By Shishank Gupta SVP & Head of the Digital Workplace Ecosystem and Microsoft Practice, Infosys
Leaders around the world are quick to attribute productivity gains and accelerating efficiency to the adoption of AI in their organisations. However, the catch often comes when they are asked for hard numbers.
According to the Infosys AI Business Value Radar, while enterprise investment in AI continues to surge, only 19 percent of AI use cases fully deliver on all their intended business objectives, underscoring the critical need to connect adoption to core enterprise outcomes.
The productivity wave, and where it stalled
The first wave of enterprise AI adoption was powered by a promise that was straightforward at its core. Give every knowledge worker a capable AI assistant and watch productivity compound.
The most common use cases were designed around the individual. They would often make people faster at the task level, without any redesign at the workflow level. Adoption was near immediate and exponential.
However, individual productivity rarely features as metrics in boardroom dashboards. The reason is simple. Time freed up because of AI use does not readily translate into lower costs or improved margins. AI operating at the task level inherits limitations of the existing process. This often reduces its impact at the organisational level.
Customer experience: a step forward, but still a softer measure
Then came the next wave of AI adoption. According to a recent McKinsey research, nearly 35 percent of organisations plan to automate more than 60 percent of inbound inquiries by 2028, while 62 percent expect authentication processes and call summarization to become fully automated over time. These figures are hardly surprising, given that AI-powered chatbots and virtual assistants now enable enterprises to provide support well beyond traditional business hours. In fact, customer and employee familiarity with AI is growing so rapidly that interactions with the most widely used AI bots have surpassed 1.5 billion requests per day.
While this is a notable shift in the AI adoption roadmap, it still is a softer measure in most industries. For example, in regulated sectors, customers have limited alternatives, and CX improvements do not always translate to the figures that matter to leadership. However, organisations that harness this shift will not merely serve customers better; they will redefine the standards by which customer experience is measured.
Operations experience: where the metrics start to move
Organisations that show measurable outcomes tend to embed agentic AI directly into numerous systems that run the business. These include inbuilt agents from major ERP, CRM, HR, and inventory platform providers, plus custom agents that close the gaps that those platforms leave open.
This is where AI begins impacting figures that often matter to leadership directly: sales conversion, cost to serve, and compliance exposure. The value is not in standardizing a process faster. It is in reimagining how a workflow is designed in the first place. The ability of AI systems to correlate data in real time, surface decisions at the point of action, and automate bounded triggers, indicates that enterprises can do things that were structurally almost impossible before. This is the key: reimagine what can be done, not just do the same things more efficiently.
Today, organisations are increasingly focusing on AI-driven technology transformations. For instance, in CRM modernisation, disparate legacy instances are being consolidated into a unified platform using AI-powered migration, driving improvement in prospecting velocity and information capture. AI-native eLearning platforms are delivering personalized, voice-enabled workforce training at scale. Agentic AI deployments across business functions are helping to reduce query resolution time and enhance customer experience significantly.
The pattern across such engagements is consistent: AI that is engineered into the workflow, not layered on top of it, helps improve speed, accuracy, and savings. This is the difference between automating what exists and reimagining what is possible.
As AI adoption matures, the areas with the biggest upside are the ones with real economic leverage: places where margins can shift, or growth can be unlocked, such as compounding proprietary data and complex workflows.
Customer, employee, and operations experience (C.E.O.X) is increasingly functioning as one connected system rather than three separate investment areas — and companies that bring data, workflow, and decision-making together across all three are the ones turning AI adoption into a strong competitive edge.