95 percent of generative AI projects\u00a0across industries\u00a0failed to deliver<\/a>\u00a0meaningful return on investment. Pilots launched quickly, demonstrations looked promising, yet many initiatives quietly stalled or were cancelled.\u00a0<\/p>\nCustomer experience has been one of the clearer areas of progress, where\u00a0GenAI has already delivered practical gains through agent\u00a0assistance, knowledge\u00a0surfacing\u00a0and workflow support, even as other sectors struggled to move beyond experimentation.\u00a0<\/p>\n
According to\u00a0Martin Taylor, Deputy CEO and Co\u2011Founder of\u00a0Content\u00a0Guru, the issue was discipline.\u00a0<\/p>\n
\u201cThe problem with a lot of generative AI projects was not the technology. It was that organizations never baselined what they were starting from, so they\u00a0couldn\u2019t\u00a0demonstrate\u00a0the delta.\u201d\u00a0<\/p>\n
\u201cROI is the delta between the previous way of doing things and the new way of doing things,\u201d Taylor said.\u00a0\u00a0<\/p>\n
\u201cWhere a lot of\u00a0AI\u00a0has gone wrong is that [teams] were not baselining the cost that they were starting with and therefore they weren\u2019t able to demonstrate the savings or efficiencies that they had gained from introducing the AI.\u201d\u00a0<\/p>\n
Why ROI was Hard to Prove with GenAI\u00a0<\/p>\n
In customer experience, GenAI\u00a0has\u00a0delivered\u00a0visible operational benefits.\u00a0The challenge is\u00a0whether those improvements have been consistently tied to measurable financial outcomes.\u00a0<\/p>\n
There was no lack of\u00a0ambition,\u00a0\u00a0What\u00a0was often missing was the operational groundwork needed to measure and sustain value. Without a clear understanding of existing costs, organizations had no credible way to measure savings or efficiency gains.\u00a0<\/p>\n
The result was a surge of loosely defined pilots that produced impressive demonstrations but little financial evidence. When those pilots concluded, CFOs had little reason to approve broader rollouts.\u00a0<\/p>\n
The discipline\u00a0required\u00a0to prove value begins long before deployment, Taylor said.\u00a0<\/p>\n
\u201cWe\u2019re very much all about baselining the use cases\u2014firstly\u00a0identifying\u00a0the use cases, and then which ones are going to be suitable, preferably high\u00a0volume,\u00a0not too complex ones are best.\u201d\u00a0<\/p>\n
Organizations need a clear operational picture before any automation begins.\u00a0<\/p>\n
\u201cAnd then\u00a0you\u2019ve\u00a0got\u00a0a good idea\u00a0of the cost to serve now.\u00a0You\u2019ve\u00a0got stats like average handle time, first contact resolution to fall back on.\u201d\u00a0<\/p>\n
Once that baseline exists, results become measurable.\u00a0<\/p>\n
\u201cOnce you\u2019ve got a very clear vision of the starting point, then like any good science experiment at school, you can see what the actual results and conclusions are.\u201d\u00a0<\/p>\n
Why Agentic AI Starts\u00a0From\u00a0a Stronger Foundation\u00a0<\/p>\n
Agentic AI is\u00a0emerging\u00a0into a different enterprise climate. Budgets face greater scrutiny and expectations are more grounded. And organizations are returning to AI adoption with the benefit of hindsight, Taylor noted.\u00a0<\/p>\n
\u201cWhen we\u2019re looking now at agentic AI, there\u2019s a more thoughtful approach because organizations are learning from where they went wrong with GenAI.\u201d\u00a0<\/p>\n
There\u2019s\u00a0a more thoughtful approach to how projects are scoped. Use cases are more operational and closely aligned with measurable business processes. Enterprises are prioritising high-volume, low-complexity tasks where outcomes can be measured quickly and credibly.\u00a0<\/p>\n
What agentic AI changes is the complexity of what can be\u00a0contained. Customer journeys that previously\u00a0required\u00a0multiple interactions can now be managed automatically, with intelligent handover to human agents when judgement, empathy, or regulatory oversight is\u00a0required.\u00a0<\/p>\n
Clear metrics sit at the centre of these initiatives. Measures such as average handle time, first contact resolution, containment rates, and cost-to-serve are defined before deployment rather than retrofitted afterward.\u00a0<\/p>\n
\u201cCustomer expectations have continued rising. That started in the pandemic era, where people were at home a lot more, they\u00a0were not able to\u00a0conduct face-to-face commerce, and organizations invested in better experiences online, better customer experience, better contact center investment,\u201d\u00a0Taylor said.\u00a0<\/p>\n
Consumers quickly adapted to those improvements, creating a growing delivery gap\u00a0between customer expectations and what organizations were realistically able to deliver.\u00a0\u00a0<\/p>\n
\u201cNow there\u2019s a chance to bridge some of that expectation gap, because a lot of what we can do in the agentic world around containment, particularly, is speaking to expectations of customers and you might, if you\u2019re lucky, be able to exceed those expectations.\u201d\u00a0<\/p>\n
While CX made early progress with GenAI,\u00a0Agentic AI raises the stakes\u00a0by introducing greater autonomy, making upfront planning,\u00a0baselining\u00a0and governance even more important.\u00a0<\/p>\n
Containment, not deflection\u00a0<\/p>\n
One of the clearest indicators of agentic AI\u2019s impact is containment, Taylor said.\u00a0\u201cRealistic expectations are that there will be containment of a greater number of use cases.\u201d\u00a0<\/p>\n
