exposed to the AI (refund, reschedule, rebook, restock) each fenced by policy and telemetry. That shift reduces hallucination risk and raises trust internally because success is defined by completed actions, not chats.

Multilingualism becomes table stakes. Code-switching mid-call stops being exotic and starts being expected, especially in multicultural markets. The winners will invest in locale-specific policies, carriers, and escalation etiquette, not just translation.

A proactive voice shows up. Replenishment reminders, back-in-stock alerts, and delivery nudges will move from SMS to voice in segments where speaking is faster than tapping. The operational lift is real: fewer misses, fewer reships, happier customers.

Lessons from QSR spill into retail care. The drive-thru has become a crucible for voice AI. Retailers will borrow what works (abuse controls, deterministic handoffs, environment selection) and avoid the mistakes. Expect more Rufus-style conversational shopping inside owned apps and sites, not as detours to third-party assistants. And expect enterprise “agentic AI” to become a board-level framework that standardizes tools, policies, observability, and escalation.

What does Dasha bring on day one?

A voice that sounds like a person. A brain that follows your policies. Tooling wired to your systems, with a builder your CX team can use and a scripting layer your engineers respect. 

We support more than 30 languages with in-call switching, we scale to peak loads with high concurrency and we work with your carrier or ours. We ship guardrails, redaction, audit, and observability by default, so you get outcomes, not demos.

You can test Dasha.ai right now and see what it is capable of. You can also schedule a call with me and we can go through how we can help your retail business adopt retail.

Start where customers already talk to you most and where resolution is procedural. Turn those procedures into a conversation that completes the task and keep humans in the loop where empathy and judgment are the product.

Once service stabilizes, add conversational discovery. Then go proactive with replenishment, back-in-stock, and winbacks. You’ll see it first in weekly dashboards and you’ll feel it next in the quarter.

If you’re ready to move from pilots to production, we’ll build your first retail voice agent, wire it to your stack, and target measurable wins in the first 90 days. That’s how “retail and AI” stops being a headline and starts being a habit.

Want to see if Dasha is the right fit for your retail business? Schedule a call with me and we can go through your brand automation needs.