Ep 45 - Clarity Over Speed: The AI skills that actually matter

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Have you fallen into the AI execution trap? While everyone races to master prompt engineering and ChatGPT tricks, this episode reveals the uncomfortable truth: execution has never been the real bottleneck in innovation. The true differentiator—in every technological revolution—has always been clarity.

We dive deep into how the democratization of AI creates a dangerous "execution obsession" where teams become incredibly efficient at solving the wrong problems. Through examples like Instagram and Tesla, you'll discover why companies don't win through faster building but through sharper problem finding. When AI makes everything easier to create, knowing what's worth creating becomes your only durable advantage.

The episode introduces a powerful framework combining design thinking with AI capabilities to ensure you're solving real problems, not just generating impressive outputs. You'll learn specific practices for avoiding execution traps and developing the clarity that AI can't provide. Beyond problem finding, we explore why communication has become the essential translation layer in an AI-powered workplace. As organizations drown in unused AI outputs, the ability to transform technical insights into human understanding and action separates leaders from followers.

Whether you're a manager navigating AI implementation, a professional concerned about staying relevant, or a leader charting digital transformation, this episode provides a refreshing perspective on what truly matters in the AI era. Challenge your assumptions about productivity and discover why the future belongs not to the fastest builders, but to those who know what's worth building and can explain why it matters. Listen now and gain the strategic advantage that transcends technological trends.

Want to join a community of AI learners and enthusiasts? AI Ready RVA is leading the conversation and is rapidly rising as a hub for AI in the Richmond Region. Become a member and support our AI literacy initiatives.

Episode Transcript

Speaker 1 (00:00):
Welcome back to Inspire AI, the podcast where we
explore how artificialintelligence is reshaping work,
creativity and society.
I'm your host, jason McEntee.
Let me start with this.
Every professional I know islearning prompt engineering.
They're taking courses onChatGPT, udemy, swapping prompt

(00:24):
templates, maybe even studyingmachine learning basics.
Linkedin is flooded with postsabout how to master prompts or
become AI literate, and sureit's exciting.
But here's the uncomfortabletruth Execution has never been
the bottleneck.
The real bottleneck in everyera of innovation is clarity.

(00:48):
In this episode, we're going todig into two skills that matter
far more than prompt tricksProblem finding, which is the
ability to identify what'sactually worth solving.
Communication, the ability totranslate AI outputs into human
understanding and action.
Master these skills and youwon't just keep up with AI,

(01:12):
You'll stand out.
When GPT and other largelanguage models hit the
mainstream, we all became prompttinkerers.
Suddenly, people wereexperimenting.
Explain this like I'm five, oract as a world-class lawyer, or
even walk me step by step.
We call these zero-shot,few-shot, chain-of-thought

(01:37):
prompting, and it felt likemagic.
Let's give prompt engineeringits credit right.
It's lowered the barrier toentry.
It gave people a way to harnesscomplex models without needing
to code.
But there's a catch Promptengineering is brittle.
The right wording today mayfail tomorrow.

(01:58):
The perfect system prompt cancrumble with a model update and,
let's be honest as thesesystems evolve, they're getting
better at understanding what wemean, not just what we say,
which means this Promptengineering may help you get
short-term wins, but it's notthe timeless skill we all think

(02:19):
it is.
Here's where AI creates a realdanger.
Let's call it executionobsession, because when it's
easier to generate, create andbuild, what do most teams do?
They crank out more and moreMore reports, more campaigns,
more features, and they feelproductive because the wheels

(02:42):
are spinning faster.
But speed doesn't equalprogress, you see.
In fact, it can backfire invarious ways.
Let's think about how AIdemocratizes solution building.
But if you're not good atidentifying real problems,
you'll end up building elaborateanswers to issues that never

(03:04):
mattered.
This is called solving fakeproblems.
Think about the endless appsnobody needed or chatbots
solving problems nobody had.
Next, ai doesn't ask is thisthe right problem?
It just helps you move fasterwith the assumptions you already
hold.
If your team believes lowengagement means produce more

(03:28):
content, ai will happily floodyou with posts, but if the real
problem is poor product fit, youjust wasted everyone's time,
including your customers.
We call that amplifying biases.
No-transcript.

(03:53):
I've seen teams celebrate AIproductivity gains without
asking did any of this actuallymove the needle?
This is what we call confusingmotion with progress, and the
riskiest teams today aren't theones dragging their feet on AI
adoption.
They're the ones that havebecome incredibly efficient at

(04:15):
solving the wrong problems.
There's an overlooked truth hereExecution has never been the
limiting factor In tech,specifically in data science and
in product design.
Projects rarely fail becauseteams couldn't build the
solution they imagined.
They fail because they builtsomething nobody needed.