This development sits within a longer progression of automation rather than a clean break from the past.\u00a0\u00a0<\/p>\n
\u201cRather than see it as a complete kind of restart,\u00a0it\u2019s\u00a0important to see it in this evolutionary sense.\u00a0It\u2019s\u00a0not a meteor strike\u00a0moment,\u00a0it\u2019s\u00a0a dinosaur\u2019s grown feathers and can take wing.\u201d\u00a0<\/p>\n
The metaphor reflects how decades of process knowledge, sector\u00a0expertise, and operational learning continue to matter. Agentic systems inherit those foundations rather than discarding them.\u00a0<\/p>\n
\u201cLet\u2019s remember we\u2019re building on 40 years of the evolution of the call\u00a0center\u00a0to the contact\u00a0center\u00a0to intelligent automation, first GenAI and now this agentic era.\u201d\u00a0<\/p>\n
That accumulated knowledge\u00a0remains\u00a0essential, Taylor said.\u00a0<\/p>\n
\u201cThere\u2019s no need to scrap it all and start again because we spend a long time as an industry getting to know the business processes very well.\u201d\u00a0<\/p>\n
Taylor described a \u201cbefore, during, after\u201d model that reflects how AI supports customer interactions.\u00a0<\/p>\n
Before an interaction, AI handles triage, intent capture, and data gathering. During the interaction, it provides relevant knowledge and decision support for agents. Afterward, it supports quality management and compliance.\u00a0<\/p>\n
Agentic AI expands the \u201cbefore\u201d stage. More work happens upstream, and in many cases that stage becomes the entire interaction.\u00a0<\/p>\n
Utilities\u00a0provide\u00a0a clear example, where inquiry types are limited and processes are well understood.\u00a0\u00a0<\/p>\n
\u201cFor one of our customers, UK Power Networks,\u00a0we\u2019re\u00a0typically automating about 94 percent of\u00a0all of\u00a0their customer inquiries fully\u2026 This\u00a0isn\u2019t\u00a0deflection, its containment.\u201d\u00a0<\/p>\n
The distinction matters. Deflection shifts work elsewhere. Containment resolves it. Customers receive outcomes rather than redirection, and humans are engaged only when necessary.\u00a0\u00a0<\/p>\n
As agentic capabilities mature, the complexity of issues that can be fully\u00a0contained\u00a0continues to increase, while intelligent handover ensures human agents step in at the right moment.\u00a0<\/p>\n
What Realistic ROI Looks Like\u00a0<\/p>\n
As expectations reset, the definition of success is shifting as well.\u00a0<\/p>\n
Taylor placed improved customer experience at the top of the list. In competitive service environments, a single negative interaction can erode years of brand loyalty.\u00a0<\/p>\n
Operational efficiency follows\u00a0as a consequence, but the impact extends beyond direct cost savings. Automation can reduce training time, improve agent satisfaction, and lower attrition in an industry historically characterised by high turnover.\u00a0<\/p>\n
Taylor also cautioned against expecting immediate workforce reductions. Many organizations use AI to expand service capacity and improve quality while\u00a0maintaining\u00a0similar staffing levels.\u00a0<\/p>\n
\u201cAs far as possible,\u00a0it\u2019s\u00a0about slotting in these AI agents into this proven model.\u00a0It\u2019s\u00a0not that we just want to scrap it all and start again.\u201d\u00a0<\/p>\n
Industry analysts share that realism. Gartner predicts that\u00a0no Fortune 500 company will eliminate all human agents by 2028<\/a>, even as the automation of routine inquiries accelerates.\u00a0<\/p>\nAgentic AI offers a more credible path to ROI because enterprises are applying the discipline that earlier AI initiatives lacked.\u00a0<\/p>\n
Use cases are clearer. Metrics are defined upfront. Risks are acknowledged. And AI is being treated as a business transformation rather than a novelty. For leaders evaluating agentic AI, expectations matter.\u00a0\u00a0After the first phase of GenAI adoption,\u00a0it\u2019s\u00a0clear that\u00a0where organizations plan carefully, define value upfront\u00a0and apply AI to the right problems, returns follow. Agentic AI will reward that\u00a0discipline, and\u00a0expose its absence.\u00a0<\/p>\n
Related Stories: <\/p>\n","protected":false},"excerpt":{"rendered":"For many enterprises, the first wave of generative AI left leaders with a troubling question. Where was the…\n","protected":false},"author":2,"featured_media":12403,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[9897,179,509,7493,405,512,513,807,2567,223],"class_list":{"0":"post-12402","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-agentic-ai","8":"tag-agent-assist","9":"tag-agentic-ai","10":"tag-agentic-ai-in-customer-service","11":"tag-agentic-artificial-intelligence","12":"tag-ai-agents","13":"tag-automation","14":"tag-autonomous-agents","15":"tag-ccaas","16":"tag-customer-engagement-center","17":"tag-generative-ai"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/12402","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=12402"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/12402\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/12403"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=12402"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=12402"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=12402"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}