In just a few years, AI has gone from a specialist topic to something everyone has opinions on.
asserted
everyone → go → opinions
It has been praised as miraculous, condemned as dangerous, and debated everywhere from boardrooms to dinner tables.
asserted
It → praise → tables
But between these poles of euphoria and dread lies the reality most of us now face: a technology powerful enough to reshape how work gets done, yet still deeply dependent on human judgment.
asserted
work → lie → judgment
Too often, headlines focus on extremes, but the practical questions are far more grounded.
asserted
questions → focus → extremes
How do we manage this technology?
asserted
we → manage → technology
How do we and our teams work alongside it effectively?
asserted
we → work → it
How do we capture its benefits without compromising our values or our goals?
asserted
we → capture → values
To find useful answers, we have to start by understanding where we are, what has truly changed, and what has not.
asserted
what → find → answers
It has been with us since at least 1956, when a small group of computer scientists gathered at Dartmouth College to explore a deceptively simple question: Can machines think?
asserted
machines → gather → question
Since then, the technology has seen decades of progress and setbacks, so-called AI summers and winters.
asserted
technology → see → progress
AI didn’t suddenly appear in the world in the 2020s.
asserted
AI → appear → 2020s
But powerful, general-purpose AI systems pushed the technology into mainstream awareness in ways that earlier breakthroughs never did.
asserted
breakthroughs → push → ways
It’s natural that the reactions have been polarized; rapid change often leaves people
unsure what comes next.
asserted
what → ’ → people
Today’s systems are powerful and raise new challenges, and we will take those challenges seriously throughout this book.
asserted
we → raise → book
But they are not as alien or unmanageable as they are sometimes made out to be.
asserted
they → make → ?
We already have decades of lessons and frameworks from earlier generations of AI and from other complex technologies, from aviation to automobiles to power plants, all of which can be adapted to this moment.
asserted
all → have → moment
Understanding what’s the same and what’s different about this moment for AI and work, and how we can build on what we already know, is crucial.
asserted
we → understand → what
If you’re new to AI, I will offer you a map of essential concepts so you can navigate confidently.
asserted
you → ’re → concepts
If you’ve been here for a while, I will reframe the challenge, moving the conversation toward leadership and collaboration rather than technical mastery alone.
asserted
I → reframe → mastery
After all, how we choose to work with AI, and who we’ll become in the process, is something we still get to decide.
asserted
we → choose → process
A number of factors have propelled today’s AI from data science laboratories into the center of everyday business conversations.
asserted
number → propel → conversations
Generalists, not specialists:
For decades, AI was used behind the scenes, embedded in models that (for example) predicted customer churn or flagged fraud.
asserted
that → use → fraud
Those systems were specialists, usually trained for one narrow task and confined to it.
asserted
systems → train → it
These “foundation models” are vast neural networks trained on oceans of data and capable of being adapted across contexts.
asserted
models → train → contexts
The same model that helps a developer write code can also be harnessed to help a marketer write copy or an HR leader write a job description.
asserted
leader → help → description
That versatility is what brought AI out from the back office and into nearly every corner of knowledge work.
AI
asserted
what → bring → work
that speaks like humans
asserted
that → speak → humans
A second shift is that AI now speaks our language, literally.
asserted
AI → speak → language
We no longer need to code or click through rigid menus to interact with AI.
asserted
We → need → AI
We can simply use everyday language, and the responses come back in polished, humanlike prose, making the technology broadly accessible.
asserted
technology → use → prose
These systems can also generate new content, including text, images, and beyond—which is why this wave is often called “generative AI.”
asserted
wave → generate → text
But that ease of use creates a new kind of business responsibility.
asserted
ease → create → responsibility
We must now learn when to trust AI’s output and when to challenge or shape it.
asserted
We → learn → it
AI that takes the next step:
The third change, and perhaps the most profound, is agency.
asserted
change → take → step
AI no longer just analyzes or predicts.
asserted
AI → analyze → ?
Now it acts.
asserted
it → act → ?
“Agentic AI” can plan steps toward a goal, call for the right tools, check its own work, and keep going from there.
asserted
AI → plan → work
A single AI agent can draft an email, open a ticket, schedule a delivery, and log the transaction, all without a human clicking “send.”
asserted
human → draft → transaction
This can bring immense productivity potential but also raises new questions.
asserted
This → bring → questions
How much autonomy should we give AI?
asserted
we → give → AI
…and 11 more, not listed.
The Aporia
