Voice AI is likely to become a common option inside many apps by 2028, especially in customer service—not a feature that literally every app will have. Gartner’s forecasts point to a shift toward embedded assistants and software agents that can work across apps, while its customer-service survey shows companies moving from exploration to pilots and deployment. The change is less about adding a microphone button everywhere and more about making speech one way to ask software for help or get a task done.
What “every app” is likely to mean
There is no authoritative forecast here that counts all apps worldwide or says every developer will add voice. Gartner’s figures concern Fortune 500 companies, enterprise software, customer-service journeys and business applications—not the entire universe of consumer apps. The defensible reading of the headline is that voice will become an available interaction layer across many major software categories.
That layer may be built into an app, supplied by a shared AI service, or reached through an assistant on a phone. In some cases, the user will speak directly to a product’s assistant. In others, an agent may handle a request across several services without requiring the user to open each app and navigate its screens.
Voice also does not have to replace touch, typing or visual interfaces. Speaking can be useful when a user is occupied, needs a quick answer, or wants to describe a problem naturally; screens remain important for reviewing details, making choices and confirming consequential actions.
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Why 2028 is a plausible turning point
Gartner’s forecasts describe two connected shifts: AI agents becoming part of business software, and some user experiences moving away from app-by-app navigation. They are forecasts, not guarantees, but together they help explain why voice could spread as one way to interact with those agents.
| Forecast | What Gartner said | What it suggests for users |
|---|---|---|
| Task-specific agents | In 2025, Gartner predicted that 40% of enterprise applications would include task-specific agents by the end of 2026, up from less than 5%. | More apps may be able to carry out bounded tasks rather than only present information or forms. |
| Agentic front ends | In 2025, Gartner estimated that one-third of user experiences would shift from native applications to agentic front ends by 2028. | Some requests may be handled through an assistant coordinating work across apps, rather than through separate app interfaces. |
| Multimodal service | In 2024, Gartner predicted that 30% of Fortune 500 companies would offer service through a single AI-enabled channel supporting text, image and sound by 2028. | Customer service may combine spoken conversation with text or images in one interaction. |
| Third-party assistants | In 2024, Gartner predicted that 70% of customer-service journeys would begin and end in third-party conversational assistants built into mobile devices by 2028. | Some customers may get service without opening the company’s app themselves. |
These forecasts concern different things: the first is about enterprise applications, the second about user experiences, and the last two about customer service. They should not be added together or treated as a measured adoption rate for all apps.
Which apps are most likely to add voice first?
Customer support is the clearest early use
Support is the best-documented starting point because companies can use it for recurring questions and track whether an interaction solved the customer’s problem. Gartner reported in 2024 that 85% of customer-service leaders planned to explore or pilot conversational generative AI in 2025. In the same survey, 44% said they were exploring a customer-facing GenAI voicebot, 11% were piloting one, and 5% had deployed one. Those are reported plans and adoption stages among surveyed leaders, not percentages of all businesses or completed deployments in 2025.
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The spread between exploration and deployment matters: interest does not mean a reliable, widely available service. Gartner analyst Patrick Quinlan said voice-based customer service would evolve as generative AI matures, rather than disappear. That points to a likely path from limited, supervised support use toward more capable service conversations—not an overnight switch to fully autonomous call handling.
Other plausible candidates
Product search, scheduling, sales qualification, field service, healthcare navigation and internal enterprise workflows are reasonable places to look for voice features. These are potential applications, not categories whose adoption rates are established by the cited forecasts. Their fit depends on whether speaking is easier than the existing interface and whether the agent can safely access the right information and systems.
What makes an in-app voice assistant useful
A voice feature is only as useful as its ability to understand the user, respond without awkward delay and complete the task correctly. A conversational model alone is not enough when a request depends on a company’s current policies, product details, account information or operational systems.
