The New Math Behind ROI In AI

The formula for AI ROI is changing as businesses measure productivity, trust, customer outcomes and enterprise value instead of adoption

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The race to deploy AI is heating up, but organisations no longer want just deployment. They’re asking whether copilots, AI agents and automation systems are translating into measurable business value. Both enterprises and startups are under pressure to justify their investments. 

The challenge? Unlike traditional software, AI rarely transforms just one part of the business, making its return on investment (ROI) far more complex to calculate. But the answer also varies from company to company.  

We spoke with AI-native startups, enterprise giants and other scaled-up ventures to understand what ROI in AI actually looks like today. 

Which metrics are being tracked and are factors like productivity and efficiency coming into play in a measurable way rather than remaining subjective? And what happens when businesses find that their AI investments are not delivering meaningful returns? Let’s unpack these questions because in the age of AI, pretty much every company has to grapple with them. 

Even though there is no universal formula or playbook for calculating ROI from AI, organisations are increasingly measuring AI against the outcomes that matter most to their businesses. What we learnt is that ROI in AI does not carry the same meaning for every organisation. Across industries, it is increasingly being measured by the business outcomes that matter most — productivity, customer experience, or revenue growth.

For edtech platform upGrad, for example, AI has been embedded across internal knowledge work and customer-facing workflows, including its examination platform and sales funnel. These deployments have generated close to a 5X ROI, largely by reducing manual intervention.

“Using AI has lifted output across our tech, content and marketing teams by 1.4X to 2X,........

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