Trends
Leaders are boosting traditional financial forecasts by using workforce data as an early indicator of business performance. Combined with AI, HR insights on engagement, turnover and skills help spot trends sooner, enabling faster, more accurate decisions and better margin management.
What business leader doesn’t want clearer foresight and better visibility into next quarter’s revenue, upcoming cost pressures and the health of their margins? Yet most forecasting models still rely almost entirely on financial data, even though financials tend to tell the story last.
The earliest signs of change, good or bad, nearly always appear in HR data analytics. And workforce trends may surface weeks or months before they show up on a balance sheet or income statement.
That’s why forward-looking leaders are rethinking their approach. With clean people data supported by responsible artificial intelligence (AI), organizations can turn early signals into reliable forecasts, spot risks sooner, plan resources more effectively and make decisions faster.
You don’t have to replace your existing financial forecasting model. Instead, power it with workforce insights you’ve been leaving untapped.
HR data analytics in motion
Financial outcomes tend to build slowly. When leaders learn to treat people data as “data in motion,” they discover workforce signals consistently move ahead of financial results.
For example, when employee engagement scores slip, production delays, quality issues and customer experience impacts often follow, creating avoidable revenue drag. Higher attrition drives recruitment costs, onboarding time and overtime to cover gaps, forecasting compressed margins in the next quarter. Slow time-to-fill or shortages in critical skills signal that teams may struggle to meet demand, limiting revenue potential or delaying projects.
None of these insights requires complex analytics. They simply require paying attention to how fast certain signals are changing and in which direction. When viewed this way, people data enhances forecasting. Instead of guessing where performance is headed, you observe it in real time through the people who drive it and maximize the value of your investments.
“You need to align your HR data analytics efforts to what’s important to the organization and the HR strategy that you have in place,” said Kathy Gawronski, VP Value Engineering at WorkForce Software – an ADP Company.
“First, it’s important to get support for driving more value out of your HR system and using available data to drive that value. It won’t be that difficult to get support to do that, because it’s getting more value out of what you’ve already invested in,” said Gawronski. “Begin small and leverage the value of initial studies to establish additional support and necessary investments. Be sure to communicate the impact to the bottom line of the initial insights. Lather, rinse, repeat.”
AI as the accelerator
High-quality people data creates early visibility, but responsible AI turns that visibility into sharper, faster and more reliable forecasting. Instead of manually stitching together spreadsheets, leaders can use AI to reduce reporting errors, uncover patterns earlier and model scenarios in seconds.
The benefits are practical and immediate:
- Fewer reporting mistakes: AI reduces manual entry and reconciliation work, helping eliminate the small but costly inaccuracies that distort forecasts.
- Faster scenario modeling: Leaders can test questions like, “What if attrition rises 35%?” or “How would a slowdown in hiring velocity affect project capacity?” without days of analysis.
- Improved budgeting accuracy: By detecting small shifts in workforce behavior early, AI gives HR and finance teams more time to adjust headcount plans, training investments and labor budgets.
According to McKinsey & Company, organizations using AI-driven forecasting saw forecasting errors drop 20% to 50% compared with traditional spreadsheet methods.
Forecast in action
Consider a regional business that began tracking absenteeism in conjunction with team performance. Initially, the metrics appeared unrelated. But eventually leaders noticed a pattern: when absenteeism crept up, productivity dipped shortly afterward, often before anyone flagged a problem.
By linking these signals, the company built a simple model that projected how rising absenteeism would affect output and labor costs. When the data began trending upward for one quarter, the model forecasted a margin decline nearly two months earlier than traditional financial reports would have. Armed with that insight, leaders acted quickly. They adjusted staffing plans, shifted workloads, and tightened scheduling practices, avoiding the overtime expenses that would have eroded margins.
This is the power of forecasting through people data. Even a basic connection between workforce behavior and financial outcomes can reveal issues earlier, strengthen planning and prevent cost overruns before they occur.
Quick start framework
You don’t need a full analytics function or complex modeling to begin forecasting with workforce insights. A simple, structured approach can help HR and business leaders build confidence, improve accuracy and demonstrate value quickly. But you do need technology that’s up to the task; a platform that can flex and expand with your needs and has AI capabilities built in. And it’s essential that you evaluate and address any shortfalls in the quality of your data. Clean and accurate data is essential to the success and output of everything that follows.
Step 1: Clean existing workforce data and link it to business goals
Data quality is critical. Start by validating what you already have, such as turnover, time-to-fill, engagement scores, training hours or skills inventories. The goal is consistency. Tie each data point back to a business question, such as “How does turnover affect margin?” or “How do skills gaps impact project delivery?”
Step 2: Partner with finance to define key forecasting inputs
HR shouldn’t forecast in a vacuum. Collaborate with finance to identify which workforce signals meaningfully impact revenue, cost or capacity. Agree on shared definitions, data sources and the thresholds that should trigger a conversation.
Step 3: Start small: one metric, one model or one reporting cycle
Pick a single leading indicator, such as voluntary turnover. Build a simple predictive workflow around it and track how that metric moves and signals change. This keeps experimentation manageable and shows value early.
Step 4: Expand as patterns emerge
Once you see reliable relationships between people data and financial results, scale gradually. Add new metrics, automate reporting, incorporate AI and refine your models. Each added layer improves accuracy and strengthens long-term planning.
See the road ahead with workforce intelligence
Forecasting through people data transforms traditional HR metrics into true leading indicators of business performance. When leaders understand how shifts in engagement, turnover, skills and hiring velocity shape operational capacity and financial outcomes, they gain a clearer view of what’s coming next.
With an integrated perspective across workforce, payroll and financial data, ADP empowers leaders to make smarter, more confident forecasting decisions.
Learn how ADP helps businesses connect people analytics with profit prediction.