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Conversational AI in customer service and sales can answer routine questions, guide callers, help shoppers find products, and support sales teams. The most useful deployments are built around a clear task, connected to the information and systems needed to complete it, and able to hand complex or sensitive cases to a person.
The examples below show several distinct patterns, from order tracking and voice support to image-based product discovery. Their reported results come from the organizations and vendors describing each deployment; they are not a shared benchmark or a forecast for another business.
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What conversational AI does in customer service and sales
Conversational AI lets people interact with software through natural-language chat or voice. In customer service, it may answer a question, retrieve account or order information, guide a caller through a process, or gather context before a human agent takes over. In sales, it may answer product questions, recommend items, guide a shopper through a catalogue, or help a sales employee draft communications and organize follow-up.
These are different jobs, not interchangeable versions of one chatbot. A customer-facing shopping assistant needs reliable product information and a route to a sales associate. A service agent needs accurate help content and, when appropriate, access to order or case workflows. An internal sales agent may assist an employee rather than speak to a prospective customer at all.
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Examples at a glance
| Example | Primary job | Channel or interaction | Reported evidence |
|---|---|---|---|
| Salesforce Agentforce on help.salesforce.com | Customer support and troubleshooting | Web help experience | Salesforce reported 45,000 conversations weekly and resolution rates in the 85% range ten months after launch (2025). |
| Tiendas CUADRA with Microsoft Copilot Studio and Dynamics 365 Customer Service | Order support, then retail product discovery | Customer-service assistant; image-based retail features | Microsoft Learn describes the phased deployment and its cost-control changes; no comparable resolution figure is stated. |
| DoorDash Dasher support | Answering support questions and reducing routine call escalations | Voice IVR | AWS says it handles hundreds of thousands of calls daily and reduced live-agent escalations by thousands a day. |
| Intercom Fin Voice | Resolve calls or complete initial steps before handoff | Phone | OpenAI’s 2025 enterprise report presents Intercom case-study claims: 53% average end-to-end call resolution and 48% lower latency. |
| Ryanair assistant | Passenger questions about baggage, refunds, bookings, and disruptions | Multilingual chat and voice | AWS reports deployment metrics for a service launched in October 2024, including 10 million chatbot answers and 94% accuracy. |
| Microsoft Store Assistant | Product advice and guided shopping | Store chatbot with page context and human sales handoff | Microsoft reported several million annual conversations, four-star satisfaction, and revenue above 140% of forecast six months after launch. |
| Salesforce business agents for sales work | Support sales employees with routine actions | Email drafting, to-dos, and meeting requests | Salesforce’s H1 2025 index identifies these among common sales actions; it is not a customer-facing shopping assistant. |
The figures in this table describe different tasks, periods, and measurement methods. They should not be used to rank the deployments against one another.
Customer-service examples
1. Salesforce Agentforce: web help and troubleshooting
Salesforce describes its deployment on help.salesforce.com as a “customer zero” example: selected visitors could use a digital help agent for questions and troubleshooting beginning in fall 2024. Salesforce reported that ten months after launch the agent handled 45,000 conversations per week, with resolution rates in the 85% range. Those are Salesforce’s figures for its own deployment, not an independently measured result for other Agentforce customers.
Salesforce’s account emphasizes the work around the agent as much as the model: keep knowledge content curated, train and refine the agent over time, give it a conversational and empathetic persona, and provide seamless handoffs for nuanced problems. In practical terms, an answer agent needs maintained source material and a clear boundary between questions it can resolve and cases that need a person. Salesforce’s Agentforce customer-support account describes the deployment and its lessons.
2. Tiendas CUADRA: from order tracking to retail discovery
Mexican retailer Tiendas CUADRA used Microsoft Copilot Studio with Dynamics 365 Customer Service, initially targeting high-volume operational requests such as order status and delivery tracking. The implementation included structured escalation into human-assisted workflows. It later expanded toward retail discovery, with capabilities described for interpreting product images, recommending complementary products, and suggesting outfits.
