AI in customer experience: Driving measurable business value

Artificial intelligence (AI) is now a key topic in enterprise boardrooms, promising to revolutionise, optimise and unlock efficiency at scale.

In a recent survey conducted by MES Computing, 75 IT leaders shared their insights on major trends facing IT and business leadership. One of those trends, of course, was generative AI.

So, what was the verdict?

• 43% view it as promising but not fully developed.
• 29% consider it to be the most significant innovation since the smartphone.
• 13% believe it represents the largest bubble since the dot-com era.
• 12% brand it a ‘golden opportunity’.
• 3% regard it as risky.

With 83% of those surveyed expressing positive sentiments toward generative AI, it's clear that AI adoption is no longer just hype but reality. And while many AI models have been around for many years, those racing to adopt AI-powered solutions now find themselves sitting on a critical question: Where does the hype end and the value begin?

To understand how gen AI is demonstrating tangible business value, let's take customer experience (CX) as a use case.

In today's 'everything now' economy, customers expect instant, personalised interactions at every touchpoint. For mid- and large-scale enterprises, where the customer base often spans hundreds of thousands rather than just a few hundred, meeting these expectations isn't simply a competitive advantage. It's a high-stakes necessity.

If that's the case, then what's the hold up?

The reality check: AI in enterprise software

According to Gartner, 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs or unclear business value.

The reality is that AI isn’t a plug-and-play solution; it’s a tool that requires strategic implementation and a robust foundation to build from. Deployment success depends on addressing several structural challenges inherent in enterprise orgs: 

• Data readiness. AI is only as reliable as the data it's trained on. With disparate data silos, inconsistent governance and accessibility issues, the effectiveness of AI is undermined before implementation even begins.
• Infrastructure limitations. 66% of CTOs report that their infrastructure cannot fully support generative AI's processing requirements.
• Legacy systems. Compatibility/integration issues between AI workloads and existing tech stacks create significant deployment challenges.
• Elusive value metrics. Despite a 97% YoY increase in spending on AI infrastructure, organisations are struggling to establish clear KPIs that capture and measure AI's business impact, leading to stalled initiatives and abandoned projects.
• Alignment. Successful AI implementation demands that all areas of the organisation share a common understanding of AI capabilities, opportunities, and limitations. People need to understand what the purpose is, why it matters and how it will impact their roles.
• Security and compliance. As AI tools and workflows access sensitive data, robust risk management and is not just a technical requirement but a business imperative.

This tells us that it's not that AI is out of reach; it's making AI work effectively and securely that presents the real challenge.

The key differentiator? A focused approach, starting with specific, high-value use cases, rather than an all-at-once transformation. In this way, customer experience has emerged as the perfect proving ground for AI’s business value. Here, the technology isn’t just theoretical; it’s making a practical, measurable business impact.

Two ways AI is supercharging CX to drive business growth

By 2029, Gartner predicts that 80% of customer service interactions will be handled by AI. Right now, we can identify two key AI use cases that are leading the charge in delivering real business value in customer experience:

1.    Hyper-personalisation

In an era of tailored marketing efforts, personalisation is non-negotiable. In fact, personalisation alone is no longer enough, we’re talking hyper-personalisation.

AI is enabling enterprises to analyse customer behaviour, preferences, and historical data to deliver recommendations and offers that feel uniquely individualised, timely and relevant.

Take these examples:

• Starbucks didn’t just settle for app-based ordering, they developed Deep Brew, a proprietary AI and data analytics platform to power it. By leveraging purchase history, drink preferences, and even the times of day customers typically grab their coffee, their app delivers tailored perks and recommendations in real time, engaging customers and driving repeat revenue.
• Netflix. Yes, it’s the giant in the SVOD (Subscription Video on Demand) space, but its personalisation tactics have been helping win back customers in the face of subscription fatigue for years. Its recommendation engine, driven by AI and machine learning, suggests movies and shows based on viewing history to deliver personalised entertainment options. These personalised prompts start on the site and continue through targeted messages and app alerts.

The business value? Sales and revenue. For Starbucks, the app now drives 31% of U.S. sales. For Netflix, the algorithms that keep viewers coming back for more are alone responsible for over a billion dollars in customer retention.

2.    Agentic AI: Proactive AI assistants

AI-driven chatbots, digital concierges, and virtual assistants are revolutionising customer support with round-the-clock responses. Leveraging natural language processing (NLP) and process automation, these intelligent assistants handle repetitive tasks, freeing up front-line CX teams to focus on more complex interactions.

Enterprises of all sizes are using AI assistants to enhance operational efficiency across the customer journey. By automating routine enquiries and escalating more complex issues to front-line teams when necessary, these assistants are proving valuable in multiple ways:

• Predicting the optimal time to engage with customers
• Speeding up response times
• Automating repetitive queries
• Routing interactions based on intent, sentiment, and language
• Reducing agent workload by automating after-call tasks and real-time data retrieval
• Enhancing agent confidence and effectiveness
• Standardising processes like data entry and call scoring for greater accuracy and consistency

Take publisher Wiley, for example. To manage surges in customer service requests, they implemented a customer contact triage agent to draft personalised responses to support reps, help support teams resolve customer issues and track service trends over time.

Meanwhile, a direct-to-consumer (DTC) retailer featured in a McKinsey article is using gen AI to automate key process steps (for example, retrieving information at the back end, making necessary changes, and replying to customers in the brand’s voice) to enable faster resolution of queries, from order-taking to repair requests.

The business value? For Wiley, AI-driven automation led to a 40% increase in improved case resolutions, meanwhile, the DTC retailer saw an 80% decrease in time to first response and a four-minute reduction in average resolution time per ticket.

By shifting from reactive to proactive customer support with AI-powered solutions, these organisations aren’t just improving operational efficiency, they’re reducing overheads, building trust and strengthening customer loyalty.

Bridging the gap between AI hype and business value

While these examples highlight AI's potential, not all implementations succeed. Many enterprises fall into the trap of adopting AI for its own sake rather than aligning it with clear business value.

To move beyond technology hype (whether AI or any enterprise software initiative) and achieve measurable results, IT pioneers apply our proven V5 Framework: five essential pillars that help evaluate commercial advantage against technological potential.

• Value: Start with business value. Define measurable outcomes tied directly to improvements (e.g., reducing response times by 50%, increasing customer retention by 20%).
• Vision: Ensure there is a clear vision for AI's role within your long-term business objectives and ensure everyone involved in its implementation has a say in shaping that vision.
• Viable: For AI to succeed, you need to have invested in the right foundations first.
• Velocity: Iterate continuously. Use agile methodologies to test small-scale implementations before scaling up.
• Verification: Prove benefits delivery at the end of each development sprint, aligning AI use cases with business objectives at every stage to ensure real impact.

By approaching AI adoption with a commercial lens, enterprises can move beyond the hype and unlock its true potential as a driver of business transformation. The CTOs and IT pioneers who embrace technology strategically focus on delivering business value one step at a time.

The question isn't whether AI will shape enterprise software; it's whether your company will lead or lag in this transformation.

Success with any technology initiative hinges on one critical factor: alignment between IT and business priorities. Successful implementations create true business value.

For over three decades, Griffiths Waite has partnered with large-scale enterprises, guiding CTOs and technology leaders through transformations that fundamentally reshape how businesses operate, compete and grow. We've witnessed every technological wave, and today's innovations represent perhaps the most significant opportunity yet for those prepared to harness them strategically.

Download our guide: The V5 Framework: A commercial lens on transformative software development. Discover how to translate technology's promise into measurable business outcomes that drive your organisation forward and help you act fast with confidence.