AI is accelerating CX for tech companies. So why are manual processes still rising?

Tech companies are under intense pressure right now. Renewals are harder to secure, users want instant answers, and AI-native competitors are moving fast. Customer experience (CX) and customer success teams are expected to respond to this demand while managing higher volumes of complex interactions — all under tighter efficiency mandates.

The latest CallMiner CX Landscape Report shows a clear paradox emerging across the tech sector. AI adoption is surging, and leaders overwhelmingly see it as essential to improving both CX and employee performance. Yet many tech organizations still rely on manual analysis, survey-heavy customer feedback programs, and disconnected systems that make it difficult to act on insights in real time.

Whitepaper

Tech Leads in AI Adoption, Lags in Turning CX Data into Action

Tech sector leads in AI investment, yet many still rely on manual analysis, solicited feedback, and disconnected systems

Learn more

It’s a gap between aspiration and reality, which tech leaders are working quickly to close.

AI adoption is up, but readiness hasn’t fully caught up

Tech organizations are embracing AI at a remarkable pace. Eighty-one percent of tech organizations say AI is at least partially implemented, up from 58% in 2024. Nearly all agree it will be a key strategy for their CX and customer success teams in the year ahead. Many also believe AI will be central to unlocking the full potential of their employees.

But enthusiasm is only part of the story. Seven in ten tech CX leaders admit they’re deploying AI without the governance structures needed to manage risk. This creates uncertainty around data quality, accuracy, and compliance, especially in customer-facing scenarios.

In other words, leaders clearly want AI to accelerate CX transformation. They just need better foundations to support it.

Manual CX data analysis is still the norm

Even with strong AI investment, many tech organizations continue to lean heavily on manual analysis. The report shows that manual CX data analysis has increased 17% since last year, while automated analysis has also gone down by 17% year-over-year.

This shift slows down:

• Real-time agent assistance
• Predictive churn analysis
• Identification of product issues
• Operational decisions that depend on fresh customer insight

This year’s findings show that tech companies often have powerful tools available for CX data analysis, but struggle with adoption, integration, or alignment of data across teams.

Survey-heavy feedback programs are creating blind spots

Another consistent theme: most tech companies say the majority of their customer feedback is still solicited — think traditional NPS, CSAT, and structured surveys. Only a small share say unsolicited signals make up most of what they review.

Here’s the issue: Unsolicited feedback from calls, chats, tickets, reviews, and social media or community discussions often reveals friction long before surveys do. When organizations look primarily at solicited feedback, they may miss these signals altogether.

The upside is that teams see the opportunity to improve the quality of their solicited feedback. Many leaders say personalized survey questions, behavior-based triggers, and smarter timing would improve their solicited feedback quality, especially when paired with rich unsolicited data.

Where tech teams are turning to AI today

Despite the challenges, many tech companies are already using AI in practical ways that make work easier for both customers and employees. The most common use cases include:

• Helping customers resolve issues independently through self-service
• Improving agent training and coaching
• Personalizing and contextualizing customer outreach

These applications show that AI is being used to help employees work smarter and support customers more effectively. And confidence in AI is rising. Roughly nine in ten leaders say they can already measure ROI from their AI investments.

Governance and risk management need more focus

While most organizations say they have some form of AI governance, it’s often not fully developed. Less than half say their governance structures oversee AI accountability and risk management.

Tech leaders express several concerns over AI’s risks, including:

• Siloed customer data that limits AI’s effectiveness
• Security and compliance risks
• Uncertainty about whether AI can capture nuance and emotion in customer interactions

As AI becomes more deeply embedded in CX operations, these gaps will matter more — especially for regulated industries or companies supporting sensitive user data.

Employee experience is emerging as a CX multiplier

Leaders overwhelmingly agree that employee experience (EX) and CX are deeply connected. When agents have the right tools and coaching, customers feel the impact.

Most tech organizations are already investing in:

• Real-time agent guidance
• Group training and retraining
• Employee well-being programs

But opportunities remain. Only a minority offer personalized 1:1 coaching or use AI to support unbiased performance scoring. These are areas where AI can dramatically reduce manual workload and help managers scale individualized support. This is one of the biggest areas of potential growth in the coming year.

The path forward: Turning insight into action

Tech leaders face the challenge of accelerating efficiency while maintaining customer trust and product quality. But across the industry, a clear roadmap is emerging. Organizations making the most progress are those that:

• Automate CX insight analysis with conversation intelligence
• Strengthen frontline coaching with AI-powered scoring and real-time guidance
• Combine solicited and unsolicited feedback for a complete voice-of-customer view
• Make solicited feedback smarter with personalized and data-driven customer outreach
• Share customer insights across support, success, product, and revenue teams

AI gives tech companies a way to work faster and smarter, but only when paired with the right processes, governance, and data foundations. Teams that strike this balance will be better positioned to respond to customer needs, empower their employees, and turn their CX insights into meaningful business value.