Boosting CX Worker Productivity in the AI Age

Today’s frontline customer experience workers face a confluence of forces that will dramatically reshape business models, impact employee experience, transform customer experience, and affect stakeholder satisfaction.

An aging population, a war for talent, and a shift to mass automation will forever transform the market for physical labor. Meanwhile, artificial intelligence (AI)-native vendors such as Cursor, Lovable.AI, and Devin are already delivering “digital labor” and agentic AI solutions that come close to human throughput on routine engineering tasks at an exponential scale. This same approach will impact customer experiences and employee experiences at a machine scale never seen before and play a decisive role in frontline worker productivity.

This once-in-a-generation opportunity to blend autonomous digital labor with human experiences will satisfy unmet demand while preserving accountability, client trust, and profitability. Success will require a level of contextual relevancy that makes experiences feel anticipatory.

Companies will need agents that span multiple departments and traverse multiple business processes. Constellation believes that early adopters and mature agentic AI enterprises eventually will prioritize platform-agnostic agents and partner with services firms and agentic factories that can help them design, build, govern, orchestrate, refine, and retire agents.

Legacy Firms Caught Between Two S-Curves

As the renowned Harvard business professor Clayton Christensen pointed out in 1992, the technology S-curve framework describes how organizations and industries substitute new technologies for old technologies. His report “Explaining the Attacker’s Advantage” details how an architectural innovation (such as agentic AI) could upend legacy players.

This quote from Christensen could describe the scenario faced by companies that are not AI-native: “I show that it is in architectural, rather than component, innovation that entrant firms exhibit an attacker’s advantage.…”

As the attackers, AI natives and AI exponentials have the advantage via agentic AI that will upend legacy software firms, technology services providers, and industries that fail to make the required architectural shifts. Legacy software-as-a-service (SaaS) AI lacks contextual relevancy; in fact, most SaaS AI systems cannot deliver on advanced AI capabilities such as AI-led troubleshooting, hyper-care, and resource optimization due to a lack of context. In the customer support world, systems that support frontline worker productivity must have access to customer data, configuration settings, and standard operating procedure (SOP) documents. Access to internal intellectual property, best practices, and the organization’s business and knowledge graphs is essential to delivering relevancy on the front lines for AI natives and AI exponentials.

By delivering deeper and more accurate decisions in-network, these AI-native firms achieve decision automation, transforming experiences from zero latency to anticipatory intent. The ability to achieve automation, decision velocity, and compliance breaks the paradox of choosing only two out of three when it comes to faster, better, and cheaper for new product offerings. To deliver the decision automation and decision intelligence required for an AI world, organizations seek a platform that both enables AI to make deeper and more accurate predictions and automates a wider array of processes. 

In the past, organizations had to build these capabilities themselves or hope that a solution could be configured to requirements. New solutions that have AI deployed in-network will enable AI to access contextually relevant signals as needed. In addition, these platforms will provide a stronger collaboration model to work with internal AI development efforts to supplement gaps, instead of the rip-and-replace model normally encountered when bringing in a legacy SaaS provider.

The Bottom Line: Frontline Workers Gain Importance in the AI Age

Robotic process automation (RPA) and a lot of early AI was about automating back-office processes and eliminating back-office workers, who had little impact on the customer experience. Frontline workers have a very strategic role. These individuals are the voice and face of the organization across the entire customer journey. Frontline worker productivity focuses on empowering and elevating these workers to address escalations, exceptions, and strategic relations. 

Allowing AI to have the full context of the customer will help move AI for frontline workers from solving problems to anticipating them. Thus, Constellation expects that capabilities such as automatic assignment based on technical depth and agent capability, ambient information gathering, and pattern recognition of tribal knowledge will become essential tools for frontline worker productivity.

R “Ray” Wang is the author of Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants (HarperCollins Leadership) and founder of Constellation Research.

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