My feed is filled with answers. Gurus and experts constantly say, “Do this and solve your performance problems in the next quarter!” One, constantly posts, “I can redesign [X] role in 30 days.” These are often followed by prompts for AI to develop and tune the answers. I suspect this individual has never held many of the roles he expertly redesigns.
Most of the time, my reaction is, “Why is it taking you so long?”
After decades of doing this, I can walk into an organization, and within about a week, I have an idea of the biggest issues, and potential solutions. Start-up, Global 50, it doesn’t matter.
Developing answers is easy!
And we see this in so many of our engagement strategies. Our salespeople have the answer, “Buy my product!” And the response, 100% of the time is, “You don’t understand!”
We leave the customer with a choice of answers, none of which is wrong, but answers that still don’t address their problem.
And with AI, we can make it easier, giving it an aura of personalization. The problem isn’t AI gives bad answers. In fact, they may seem highly personalized to you and your business. They are still generic answers, but harder to recognize as such. It doesn’t know the dynamics of what’s happening now. It doesn’t know what each person is thinking, their ideas, or what they are worried about. It can’t develop a unique answer that is most workable now.
But are these the right answers for this organization right now? Two competing organizations, about the same size, about the same performance, will have differing “best answers.”
For one, it might be expanding into new markets and acquiring new customers in those markets. For another, it might be expanding their product lines to expand penetration of their current markets. Each is a great strategy, but each fits each organization differently.
As we look to develop the answers, there is never a single answer. There is never a best answer. It may be one that’s fastest to put in place with current resources. It might be one that addresses the risks differently. It may be one that is all that they can do right now.
The hard work isn’t coming up with answers, it’s exercising the judgment to choose the one that’s most workable now.
Experienced leaders may be seduced by certainty. “I’ve seen this before,” fails to recognize
“it’s always different.” Others may struggle in different ways. They recognize past failures, they may struggle with choices. They will consider the risks and choose the solution, based on all these factors. In doing so, they recognize this choice is a starting point.
Inexperienced leaders face these decisions differently, They think they may be taking a decisive step, the rest is all in the implementation. They think the hard part ends, when the decision is made
And now with leaders using AI, AI produces a confident choice, with none of the hesitation. Yet it doesn’t know the dynamics, the reality of what the organization faces to know whether it is the best choice for the situation. Leaders, succumbing to this faux confidence surrender their judgment of what might be achievable.
The real issue is how leaders understand the decision itself. Some see it as an end, “Here’s the decision, now go execute!”
Others treat it as a hypothesis, “Given what we currently know, this is our best choice.”
The answers are easy.
Choosing the better answer for current circumstances is difficult.
Whether experienced or inexperienced, or AI assisted; we invest a disproportionate amount of time in developing and choosing the answer.
But the answers just set a direction or goal. And the reason we may struggle with these answers is that we recognize this is the starting point. However, the judgment that matters is that which is exercised in implementation.
Implementation isn’t the execution of the answer. It’s where we learn what the real answer needs to be.
People don’t respond as expected, capabilities we thought were in place may be non-existent. Customers don’t react as expected, competition changes, people interpret the change differently. The assumptions that drove the decision may no longer be valid.
The change itself is where the learning happens.
We start the process with what we thought was the best answer, but in implementing it, we discover something new, we learn, we adapt. We make changes in what we do.
And sometimes, we realize we are pursuing the wrong answer.
Discovering that our original answer wasn’t quite right, or even may have been wrong, isn’t necessarily a failure of the analysis in choosing the answer. It’s what we should expect when the answers meet reality.
When we fail to recognize this and adapt, most often we don’t achieve what we hoped. When we recognize the real work is in the implementation and the learning that creates is where we sometimes achieve more than we thought possible.
Answers are easy! A dime a dozen.
Choosing the answer most appropriate to your current situation is tougher.
But the work of making it happen is where we discover what the good answers are.
Afterword: Another fantastic AI based discussion of this post. They even make an observation that I’m arrogant…… Hmmmm…… Enjoy!
