The Significance of the Top Challenges Contact Centers Face in 2025

CallMiner’s CX Landscape Report 2024 reported the following findings:

• 87% of global CX leaders believe that implementing generative AI is key to their customer experience and customer service teams.
• 91% believe that AI will help optimize CX strategies.
• 27% of organizations do not know how to quantify the ROI of AI investments.
• 62% of organizations report having at least partially implemented AI technologies.
• 44% believe that the top motivator of AI adoption is the automation of tasks to improve efficiency and agent productivity.
• Only 21% feel that AI implementation is complex, and only 38% express security concerns around AI implementation, which is a significant drop compared to last year.
• 43% believe that AI will unlock employees’ full potential by automating repetitive tasks.
• 48% employ AI-powered real-time guidance during live customer interactions.

CallMiner surveyed contact center and customer experience leaders across the UK, US, Republic of Ireland, South Africa, France, and Germany across retail, technology, financial services, and healthcare verticals.

Globally, this isn’t going to be too different.

Let us try to understand the significance of these findings.

Contextual Significance of These Findings

For many years now, AI has been discussed in broad, visionary terms, often hanging around the edge of hype. Most organizations implemented chatbots, virtual assistants, and automation tools, but very few had a structured roadmap for implementation.

The narrative is completely changing now. Generative AI is no longer an experimental activity but a business imperative. If you are not embedding AI as a part of your customer experience strategies, you risk losing competitive ground.

AI Is Delivering Results!

Consider real-time agent guidance during customer interactions, which 48% of organizations now employ. AI-powered solutions suggest the best responses to agents by analyzing live interactions and customer sentiments.

Let me give you an example of a leading airline.

They implemented AI-driven agent assistance using speech analytics to monitor customer calls in real-time. Whenever passengers expressed frustration due to delayed flights, the AI system prompted agents with empathetic language and tailored compensation offers.

What was the result of this?

• 18% improvement in first-call resolution (FCR)
• 26% drop in escalations to supervisors
• 15% increase in customer satisfaction (CSAT)

What about the ROI roadblock of AI implementations?

The uncertainty of ROI often leads to hesitant executive buy-in and slower adoption cycles. The impact of AI cannot be quantified easily with cost savings; instead, we will have to look at employee experience, customer experience, efficiency improvement, and better business outcomes.

The easiest way to bridge the ROI gap is by defining clear metrics:

• Is AI reducing the need for multiple interactions?
• Are AI-powered engagements improving CSAT and NPS scores?
• Are AI-guided agents handling interactions faster and more accurately?

Align AI with Business Objectives

Should you implement AI for AI’s sake? Ideally no. You should align AI initiatives with business objectives. For instance:

• I want to reduce customer churn
• I want to upsell to my existing customers
• I want to drive self-service adoption

These can be some of the objectives for your AI initiatives, and you can measure them to see if they result in driving these objectives.

One of our banking customers took this approach by implementing an AI-powered virtual assistant for routine transactional inquiries.

Within six months, they benefitted considerably:

• 24% reduction in call volume to human agents
• 35% increase in self-service adoption.
• Higher engagement in digital banking services.

When you look at it, AI is not a plug-and-play solution. It requires continuous learning and refinement. You should start with specific AI use cases, measure, gather insights, and expand based on success.

AI as an Enabler of Human Potential

AI’s true value lies in augmenting human capabilities.

AI-guided support enables agents to focus on complex customer interactions, boosting morale and retention by automating routine transactional queries.

One of our BPO customers deployed an AI-driven knowledge assistant that delivered real-time coaching to agents handling complex support tickets.

What are the results?

• 20% faster resolution times
• 30% reduction in agent onboarding time
• Higher agent satisfaction and lower attrition

Agents can focus on handling nuanced issues, building rapport with customers, and providing high-value support, while AI takes over time-consuming manual processes.

AI Security Concerns – Are They Real?

With careful implementation and deployment of AI-powered customer experience strategies, the concerns around security are dropping significantly.

Some of the common reasons include:

• Stronger AI governance frameworks ensure compliance with GDPR, TCPA, and other data privacy regulations.
• Increased familiarity with AI tools, leading to smoother implementation and integration.
• Cloud-based AI solutions reduce barriers to deployment and make AI adoption more accessible than ever.

The CallMiner CX landscape report clearly points out that AI is already reshaping CX. If you hesitate to adopt CX, you risk being left behind.

A few home truths all of us should be aware of about AI deployments as a part of your CX strategy:

• AI is about enhancing customer relationships and not just about cost-cutting.
• The barriers to AI adoption, like security, complexity, and ROI uncertainty, are diminishing.
• AI is shifting from an experiential phase to a core CX enabler.

How fast can we scale AI to stay ahead? This should be the only question on your mind while considering leading your CX strategy with AI.