Microsoft Learn also reports a useful implementation trade-off: early versions applied generative AI too broadly, increasing credit consumption. The team refined prompts, used generative AI selectively, and shortened responses to control costs. The case study describes customer photo uploads for product matching, styling suggestions, and leather-care guidance as an intended enhancement; it does not establish that this planned feature was live. Microsoft Learn’s CUADRA case study outlines the rollout.
3. DoorDash: voice self-service for Dasher support
DoorDash developed a generative-AI self-service flow for Dasher support calls using voice IVR. AWS says the solution was tested in early 2024 before rollout to all Dashers, handles hundreds of thousands of calls daily, and reduced live-agent escalations by thousands per day. DoorDash’s stated direction was to move beyond answering questions toward taking actions through its logistics workflow.
This example shows why an AI voice system is more than a spoken FAQ: its usefulness depends on the call flow and its connection to the operational systems that can answer or act on a Dasher’s request. The deployment’s reported scale and reduction in escalations are DoorDash/AWS claims, not a general outcome guarantee. AWS’s DoorDash case study describes the test and rollout.
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Intercom presents Fin Voice as its customer-service agent extended to phone calls. OpenAI’s 2025 enterprise report says Intercom reported that the Realtime API reduced latency by 48% since March, that Fin Voice resolves 53% of calls end to end on average, and that calls still requiring a human are resolved 40% faster after the AI completes initial steps.
These are case-study claims reported by OpenAI and Intercom, not independent comparisons with other voice systems. The figures also describe different outcomes: latency is not resolution, and faster resolution after handoff is not the same measure as calls resolved entirely by the agent. OpenAI’s Intercom enterprise report provides the case-study context.
5. Ryanair: multilingual passenger support over chat and voice
AWS describes Ryanair’s assistant as supporting questions about baggage, refunds, bookings, and disruptions through multilingual chat and voice, integrated with Amazon Connect. The AWS case study says that in February 2026 Ryanair consolidated 12 domain-specific agents into one agent architecture. For a service launched in October 2024, AWS reports 10 million chatbot answers, 94% accuracy, 120,000 daily answers, and seven languages. It also reports an 84% response-time reduction, from 18 seconds to 2.9 seconds, and more than 500,000 agent hours saved.
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These numbers are AWS/Ryanair case-study figures with the case’s own definitions and time context. They do not establish that another airline, or a business with a different request mix, would see the same results. Consolidating agents is also an architectural detail, not by itself evidence of better answers. AWS’s Ryanair case study describes the system and reported metrics.
Sales and guided-shopping examples
6. Microsoft Store Assistant: product guidance with a human option
Microsoft replaced a rule-based Microsoft Store chatbot with the Store Assistant in October 2024. Microsoft says the assistant uses Azure OpenAI, Semantic Kernel, and real-time page context to answer questions across Microsoft’s product portfolio, and can recognize when to involve a human sales associate.
Six months after launch, Microsoft reported several million annual conversations, a four-star satisfaction rating, and revenue exceeding 140% of forecast. Those are Microsoft’s figures for its own store deployment; they do not isolate the assistant’s effect from other influences on revenue. Microsoft’s Store Assistant account describes the system and its reported results.
7. CUADRA: image-based product advice and outfit suggestions
CUADRA’s retail assistant illustrates a shift from answering operational questions to helping customers choose products. Microsoft Learn says it can interpret product images, suggest complementary items, and generate outfit suggestions. The case study separately describes customer-uploaded shoe photos for product matches, styling advice, and leather-care guidance as a planned enhancement, so those photo-upload functions should not be treated as confirmed live features.
This pattern requires product information that can support useful recommendations, not merely fluent conversation. It also demonstrates a phased approach: begin with recurring support requests, add retail discovery where the product catalogue and workflows can support it, and manage the cost of generative responses. The implementation and cost-control account is in Microsoft Learn’s CUADRA case study.
8. Salesforce sales agents: assistance for sales employees
Salesforce’s H1 2025 Agentic Enterprise Index describes sales and service as prominent AI-agent use cases. Common sales actions included drafting and sending emails, creating to-dos, and sending meeting requests. This is a distinct form of conversational AI: the agent supports business sales work rather than advising a shopper on a product page.
