{"id":646869,"date":"2026-10-05T10:09:48","date_gmt":"2026-10-05T17:09:48","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/most-ai-projects-struggle-to-deliver-value-heres-the-roadmap-to-fix-that"},"modified":"2026-10-09T05:31:49","modified_gmt":"2026-10-09T12:31:49","slug":"most-ai-projects-struggle-to-deliver-value-heres-the-roadmap-to-fix-that","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-gb\/blog\/post\/most-ai-projects-struggle-to-deliver-value-heres-the-roadmap-to-fix-that","title":{"rendered":"Most AI projects struggle to deliver value. Here’s the roadmap to fix that."},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text css=””]The numbers on enterprise AI projects are hard to ignore, and harder to justify. MIT's Project NANDA found that 95% of organisations see no measurable return from their AI investments<\/a>. BCG puts the number of organisations creating substantial AI value at just 4%<\/a>. CX leaders admit they\u2019re struggling with the gap between AI investment and returns. In the <\/span>Genesys 2026 State of Customer Experience Report<\/span><\/a>, <\/span>42% of CX leaders surveyed cite demonstrating AI value or ROI as one of their top technology adoption challenges, even though 27% say proving ROI is a strategic priority.<\/span> <\/span><\/p>\n

None of this means AI can\u2019t deliver value. The problem lies in how organisations plan and deploy their AI initiatives.<\/span><\/p>\n

At Xperience 2026, two experts from Genesys, Juan Perez, Director of AI Solutions & Services, and Niels Kusters, Senior CX Advisory Consultant, laid out a practical, five-step method for building AI use cases that deliver measurable value.[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Value lives in the reducing friction<\/span><\/span>\u00a0<\/span><\/span><\/h2>\n

Most organisations start AI initiatives by asking the wrong question: What can this technology do for us? That sends them straight into a catalogue of capabilities, like virtual agents, routing, predictive engagement and copilots, which leads to a search for somewhere to apply each one. Focusing on technology first feels logical. But it leaves organisations with a portfolio of technically impressive AI features that don\u2019t deliver outcomes that matter to the business.<\/span>\u00a0<\/span><\/p>\n

The better question is: Where are your customers struggling? Where do they abandon a task, escalate in frustration, or call back three times to get the same issue resolved? That friction is expensive, as the numbers Perez and Kusters unpacked at Xperience show: 84% of retail shoppers say they'd consider switching brands after a poor returns experience<\/a>; 67% of telecom customers would leave after unexpected charges<\/a>. Friction doesn't just annoy customers. It costs organisations their loyalty.<\/span>\u00a0<\/span><\/p>\n

Starting from friction rather than capabilities keeps you anchored to business outcomes instead of technical possibilities.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Seeing the whole iceberg<\/span><\/span>\u00a0<\/span><\/span><\/h2>\n
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Perez and Kusters used an iceberg visual to illustrate how easy it is to start with the wrong focus. At the very top of the iceberg are the AI capabilities, which are what typically comes to mind first as organisations contemplate deploying AI. Just below the AI models is the technology and data layer: the platforms, integrations and data pipelines that make the AI models usable. <\/span>\u00a0<\/span><\/p>\n

Most of the iceberg sits fully submerged and out of view. It includes the people, processes and change management required for successful outcomes. This is the part almost nobody budgets for. It\u2019s also the part that sinks most AI initiatives. While critical, it's not enough to have the right capabilities and data pipeline. You need employees who trust the AI tools enough to use them, supervisors who've bought into a new way of measuring performance, and workflows redesigned around what AI changes in your CX operation. <\/span>\u00a0<\/span><\/p>\n

Skip that layer, and you get a technically functional deployment with low adoption and even lower returns.<\/span><\/p>\n

And financial services may be providing one of our clearest early signals.<\/span>\u00a0<\/span><\/p>\n

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Five steps to build AI use cases that deliver value<\/span><\/span>\u00a0<\/span><\/h2>\n

