AI has entered the CX mainstream, but organizations are struggling to turn bold investments into measurable value. Despite widespread deployment, operational challenges and fragmented governance continue to undermine ROI.
CallMiner's 2025 CX Landscape Report, released on Sept. 16, highlights a persistent disconnect between AI ambition and execution. It shows organizations are investing heavily in AI infrastructure. However, those investments do not always translate into measurable value or outcomes.
The annual report surveyed leaders across health care, financial services, technology, retail, and business process outsourcing industries. Nearly a quarter of organizations identified the failure to prove ROI from tech investments as a key CX challenge, while others cited difficulty turning insights into meaningful action.
It also captures perspectives from senior CX and contact center decision-makers on AI implementation, highlighting key trends, concerns, and the ongoing struggle to align data, technology, and outcomes.
Two primary findings this year suggest business leaders need to reassess their goals and strategies for CX. One is that 62% of leaders admit they seldom use CX data to their best advantage, up from 59% in 2024. The second is that 98% report difficulties aligning CX data and feedback across departments.
"The leaders of tomorrow will be those who bridge this gap by connecting AI investments directly to CX improvements, ultimately turning insights into enterprise-wide action,” said Jeff Gallino, CEO and founder of CallMiner.
Managing AI’s Strategic Tradeoffs
According to the survey, 96% of CX organizations view AI as a key strategy, but governance structures lag behind implementation. Organizations are scaling AI deployment while still grappling with organizational challenges, such as analyzing data and acting on insights.
AI is widely implemented across CX initiatives, yet organizations still struggle to align its strategic promise with the realities of deployment. The report found that, even with growing investment, many teams remain hindered by inefficient execution. In fact, 42% still rely on manual processes to analyze CX data.
Further, organizations face critical challenges when aligning on CX data. These include a lack of effective communication between departments (48%), a lack of understanding of how to analyze data (47%), and a lack of clarity on how to act on data insights (45%).
Gallino cautioned that without clear communication and the ability to interpret what the data is really saying, technology alone falls short. AI adopters must focus on purpose-fit technology and well-designed processes to transform simple data collection into meaningful action.
"The organizations effectively using AI are those that understand the right places to automate and the right places to augment," said Gallino. "Success comes from finding the sweet spot where technology handles routine tasks, while providing support during human interactions that elevate the customer experience.”
Governance Gaps Undermine CX Trust
Companies must ensure their governance teams are, at a bare minimum, cross-functional, according to CallMiner’s Chief Marketing Officer Eric Williamson. The immediate risk is trust — internally, when employees don’t believe in the systems they use, and externally, when customers encounter inaccurate or unhelpful experiences.
"No single department should dictate investment or use cases," he told CRM Buyer.
An immediate initiative for those teams should be to align on their organizational goals for AI investments and what ROI or outcomes they are trying to achieve, he noted. A collaborative, strategic framework ensures alignment on objectives, risk management, and customer impact.
Automate CX Data to Unlock Value
These manual practices limit visibility into emerging trends and prevent teams from acting on CX insights in real time, which is why leveraging conversation intelligence and AI is critical to eliminating this bottleneck, Williamson noted.
Automated quality assurance (QA) is a proven entry point that delivers immediate ROI, he continued. Traditionally, most organizations can only manually review three to 10 interactions per agent each month. That leaves the majority of conversations unanalyzed, limiting the ability to spot trends or coach effectively.
"Start small, with high-impact use cases, rather than trying to overhaul everything at once," Williamson recommended.
Automating QA with AI enables organizations to analyze 100% of omnichannel conversations, providing them with better visibility into compliance, agent performance, customer sentiment, and more at scale.
"This enables faster feedback and more accurate benchmarks. Quick-win deployments like this prove AI’s value, while building a foundation for enterprise-wide, data-driven CX strategies," he explained.
Turning CX Data Into Action
Businesses must align on how CX data will be applied — clarifying which goals it supports and how insights will drive decisions across departments. To turn data into action, leaders must be aligned on how to utilize that data, according to Williamson.
This challenge is a significant barrier. The goal is for teams to agree on how data will be utilized, what goals it supports, and how insights will be implemented.
"When CX data simultaneously informs customer service, marketing, product, and operations, it shifts from passive collection to the engine of meaningful business action," he said.
As organizations mature in data strategy, the call center becomes the proving ground for AI’s real impact on CX performance.
AI’s Role in Call Center Performance
The common belief is that AI will unlock employee potential and drive operational efficiency in the call center. That is achieved when AI successfully automates routine tasks while also elevating the human agent's performance in complex interactions, according to Williamson.
He offered the process of AI contact summarization as an ideal example. By automatically generating accurate, standardized interaction summaries (either post-interaction or in real-time), AI reduces administrative and after-call burdens.
This approach gives agents more time to focus on higher-value tasks. AI summaries also eliminate inconsistency, so organizations have reliable records for compliance and coaching.
"Beyond routine tasks, AI can support agents in other ways, such as delivering real-time guidance during live interactions. Agents receive context-sensitive prompts or support as conversations unfold, ensuring they can confidently address complex or emotional customer needs," he explained.
Balancing AI ROI and Customer Trust
Many organizations see the failure to prove ROI from tech investments as a CX challenge. The key is balancing efficiency, effectiveness, and loyalty.
Beyond straight cost savings, organizations should also measure whether AI improves customer and agent outcomes, such as first-call resolution, churn reduction, and customer satisfaction. In contact centers, connecting AI to higher resolution rates or fewer repeat calls shows direct value.
"Ultimately, the clearest ROI is when AI both saves money and strengthens the customer relationship," Williamson said.
The next CX challenge will be customer acceptance of AI. Williamson sees that shift coming as early as next year. AI is advancing faster than customer comfort levels, putting pressure on organizations to balance innovation with trust.
"Some customers remain skeptical or resistant, especially in critical interactions. Even with effective governance and ROI, AI investments will miss the mark if customers don’t want AI to handle their most sensitive, complex issues," he warned.
He sees customer acceptance growing, but only if CX leaders ensure that implementations align with brand values and customer expectations.
"The CX leaders of the future won’t just be those who deploy AI successfully, but those who earn customer trust in the process," he concluded.
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