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Conversational AI can help a business answer questions, support sales and marketing, and engage customers—but flexibility alone is not a reason to buy it. Start with one clearly defined customer or staff workflow, then assess whether the system can handle it accurately, hand off difficult cases to a person, and be maintained and monitored over time.
What conversational AI can do for a business
Conversational AI lets people interact with software through natural-language chat or voice. Depending on the system and its connections to business data, it may answer routine questions, guide customers through a task, or help route an inquiry. It is a category of tools, not a guarantee that a bot can resolve every conversation.
Potential uses include customer service, sales, marketing, and customer engagement. The best starting point is a specific, frequent task with a clear measure of success—for example, answering a defined set of questions about opening hours or helping a shopper find information about a product. A broad goal such as “automate customer service” is harder to evaluate and can conceal cases that need human judgment.
Customer service and routine inquiries
A system may handle common questions when the answers are available in reliable, current material. Before using it with customers, determine which questions it is allowed to answer, how it should respond when it is unsure, and how a customer reaches a person. If the bot gives a confident but incorrect answer or makes escalation difficult, it can add friction rather than remove it.
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Sales, marketing, and engagement
Conversational tools can also guide product discovery, collect an inquiry, or support customer engagement. In a 2025 survey by Reimagine Main Street and PayPal, 77% of surveyed active small-business AI users said marketing and customer engagement were areas where new AI solutions could have the greatest impact; 59% said they were willing to automate customer service inquiries. These are respondents’ views and willingness, not evidence that automation improved sales or reduced costs.
What adoption figures do—and do not—show
Reported adoption varies with who was surveyed and what the survey counted as AI use. These figures describe different populations and measures; they should not be read as a single trend line or as proof that a particular tool is right for an individual business.
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| Source and date | Who or what was measured | Reported result |
|---|---|---|
| U.S. Chamber of Commerce, 2025 report | U.S. small businesses self-identifying as using generative AI | 58% in 2025, compared with 40% in 2024 and 23% in 2023 |
| OECD, 2025 | SMEs across surveyed countries; use by a respondent or colleague | 31% reported generative AI in use |
| Gartner, survey conducted July–August 2024 | 187 customer service and support leaders; customer-facing GenAI voicebots | 44% were exploring, 11% piloting, and 5% had deployed one |
| Gartner, 2024 survey | Plans reported by customer service and support leaders for 2025 | 85% planned to explore or pilot a customer-facing conversational GenAI solution in 2025 |
The Gartner plan is a forecast reported in 2024, not a measure of adoption in 2026. The Chamber figure concerns U.S. small businesses and self-identified generative AI use; the OECD figure concerns SMEs across surveyed countries and use by a respondent or colleague. Neither is directly interchangeable with Gartner’s findings about customer-facing voicebots among service leaders.
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Evaluate the work the system must do, not just its conversational polish. A useful assessment asks whether it can resolve the customer’s issue, use dependable information, fit the channels and systems the business already relies on, and transfer the conversation when automation is not appropriate.
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- Task and answer quality: Define the questions or actions in scope. Check answers against realistic customer requests, including ambiguous questions and cases where the correct response is to say it does not know.
- Human handoff: Decide when the system must escalate, what context it should pass along, and how the customer can reach a person without having to start over.
- Channel coverage and integrations: Confirm that the channels customers use—such as web chat, messaging, or voice—are supported, and identify what connections are needed to provide useful answers or complete a task.
- Knowledge upkeep: Assign responsibility for reviewing the material the system uses, correcting errors, and removing outdated information. Gartner reported that 61% of surveyed service and support leaders had a backlog of knowledge articles to edit, and more than one-third lacked a formal process for revising outdated articles.
- Privacy and security: Review how customer information is handled, what data the system receives, and the provider’s applicable data terms and security practices before connecting business systems or exposing the tool to customers.
- Monitoring and operating effort: Establish who reviews conversations, how failures and complaints are recorded, and how often performance and knowledge are checked. Include setup, integration, oversight, and maintenance—not just the initial purchase—in the operating effort.
Gartner’s readiness findings underline why content management matters. Kim Hedlin, a Gartner senior principal in its Customer Service & Support Practice, said: “Service and support leaders are eager to deploy conversational GenAI, but they cannot ignore existing issues with knowledge management.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why customer experience needs to be measured directly
In Twilio’s 2025 survey, 90% of surveyed business leaders believed customers were satisfied with conversational AI experiences, while 59% of surveyed consumers reported satisfaction. This is a perception gap between two respondent groups, not a controlled comparison of the same customers or a measure of one specific product.
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That gap is a reason to assess the experience from the customer’s side rather than rely on internal confidence. Track whether conversations actually resolve the issue, whether customers need to repeat themselves after escalation, and where users abandon the interaction or report a problem. Keep a clear route to human support for cases the system cannot reliably handle.
How to start without overcommitting
- Choose a narrow workflow. Pick a recurring inquiry or task with dependable answers and a clear boundary. Do not begin by giving a bot responsibility for every customer conversation.
- Set the rules and fallback. Specify what the system may answer, what it must not do, how it signals uncertainty, and when it transfers the conversation to a person.
- Prepare the knowledge and connections. Review the source information for accuracy and freshness, assign an owner, and verify any needed channel or business-system integrations.
- Review conversations before widening use. Check answer quality, unresolved requests, handoffs, and customer feedback. Correct underlying information or workflow problems before expanding the system’s scope.
- Compare results with the existing process. Decide in advance what would make the workflow worthwhile for your business, and include ongoing maintenance and oversight in that assessment. Adoption statistics and stated interest do not establish a return on investment.
What the available evidence cannot establish
The cited surveys show interest, reported use, plans, and perceptions within their respective respondent groups. They do not establish that conversational AI produces a positive return for every business, that every business needs a customer-facing bot, or that one vendor performs better than another. Product features, pricing, privacy terms, and geographic availability also depend on the specific provider and should be checked directly before a purchase.
Quick Recap
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