Tag: artificial_intelligence

Teams goals outweighing personal KPIs?

Tyler Cowen’s August 17th Bloomberg column (Bloomberg) highlights a significant shift in how how talent is evaluated and rewarded in modern organizations.

The Question

If the output to be created is a result of team-based functions, how do you determine who to put on the team and how to compensate that person for their inputs?

The Problem

  1. Giving credit where credit is due. Who owns the largest % of meaningful contribution? How does that get measured?
  2. The use of AI. How much was AI leveraged? Do you compensate people for improving the AI database? To reward or not reward for effective prompt engineering?
  3. How do we find the right people to work on the team? Tyler notices that firms are starting to focus on ex ante signals of quality (a degree, signals of status, etc.) vs taking a chance on outsiders that may prove more beneficial.

My Take

  1. Talent Spotters/Hiring Managers: Get better at spotting talent! Talk with other leaders about how they assess and look for talent. Talk with highly talented contributors and learn about their work. Learn about other disciplines and imagine how talent from that discipline may help you in yours. I found that engineering and music professionals are fantastic customer success managers.
  2. Managers: Depending on your business, it’s possible you’ll need to rethink your KPIs. Perhaps team-based KPI and comp plans are best when the ideal outputs are a result of team dynamics.
  3. Talent: Realize that the signals you put up to indicate your availability will need to change. Networks will become more important for people who have and don’t have credentials.
  4. Managers: How do you reward people who improve the use and adoption of AI in the firm? It’s not enough to suggest good prompts. How are people incentivized to use AI as an efficiency and problem solving partner?

One Useful Action

If nothing else, simply ask yourself: do I have the right talent in the right seats? And, how sure am I that I’m not missing out on undervalued talent?

Cultural Stagnation = f(AI, and Creative Output)? And why team managers should care.

I’m thinking about the connection between AI and human creativity. The question is: what’s the connection creativity stagnation and AI’s transformative potential? And what can managers do about it?

I observe two things.

  1. Scott Buchanan of Economists Writing Every Day writes that investors had high hopes for AI-related investments. The thinking is that AI would revolutionize the world. Recently, analysts wonder if they’ll see an ROI.
  2. Ted Gioia of The Honest Broker writes that the entertainment industry is stagnating creatively. Music preferences are regressing to the past, the New York Times 100 best books of the 21st century contained writers who were mainly known in the 20th century.

Here’s what I think I know.

  1. AI is built on human knowledge.
  2. Human knowledge is an output of humans — largely from some creative/scientific (they can be the same) production function.

Is it possible that we’re realizing that AI is not as revolutionary as we thought because we’re not as revolutionary as we hoped?

This is not the blog for people to learn how to adopt AI into their workflows. Plenty of smarter people are writing about that.

This is the blog for people who obsess about talent and want a (often contrarian) perspective. And because you’re a person who cares about talent, here’s how I believe like us act.

  1. We do all we can to find generative talent. We open our minds to people who are different or don’t have the “perfect” resume and look for people with skills we can use. We increase the breadth and depth of our human capital!
  2. We adopt management styles that promote creativity. We engage in brain storming, ask for talent to give us inputs into decisions, we give inputs, we let people experiment, and we help coach decision-making versus coaching outcomes.
  3. We stop talking and listen more. We let talent give their ideas and we engage with their ideas. We say, “I think you’re trying to accomplish z, and you got outcome y, and and if you take path x your process may get you closer to z.”

Happy generating.

I like these 4 ways to use AI for coaching and productivity

Taken from Ethan Mollick’s, Co-Intelligence: Living and Working with AI. The book is a practical guide for understanding AI, it’s implications, and how to leverage it for personal and professional life. Recommended.

I like the below applications because it forces the user, presumably your team member, to be curious. I’m not an AI expert, but based on what I’ve read it appears that AI’s utility may be a result of the quality of a user’s curiosity.

I’m pushing the Customer Success Managers (CSMs) on my team to use prompts 1, 3, and 4 daily.

  1. As a coach. 
    • Prompt: I was thinking of sending an email to a client, but I am busy and I’m scared of the outcome and I don’t think I want to send the email until I have…..  Can you reframe my failure to send an email as a loss rather than a default option?  Make the framing vivid.
  2. Ask the AI to impersonate people.  Famous public figures are best:
    • Prompt: “Act as Bill Gates” and then ask for business advice.  
    • Prompt: “Act as a witty comedian and generate some subject lines for my email that get people to laugh”
    • Prompt: “You are an expert and consultant at performance marketing. How would you present these performance insights to your clients in a way they would understand and feel secure in their investments?”
  3. As a teacher.
    • Prompt: “You will be my negotiation teacher.  You will simulate a detailed scenario in which I have to engage in a negotiation.  You will fill the role of one party, I will fill the role of the other party.  You will ask for my response in each step of the scenario and wait until you receive it.  After getting my response, you will give me details of what the other party does and says.  You will grade my response and give me detailed feedback about what to do better using the science of negotiation.  You will give me a harder scenario if I do ell, and an easier one if I fail.”
  4. As a data analyst partner:
    • Prompt: “Adopt the persona of a data scientist.  Here is a spreadsheet I’ve uploaded.  (define the data).  How would you answer these questions…. using the data given?”