What Senior Decision Makers Reveal About CX, EX, AI and Digital Transformation in the Utilities Sector

ABOUT THE RESEARCH

Customer experience, employee experience, and digital transformation are closely connected. The tools employees use, the information they have, and the technology supporting them all shape the experience customers receive.

To understand how organisations are approaching these priorities, we surveyed senior decision makers across the Utilities Sector responsible for CX, EX, AI, and digital transformation. They shared how their organisations prioritise experience, where the biggest challenges lie, and how technology and data support improvement.

The results highlight different approaches to experience, alongside common barriers such as budget pressure, system complexity, and organisational alignment. This report summarises the findings and explores what they mean for leaders working to improve experience across their organisations.

AI Is Everywhere—But Focus Is Split

We asked: Which areas of communications, if any, is your organisation currently exploring the application of AI in?

AI is firmly on the agenda across the utilities sector, with organisations split between CX, EX, and a combined approach—signalling exploration rather than full strategic alignment..

• 35% are exploring the application of AI in CX only

• 37% are exploring the application of AI in EX only

• 28% are exploring the application of AI in both CX and EX

• 1% are not exploring the application of AI in either of these areas

The near-even spread highlights a sector still working out where AI will have the greatest impact.

What this means for you
In utilities, field teams, contact centres, and back-office operations are tightly linked—focusing on EX can directly improve outage response, billing accuracy, and customer trust. A joined-up CX and EX strategy will be critical to delivering consistent service across complex operations.

From Curiosity to Commercial Intent

We asked: What, if anything, best describes your organisation's intent for using AI in communications in this area(s)?

Plans are moving beyond experimentation, with a clear focus on delivering measurable improvements and solving real operational challenges.

• 31% want to drive measurable improvement

• 33% are looking to solve specific challenges

• 19% are reshaping experience delivery

• 17% are in an exploration phase

AI is no longer a side project—it’s becoming embedded in how organisations improve performance.

What this means for you
Priority use cases in utilities—such as handling service disruptions, improving first-time resolution, or reducing call volumes—should anchor your AI strategy.

AI’s Value Is About Better Service

We asked: What type of value, if any, does your organisation most expect AI to deliver

The focus is firmly on better service—faster, more personalised, and more consistent—rather than simply reducing costs.

• Improved speed and responsiveness: 29%

• Service consistency: 21%

• Personalisation: 26%

• Cost efficiency: 11%

• Competitive differentiation: 13%

This reflects the need to deliver reliable, real-time service in high-pressure environments.

What this means for you
For utilities, speed and consistency during high-pressure moments—like outages or billing issues—are critical. AI should help you deliver timely, accurate, and proactive communications that reduce complaints and regulatory risk.

Customers Are Ready—But Expectations Are High

We asked: How positively or negatively do you believe customers will perceive AI-enabled interactions?

Customer sentiment is overwhelmingly positive, with strong confidence that AI will enhance rather than hinder interactions.

• Net Positive: 77%

• Net Negative: 13%

• Neutral: 10%

However, a small but notable minority still signals potential trust or experience concerns.

What this means for you
Utilities customers expect reliability above all else—AI must deliver clear, accurate information, especially during critical events. Poor experiences during outages or billing queries will quickly erode trust and increase regulatory scrutiny.

The Rise of the Hybrid AI Model

We asked: What, if anything, best reflects your organisation's most preferred service model for AI?

The dominant model is hybrid—AI handles the routine, humans step in where it matters most.

·       AI supports people during interactions: 21%

·       AI leads routine interactions with escalation: 26%

·       AI is the primary interface: 12%

·       AI invisible to users: 24%

·       Human-only service: 17%

Overall, the findings indicate that augmentation—not full automation—is the emerging approach in this sector.

What this means for you
In regulated environments like utilities, maintaining human oversight—especially for vulnerable customers or complex cases—is essential. AI can reduce pressure on teams, not remove accountability. 

Confidence High in Delivering AI Outcomes

We asked: How confident or not confident are you that your organisation can turn AI ambition into practical outcomes?

