The Rise of AI in Decision Making
Artificial intelligence (AI) has become increasingly prevalent in our lives, with the launch of ChatGPT in November 2022 being a significant milestone. Terms like “inference,” “reasoning,” and “training data” are now commonly used, even in everyday conversations. However, it’s essential to remember that AI is meant to augment human decision-making rather than replace it entirely.
In the realm of enterprise data, various AI tools like ChatGPT, Glean, and Perplexity are assisting professionals in making informed decisions. For example, a product marketing manager can use a text-to-SQL AI tool to determine which customer segments have given the lowest Net Promoter Score (NPS). This information can then be used to tailor promotional strategies effectively.
This represents AI augmenting human decision-making, but what about the future? It is possible that we will reach a point where a CEO can simply instruct an AI to design a promotions strategy based on existing data, industry best practices, and past experiences. The AI would then produce a strategy comparable to that of a skilled human product marketing manager. In fact, there may even come a time when the AI acts autonomously as a Chief Marketing Officer (CMO), independently working on promotions strategies to share with the CEO.
However, until artificial general intelligence (AGI) becomes a reality, humans will likely remain involved in significant decision-making processes. While many speculate on how AI will transform our professional lives, it’s important to recognize what it won’t change anytime soon: good human decision-making.
Tips for Leveraging AI in Decision Making
When working with AI-generated data and analyses, it’s crucial to adopt effective decision-making strategies. Here are some tested ideas for making the best possible decisions:
1. Decide beforehand: Set clear go/no-go criteria before analyzing the data. Humans often change their criteria in the moment, leading to biased decision-making. By establishing criteria in advance, you can make unbiased decisions based on expert judgment. This prevents reverse-engineering new criteria based on factors like data appearance or the sentiment in the room.
2. Document individual opinions: After presenting the data, allow decision-makers to independently document their thoughts before sharing them with the group. This ensures that everyone’s expertise is considered without fear of disagreement being suppressed. By discussing areas of divergence in opinion, you can leverage the broad expertise of the group.
3. Discuss smaller decisions: Recognize that big yes/no decisions are composed of smaller decisions. Explicitly discuss these smaller decisions before addressing the main one. This helps improve decision quality by making implicit questions explicit.
4. Document decision rationale: Documenting the rationale behind a decision allows for honest evaluation during future business reviews. This feedback loop helps improve decision-making and separates skill from luck.
5. Set kill criteria: Determine criteria that, if unmet after launch, signal that a project should be terminated. This prevents selective blindness and ensures intellectual honesty when evaluating project performance.
Although these strategies may initially require additional effort, they quickly become second nature and yield high ROI. By ensuring that all expertise is expressed and setting guardrails, you limit decision downsides and facilitate continuous learning.
The Value of Human Expertise in Data-Driven Decision Making
As long as humans remain involved in decision-making processes, the ability to work with data and analyses generated by both human and AI agents will remain crucial. Navigating cognitive biases while working with data is a valuable skill set that professionals must develop.
In conclusion, AI is revolutionizing decision-making, but it is not replacing human judgment entirely. By understanding how to effectively leverage AI and combining it with human expertise, professionals can make informed decisions and drive success in their organizations.
Sid Rajgarhia, an investment team member at First Round Capital, has spent the last decade working on data-driven decision-making at software companies. His insights highlight the significance of incorporating AI into decision-making processes while recognizing the ongoing value of human expertise.
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