Aligning CCM AI Investment – Where Hype Meets Reality

When respondents were asked how much their organisation expects to invest in CCM-related AI technologies over the next 24 months, the average projected spend was £1.4 million.

At first glance, this appears to be a significant commitment. For organisations still in the early stages of exploring AI, the figure may seem surprisingly high, particularly given that many projects remain at the proof-of-concept or planning phase.

However, viewed within the wider context of global AI investment, the number becomes far more credible.

Worldwide spending on artificial intelligence continues to accelerate at an extraordinary pace. Industry analysts estimate global AI expenditure will reach hundreds of billions of dollars annually over the next two years, with particularly strong growth in customer-facing technologies. Customer service applications - including virtual assistants, conversational AI, intelligent routing, sentiment analysis and automated communications - are among the fastest-growing segments of the AI market.

Much of this activity sits firmly within the wider CCM landscape.

For larger enterprises, particularly those operating in highly regulated sectors, a £1.4 million investment is relatively modest. Many organisations have already established dedicated AI teams, commissioned consultancy support, developed governance frameworks and begun integrating AI into wider digital transformation programmes. In many cases, those activities alone will exceed the average investment identified in our research before technology implementation costs are even considered.

The headline figure therefore reflects something important - organisations are no longer viewing AI as an experimental technology. Increasingly, it is becoming a strategic investment area that demands budget, executive sponsorship and long-term planning.

The bigger question is where to invest

If investment levels are beginning to crystallise, deciding where AI should be deployed remains considerably less certain.

The technology landscape is evolving rapidly, with almost every aspect of customer communications presenting a potential opportunity. Industry forecasts continue to paint an ambitious future. Gartner, for example, predicts that agentic AI - systems capable of independently making decisions and completing tasks - will resolve the majority of routine customer service interactions before the end of the decade, significantly reducing operational costs.

Whether those forecasts materialise on the predicted timescales remains to be seen.

Our own findings suggest organisations are taking a far more balanced approach.

When respondents were asked which CCM-related AI capabilities they plan to implement over the next two years or beyond, no single application emerged as a runaway priority.

Instead, planned adoption was spread remarkably evenly across eight different capabilities:

• Analysing past customer conversations to improve future communications (62%)
• Managing consent and compliance automatically (60%)
• Detecting fraud or suspicious communications (59%)
• Optimising send times and communication channels (59%)
• Personalising customer messages at scale (51%)
• Analysing customer sentiment and feedback (51%)
• Automating responses through chatbots or virtual assistants (49%)
• Generating customer communications and content (48%)

This relatively narrow range - from 48% to 62% - is one of the most revealing findings in the research.

Rather than indicating a clear consensus around where AI creates the greatest value, it suggests many organisations are still evaluating the possibilities. Few appear ready to place significant bets on any single capability, reflecting the fact that AI strategies remain under active development.

In many respects, organisations are still learning where AI fits best within their existing customer communication processes.

Governance is shaping adoption

The strongest areas of intended adoption also reveal something important about organisational priorities.

The three highest-ranked use cases all focus on operational intelligence, compliance and risk management rather than customer-facing automation.

Analysing previous customer interactions, automating consent management and detecting fraudulent activity all represent applications where AI can improve efficiency while supporting governance objectives.

This is perhaps unsurprising. Regulated organisations operate under increasing scrutiny regarding data usage, customer treatment and communication practices. Introducing generative AI directly into customer-facing communications naturally raises additional questions around explainability, oversight, accuracy and accountability.

By comparison, applications such as automated content generation and chatbot deployment ranked slightly lower. This should not be interpreted as a lack of interest. Rather, organisations appear to be recognising that customer-facing AI carries greater reputational and regulatory risk. Before allowing AI to generate personalised communications or interact autonomously with customers, many businesses understandably want stronger governance, clearer controls and greater confidence in the technology.

In other words, organisations appear to be prioritising AI that supports human decision-making before AI that replaces it.

Enthusiasm remains universal

Perhaps the clearest message from the research is that AI is no longer considered optional. Every respondent indicated that their organisation intends to invest in AI within the next two years. That level of consensus is striking.

Only a few years ago, many organisations were questioning whether AI was relevant to customer communications. Today, the discussion has shifted entirely. The conversation is no longer about whether to invest, but how, where and at what pace. The uncertainty lies not in the destination, but in choosing the right route.

Turning ambition into measurable value

The findings ultimately suggest that organisations are approaching AI with cautious optimism.

The appetite to invest is strong, budgets are beginning to emerge, and leaders clearly recognise AI's transformative potential. At the same time, uncertainty around governance, regulation, skills and implementation means many organisations are deliberately avoiding large-scale deployment before demonstrating tangible business value.

For many, the most effective strategy will be to adopt a structured "test, learn and scale" approach.

Rather than attempting wholesale transformation, organisations can begin with carefully selected, lower-risk initiatives that deliver measurable outcomes. Applications such as communication optimisation, sentiment analysis, compliance monitoring or intelligent content recommendations offer opportunities to demonstrate value while operating within established governance frameworks.

Successful pilot programmes create more than immediate operational benefits – they build organisational confidence, strengthen internal capability and provide the evidence needed to support broader investment decisions.

The organisations that succeed will not necessarily be those spending the most on AI. They will be those that align investment with clear business objectives, implement appropriate governance from the outset, and scale adoption only when the technology has proven its value.