Two important articles landed in the past two months — one from MIT Sloan Management Review, one from Harvard Business Review — and read together, they make a compelling case that most organizations are flying blind at exactly the wrong moment.

Brian Elliott’s MIT Sloan column, “An AI Reckoning for HR: Transform or Fade Away,” opens with a challenge that HR leaders have been putting off for decades: the shift from compliance function to strategic partner. What’s different now is that AI has made the cost of inaction undeniable. The HR technology market is on track to grow from $40 billion in 2024 to over $82 billion by 2032, driven largely by tools capable of absorbing work that HR currently owns. As Elliott frames it, HR will either lead its own transformation or have change imposed on it.

Meanwhile, the HBR piece, “9 Trends Shaping Work in 2026 and Beyond,” authored by Gartner’s Peter Aykens, Kaelyn Lowmaster, Emily Rose McRae, and Jonah Shepp, paints a sobering picture of where most organizations actually are. Gartner research finds that only one in 50 AI investments delivers transformational value, and only one in five delivers any measurable return on investment at all. The gap between CEO expectations and workforce reality has never been wider.

These two articles are exploring the same underlying problem from different angles. And that problem has a name: organizations are making consequential decisions about their workforces, their cultures, and their AI investments without the data to know whether those decisions are working.

The “Completion, Not Impact” Trap

Elliott’s most striking illustration comes from Eric Severson, who describes walking into a room full of meticulously completed performance review binders when he was head of HR at The Gap. The team was proud of 98% compliance. But the reviews didn’t answer whether the company was reducing unwanted attrition or developing employee skills. The metric was completion, not impact.

That story is nearly ten years old, but it describes a dynamic that remains the norm. SHRM research cited in Elliott’s piece found that only one in eight HR teams operates at a high maturity level — which includes the ability to apply data well and retain the right people. The average maturity score across HR organizations is just 3.85 out of 6.00.

Former Levi Strauss & Co. CHRO Tracy Layney put it plainly: HR leaders should be held accountable for people outcomes with the same rigor “as you would around financial rigor, around customer rigor, around all your marketing metrics.” The aspiration is decades old. The gap is still wide.

Elliott’s recommendation to “jettison low-value work” hits the same note. Samantha Gadd, founder of Humankind, suggests HR teams put every active initiative on a wall and ask which ones employees would actually notice if they stopped. Common examples of “activity without outcomes” that tend to survive that exercise far longer than they should:

• Engagement surveys that produce reports but no follow-through action
• Performance review cycles measured by completion rate, not development impact
• Learning programs tracked by hours attended rather than skill applied
• Onboarding checklists designed around HR compliance, not new-hire productivity

Identifying which programs fall into this category — and which actually move the needle — requires visibility into how work is really happening, not just how many boxes got checked.

When Workforce Decisions Outrun the Evidence

The HBR piece makes the stakes concrete with Trend 1: AI layoffs are outpacing AI productivity gains. Gartner analysis found that less than 1% of layoffs in the first half of 2025 were the result of AI actually increasing employee productivity. The rest were bets placed in anticipation of AI returns that haven’t materialized — and in many cases, may not.

The result? Organizations now face the costly task of rehiring talent they prematurely let go, often at greater expense than before. As the HBR authors note, HR will be responsible for ensuring that workforce size, structure, and skills can support current priorities without sacrificing the organization’s path to an AI-infused business model. That’s an extraordinarily difficult mandate to execute without real visibility into how work is actually flowing.

The same evidence gap shows up in Trend 2, culture dissonance. Organizations are quietly expecting more from employees without offering more in return — and employees are noticing. This dissonance creates lower performance, declining engagement, and a degraded employer brand. But most leaders are only aware of it once the damage is done, because they have no mechanism for detecting the gap between stated culture and lived experience until attrition or engagement scores surface it, months after the fact.

