Chapter 3

The Road Ahead – Investment, Measurement, Trust, and Strategic Priorities

Where, then, will the next wave – the next act – sweep the industry?

Sixty-two percent of organisations expect to increase AI investment over the next 12 months, with 17% anticipating budget growth in excess of 25%. A further 27% expect spending to remain broadly stable.

These numbers are remarkable in context. Investment is accelerating, and commitment is deepening, even as organisations struggle to bridge the gulf between deployment and commercial impact. The calculus is shifting. This contradiction shows AI is crossing a threshold in how it’s evaluated, with the rationale underpinning investment evolving in real time. Earlier rounds of spending were justified by the promise of future value. Increasingly, investment is sustained by the perceived cost of falling behind. Few technology cycles have generated such a powerful sense of executive FOMO as AI – a perception that standing still is itself a competitive risk.

Alongside rising investment levels, a countervailing discipline is also emerging. One in 10 organisations are closing the cheque book while the evidence base catches up with the ambition. This speaks to a market applying greater scrutiny to where and how capital is deployed, and signals the crystallisation of a more disciplined model of enterprise adoption anchored in economic accountability.

Figure 10: How is your organisation’s investment in AI for CX expected to change over the next 12 months?

Which raises an immediate question: measured how?

At this juncture, CSAT, NPS, and FCR dominate the measurement landscape – familiar, established, and consistently leveraged within customer-facing operations for a long time. Their prevalence is understandable. They are the metrics boards recognise, benchmarks exist for them, and they link to customer experience outcomes in ways that are intuitive to communicate upward.
In amongst them, revenue growth is directly tracked by 40% of organisations and CLV by 24% – present, certainly, but far from dominant. And that’s interesting because these are the KPIs that connect AI investment to the commercial performance the C-suite actually holds to account. Their relatively modest position in the measurement hierarchy is instructive: AI in CX is still typically assessed through a service-based lens, at a moment when boardrooms are asking for signs of impact that transcend the function itself.
The organisations likely to sustain investment over time are building measurement frameworks that span both. CSAT reveals whether an interaction landed well. CLV and retention indicate whether it built something. The most durable AI programmes in CX are those that have learned to speak both languages fluently – reporting experience metrics to the function and commercial metrics to the board, and treating the relationship between the two as the analytical terrain worth understanding.

Figure 11: Which metrics are the most important indicator of AI success in CX? (Select up to 3)

_54%

_24%

_40%

_34%

_30%

_24%

_19%

_16%

_10%

_9%

What connects every metric on that list is the variable underneath them all: customer trust in the AI delivering the experience. And right now, 24% of organisations are taking no specific steps to build customer confidence in their use of AI. In a low-trust environment, that is ground ceded to competitors willing to do the work.

In broad strokes, consumer trust in AI is structurally low. The instinct among many organisations has been to treat this as a communications challenge – a box to be ticked through disclosure statements, opt-out mechanisms, and carefully worded privacy policies. However, a much more fundamental reorientation is required.

Trust in AI is built through design. The 50% testing features in small groups before more comprehensive rollouts are making a sound architectural decision as much as a prudent risk management one: they’re generating evidence of reliability before claiming it. The 27% offering customers a human alternative are acknowledging an important reality about the current state of customer readiness – and protecting the relationship in interactions where AI confidence hasn’t yet been established.

Collectively, the industry is still at an early, tentative stage of earning the permission AI ultimately requires to operate at scale. Customers will extend that permission when their experience consistently justifies it – when AI interactions resolve faster, feel more relevant, and produce fewer frustrations than the alternatives. Today’s leaders are driving that outcome through deliberate design, consistent governance, and transparent communication – accumulating a reputation for AI that works. Those building AI programmes without a corresponding trust strategy are, in effect, borrowing against a balance they haven't yet built.

stage of earning the permission AI requires to operate at scale. Customers will extend that permission when their experience consistently justifies it – when AI interactions produce fewer frustrations than the alternatives and feel more relevant. Today’s leaders are driving that outcome through deliberate design, transparent communication, and consistent governance – accumulating a reputation for AI that works. Those building AI programmes without a trust strategy are, in effect, borrowing against a balance they have yet to build.

Figure 12: What actions is your organisation taking to foster customer confidence in its use of AI?

Looking ahead, the strategic agenda for the next year reads like a concerted effort to continue constructing the foundations that enable AI technologies to thrive.

The three leading imperatives – scaling automation and self-service maturity (35%), improving agent enablement and workforce productivity (29%), and building unified customer data and insight capabilities (28%) – represent the priorities of organisations looking to consolidate their current activations. Each addresses key gaps in execution strength: automation maturity tackles the pilot-to-scale problem; agent enablement connects to the human-in-the-loop design challenge; unified data overcomes the fragmentation barrier that undermines progress across the board.

In short, the agenda aligns closely with the realities of the current landscape.

Yet one finding stands out for the wrong reasons. Strengthening executive ownership and cross-functional governance – cited by just 13% of organisations – is one of the foremost capabilities reliably associated with AI programmes that scale and sustain. Its position in a dataset where governance fragmentation and organisational silos are among the most prominent barriers is a sharp contradiction.

Indeed, to establish genuine AI outperformance in CX is to have senior business leaders in the room driving decisions and holding the outcomes. Until that enterprise characteristic is embraced, the rest of the agenda remains harder to action than it needs to be.

– cited by just 13% of organisations – is one of the foremost capabilities reliably associated with AI programmes that scale and sustain. Its position in a dataset where governance fragmentation and organisational silos are among the most prominent barriers is a sharp contradiction.

Figure 13: Where is your organisation placing the greatest strategic emphasis to strengthen AI-enabled CX performance over the next year? (Select up to 2)

Next Steps?

Check out the

CCW Europe Summit

How are Europe's biggest brands scaling AI?

Find out at the CCW Europe Summit.

Over three days, senior CX, service operations, digital transformation, and technology leaders will come together to share how their organisations are operationalising AI – from redesigning customer operations and earning customer trust to measuring commercial impact.

Date: 5 - 7 October 2026
Location: Hôtel Mövenpick & The
Passenger Terminal, Amsterdam, The Netherlands

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