Name: 3 AI puzzles workplaces must solve | Martin Gonzalez for Big Think +
Uploaded: 2025-12-04T17:51:36.000Z
Duration: 8 min 51 s
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I'm a principal of Organization and Leadership Development at Google and I'm the author of the Bonfire Moment.

The book explores this idea that teams are harder than tech in the process of innovation.

It's so important for leaders and CEOs and founders to pay attention to the people side of the business because that could easily derail your best laid out plans.

We know a lot of employees and organizations are starting to use AI for their work.

We also know that we flip flop between these really intense narratives of substitution.

There will be less of people playing my kind of work, my kind of role because of AI and a narrative of augmentation, which is these tools give me superpowers that allow me to do more within my role and, and

I will succeed and do well in the future if I can only adapt these new technologies.

There's a lot we need to think about when we think about the augmentation model.

Because as early research is showing, as we bring these tools into the workplace, we're not quite seeing the kind of transformative potential that AI has been talked about by its inventors.

So I've started to think about three puzzles we need to solve for as we bring these technologies into our organizations.

One of the challenges in bringing AI into an organization is what I've started to call the selective upgrade puzzle.

This is when these tools endow its users with superpowers, but not all users.

And somehow there's a selective upgrade that happens when these tools get shared in an organization.

This, this one randomized control experiment that was run by researchers from places like Harvard and mit, engaged the Boston Consulting group and set up their junior consultants in control groups and a couple of experimental groups.

What they did was they gave them access to a large language model and they were asked to do two kinds of tasks.

The first task was a creative ideation task.

They had to help a fictitious client come up with different product ideas that they can go to market with.

The second was a business analytics task where they had to analyze why a business was struggling and create recommendations.

What the study went on to discover was that when they looked at the top performers, they tended to do much better.

And when they looked at the lower performers, they tended to do much worse.

When you think of this selective upgrade effect spread across thousands of employees over a span of time, what we might see is an ever growing gap between your best and worst performers.

And this variability would have been attributed to the use of these AI tools where that gap didn't exist before you deployed these tools.

There are a couple of things that leaders can think about as they deploy AI in their organization.

The first is to create really clear guardrails around what these tools should be used for and what they shouldn't be.

And those guardrails will possibly diminish over time as these tools become much more, much more effective.

It's important to go through this experimental period understanding where it actually augments the work and where it actually takes away from the work.

Another thing to consider is it's important for the users as they leverage these tools for certain domains, that they have a certain basic level of expertise in these domains.

It allows the users to apply good judgment when a tool is actually leading them in a worse direction and when it actually is.

Augmenting the work Using a tool when you have zero knowledge of that domain is a very, very dangerous proposal.

As we think about bringing AI into our organizations, we need to think about this agentic preference puzzle.

We as humans have a tendency towards control.

And when these tools take away control from the work, we see that adoption rates drop.

There are some fascinating studies done out of Wharton that explore this idea that they called algorithmic aversion bias.

For example, when was the last time you decided to override what Google Maps or Waze told you was the right way home?

We'll sometimes believe that we actually have better or have a lower error rate than these machines.

And what this branch of study has looked into was when individuals actually see an algorithm commit an error, even if that error rate is still lower than the human error rate, we would much rather trust our human judgment over the algorithm.

It goes on to explain that perhaps one way to think about this is when we think of algorithms and these AI bots.

Their error rates are knowable and they're static, but human intuition and human intelligence is perfectable.

And perhaps we therefore trust that we can perfect our own judgment in certain tasks.

The research goes on to then try to figure out what's the right antidote to, to this.

And they allow, in one study, they allow users of these algorithms to tweak ever so slightly different parameters of these algorithms.

When these people are given that leeway to control the algorithm, what you find is that the error rates will increase as a result, as you would expect.

But then you also see the adoption rates significantly increase because people can control it.

And this drives home a really valuable point around adoption of these AI tools.

As a leader, you might think about what is an error rate that is acceptable, if only it means that you then create a lot more adoption in the workplace.

The ideal scenario is that people adopt these tools fully without tweaking them.

But we know that that comes at a cost of lower adoption.

Are we willing to sacrifice some amount of precision in the use of these tools in exchange for an improved level of adoption?

The final puzzle is this self sufficiency spiral.

If you think about all the work we do in an organization, you can categorize them into solo work and interdependent work.

And you might say that in the future, these tools will allow us to do a lot more solo work.

And a lot of the solo work will colonize parts of the interdependent work.

And then what gets left behind as interdependent work, whether it's writing emails or doing presentations or conducting meetings, a lot of these tasks will then get intermediated by these AI tools.

When you think about what it takes to create culture in an organization, or the role of the leader in kind of bringing people together around a shared mission, a lot of that is about interactive tasks.

A lot of that is about not being in solitude and doing isolated work, but

And if the future of the workplace is a lot more solo and a lot more isolated, I worry a little bit about what this means for the future of organizations and our ability to create cultures and create a sense of identity with the organization.

We've seen other technologies in the past kind of deliver to us a future that we didn't quite want.

You take for example, social media, where it had this promise to create a more connected world, but

instead what it gave us is possibly a more fragmented, polarized world where we perhaps have expected less from each other.

And as MIT ethnographer once said, we are alone together through these tools.

So we don't want this feature for the workplace.

And we need to think about ways that we can bring people together through perhaps different means and different approaches, so we can continue to create thriving environments for people as they engage with these tools.

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3 AI puzzles workplaces must solve | Martin Gonzalez for Big Think +

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