The data from both articles makes the cost of this guesswork concrete:

• Only 1 in 50 AI investments delivers transformational value (Gartner)
• Only 1 in 5 AI investments delivers any measurable ROI (Gartner)
• Less than 1% of 2025 layoffs were driven by actual AI productivity gains (Gartner)
• Only 1 in 8 HR teams operates at a high maturity level, including the ability to apply data well (SHRM)
• 91% of CIOs and IT leaders say their organizations devote little to no time scanning for behavioral byproducts of AI use (Gartner)

Each of these numbers points to the same gap: organizations are acting, but not measuring whether their actions are working.

“Workslop” Is a Visibility Problem

Trend 4 in the HBR piece — what the authors term “workslop,” or quickly-produced, low-quality output generated by or with AI — is often framed as a change management or adoption problem. But at its core, it’s a measurement problem.

Employees are being pressured to use AI tools and produce more output, with no time or authority to assess whether that output is actually fit for purpose. The result is work that looks productive but adds minimal or even negative value. One study cited in the article found that employees spend an average of nearly two hours dealing with each instance of “workslop” they encounter — making it a significant drag on the financial returns organizations hoped AI would deliver.

The path out, according to the HBR authors, is focusing AI investments on employees’ actual pain points rather than mandating adoption for its own sake. Organizations that take this approach see lower adoption rates, but higher-quality work and better financial returns. The question that unlocks this path is: where is work genuinely getting stuck? That’s not a question you can answer without data on how work actually happens.

Process Pros Need Process Data

Perhaps the most practically important insight in the HBR piece is Trend 8: it’s process professionals — not technical AI specialists — who are unlocking real AI value. Gartner research finds that business units focused on redesigning how work gets done with AI are twice as likely to exceed their revenue goals. The winning profile isn’t the AI power user; it’s the person with the creativity and systems thinking to redesign entire processes, not just automate individual tasks.

Elliott makes an almost identical argument from the HR side. The second path forward for HR — the one that leads toward strategic relevance rather than marginalization — is what Kit Krugman of Foursquare calls an “internal organizational effectiveness engine”: a function staffed with designers, strategists, and systems thinkers who scope problems, establish useful metrics, run experiments, and iterate on results.

Both of these paths require the same foundation: an accurate picture of how work is actually flowing before you try to improve it. You cannot redesign processes you cannot see.

Workforce Intelligence as the Missing Link

This is where Sapience fits into the picture — not as another AI tool to adopt, but as the data infrastructure that makes evidence-based workforce decisions possible in the first place.

Elliott calls for HR to jettison “activity without outcomes” — programs and surveys that generate reports but don’t drive action. Doing that requires knowing which activities are actually producing value. The HBR authors urge organizations to focus AI where employees genuinely need it. Doing that requires understanding where work is genuinely getting stuck.

The trends in both articles share a common root: consequential decisions being made without visibility into how work is actually happening. Workforce intelligence directly addresses the measurement gaps at the center of each:

• Premature workforce reductions → Know where AI is genuinely improving output before making headcount decisions
• Cultural drift → Detect changes in work patterns — collaboration, capacity, focus time — before they surface as attrition
• Workslop → Identify where AI adoption is adding volume but not value, and focus investment where it actually helps
• Process redesign → Map how work flows today before deciding where to intervene

Workforce intelligence doesn’t solve every problem these articles identify. But it closes the measurement gap that runs through all of them — giving HR leaders and executive teams the data to move from “activity” to “impact,” from aspiration to evidence, and from reacting to what already happened to shaping what comes next.

That’s not a small thing. In a moment when the cost of flying blind has never been higher, it might be the most important thing.

Sapience provides workforce intelligence that helps organizations understand how work is now occurring in the modern, AI-enabled enterprise — so leaders can make smarter decisions about where AI creates real value, where processes need redesign, and how to build a workforce that performs with resiliency.

Learn more about how Sapience works for Human Resources leadership.