AI Agents for Sales

AI agents for sales, currently at the Peak of Inflated Expectations, are autonomous or semi-autonomous software entities that leverage advanced AI techniques to perceive, decide, and act within digital sales environments.

Powered by large language models (LLMs), these agents promise to revolutionize sales by autonomously planning and executing tasks, moving beyond the limitations of traditional AI assistants. Sales organizations see these agents as proactive partners capable of independent work and human-like reasoning to drive revenue.

The rapid surge in investment from major platform vendors and stakeholders since 2024 has fueled high hopes for AI agents in sales. However, the anticipated capabilities often outpace current technological realities, placing AI agents for sales firmly at the Peak of Inflated Expectations.

Emotion AI 

Emotion AI, currently in the Trough of Disillusionment phase, leverages AI to assess users’ emotional states through computer vision, voice analysis, sensors, and software logic. It enables personalized responses tailored to a customer’s mood.

However, privacy and government regulations, such as the EU AI Act’s ban on computer vision-based emotion detection in education, remain a major barrier to enterprise adoption.

Initial excitement around emotion AI has given way to a more cautious approach, as organizations encounter significant implementation challenges—explaining its current position in the Trough of Disillusionment.

Digital Twin of a Customer

A digital twin of a customer (DToC), in the Innovation Trigger phase, is a dynamic virtual representation of a customer that organizations use to simulate, emulate, and anticipate customer behavior. DToCs enhance the strategic value of data and analytics by broadening the insights organizations can derive from customer data while helping to mitigate associated risks.

Currently, organizations employ digital twins to monitor product performance and determine optimal actions. With DToCs, they can simulate how customers might respond to specific scenarios, enabling more personalized experiences, stronger customer relationships, and increased revenue.

Despite its transformative potential, DToC technology is still in its infancy, with adoption and real-world applications only beginning to emerge. Its utility depends on customization for specific use cases, highlighting its evolving and immature state. As a result, DToCs remain in the Innovation Trigger phase, with significant growth expected as the technology matures.