Businesses are exploring the immense revenue potential of Conversational AI Agents, as these exciting new technologies quickly become key growth streams.
By 2026, more than 50% of enterprise applications will be conversational. That is a massive leap from less than 5% today, yet unsurprising considering how many businesses see conversational AI as a key AI technology market.
Thanks to custom language models geared to specific tasks and industries, conversational AI Agents can now complete high-value, domain-specific tasks normally performed by people cost-effectively and intelligently.
According to Gartner, the conversational AI market, driven by the growth in advanced AI Agents, Internet of Things (IoT) device integrations, and Agentic AI contact center solutions, is expected to grow to $36 billion by 2032.
What are these technologies, and how does Gartner see each advancing the growth of the conversational Ai Agent market?
According to Gartner, Advanced VAs comprise the biggest current and projected market opportunity ($27.5 billion by 2032) with a 26% CAGR. Advanced purpose-built industry and use-case-specific virtual assistants can now support business customers and staff in a wide variety of tasks through process and workflow automation.
Virtual assistants use natural language processing, prediction models, recommendations and personalization engines that interact with people via voice, image, gestures, maps, tables, audio, video, or text (i.e., multi-modal) to complete tasks normally completed by skilled people.
AVAs use task automation (Robotic Process Automation) and workflow augmentation to complete actions that deliver operational efficiency, employee productivity, customer satisfaction, and cost reduction. For example, AVAs can take customer data, conduct KYC (Know Your Customer) checks and then update compliance, sales, and financial systems in real time without the need for a person to be involved.
As AVAs interact with enterprise applications and data, they can learn from user behavior and build more sophisticated data models. Further fine-tuning using machine learning helps AVAs recognize semantics, context, and intent in domain-specific areas such as healthcare, legal, or manufacturing, helping them answer questions that previously would have had to be answered by a medical professional, lawyer, or engineer. As a result, users receive more intelligent, personalized conversations and information relevant to their questions, further building confidence, engagement, and utilization of advanced virtual assistants.
For example, virtual legal assistants can assist with case management by helping draft contracts, retrieving legal information, and providing recommendations on legal arguments based on court case history. Virtual clinician assistants can summarize and translate patient conversations in real time, offer diagnosis assessments, predict future health issues, recommend prescriptions, conduct medical record searches, and schedule follow-up appointments with relevant doctors.
IoT device integrations represent the second-largest conversational AI growth market, with a 26% CAGR and $982 million in projected revenue by 2025. IoT uses internet and network-connected devices and applications to automate, optimize, and manage smart home devices and industrial processes in manufacturing, retail, transportation, healthcare and the utilities industry.
Internet of Things (IoT) device integration refers to integration capabilities that allow users to onboard, manage and interact with a wide variety of regular IoT devices with conversational AI Agent capabilities. For example, IoT devices in the home might include smart speakers or smart home devices (such as microwaves and ovens).
In industry, the richer market opportunity for IoT devices might include agricultural sensor systems, smart irrigation devices, vending machines or medical equipment. Advanced IoT device integration capabilities allow scheduling, automating, and orchestrating data flows across IoT architecture and organizations, converting them into actionable insights for individual households and industrial companies.
By integrating conversational AI techniques into IoT interfaces, information and data can be exchanged more easily using voice commands, enabling workers to complete a range of tasks. So, rather than having to access keyboards and type, workers can use speech to accomplish tasks as they complete their duties.
Gartner suggests that 2026 advancements in Agentic AI will automate up to 70% of call center agent tasks. This is a significant rise from 30% in 2023. Others, such as Grand View Research, are similarly predicting the global conversational AI Agent market to grow rapidly, i.e., by an anticipated compound annual growth rate (CAGR) of 23.6% from 2023 to 2030.
These are exciting numbers, and businesses are seeing the potential for generative AI-infused conversational AI Agents to significantly transform customer and employee experiences, supporting new levels of business growth and cost efficiency.
How?
By facilitating more human-like AI Agent relationships, businesses can resolve more customer queries digitally, collect more relevant information to share with call center agents, and help call center agents to deliver better, cheaper, and faster customer service.
Whilst the conversational AI market growth is exciting and bodes well for businesses in this space, this technology should not just be implemented with impunity – there are risks. For example, the black-box nature of LLMs raises issues with model transparency, monitoring and compliance. In addition, as more and more countries introduce AI legislation, conversational AI businesses need to pay closer attention to what LLMs produce.
A lack of model explainability will inhibit their scalability, so conversational AI businesses must understand their models and outputs.
While conversational AI, thanks to generative AI, is developing human-like capabilities, it should not replace human decision-making, such as approving or rejecting health insurance applications or providing business advice, particularly without human supervision. These are key moments of truth that need human oversight, judgement, and decision-making. Humans must detect more nuanced tones in customer conversations and proactively resolve issues or reveal significant cross-sale opportunities.
The emergence of generative AI and Agentic AI is revolutionizing how businesses interact with technology, offering unprecedented opportunities for automation, personalization, and proactive intelligence.
By harnessing the power of conversational AI Agents, businesses can achieve remarkable efficiency and cost-effectiveness. The question now is not if but when your business will begin using this technology.
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