2 Things You Lose Forever When You Ask AI to Decide Your Future
Delegating the hard things won't make your life easier in the long-term
Product Leaders, VPs of Product, Product Managers – the higher you go, the more you need to write, tell stories, and persuade others so they can take your ideas and bring them to life.
When ChatGPT and AI started to take off about a year ago, many leaders secretly rejoiced at the prospect of finally being relieved of the drudgery of writing of any kind. Analysts gushed about getting “perfect” strategies and industry-leading “finished” product requirements documents delivered “in seconds.”
While I understand the temptation, it’s important to be aware of the two things we lose when we jump to the end result without going through the process:
You’ll miss out on the hard thinking necessary to build skills and mastery
You’ll lose key collaboration opportunities
1. Digging Deep & Slow
Daniel Kahneman, who recently passed away, earned a Nobel prize for developing his concept of the two modes of thinking in his revolutionary work, “Thinking, Fast and Slow”:
Type 1 Thinking – Fast, intuitive, automatic, but prone to biases beneath the level of our conscious awareness. Type 1 allows us to drive a car or ride a skateboard without having to think about how to do it. We can also call this “Reflexive” thinking.
Type 2 Thinking – Slow, thoughtful, effortful, aware of the problem to be solved, and the process of breaking the problem down. Crucial for untangling complex challenges and allowing us to learn new skills. We can also call this “Reflective” thinking.
One mode of thinking isn’t better than the other – both are essential to survive.
The toll that ubiquitous fast Internet and smartphones have reduced every challenge in life to an instant web search away, increasingly allowed our Type 2 thinking muscles to get soft and flabby. When we get lazy and avoid exercising our Type 2 thinking muscles, we’ll increasingly lose the ability to learn new skills or deepen them.
And this is AI’s “siren song” –
“You don’t have to do all that hard thinking. Let us do it for you.”
There might be areas you can do this, like tracking down a “Jobs to Be Done” format, or doing some high-level research. But using AI as a crutch and trusting it too much to actually write those “Jobs to be Done” stories for you can and will cause you tremendous pain.
Instead, exercise your own Type 2 Thinking brain power to focus on and work backwards from your users’ needs.
2. The Power of Collaboration
Whatever your building, you’re most likely not going to do it all on your own,
1. Designing Strategy as a Team Sport
Designing Strategy benefits from the combined efforts of a small, diverse, cross-functional group committed to the process.
As they collaboratively step through the 7 Steps of the Strategy Process Map, they ideate through multiple sets of strategic choices, focused on solving a specific problem, their target set of users, and matched with their company’s capabilities.
The final completed strategy “deliverable” is only part of the goal.
Having a range of temperaments, experiences, and areas of expertise in the room will help design the best possible strategy. Even more importantly, it’s the shared experiences and collaborative learning that unfold when the group is doing deep, “Reflective” hard work of designing, discussing, testing, diverging, reviewing, converging, and ultimately making the best set of choices they can.
Think of it as shared “Type 2” thinking, with a more open-minded, flexible and capable group coming out the other side.
2. OKRs Crafted Collaboratively by Both Leadership and the Cross-Functional Team Doing the Work
Once you have a clear set of Strategic Choices, setting metrics that let you know you’re on track via Objectives and Key Results is the next step.
Facilitating this rapidly in a lightweight fashion is key.
Leadership inspires
OKRs are done best when leaders set the inspirational Objectives, or a “mini-missions for the quarter,” against the context of the strategy.
Teams can then provide feedback and input into refining them.
Teams “nest” their Key Results to the Objective
After collaboratively mapping their User Journey and the Opportunity Solution Tree, teams can identify Client Behavior Change Outcomes to influence along that journey.
The teams of teams can then collaborate with each other and stakeholders to craft the Key Results that will contribute to the achievement of the Objectives.
The key to this entire effort is having the foundation of strategy, working backwards from client-centric problems, and laying the aspirational path towards the achievement of the Objectives.
Delegating the OKR drafting process to Generative AI would mean missing out on the countless valuable conversations between leaders and their teams to align on shared understanding.
Product Management: Seeing the Best Decisions Get Made
Product Management involves a wide array of skills and competencies that differ depending on which expert you ask.
Some involve tons of research, grokking tons of data. This is exactly the problem that Kraftful.com’s Product Research platform seems to serve brilliantly well, as it “synthesizes user feedback from app store reviews, support tickets, call transcripts” and returns insights in plain language.
But it’s been said that “product managers aren't the ones who make the best decisions, they're the ones who see that the best decisions get made.”
And I can absolutely guarantee that outsourcing your decision-making process to ChatGPT will return “plausible” but generic results, or worse, pure “hallucinations.”
Translating Strategy into Software
One big part of the Product role involves creating the central artifacts to guide teams through their Design and Engineering work to produce working, tested software.
Teams come to rely on Product Requirements documents (PRDs) or Epic Business Cases, or User Stories so they can take action.
In some teams and organizations, they “delegate” these tasks to Business Analysts, who go off by themselves and churn through and write all of the necessary documentation, then hand them off to the team for “execution.”
Siloing this work with one person, and unquestioningly taking their output to mindlessly code against seems to return us to the Waterfall value of big, up-front planning.
And even the original framer of Waterfall, Winston Royce, felt his approach was “risky and invites failure.”
Design artifacts collaboratively
The best teams create these artifacts as a shared, cross-functional, collaborative experience.
(Seeing a pattern here?)
If we do have to slice it down into a smaller group, look to involve “The Product Trio” at the very minimum – the shared perspective of the Product, UX, and Tech Lead/Architect roles.
All three of those perspectives are crucial to lean in and design the artifacts that the team use to guide their activities.
TL;dr– Outsource Research and Formats, Do the Hard Thinking Work Yourself Or Collaboratively
Unless you already have a Virtual Assistant who’s brilliant at doing research, the best use of Generative AI tools is to learn to prompt them effectively and outsource some aspects of research to them.
It will be up to you to go through that research, owning for yourself the hard work of “System-2” thinking.
And having the courage to collaboratively design your company’s future with a small, diverse, collaborative, cross-functional team.
But you still need to learn how to prompt
As long as we delegate the right tasks, there’s enormous value to having a research assistant who’s always ready, and never gets tired.
Being able to prompt Generative AI, like being able to cook a meal, engage a friend in conversation, sell, negotiate, give a speech, draw a picture, use a computer, get the most out of a phone or a tablet, or even run a Google search, will be just one more necessary measure of effectiveness in today’s world.
I learned more about business strategy when I started writing on the topic than I had known before. Writing makes us structure our thoughts because the language is structured by nature. If our writings are as chaotic as our thoughts are, no one will understand us. So, when we put our thoughts on paper we have to think through them carefully. And this, in turn, sharpens our thinking. So, I will never delegate my writing to AI.
Thank you for this article. I've been trying to put this emotion into words for a while now, and this helps me articulate my thinking here. It also reminds me of what I tell people when sharing documentation, diagrams, slides or other materials. They are the "evidence of thought and collaboration." While important artifacts for future reference and new joiners to the team, the Work of thinking must still be done, and we need practice, hard work, failure and success to get better at it. Simply asking an AI to think for us contributes to the mental flab - like the passengers in Wall-E.