How to Fail at AI Strategy: Hamel Husain & Greg Ceccarelli

Summary

In this engaging and humorous presentation, Greg and Hamill guide audiences through a satirical approach to creating disastrous AI strategies. With a focus on embracing poor practices, they highlight how to break down company collaboration, define vague strategies, and foster miscommunication. They stress the importance of confusion, jargon, and ineffective metrics, all while emphasizing the avoidance of data analysis and customer feedback. The duo’s tongue-in-cheek advice serves as a warning on what to avoid when implementing AI strategies, with a mockery of corporate dysfunction and misguided leadership.

Takeaways

• 😀 Embrace disconnect between teams and create impenetrable silos to ensure failure in AI strategy.
• 😀 Make unrealistic promises about AI's capabilities (e.g., solving world peace), but ignore the details and costs.
• 😀 Create a vague and ambiguous AI strategy, avoiding clear definitions of success or specific goals.
• 😀 Use jargon liberally to confuse others and make everything sound more complex than it needs to be.
• 😀 Randomly assign AI tasks to people with no relevant experience to foster disorganization and dysfunction.
• 😀 Launch untested AI systems straight to production without beta testing or quality assurance.
• 😀 Focus on tools, not processes. Whenever something goes wrong, throw more tools at the problem rather than analyzing the root cause.
• 😀 Use arbitrary metrics that have no meaningful connection to actual business outcomes to measure progress.
• 😀 Avoid looking at or trusting data. Leaders should never engage with data themselves and should delegate everything to tools.
• 😀 Put data into complex systems that only engineers can access, ensuring that no one else understands it.
• 😀 The ultimate goal is to waste resources, alienate your team, and create chaos while claiming success based on meaningless metrics.

Q & A

What is the main theme of the presentation?

The presentation humorously discusses how to completely fail at AI strategy by highlighting poor practices, organizational dysfunction, and misguided decision-making. It is an inverted guide on what to avoid when building an AI strategy.

How does the presenter suggest dividing and conquering a company in an AI project?

To set up failure, the presentation recommends creating silos within the company, promoting secrecy, and discouraging communication between teams. The idea is to prevent collaboration and understanding, which leads to inefficiency and confusion.

What is the 'anti-value stick' mentioned, and how does it relate to AI strategy?

The 'anti-value stick' is the opposite of good value creation. It suggests focusing on wishful thinking, making extravagant promises to customers without a clear plan, and ignoring the cost-benefit analysis of AI infrastructure, which will inevitably lead to technical debt.

What is the 'wishful thinking' approach to AI, and how does it affect customers?

The wishful thinking approach involves over-promising AI capabilities, like solving world peace or writing emails, without addressing the technical realities. This strategy leads to disappointing customers and undermines trust in AI projects.

What does the presenter recommend for defining an AI strategy?

The presenter advises creating a vague and ambiguous strategy that includes unrealistic goals, such as becoming the global AI leader in everything, without defining what 'everything' means. This ensures confusion and lack of direction.

How should executives communicate their AI strategy to employees?

Executives should use jargon-laden language and create a flood of documents to make the strategy sound impressive and complex, even if nobody understands it. The goal is to create confusion and obfuscate the actual purpose of the strategy.

What role does jargon play in this AI strategy?

Jargon is used strategically to prevent clarity and hide the actual objectives. By using complex technical language, it becomes difficult for employees to understand the real goals, ensuring that they remain disengaged and disconnected from the work.

What does the presenter mean by the 'zoning to lose' framework?

The 'zoning to lose' framework involves assigning AI tasks to individuals with no relevant experience and outsourcing critical activities to offshore teams with no understanding of the business. The goal is to create an environment where failure is inevitable due to lack of expertise and preparation.

How should AI systems be mobilized in this disastrous strategy?

The mobilization phase should involve deploying AI systems that are untested, poorly designed, and full of bugs. The emphasis is on shipping products to customers without any quality assurance, resulting in poor customer experiences and potential PR disasters.

What advice is given regarding the use of data in AI projects?

The presenters advise ignoring data analysis, avoiding any real engagement with data, and instead trusting AI outputs blindly. The focus is on using tools without understanding them and avoiding any critical evaluation of results, which leads to poor decision-making.