I’ve spoken with people who are explicitly told they are expected to use AI to do their job. And feeling as if they’ve been placed on a forced march toward the much-hyped expectation of higher productivity through use of AI.
But if you dive into the details, it's a very different story. What happens when people are expected to use AI, but aren't provided with the right tools or guidelines to do so? Or the opposite scenario, where no expectations are communicated, no AI strategy exists?
With these types of realities happening now across organizations, what are organizations really asking of their people when they hand them a vague AI directive and expect magic? Where exactly is the human needed, how much more productive are they? It's not that suddenly AI is magically cranking out a bunch of work - a human HAS to spend time, lots of time potentially, to do a disciplined setup of guidelines, standards, and contextual knowledge -- and this in itself is time-consuming.
What I’m describing is the awkward adolescent phase of AI adoption. Organizations can be quick to jump on the productivity promise without accounting for the learning curve. And people left behind after workforce reductions are suddenly expected to:
Each of these steps requires significant cognitive bandwidth—the very resource that's already stretched thin after downsizing. And maybe #5 is a bit cheeky, but really, are we not all ruminating on the future implications of what is happening to our work, and what it means for “later on?”
Here’s one pattern: Leadership announces AI integration as a strategic initiative. Headcount reductions follow. Then the painful reality: the remaining team has to figure out how to incorporate human finesse to produce value from AI.
What do you get? If you’re looking at this from on high, say, at a productivity chart that a consultant has provided, it’ll be some version of a J-Curve1.
If you’re evaluating the actual output of anything involving words, you get what I call “the wily watermark of predictable writing.” Output that looks a bit too polished, too logical, missing the nuanced thinking that makes human communication valuable. Work that somehow sounds like everything else. Material that makes other people not want to read it—or trust it.
Maybe this isn’t a critical, business-threatening shortcoming. Perhaps it’s seen as a necessary sacrifice to keep pace with the dancing landscape of balance sheets. It’s good enough. It passes a surface-level inspection, but lacks the depth that comes from genuine human insight, and…oh well.
For the organization or team whose work depends on preserving authenticity and nuance (creative teams, marketers, learning designers, sales enablement, communications…it’s a pretty long list actually) then the human-based job to be done is to figure out the specific places in a workflow where human intelligence is irreplaceable:
The best outputs come from thoughtfully designing shared workflow in which it’s clear what the AI tool can do well, and what the human does to make the difference. This also means getting to know the AI tools, tiered fee options, and limitations. If your team is constrained by using just the one tool that your company pays for, you can expect them to spend a lot more time figuring out how to get the most out of that tool…which might not be much. And what did you expect? About that…
Organizations expecting magical productivity boosts need a reality check. Remember the J-curve, where the first wave of AI implementation often slows things down before it speeds things up. Moreover, as the landscape of AI capabilities continues to shift, teams need room to adapt and at least stay knowledgeable about the latest developments. Here are a few other things that your teams need:
Without these investments, organizations are simply setting their people up for frustration, burnout, and ultimately, failure. In our experience, teams typically face a productivity dip during the initial implementation phase.
The organizations getting this right understand that AI implementation requires careful change management—not just tool deployment. They recognize that the human element becomes more important, not less, when implementing AI. They're creating spaces for experimentation, sharing learnings, and establishing thoughtful guidelines.
Most importantly, they're not using AI as an excuse to blindly cut headcount. Instead, they're carefully examining which tasks are ripe for augmentation and which require the irreplaceable human elements of creativity, judgment, and empathy.
Because at the end of the day, AI isn't thinking. It's pattern-matching on an extraordinary scale. And when the stakes involve your organization's voice, reputation, and connection with your audience, is pattern-matching alone really enough?
What are we gaining, and what might we be losing in our rush to do more with less?
1. The Productivity J-Curve describes the phenomenon where the adoption of transformative technologies like AI initially appears to reduce measured productivity before generating significant gains. This pattern occurs due to unmeasured investments in intangible assets required to integrate new technologies effectively.
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