Indian businesses are adopting artificial intelligence in places that customers may never see: quotations, lead scoring, supplier selection, hiring screens, product descriptions, demand forecasts and service replies. Each use can appear small. Together, they begin to shape prices, promises, opportunities and relationships.
The usual management question is whether the tool saves time or money. That question is necessary, but incomplete. A business also needs to know who approved the decision, what evidence supported it, what could go wrong and how the company would reverse course.
Every meaningful AI use should therefore produce a decision receipt.
A decision receipt is not a technical log containing thousands of system events. It is a short business record that a manager, employee, customer-service lead or auditor can understand. It explains why the automation exists and who remains responsible for its consequences.
Business Unfold has recently examined models built around operational discipline, including the Tata Business Excellence Model and technology-enabled platforms such as OYO and Zetwerk. The common lesson is that scale requires repeatable management systems. AI should be treated the same way. A pilot may survive on the memory of the person who set it up. A scaled process cannot.
The receipt should contain seven fields.
First, state the business decision. “Use AI” is not a decision. “Draft first responses to routine warranty questions” is. “Recommend reorder quantities for the next seven days” is. Precise scope keeps an experiment from quietly expanding into activities that were never evaluated.
Second, name the intended benefit and baseline. The business should record the current time, cost, error rate or customer delay before automation. Without a baseline, an impressive demonstration can be mistaken for an improvement. A tool that writes faster may still create more review work, refunds or corrections.
Third, list the approved information. A retailer may permit product specifications and published policies but prohibit customer health details. A manufacturer may allow historical maintenance records but exclude confidential drawings from an external service. Clear input boundaries protect both the company and the people whose information it holds.
Fourth, identify the human owner. The owner is not merely the person who bought the software. It is the person with authority to pause the workflow, investigate a problem and approve a correction. Responsibility cannot be assigned to “the AI team” when a customer receives a false promise or an employee is screened out unfairly.
Fifth, document the exceptions. What types of cases must go directly to a person? A hotel chatbot should not improvise on safety incidents. A lending assistant should not turn uncertainty into a confident eligibility statement. A supplier-ranking tool should not silently penalize a new vendor because it has less historical data. Exceptions are where business judgment is most valuable.
Sixth, define the remedy. If the system contributes to a wrong price, misleading claim, delayed order or inappropriate rejection, what happens next? The receipt should name the correction owner, the channels that need updating and the customer or employee remedy. Fast automation without a fast correction process simply spreads mistakes more efficiently.
Seventh, set a review date and a stop threshold. A workflow should be reviewed after enough real cases have accumulated, not left running because nobody has complained loudly. The stop threshold might be repeated factual errors, a rise in returns, unexplained differences across groups or review costs that erase the promised savings.
This record is especially important for platform and aggregator businesses. Business Unfold’s explanation of the aggregator model shows how a single digital layer can coordinate many providers under one customer-facing brand. When AI influences ranking, pricing, routing or support across that network, one weak rule can affect thousands of transactions. The company needs a way to trace the decision back to an accountable owner.
The same principle applies to small firms. Consider a local travel company using AI to draft itineraries. Its decision receipt might permit the tool to assemble options from approved hotel and transport data, require a person to verify visas and safety information, and automatically escalate accessibility requests. The system can still save time, but it does not get authority it cannot safely exercise.
A manufacturing supplier could use AI to summarise enquiries and suggest production routes. The receipt would preserve the original specification, identify who verified tolerances and record why a nonstandard request was accepted or rejected. That discipline supports the sort of scalable coordination described in Business Unfold’s coverage of Zetwerk without pretending that every industrial decision is routine.
This is also consistent with India’s growing emphasis on responsible, human-centred AI. The IndiaAI Mission’s responsibility campaign encourages ethical and accountable use. A decision receipt translates that aspiration into an operating habit. The NIST AI Risk Management Framework similarly organises responsible practice around governing, mapping, measuring and managing risk. Small and medium businesses do not need a large compliance department to apply the core idea.
The receipt can fit on one page. The important point is that it exists before the workflow becomes infrastructure.
Leaders often fear that documentation will slow innovation. In practice, the absence of documentation is what makes responsible scaling slow. When nobody can explain why a system made a choice, every incident becomes a fresh investigation. When ownership, evidence and remedies are already recorded, the company can diagnose problems and improve faster.
AI can help Indian businesses grow, coordinate and compete. But speed is not the same as control. Before automation makes a decision repeatedly, the business should be able to produce a receipt for the first decision: what it was meant to accomplish, what evidence it used, who remained accountable and how the company would put things right.
That is the difference between trying a tool and building a dependable business process.
Dr. Gleb Tsipursky, dubbed “The Office Whisperer” by The New York Times, is a future-of-work thought leader and the author of seven books, including The Psychology of AI Adoption at Work: From Resistance to Results, Never Go With Your Gut, The Blindspots Between Us, and Returning to the Office and Leading Hybrid and Remote Teams. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts and as a Behavioral Scientist at the University of North Carolina at Chapel Hill. Previously, he taught at The Ohio State University for more than eight years and served as a Fellow at UNC. Over more than 20 years, he has consulted for over 100 clients, and his work has appeared in nearly 600 articles and 450 interviews. Contact: gleb@disasteravoidanceexperts.
com.