If applying GenAI to a process doesn't improve key metrics like revenue or cost, it's a sign that the original human task was likely low-value or "BS work." The AI exposes work that doesn't contribute to business outcomes, prompting re-evaluation of its necessity.

AI's primary value isn't replacing employees, but accelerating the speed and quality of their work. To implement it effectively, companies must first analyze and improve their underlying business processes. AI can then be used to sift through data faster and automate refined workflows, acting as a powerful assistant.

A critical error in AI integration is automating existing, often clunky, processes. Instead, companies should use AI as an opportunity to fundamentally rethink and redesign workflows from the ground up to achieve the desired outcome in a more efficient and customer-centric way.

The Goldman Sachs CEO differentiates between two types of AI adoption. Giving employees AI tools to make them more productive is relatively easy. The much harder, yet more impactful, challenge is fundamentally re-engineering long-standing, complex processes like customer onboarding from the ground up.

The historical adoption of electricity in factories shows that true productivity gains came from redesigning the factory floor, not simply replacing steam engines. Similarly, companies must fundamentally re-engineer processes around AI to unlock its transformative potential.

The most significant gains from AI will not come from automating existing human tasks. Instead, value is unlocked by allowing AI agents to develop entirely new, non-human processes to achieve goals. This requires a shift from process mapping to goal-oriented process invention.

Beyond individual productivity gains, AI's strategic enterprise value is its ability to re-engineer core operations. This automation creates significant efficiency savings, unlocking capital that can be reinvested into strategic technology spending without negatively impacting financial returns.

Adopting AI acts as a powerful diagnostic tool, exposing an organization's "ugly underbelly." It highlights pre-existing weaknesses in company culture, inter-departmental collaboration, data quality, and the tech stack. Success requires fixing these fundamentals first.

The productivity boom from AI won't materialize from workers simply using new tools. Citing historical parallels with electricity and computers, the real gains are unlocked only when companies fundamentally restructure their operations and business models around the technology.