(04:38):
Think about Instagram.
They weren't the only photo app.
They succeeded because theyreframed the problem.
People didn't want to sharephotos, they wanted to share
moments.
Or how about Tesla?
Love it or hate it, they didn'tdominate because they built a

(04:58):
slightly better car or fastercar.
They redefined what problemcars should solve not just the
transportation, butsustainability and energy.
The companies that win aren'tthe fastest builders.
They're the sharpest problemfinders.
Win aren't the fastest builders.

(05:19):
They're the sharpest problemfinders.
Now that AI levels the playingfield on execution speed, the
only durable edge left isknowing what's worth executing.
Let me pause here and ask you inyour own work, are you chasing
execution or investing inclarity about the problem.
So I offer you this To avoidexecution obsession and to get

(05:40):
better at problem solving.
There's a proven framework outthere.
They call it design thinking.
It's a structured approach tounderstanding human needs,
defining problems, ideatingsolutions and testing
assumptions.
And when paired with AI, it'ssuper powerful.

(06:01):
And here's how they complementeach other.
Ai can generate thousands ofideas.
Design thinking helps you pickthe one that matters.
Ai can optimize for any metric.
Design thinking ensures youchoose the metric that actually
counts.
Ai accelerates action.

(06:21):
Design thinking ensuresdirection.
So here are three habits thatmake this real.
First, ask better questions,don't ask.
How do we increase engagement?
Two, observe behavior, not justwords.

(06:45):
Users may say they want morefeatures, but if they're
abandoning your app because ofcomplexity, fewer steps might be
the real solution.
And then, finally, prototypeyour thinking.
You should test assumptionsbefore building A paper sketch.
A quick survey or a landingpage experiment can save months

(07:08):
of wasted execution.
This is how you turn AI from aproductivity gimmick into a true
innovation partner.
But even problem solving isn'tenough.
Like I said, there's anotherbottleneck communication.
Here's the dirty secret oftoday's AI boom.
Companies are drowning in AIoutputs that sit unused

(07:32):
Dashboards, reports, riskanalyses the petabytes are
piling up.
They aren't being ignoredbecause they're wrong, but
because nobody translates theminto human understanding.
That's where communicationcomes in.
Let's call it the translationlayer for the AI tech geeks out

(07:54):
there.
Your job isn't just to operateAI.
It's to make it useful tohumans by simplifying complex
insights into plain language and, of course, contextualizing
outputs so they fit your culture, your priorities and your
constraints.
You should be thinking abouthow to turn AI's what into a

(08:18):
human.
So what?
And now what?
And let's not forget abouttranslating vague business
questions into inputs AI canactually handle.
Think about it when everyonehas access to the same AI
insights, the competitive edgedoesn't go to the best operator.
It goes to the best translator,the person who can explain what

(08:42):
those insights mean and what todo about them.
So how do you build thiscompetitive communication
advantage?
You simplify.
Practice explaining AIrecommendations in one sentence.
If you can't do that, youprobably don't understand them
well enough.
Yet you also want to be able tocontextualize.

(09:03):
Ai doesn't know your company'spolitics, budget or timing.
You do so.
Always frame outputs in thecontext of what actually matters
.
Focus on implications.
Every AI insight should answerso what and what's next?
Let's not forget aboutdeveloping the right frameworks.

(09:23):
What's next?
Let's not forget aboutdeveloping the right frameworks.
Think about having repeatableplaybooks for how your team
responds to AI insights, forexample, if AI flags a customer
risk.
Here's the exact three-stepprocess you should follow.
Finally, ask better questions.

(09:44):
Translate fuzzy human requestsinto clear, structured AI
prompts.
What used to be help usunderstand customers better
becomes.
Summarize the top threefrustrations our customers
mention in support tickets.
Communication isn't fluff, it'sleverage.

(10:05):
And the irony is, the moreadvanced AI becomes at
generating outputs, the morevaluable clear human
communication becomes.
And if we quit practicing thoseskills, we're going to give up
more and more of our autonomy tothe AI outputs and forget the
origins and art of amazingcommunication that brings people

(10:27):
and ideas together.
So let's bring it all home.
Prompt engineering while it'suseful, now it's fading.
Execution obsession is a trapthat makes you efficient at
solving the wrong problems.
Problem finding is timeless.
It's the wrong problems.

(10:51):
Problem finding is timelessit's the real edge.
Design thinking is thediscipline that keeps AI pointed
in the right direction, andcommunication is the missing
link that makes AI outputsactionable.
In the end, the future doesn'tbelong to the fastest builders
and the future doesn't belong tothe fastest builders.
I'll say it again the futuredoesn't belong to the fastest
builders.
It belongs to those who knowwhat's worth building and who

(11:15):
can explain why it matters.
That's it for today's episodeof Inspire AI.
Thank you for listening.
Don't forget to like share ourcontent.
Thank you for listening.
Don't forget to like, share ourcontent.
Remember clarity beats speedand communication turns insight
into impact.