Confidence is strong, but largely sits at “somewhat” rather than “very”—suggesting belief tempered by real-world challenges..

·       Very confident: 24%

·       Somewhat confident: 63%

·       Neutral: 12%

·       Not confident: 1%

Organisations believe in AI’s potential—but recognise delivery won’t be straightforward.

What this means for you
Utilities organisations often operate with legacy infrastructure and complex integrations—bridging the gap between ambition and delivery will require strong vendor partnerships and clear execution plans. 

The Real Blockers: Data and Legacy Tech

We asked: To what extent, if at all, are the following limiting what you can do with AI today in terms of ambitions and communications?

The biggest barriers are not ambition—but data quality and outdated systems, affecting almost every organisation.

• 92%: Data availability and quality

• 94%: Existing communication and experience systems

These foundational issues are holding back progress at scale.

What this means for you
Utilities data is often siloed across billing, network, and customer systems—without integration, AI cannot deliver real-time, joined-up experiences. Modernisation and data governance should be your first priority.

AI Transformation Is a People Challenge

We asked: How challenging or not challenging do you expect organisational and people change to be when introducing AI into the organisation?

Most expect AI adoption to be highly challenging, particularly from a people and cultural perspective.

·       Extremely challenging: 11%

·       Very challenging: 60%

·       Somewhat challenging: 24%

·       Not challenging: 5%

This is not just a tech shift—it’s a fundamental organisational change.

What this means for you
From contact centre agents to field engineers, workforces will need new skills and confidence in AI tools. Training, communication, and change leadership avoids resistance and ensure adoption.

Keeping Humans in the Loop

We asked: What, if anything, best reflects your organisation's view on control in AI-led interactions?

Human oversight remains critical, with most organisations favouring shared or supervised control models.

·       Human approval for key interactions: 37%

·       Shared control: 31%

·       Automation with light oversight: 17%

·       Full automation acceptable: 13%

·       Humans must remain in control: 2%

There is clear caution around handing full control to AI.

What this means for you
Given regulatory requirements and customer vulnerability considerations, maintaining clear human accountability in AI-led decisions will be essential—particularly in billing, complaints, and service disruptions.

Risk Awareness Is High

We asked: How sensitive, if at all, is your organisation to AI-related risks in communications (e.g. trust, compliance, employee impact)?

Risk sensitivity is near universal, reflecting the high-stakes nature of the utilities sector.

·       Highly sensitive: 54%

·       Moderately sensitive: 36%

Trust, compliance, and reliability are front of mind for almost all organisations.

What this means for you
Regulatory compliance, data protection, and fairness must be built into an AI approach from day one—this is not a sector where you can “test and learn” without safeguards.

Significant Investment Underway

We asked: To what extent, if at all, is your organisation investing/planning to invest in AI?

Investment is already significant—and set to grow further—showing clear long-term commitment to AI.

What this means for you
Utilities are committing substantial budgets to AI—with investment aligned to operational priorities like resilience, customer service, and regulatory performance.

AI Impact Will Take Time—But It’s Coming

We asked: When, if at all, do you anticipate seeing impact from AI investment in your organisation?

Most organisations expect meaningful impact within the next 1–4 years, reflecting a measured, long-term view.

·       In 1–2 years: 45.24%

·       In 3–4 years: 54.76%

There is little expectation of immediate returns—but strong belief in future value.

What this means for you
AI in utilities will require long-term commitment—align initiatives with regulatory cycles, infrastructure upgrades, and customer service improvements to maximise impact over time. 

From Momentum to Meaningful Transformation

Utilities organisations are moving decisively from AI exploration to execution, with strong investment and clear intent to improve service outcomes.

However, progress is being held back by data quality, legacy systems, and the complexity of organisational change.

Success will depend on aligning technology, people, and governance to deliver reliable, compliant, and customer-focused AI-driven experiences.

Working with experienced partners like NFON can help bridge the gap between ambition and delivery—ensuring AI drives real, measurable impact.

If you’d like to explore what these findings mean for your organisation, get in touch with the NFON UK team at hello-uk@nfon.com, or call 0330 383 8000.
Let’s start the conversation.