Here are the five steps for building your AI use case that will help you work through the entire iceberg to realise compounding returns on your AI investment.<\/span>\u00a0<\/span><\/p>\n

Step 1: Start with experiences that matter <\/span>\u00a0<\/span><\/h4>\n

Before considering solutions or AI capabilities, identify which customer experiences make the biggest impact on your business, such as onboarding, billing, claims, returns and scheduling. These are the journeys where a good or bad experience visibly makes or breaks loyalty and affects revenue. Those impacts tie the experience to a business case with a specific metric, like containment rate or retention, that proves the use case\u2019s value. This step keeps the entire effort tethered to business impact rather than technical innovation.<\/span>\u00a0<\/span><\/p>\n

Perez and Kusters grounded this with a real example: a telecom operator handling more than 1.5 million voice interactions a month identified billing as one of its highest-stakes experiences \u2014 the kind of journey where a single bad interaction can undo months of loyalty building.<\/span>\u00a0<\/span><\/p>\n

Step 2: Find the friction<\/span>\u00a0<\/span><\/h4>\n

Map the experiences you\u2019ve identified, and pinpoint where things break down. Walk the journey the way a customer does: the confusing bill, the bot that loops the same generic FAQ, the repeated explanation to another agent, the resolution that leaves the customer unsure whether they\u2019re still on the hook for a late fee. Each moment of friction is an opportunity for intervention \u2014 and adding value.<\/span>\u00a0<\/span><\/p>\n

Forcing customers to repeat information is one of the clearest signals of broken orchestration. Genesys' 2026 State of Customer Experience report found that 95% of consumers want their information carried across channels so they don't have to repeat themselves, yet 52% of companies still don't pass it along to a human agent.<\/span>\u00a0<\/span><\/p>\n

Mapped out, the telecom billing journey looked like this: a customer gets an unexpected charge, turns to self-service and gets looped through generic articles, calls in and has to explain the issue from scratch, gets bounced between teams and starts over each time, and finally gets a ticket with no clarity on the late fee. Five moments, and at least three of them are pure friction.<\/span>\u00a0<\/span><\/p>\n

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Step 3: Practice the art of the possible<\/span>\u00a0<\/span><\/h4>\n

Once you've found the friction, resist the urge to jump straight to a solution. Perez and Kusters recommend a classic technique to look at the problem through three lenses. The first is unconstrained \u2014 no budget, no legacy system, no limits. At each point of friction or moment of truth in the customer journey, what\u2019s the best-case scenario? Then take a second, deliberately critical pass: Where are the real risks, security concerns and technical limitations? The third pass takes a balanced approach, optimistic but grounded. These three passes keep you from defaulting to whatever your team assumes is most feasible.<\/span>\u00a0<\/span><\/p>\n

Running the billing journey through all three lenses produced a redesigned version: the customer gets a proactive alert before the charge feels like a surprise, a guided self-serve flow resolves the simple cases, and when a live agent is needed, they already have full context and can close the issue in one contact.<\/span>\u00a0<\/span><\/p>\n

Step 4: Design a solution around your vision<\/span>\u00a0<\/span><\/h4>\n

With a clear picture of what's possible, get specific about how you'll build the solution. Identify the technology and platform capabilities you\u2019ll need, what data must be in place and what metrics will tell you whether the use case is working. Use case KPIs often differ from the business metrics they ultimately serve. You\u2019ll need both to know it\u2019s working.<\/span>\u00a0<\/span><\/p>\n

For the billing journey, the vision translated into specific use cases with their own KPIs: an agentic self-service journey to lift containment rate, predictive routing to improve first-contact resolution, and Genesys Cloud Agent Copilot to give agents real-time guidance to improve the customer experience<\/span>. <\/span><\/p>\n

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Step 5: Build, pilot and monitor<\/span>\u00a0<\/span><\/h4>\n

It\u2019s crucial to ensure that you\u2019ve defined a use case that will provoke positive changes and deliver value that compounds over time. So, before committing resources, run the use case through a readiness check across four dimensions: <\/span>\u00a0<\/span><\/p>\n