Indian businesses are pouring crores into Artificial Intelligence, but a crucial question remains: is it actually paying off? A new perspective from McKinsey argues that the key isn't tracking AI usage, but measuring its impact on real work outcomes.
The Common 'Activity Trap' in AI
For years, the story of enterprise AI has been one of adoption. Companies have focused on metrics that prove activity rather than impact. These include the number of AI models deployed, the percentage of employees using a new generative AI tool, or the sheer volume of data processed. While these figures look impressive in a presentation, they often fail to connect to the company's profit and loss statement. Recent analysis from firms like McKinsey highlights a growing 'gap between AI activity and AI impact'. Many businesses can show that their people are using AI tools more, but struggle to prove that this usage has improved customer experience, reduced operational costs, or generated new revenue. This is the activity trap: being busy with AI, but not necessarily getting business results.
McKinsey's New Value Framework
In response, McKinsey proposes a fundamental shift in perspective. Instead of just measuring adoption, they advocate for measuring AI's effect on tangible work outcomes. This means moving beyond 'how many people are using the chatbot?' to 'has the chatbot reduced customer service call times by 15%?' or 'has our AI-powered marketing tool increased sales conversion rates?'. The core idea is to define the business key performance indicator (KPI) first and then deploy AI as a solution to improve it. This framework connects the technical performance of an AI model directly to operational metrics and, ultimately, to financial results like revenue growth or cost savings. Only a small fraction of companies, which McKinsey calls 'AI high performers', currently operate this way, attributing at least 5% of their earnings to AI.
Why Measuring Outcomes Is Crucial
This focus on outcomes is vital because AI is not cheap. McKinsey estimates that the global economic value from AI could reach USD 2.52 trillion annually, but that this figure could drop significantly if the costs of deployment aren't managed. Without a clear line to financial returns, multi-crore investments in AI infrastructure and software are a massive gamble. When leaders measure outcomes, they can make smarter decisions. They can identify which AI projects are delivering real value and deserve more investment, and which ones are simply expensive experiments with low user adoption or no impact on the bottom line. This approach forces a disciplined, business-first mindset, turning AI from a shiny object into a managed investment with clear expectations for returns, much like any other capital expenditure.
Putting Outcome-Based Measurement into Practice
For an Indian enterprise, implementing this approach starts with asking different questions. Before launching an AI initiative, leadership must define success. The first step is to identify a specific, measurable business problem—for example, 'our product development cycle is too long' or 'we lose too many customers at the final stage of purchase'. The next step is to establish a baseline: what does this process cost and how long does it take today? Only then should the team explore how AI can help. As the AI solution is rolled out—first as a pilot, then as a minimum viable product (MVP)—the focus should be on tracking changes to that pre-defined metric. Did the AI tool for developers actually shorten the coding and testing phase? Did the new recommendation engine reduce cart abandonment? Success is measured not by AI usage, but by the movement of these core business needles.
From Tech Projects to Business Transformation
Shifting to an outcome - based model is not just a technical challenge; it is a cultural one. It requires deep collaboration between IT departments and business unit leaders who own the P&L. It also requires a commitment to transparency and a willingness to kill projects that are not working, a difficult task in any large organization. According to McKinsey's findings, 75% of the value from generative AI comes from its application in four key areas: customer operations, marketing and sales, software engineering, and R&D. By focusing on improving the outcomes in these specific functions—automating customer service, generating more effective marketing content, or accelerating research—companies can move beyond isolated productivity gains for individual employees and begin to see structural improvements to their entire operation.