Salesforce reported that agent creation among participating first movers rose 119% between January and June 2025. Its index also said average customer-service conversations led by an agent grew 22-fold in H1 2025 and reported human escalations increased from 22% in Q1 to 32% in Q2. These are Salesforce index figures from participating organizations, not a universal market census; the rise in escalations also illustrates why human involvement is part of deployment rather than evidence that every interaction should be automated. Salesforce’s H1 2025 index explains its measures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported results do—and do not—show
Vendor and platform case studies are useful for understanding what a deployment was built to do, how it connected to other systems, and what its publisher says happened. They are not directly comparable effectiveness tests. For example, a call-resolution rate, chatbot accuracy, response-time change, weekly conversation volume, and forecasted revenue measure different things. A result can also depend on which interactions were included, the deployment period, and how the publisher defines resolution or accuracy.
Salesforce’s 2026 survey report offers a broader but still specifically defined view: it surveyed 3,075 customer-service professionals worldwide. Salesforce says 66% of service organizations use agentic AI, up from 39% in 2025, and that 70% of organizations with AI service agents reported measurable value within 60 days. These are survey findings from Salesforce’s respondent base, not an independent census of all organizations. Salesforce’s 2026 service-agent research article describes the survey.
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How to choose a useful conversational AI use case
Start with a repeatable job
Order status, delivery tracking, common product questions, and routine account support are bounded starting points reflected in published deployments. A well-defined request makes it easier to decide what information the agent needs and what counts as a successful answer. Begin with an answer or guided next step before permitting actions that change orders, accounts, or other operational records.
Match the channel to the job
Web chat can fit product-page guidance or help-center questions; voice can handle callers who need spoken support; messaging may suit asynchronous exchanges. The cited cases establish chat and voice examples, but do not establish a single channel as best for every business. Consider language coverage, expected latency, the user’s context, and how a conversation can move to staff without forcing the customer to start again.
Connect only the systems needed to answer or act
A useful agent needs dependable access to relevant knowledge and, where required, current product, order, ticket, or account information. A voice flow intended to progress a support issue may need contact-center and operational integrations; a shopping assistant needs usable catalogue context. Decide whether the agent is only answering, gathering details, or authorized to perform actions, and make that boundary explicit.
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Define which situations require a person, how the customer reaches one, and what context the AI passes along. Complex, sensitive, uncertain, or unresolved cases are natural escalation points. The Salesforce, Microsoft Store, and DoorDash examples all place human workflows alongside automation rather than treating escalation as an exception to be hidden.
Measure the outcome that matches the task
Choose a metric before rollout and define it precisely. Containment or resolution, accuracy, response time, successful task completion, customer satisfaction, and agent time saved are not substitutes for one another. Record the baseline, population, time period, and exclusions so that later results can be interpreted without overstating what changed.
Control quality and operating cost as usage grows
Keep source content current, review failures and escalations, and refine prompts and responses as real interactions reveal gaps. CUADRA’s case shows that using generative AI for too many responses can raise consumption, while selective use and shorter answers were used to control costs. DoorDash’s early test before wider rollout offers another example of validating a flow before expanding it.
Frequently Asked Questions
What is an example of conversational AI in customer service?
A help agent that answers common troubleshooting questions, a voice IVR that handles routine support calls, or a multilingual airline assistant that responds to baggage and booking questions are all examples. Salesforce, DoorDash, and Ryanair describe deployments of these kinds.
How is conversational AI used in sales?
It can guide shoppers through a product catalogue, answer product questions, recommend complementary products, or help sales employees draft emails and organize follow-up. Microsoft Store Assistant and CUADRA illustrate customer-facing guidance; Salesforce’s index describes agents supporting sales staff.
Can conversational AI hand a conversation to a human?
Yes. The cited deployments include human escalation or sales-associate involvement. A useful handoff gives staff the customer’s issue and relevant conversation context, so the customer does not have to repeat the whole request.
Do published case-study results predict what another business will achieve?
No. The examples report different measures for different organizations and tasks, and most figures are published by a vendor or the company operating the deployment. They show reported outcomes in those settings, not guaranteed results or a common comparative ranking.
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