Building for Purpose: Why HR Matters More in the Age of AI

Editor’s Note: This is the seventh article in a seven-part GrowthBits series on AI in HR, exploring how leaders can preserve human judgment and embed AI responsibly as work evolves.

What Are We Actually Doing Here?

In the age of AI and increasing automation, employees—HR professionals included—may find themselves asking: does my work even matter?

The Bridge

There is a scene in J.C. Chandor’s film Margin Call¹—set the night before the 2008 financial crisis—where Eric Dale, played by Stanley Tucci, has just been laid off. He is an engineer who ended up in finance. He is sitting on a stoop as the firm he gave his career to prepares to dump its worthless assets onto unsuspecting clients. And somewhere in the conversation he says: do you know I built a bridge once?

Then he does the arithmetic. The bridge ran from Dilles Bottom, Ohio to Moundsville, West Virginia. Nine hundred and twelve feet above the Ohio River. Twelve thousand people a day. Thirty-five miles cut each way. He works through the numbers and arrives at a conclusion: that one structure saved the people who crossed it a combined 1,531 years of their lives not spent in a car. Rather than proffering a deep speech about meaning, he is simply trying to find something solid.

Technology changes what the work looks like but it does not change why it matters.

The Walls

Four thousand years earlier, Gilgamesh came home from his failed quest for immortality with nothing—the secret lost, the plant stolen, the journey over. The oldest story in human literature resolves this seeming failure by pointing at something made with human hands: the walls of his city. Fired brick, carefully laid, still standing. “Look at this, you were here, you made something that served people.” That is enough.

Four thousand years separate those two moments and the feeling is identical. What was made doesn’t matter. What matters is that it was made with full human attention and that it served people who needed it.

The Accounting

In Walden,² Thoreau wants to get to Fitchburg. His neighbor needs to get there too. The neighbor spends the day working to earn the train fare: a day’s labor for a day’s travel. Thoreau walks, thirty miles through the countryside. By the time the neighbor has finished his shift, earned his ticket and stepped off the train, Thoreau has already arrived.

The accounting looks straightforward until you ask what is being counted. The neighbor was efficient by every conventional measure: he earned money, purchased a service, arrived at the destination. Thoreau’s observation is simply that he got there first, and that the neighbor spent a day of his life in service of saving time he then didn’t have. The efficiency of taking the train versus walking was real in a vacuum, but the cost was invisible because it was never entered in the ledger.

This is the calculation AI forces every HR organization to make explicit. The administrative task automated, the hour recovered—these are real gains. The question Thoreau is asking is what the hour is then spent on. Automation that recovers time and reinvests it in the anchor work–the difficult conversation, the development relationship, the hiring decision made with full human attention—compounds. Automation that recovers time and uses it to reduce headcount, or simply accelerates throughput, is the neighbor’s bargain.

You cannot automate the demand without automating the meaning away with it.

HR’s Irreplaceable Role

The meaning of work lies in what a task asks of the person doing it. The surgeon’s meaning is in the weight of responsibility that the cutting represents, not the cutting itself. The teacher’s meaning is in the relationship of human transmission, not the information transferred. Eric Dale’s meaning was in the years of attention the calculations represented—the problem held carefully over time, the judgment required to see it through to something that stood.

AI is very good at the task. It cannot replicate the relationship between a person and work that asks something of them—that relationship requires a person, with a history and a stake in the outcome, who can experience the weight of it. The demand is the meaning. You cannot automate the demand without automating the meaning away with it.

The HR function is built to hold this question: we must ask whether the people inside an organization are flourishing—whether the work they are doing will be something they can look back at with the quiet satisfaction of a person who built something worth building.

As pattern-recognition work gets absorbed by machines, the question of whether what remains is worth the human life spent on it becomes more urgent. That is HR’s irreplaceable role. To do the accounting honestly—all of it, including what doesn’t show up in dashboards—and ensure the organization emerging on the other side of this inflection point is one where the people inside it are still building bridges.