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There’s a reason the OECD slashed US growth outlook to an anemic 1.6% this year. Inflation forecasts have risen, major companies are warning of slower sales and tariffs have led to unprecedented trade uncertainty.
Yet even as storm clouds gather, most businesses still aren’t tapping a critical resource to boost their bottom line: their own data. This isn’t a new story. For years, analysts have been lamenting the estimated $3.1 trillion in value trapped in so-called “dark data,” information companies collect but don’t use for decision-making.
Much of this is internal workforce data, information about people and operations. Companies are still failing to draw a line between siloed people data and real business outcomes, from sales to customer satisfaction and even employee retention.
But AI is suddenly changing everything. With new tools, businesses are figuring out how to use this buried data and seeing enormous payoffs. Here’s how.
The problem isn’t a data shortage. It’s a data disconnect
Despite a decade of talk about data-driven decisions, 85% of Fortune 500 companies still aren’t using their workforce data effectively. Here’s why:
- Organizational silos are alive and well. HR, finance, sales and ops all operate on different systems, using different metrics, protecting different turf.
- Tools are fragmented. Even within a single team, critical platforms like payroll, performance and learning systems don’t talk to each other.
- Insight still depends on analysts. Finding value often requires days of spreadsheet wrangling, a luxury most teams don’t have.
The result: billions of data points generated daily, but very little converted into insight or action. The usual approaches all fall short — massive investments in data warehouses, standing up centralized data teams or launching internal dashboards. These solutions often miss the mark, not because the data isn’t there, but because it lacks context, relevance or timeliness.
When that unused data is about your workforce, the risk multiplies. As organizations face mounting pressure to improve productivity and reduce costs, failing to act on workforce data isn’t just inefficient. It’s expensive.
It means workforce transformation efforts are being built on gut feel, not insight, attrition mitigation strategies are generic and talent investments aren’t targeted. Business-critical roles go unfilled not because there’s no solution, but because the data was never brought to light.
Take a leading healthcare provider we work with. Lab work routinely ground to a halt every Monday and Tuesday, costing millions in delays. The lab’s operations team blamed low demand. But the HR data told a different story: Those days were chronically short-staffed with qualified nurses.
No one had connected the dots because no one had access to all the dots.
Once the company integrated HR scheduling data with lab operations, they immediately optimized staffing and recaptured lost revenue. That’s the power of activating workforce data.
From information overload to actionable intelligence
The bigger issue: The real danger here isn’t just ‘dark data.’ It’s that critical intelligence about your people remains invisible and unleveraged at the exact moment it’s needed most.
And that’s precisely where AI comes into play. New AI tools are giving companies new ways to ask and answer business-critical questions about their workforce in real-time:
- “Which frontline location is most likely to miss its weekly sales target?”
- “What percentage of attrition is tied to one underperforming manager?”
- “Where are we overpaying for overtime due to poor scheduling?”
AI assistants now let frontline managers connect the dots by posing questions in plain language. Behind the scenes, these tools knit together a cross-section of data points from performance reports, engagement platforms, attendance systems and even compensation records. But the manager gets exactly what they need: a specific answer and a clear rationale.
When this works, it’s not just insightful. It’s operationally game-changing. A few examples I’ve seen up close:
- Reece Group used AI to go from guesswork to precision workforce planning. The global plumbing and HVAC distributor had a problem: high turnover and absenteeism were threatening a critical same-day delivery pilot. By combining absence history, engagement data and shift rosters, they predicted absences two weeks in advance, giving ops time to rebalance labor and avoid service disruption.
- Providence tapped AI to find the sweet spot for pay bumps. The healthcare provider leveraged historical data to determine if and how raising salaries would affect turnover, and what it would cost. Providence discovered that only a tiny fraction of its jobs were sensitive to compensation. By paying a targeted group of employees to stick around, the company saved $6 million a year and boosted retention by 30% in key areas.
Related: How to Effectively Integrate AI into Your Organizational Strategy
4 takeaways for leaders
For leaders looking to leverage AI to connect their own workforce data with business outcomes, it’s worth remembering that technology is only part of the solution. Some key steps:
1. Don’t start with tech. Start with shared KPIs. The most successful transformations begin by aligning cross-functional teams on business outcomes, not tool stacks.
2. Build hybrid roles to bridge silos. Functions like RevOps, FinOps and People Analytics are designed to sit between orgs. They’re the connective tissue that turns data into strategy.
3. Focus on user-first design. AI is only useful when it’s accessible. To democratize insights, prioritize tools that let frontline managers ask real questions and get actionable answers without technical skills.
4. Be ready for hard truths. Workforce data can expose inefficiencies, inequities and tough management challenges. Companies that succeed won’t just see the issues. They’ll act on them.
Almost every company has an abundance of data. It’s what they do with it that counts. Organizations that tap into the power of connecting workforce data with business data will make faster, smarter and profitable decisions. When corporations are going bankrupt at the highest rate in decades, staying in the dark isn’t an option.
There’s a reason the OECD slashed US growth outlook to an anemic 1.6% this year. Inflation forecasts have risen, major companies are warning of slower sales and tariffs have led to unprecedented trade uncertainty.
Yet even as storm clouds gather, most businesses still aren’t tapping a critical resource to boost their bottom line: their own data. This isn’t a new story. For years, analysts have been lamenting the estimated $3.1 trillion in value trapped in so-called “dark data,” information companies collect but don’t use for decision-making.
Much of this is internal workforce data, information about people and operations. Companies are still failing to draw a line between siloed people data and real business outcomes, from sales to customer satisfaction and even employee retention.
But AI is suddenly changing everything. With new tools, businesses are figuring out how to use this buried data and seeing enormous payoffs. Here’s how.
The problem isn’t a data shortage. It’s a data disconnect
Despite a decade of talk about data-driven decisions, 85% of Fortune 500 companies still aren’t using their workforce data effectively. Here’s why:
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