Too often, headlines focus on extremes, but the practical questions are far more grounded. How do we manage this technology? How do we and our teams work alongside it effectively? How do we capture its benefits without compromising our values or our goals? To find useful answers, we have to start by understanding where we are, what has truly changed, and what has not.
AI didn’t suddenly appear in the world in the 2020s. But powerful, general-purpose AI systems pushed the technology into mainstream awareness in ways that earlier breakthroughs never did.
unsure what comes next. Today’s systems are powerful and raise new challenges, and we will take those challenges seriously throughout this book. But they are not as alien or unmanageable as they are sometimes made out to be. 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.
After all, how we choose to work with AI, and who we’ll become in the process, is something we still get to decide.
What’s different about today’s AI?
Generalists, not specialists:
That versatility is what brought AI out from the back office and into nearly every corner of knowledge work.
A second shift is that AI now speaks our language, literally. We no longer need to code or click through rigid menus to interact with AI. We can simply use everyday language, and the responses come back in polished, humanlike prose, making the technology broadly accessible. These systems can also generate new content, including text, images, and beyond—which is why this wave is often called “generative AI.” But that ease of use creates a new kind of business responsibility. We must now learn when to trust AI’s output and when to challenge or shape it.
The third change, and perhaps the most profound, is agency. AI no longer just analyzes or predicts. Now it acts. “Agentic AI” can plan steps toward a goal, call for the right tools, check its own work, and keep going from there. A single AI agent can draft an email, open a ticket, schedule a delivery, and log the transaction, all without a human clicking “send.” This can bring immense productivity potential but also raises new questions. How much autonomy should we give AI? What does accountability look like when decisions are distributed across humans and machines?
But just like previous waves of AI, generative AI (which creates new content like text, images, or code) and agentic AI (which can take actions independently to complete multistep tasks) are good at some things and bad at others.
AI can amaze us with its ability to do some tasks and similarly stupefy us with
Excerpted from Manage the Machine: How to Harness Human-AI Collaboration at Work by Paula Goldman with permission from Basic Venture.
Disclosure: Salesforce chair and CEO Marc Benioff is the owner of TIME.
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