{"id":649586,"date":"2026-10-20T17:43:10","date_gmt":"2026-10-21T00:43:10","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/most-ai-projects-struggle-to-deliver-value-heres-the-roadmap-to-fix-that"},"modified":"2026-10-20T17:44:59","modified_gmt":"2026-10-21T00:44:59","slug":"most-ai-projects-struggle-to-deliver-value-heres-the-roadmap-to-fix-that","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/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\u00a0projects\u00a0are hard to ignore, and harder to\u00a0justify. MIT’s Project NANDA found that\u00a095% of organisations see no measurable return from their AI investments<\/a>. BCG puts\u00a0the number of organisations creating\u00a0substantial\u00a0AI value at just 4%<\/a>.\u00a0CX leaders admit\u00a0they\u2019re\u00a0struggling with the gap between AI investment and returns. In the\u00a0<\/span>Genesys 2026 State of Customer Experience\u00a0Report<\/span><\/a>,\u00a0<\/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>\u00a0<\/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 catalog 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\u00a0of the\u00a0iceberg\u00a0sits fully submerged\u00a0and out of view. It includes the\u00a0people, processes, and change management\u00a0required\u00a0for successful outcomes.\u00a0This is the part almost nobody budgets for.\u00a0It\u2019s\u00a0also\u00a0the part that sinks most AI initiatives.\u00a0While critical,\u00a0it’s\u00a0not enough to have the right\u00a0capabilities\u00a0and data pipeline. You need\u00a0employees\u00a0who trust the\u00a0AI tools\u00a0enough to use\u00a0them, supervisors\u00a0who’ve\u00a0bought into a new way of measuring performance, and workflows redesigned around what AI changes\u00a0in your CX operation.\u00a0<\/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\u00a0financial services\u00a0may 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\u202f<\/span>\u00a0<\/span><\/h4>\n

Before considering solutions or AI capabilities,\u00a0identify\u00a0which customer experiences\u00a0make the biggest impact on\u00a0your business, such as\u00a0onboarding, billing, claims, returns,\u00a0and\u00a0scheduling. These are the\u00a0journeys\u00a0where a good or\u00a0bad experience\u00a0visibly\u00a0makes or breaks\u00a0loyalty and\u00a0affects\u00a0revenue.\u00a0Those impacts tie the\u00a0experience to a business case with a specific metric, like containment rate or retention, that proves the\u00a0use case\u2019s\u00a0value.\u00a0This step keeps the entire effort tethered to business impact rather than technical\u00a0innovation.<\/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\u00a0identified\u00a0billing as one of its highest-stakes experiences \u2014the 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\u00a0you’ve\u00a0found the friction, resist the urge to jump straight to a solution.\u00a0Perez and Kusters recommend a\u00a0classic technique\u00a0to look\u00a0at the problem through three lenses.\u00a0The first\u00a0is unconstrained \u2014 no budget, no legacy system, no limits. At each point of friction or\u00a0moment of truth\u00a0in the customer journey,\u00a0what\u2019s\u00a0the best-case scenario?\u00a0Then take a second, deliberately critical\u00a0pass:\u00a0Where are the real risks, security concerns, and technical limitations?\u00a0The third pass takes a balanced approach,\u00a0optimistic\u00a0but grounded.\u00a0These three passes keep you from\u00a0defaulting to\u00a0whatever your team assumes is most\u00a0feasible.<\/span>\u00a0<\/span><\/p>\n

Running\u00a0the\u00a0billing 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:\u00a0Design a solution around your vision<\/span>\u00a0<\/span><\/h4>\n

With a clear picture of\u00a0what’s\u00a0possible, get specific about how\u00a0you’ll\u00a0build\u00a0the solution.\u00a0Identify\u00a0the\u00a0technology and platform capabilities\u00a0you\u2019ll\u00a0need, what data\u00a0must\u00a0be in\u00a0place\u00a0and what metrics\u00a0will tell you whether the use case is working.\u00a0Use\u00a0case\u00a0KPIs\u00a0often differ from the business metrics\u00a0they\u00a0ultimately serve.\u00a0You\u2019ll\u00a0need both to know\u00a0it\u2019s\u00a0working.<\/span>\u00a0<\/span><\/p>\n

For the billing journey, the\u00a0vision 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\u00a0to improve the customer\u00a0experience<\/span>.\u00a0<\/span><\/p>\n

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

It\u2019s\u00a0crucial to ensure that\u00a0you\u2019ve\u00a0defined a use case that will provoke positive changes and deliver value that compounds over time. So, before committing resources, run\u00a0the\u00a0use case through a readiness check across four dimensions:\u00a0<\/span>\u00a0<\/span><\/p>\n