Remembering the CoPilot

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My Sustainable Encounter with Nilesh Dayalapwar

by Albert Schiller

The Illusion of Answers

We have built machines to give us answers. What happens when we forget how to ask the right questions? The rise of ESG software offers an alluring promise to leaders: a simple, quantitative answer to the complex question of a company's impact, a nation's health. It presents a world of clean metrics and cleaner dashboards. But Nilesh Dayalapwar's philosophy issues a stark warning against this seductive simplicity. He argues that an over-reliance on automated tools risks creating a dangerous form of intellectual complacency. We become so focused on the machine's answers that we stop interrogating the reality it claims to represent. His work forces us to ask a more fundamental question of modern leadership. Are we using our tools to sharpen our judgment, or are we allowing them to replace it entirely?

The Ghost in the Circuit

An electrical circuit is predictable given that its logic is binary and absolute. A human circuit is not. What happens when we apply a tool built for the clean world of code to the messy world of human behavior? This is the conceptual problem at the heart of Nilesh Dayalapwar's experience. He argues that while technology can track KPIs with perfect accuracy, it is blind to the "ghosts" in the system: stakeholder resistance, the fear of change, and the powerful, irrational forces of an ingrained corporate culture. For instance, a tool can report that a new sustainable sourcing policy is not being adopted. It can never tell you why the procurement team is pushing back, a resistance rooted in legitimate fears about budgets, supplier relationships, and personal KPIs. This disconnect, this interactional void, is where sustainability strategies fail. Diagnosing and bridging this gap requires a human form of intelligence based on human experience. Can a tool that only measures data understand a system governed by human values, fears, and hidden incentives? Does a machine ever really “get it”?

Being Black Boxed

When a tool is easy to use but its internal logic is opaque, does it empower us or make us dangerously overconfident? This is the paradox of the "black box," and it is Nilesh Dayalapwar's primary warning. He argues that companies can be easily misled by their appealing technology, creating a "spiraling fiction" of progress. An ESG tool might be programmed to ask five convenient questions and deliver a good score, while ignoring the ten inconvenient questions that would reveal a failing strategy, thus leading the company to ultimate ruin. This creates another disconnect. Does a simple, clean output from a complex, misty tool provide feasible knowledge? Or does it merely give us information stripped of the context, nuance, and missing data that make it useful? Dayalapwar argues that without a human expert to perform an "internal orbit of the data," question anomalies, and spot errors, a company risks reporting inaccurate results, facing government penalties, and losing business. A dashboard without a skeptic to question it is a wall without a door.

Autopilot Off

This leads to Nilesh Dayalapwar's standing philosophical hierarchy. What is the fundamental, irreplaceable role of the human leader in an age of automation? It is not to manage data, but to manage meaning. A tool can run calculations, but cannot inspire a resistant team, build trust with a skeptical leader, or drive a new culture of accountability. These are the core functions Dayalapwar identifies for contextual leadership, and they are exclusively human. Dayalapwar argues that the greatest mistake a company can make is to believe purchasing a tool is a substitute for cultivating in-house expertise and human leadership. His philosophy establishes a clear order of operations: people, with their values, judgment, and ability to connect with others, are the indispensable drivers of change. Machines and software are merely the tools they execute with. The tool is an extension of the driver's will, not a replacement for it. As our tools evolve, does our most daring challenge become not questioning what they can do, but remembering what they cannot?

So what can we take from his approach?

Questions for Audience

1. Nilesh Dayalapwar warns of an "interactional void" where data fails to capture human reality. In your own organization, where does this void exist, and what kind of "contextual leadership" is needed to bridge it?

2. As our tools get better at giving us answers, what concrete practices can we implement to ensure we do not lose our ability to ask the right, inconvenient, and fundamentally human questions?