AI-driven automation is rapidly approaching
In the last few years, the technology sector has bombarded business leaders with wave after wave of AI-driven advances, from large language models (LLMs) and generative AI to emerging agentic AI. Each new innovation comes with big promises of advanced capabilities and ROI, continually pushing the boundaries of what it means for a system to be “intelligent” or “autonomous.” Advancing along these spectrums toward a future of AI-driven business automation is a key theme for agentic AI. Unlike standalone LLMs or rules-based software/hardware systems, AI agents have advanced features in planning/reasoning out a sequence of events to achieve specific goals; perception of context including visual, auditory, textual, and/or other sensory data; tool usage such as code execution, search, and computation to complete tasks, often accessed through function calling; and memory of past events (tool usage and perception) and past behaviors (tool usage and planning), used by the agent to inform and improve future actions. AI agents also tend to perform these actions autonomously, rather than cooperatively with the user.
While these technologies show great potential for various industries, business units, and use cases, CX is an area where advanced AI is already showing adoption and value. Conversational interfaces are becoming mainstream, including those supported by agentic and generative AI. IDC expects increased adoption in both voice and text, particularly in the world of CX, thanks to newer generative and agentic AI capabilities that enable the automation of complex workflows. With features including personalized customer self-service and search that provide differentiated experiences, insights and recommendations that improve customer interactions, and customized marketing campaigns and offers that drive new and increased revenue, these technologies not only improve existing processes but also promise new ways of working, interacting, improving customer satisfaction scores (CSAT), and driving new revenue growth.
The challenge for CX leaders: Maximizing AI investments
With all of these fast-paced advances, corporate leaders need to understand what each new technology—and new terms such as “AI agent” and “agentic AI”—means in terms of real-world business outcomes. Such leaders simultaneously face pressure to rapidly onboard and show ROI from AI investments while justifying and rationalizing spending in a time of economic uncertainty.
FIGURE 1: Conflicting priorities?
The top factors driving expected spending increases for 2025 are preparing for greater use of AI in the business (37.1%) and reducing IT operating costs (37.3%)
n= 863; Source: IDC’s Future Enterprise Resiliency & Spending Survey, Wave 11, December 2024
With agentic AI, many organizations consider themselves in the early stages and may believe that they need to start from scratch. In December 2024, the business leaders IDC surveyed indicated that they were at significantly different stages of evaluating and using agentic AI versus generative AI.
FIGURE 2: Status of Evaluating/Using Generative AI Versus Agentic AI
n= 863; Source: IDC’s Future Enterprise Resiliency & Spending Survey, Wave 11, December 2024
However, this is not necessarily the case. Understanding the value of different forms of AI, and where organizations can best apply them to scale these technologies across the business, can help leaders ride the crest of this new AI wave. In many cases, strong ROI comes from combining generative and agentic AI with existing investments in other forms of AI (e.g., deterministic AI, predictive AI, and AI recommendations) that have proven their efficacy.
Successfully moving to enterprise-scale AI
To truly maximize AI investments, there is a need for a centralized approach that scales AI across the enterprise. IDC’s discussions with early adopters of advanced AI, including generative and agentic AI, have highlighted the importance of choosing the right use cases to start with—those that are feasible, responsible, valuable, and expandable for the organization in question. Examples include:
- Providing AI customer support agents that can triage inbound customer calls, create or change purchase orders and support tickets, offer troubleshooting, escalate complex issues, and provide live in-call recommendations and insights to human agents, enhancing call centers’ capabilities, productivity, and CSAT/resolution scores
- Creating omnichannel customer journeys in a fraction of the usual time, allowing CX organizations to rapidly respond to, meet, and exceed modern customer expectations
- Offering personalized, differentiated shopper interactions that increase website and marketing conversion while increasing brand loyalty
AI advances are moving rapidly, and the pace of innovation is not slowing. Businesses should look for vendors that will help move their organization toward the future of agentic business automation with support for key areas such as:
- Multimodel AI—The ability to leverage multiple models will help build on existing investments to achieve richer, more accurate outcomes in CX and personalization. Other multimodel features allow organizations to leverage the right model for the job, including the optimization of different generative AI or agentic AI models across various use cases.
- Multimodal AI—AI capabilities for analyzing and deriving insights from modalities such as speech, text, image, and video enables organizations to gain value from customer data across different interaction channels, combining them into rich and actionable insights and increasing overall accuracy.
- Multiagent AI—Such systems for agent orchestration and other capabilities support a future stage of agents working together across different applications.
Generative AI can be leveraged to create hyper-personalized experiences for customers, such as personalized marketing content, recommendations, virtual assistants, and tailored in-game experiences. To learn more about maximizing existing investments in AI for CX, be sure to visit these partner collections: Presidio, RapidScale, and Uturn Data Solutions.