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Ground answers in current business information
Gartner has said most large language models are not highly effective on their own at solving specific business challenges. Its 2025 forecast said 80% of GenAI business applications would be developed on existing data-management platforms by 2028, and that this approach could reduce the complexity and time required to deliver GenAI applications by 50%. Gartner’s rationale includes retrieval-augmented generation (RAG), vector search, metadata and governance: techniques and controls that help an application retrieve relevant business information rather than rely only on a model’s general training.
For voice agents, this has practical consequences. Answers need to reflect the right account, product or policy; actions need to be authorized; and teams need to understand what information the system used. Metadata, chunking, runtime context, privacy controls and traceability are part of making that possible, not optional polish.
Keep the underlying knowledge maintained
Gartner also reported that 61% of service leaders had a backlog of knowledge articles to edit and more than one-third lacked a formal process for revising outdated articles. A fluent voice assistant can still give a wrong answer if the source material is stale or contradictory. A business adopting voice support therefore needs ownership and a regular process for reviewing the information its agent retrieves.
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Make interaction and operations reliable
Teams evaluating a voice system should assess latency and interruption handling, speech recognition and voice quality, tool calling and workflow integration, grounding in proprietary data, privacy and security controls, fraud protection, supported regions and languages, observability and evaluation, pricing and usage limits, and portability across model providers. These are decision criteria, not a claim that one implementation performs best on all of them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What current deployments show—and what they do not
OpenAI’s 2025 report described more than 7 million ChatGPT workplace seats and approximately ninefold year-over-year growth in ChatGPT Enterprise seats. Those figures indicate growth in workplace use of AI products; they do not measure how many apps have voice features.
The same report described Intercom’s Fin Voice, which uses OpenAI’s Realtime API. In that vendor case study, Intercom reported 48% lower latency since March and an average of 53% of calls resolved end to end. These are results for that implementation as reported by OpenAI, not an independent benchmark or a prediction that other voice agents will achieve the same results. The example does show how an API can put an assistant directly into a company’s customer-support experience.
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What app teams need to build voice into a product
For a product team, the useful starting question is not “How do we add a voice button?” but “Which user task is easier or better when spoken, and what must the system know and be allowed to do?” A narrow, measurable workflow is usually a more credible starting point than a general-purpose assistant.
- Choose a bounded task. Identify a frequent user need with a clear success condition, such as answering a known class of support questions or helping users complete a defined workflow.
- Connect approved information and actions. Decide which business data the agent may retrieve, which systems it may call, and which actions require confirmation or a human handoff.
- Prepare and govern the knowledge. Review source material for accuracy and ownership; plan retrieval, metadata, privacy controls and traceability so the agent can use relevant context.
- Design for speech and recovery. Evaluate recognition, response timing, interruptions, unclear requests and the transition to text or a human. Make it possible to correct an error or stop an action.
- Measure task outcomes. Track whether requests are resolved, escalated or abandoned, as well as answer quality, latency and failures. Define how the team will review problems and update the system.
- Check deployment constraints. Confirm supported languages and regions, security and fraud controls, usage limits, operating costs and how difficult it would be to move to another model provider.
Gartner also predicted in 2024 that 75% of enterprise software engineers would use AI code assistants by 2028, up from less than 10% in early 2023. That is a forecast about software development, not voice-AI adoption; it is relevant as a separate sign that AI may become more embedded in how enterprise software is built.
What users should expect by 2028
Expect more apps and services to offer spoken interactions, especially where businesses can connect a conversational agent to a well-defined support task and measure the result. Also expect voice to coexist with text and screens, and some interactions to happen through device-level or cross-app assistants rather than a voice feature unique to every app.
Gartner analyst Anushree Verma described future agent ecosystems in which specialized agents collaborate across applications and business functions so users can pursue goals without interacting with each app individually. That is a forecast of a possible direction, not proof that a seamless ecosystem will be in place by 2028. The practical transition will depend on integration, trustworthy data, user controls and whether the experience is more useful than existing ways of getting things done.
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