Artificial intelligence is increasingly becoming part of the day-to-day economics of customer operations, prompting contact center leaders to rethink how they evaluate technology investments, according to Ivan Mitov, Partner Account Manager at Voiso.
In a recent industry commentary, Mitov argues that the contact center sector is entering a new phase of AI adoption where the conversation is shifting away from experimentation and toward operational accountability.
“For several years, AI was treated as an innovation initiative,” Mitov said. “Organizations launched pilots, tested use cases, and explored opportunities. Today, AI is becoming part of the operating model itself. It influences productivity, service delivery, quality management, and increasingly, cost structures.”
The shift comes as major technology providers continue expanding AI capabilities across customer experience platforms. Industry vendors are introducing new pricing models tied to AI consumption, automation outcomes, and workflow execution, creating a closer relationship between AI usage and operational performance.
According to Mitov, this development changes how organizations should evaluate return on investment.
“Traditional software purchasing often focused on licenses and features,” he explained. “AI introduces a different dynamic because consumption can vary depending on interaction volume, workflow complexity, and customer demand. The discussion increasingly moves beyond what a platform costs toward what operational capacity it creates.”
This evolving perspective reflects broader changes across the contact center industry. AI technologies are now being used to assist with conversation analysis, quality monitoring, workforce support, workflow automation, customer engagement, and outbound operations. As adoption expands, organizations are being challenged to connect AI activity to measurable business outcomes.
Mitov believes that one of the most important questions leadership teams should ask is not how much AI costs, but what it contributes.
“The real measurement is capacity,” he said. “Does AI help teams handle more interactions? Does it improve quality visibility? Does it reduce repetitive work? Does it provide better insight into customer behavior? Those are the metrics that determine long-term value.”
Examples of AI-driven capacity gains can be found throughout modern customer operations. Automated analytics can significantly expand conversation review coverage beyond traditional manual quality assurance programs. Intelligent routing can improve resource allocation. Automation can reduce administrative workload for agents and supervisors, allowing teams to focus on higher-value customer interactions.
At the same time, Mitov cautions that many organizations still lack the governance structures needed to manage AI effectively.
“Adoption is happening faster than governance in many cases,” he said. “Teams deploy AI capabilities, but ownership, performance measurement, and accountability are often less clearly defined. Without those foundations, it becomes difficult to evaluate success or justify continued investment.”
Industry analysts have increasingly pointed to governance, transparency, and accountability as critical themes in the next phase of AI adoption. As AI becomes more deeply embedded in customer operations, organizations face growing pressure to demonstrate measurable outcomes while maintaining operational oversight.
Mitov argues that successful organizations will increasingly treat AI as part of workforce planning rather than as a separate technology initiative.
“If AI influences workload, productivity, customer experience, or quality management, then it should be part of the same strategic conversations as staffing, performance, and operational planning,” he said. “The organizations that can clearly explain what AI improved, what it cost, and what capacity it created will be in a much stronger position moving forward.”
He also believes that AI should be viewed as a complement to human expertise rather than a replacement for it.
“The goal is not simply to automate interactions,” Mitov noted. “The goal is to remove repetitive work, improve visibility, and help teams focus on the conversations where human judgment and customer relationships matter most.”
As contact centers continue to evolve, the ability to measure, govern, and optimize AI investments is expected to become a key differentiator across the industry.
“The next phase of AI adoption will not be defined by the number of features organizations deploy,” Mitov concluded. “It will be defined by how effectively they connect AI to business outcomes and operational performance.”
About Voiso
Voiso is a global provider of AI-powered contact center software that helps organizations manage customer interactions across voice, messaging, analytics, and omnichannel communication channels. The platform supports businesses across industries including fintech, travel, healthcare, ecommerce, logistics, and business process outsourcing.
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