I believe Artificial Intelligence (AI) will transform the world. This could be the specific use of AI, for example, in the field of life science, or the general use of AI, helping every individual to maximise their potential.

As it relates to business, the recent article from Jeremy Utley (Adjunct Professor at Stanford University) positions the following statement.

“Good companies reward success, punish failure, and ignore inaction. Great companies reward success and failure, and punish inaction.”

The article itself (linked below) is approximately a 10-minute read and well worth the time, it builds nicely on Jeremy’s previous article “The Most Important AI Role Has Nothing To Do With Code”.

As it stands today, many businesses are “dabbling” in the use of AI, specifically Generative AI and/or Agentic AI. It is often positioned as a recommendation, which means engagement and adoption can be inconsistent and slow.

As stated in the article, the difference between “recommended” and “required” is subtle, but can be transformative. For example, “encouragement acknowledges the old paradigm while suggesting a new one. Requirement establishes the new paradigm as the baseline.”

I completely agree with this insight, especially in the context of AI. Therefore, I am personally pushing my business to reward success and failure, and punish inaction, with specific requirements that force action.

To achieve this outcome, it is critical to have clearly defined, objective measures. Measures help remove ambiguity and enforce accountability, ensuring every individual has a clear expectation.

The article outlines a three-part framework for implementing accountability:

  • Individual Usage Demonstration: Any employee should be able to show, on the spot, how they are using AI in their regular workflow. They should be able to share their screen and walk everyone through one meaningful use case from which they are routinely deriving value. This doesn’t mean showcasing major breakthroughs, just evidence of integration.

  • Calendar Evidence: Where on an individual’s calendar is experimentation happening? Make AI experimentation a scheduled activity, not something to fit in “when there’s time”.

  • Normalizing the Question: When engaging with your team, don’t be afraid to ask the seemingly obvious question, “Have you tried AI?” Repeatedly asking is a great way to normalise AI use. It takes repetition to work AI into our workflows and muscle memory. Asking this simple question regularly helps reassure individuals that this is an expectation.

This framework is a great starting point for any business looking to drive engagement and adoption of AI, whilst also being simple enough that it can implimented with very minimal effort.

If you agree that AI will transform the world, then as a business, the highest risk associated with AI is inaction. Imagine two businesses, one that has fully embraced AI and another that has not. In the first business, every individual has a “force multiplier”, combining the best of human and machine intelligence.

In my opinion, this is a clear differentiator and will lead to a competitive advantage, accelerating productivity, automation of common tasks and unlocking new insights from data.

enterprise artificial_intelligence ai machine_learning ml generative_ai agentic_ai large_language_model llm natural_language_processing nlp transformer data prompt_engineering strategy organisational_change_management ocm lifeinwork