In the second installment of HR and Finance in the AI era, we’re diving deeper into what happens when HR and Finance data stays siloed—and how fixing it lays the foundation organizations need for faster, fairer decisions over the next decade of work.
For too long, managers and executives have been asked to make complex, high-stakes workforce decisions without having a complete picture, because HR and Finance data lived in separate systems.
People data sits in an HRIS or HCM. Financial data sits in FP&A platforms. These disconnected systems create a data trust gap, causing teams to rely on reactive, intuition-driven decisions for budgeting and forecasting.
In other words, guesswork.
That fragmentation produces what we call the “Stitching Tax,” and forces teams and people managers to stitch together inputs from multiple tools to tell a coherent story.
Key takeaways: Data alignment for HR and Finance
- The Stitching Tax slows decisions. Separate systems force managers to assemble context before they can act. You cannot build a future-ready operating model on top of fragmented systems and spreadsheets.
- AI readiness starts with governance. Clean, connected data gives organizations the foundation to use AI safely. Governed adoption, not just access to tools, is the key to successful AI-fueled workforce transformation.
- Shared definitions protect fairness. Standard metrics help teams evaluate similar roles consistently.
- Unified dashboards turn data into action. Budget, actuals, and people metrics help HR and Finance ground decisions in shared reality.
- A single source of truth supports the future of work. Connected data is the architectural foundation of the human-driven, AI-fueled operating model, helping organizations move from gut feel to faster, fairer decisions.
When data lives apart, decisions drift
Different systems create gaps, misaligned definitions, and different versions of the same numbers. For example, HR will speak in terms of employee engagement, while Finance will speak in terms of ROI.
The result is delayed decisions, an over-reliance on gut feelings when making decisions and hitting deadlines, and real consequences for fairness—especially in the middle layers of management where work gets done and where people feel the real impact of high-level decisions on planning, pay, promotion, and performance.
As part of our NextWork series exploring the future of work, this article shows why data alignment is no longer just about reporting accuracy. Drawing on insights from global HR and Finance experts, we show why data alignment is the cornerstone of a future of work defined by the partnership between people and AI, and an integrated operating model fueled by HR–Finance collaboration.
This is Step 2 of our four-part blueprint for building this new operating model—the shared ways of working, data practices, governance, and planning rhythms that help HR, Finance, and business leaders make people and business decisions from the same trusted context.
With leadership aligned in Step 1, the next phase is building a single source of truth.
By doing what Malvika Jethmalani, founder of Atvis Group and former CHRO and interim CFO, calls the “unglamorous” work of aligning data governance, companies can close the data trust gap and move from guesswork to planning, giving business leaders the connected context they need to plan smarter, act faster, and become truly ready for what’s next.
Because, as we all know, AI has the potential to speed up decision-making and improve the quality of work. But it can’t do so without a foundation of clean, accurate, and connected data.
This article discusses how to build a single source of truth and ground conversations in real time with connected insights, so leaders can stop stitching and start building AI-enhanced, proactive strategies that connect people, performance, and financial outcomes.
Siloed vs. shared
| Siloed data | Shared data |
| Manual assembly | Trusted context |
| Educated guesses | Better planning |
| Delayed decisions | Faster, fairer decisions |
Step 1: Audit the “Stitching Tax”
Map where manual data assembly slows people decisions and pulls managers away from strategic work.
- Identify where people managers switch between systems before making decisions
- Spot where HR and Finance use different numbers, definitions, or planning assumptions
- Review which decisions require repeated manual checks or clarification
- Clarify where shared context would help managers act faster and with more confidence
Before organizations build a single source of truth, it helps to understand the cost of not having one.
- 60 percent of managers spend three or more hours assembling data across systems before making a people decision
- 62 percent say they prefer an educated guess over missing a deadline
Why the data trust gap persists
John Brownhill, managing director of People & Technologies, has worked in the HR technology market for 38 years. He points out that HR–Finance alignment has always mattered and explains how the Stitching Tax challenge isn’t anything new.
ERP systems in the 1990s were partly designed to bring HR, Finance, and other business functions into a single system, but their complexity and high costs kept them out of reach for many organizations.
“HR typically owns the core people data—things like headcount, organizational structure, and workforce metrics,” John explains. “But Finance relies heavily on that same information for budgeting, forecasting, and workforce planning. And when those data sets sit across separate systems or even spreadsheets, it becomes really difficult to maintain consistency and confidence in that data.”
Start with the questions managers keep asking
Closing the gaps starts with auditing the Stitching Tax in your workflows and looking for the moments where managers repeatedly ask the same questions:
- Which system has the latest number?
- Which budget figure should I use?
- Does Finance define this role the same way HR does?
- Do I have enough context to defend this decision?
These questions reveal where the single source of truth needs to begin.
Step 2: Do the “unglamorous” data governance
Align the definitions and governance standards that trusted, AI-ready decisions depend on.
- Define the workforce, cost, performance, and capacity metrics both teams rely on
- Agree on how people and finance data is governed, accessed, and updated
- Have shared metrics across business units to reduce local interpretation
- Build trust so HR and Finance can focus on impact rather than data reliability
AI can help teams make better, faster decisions when the right foundations are in place. But acing AI transformation isn’t a race to switch on AI features everywhere. It’s a mindset shift and strategic reset that builds organizational intelligence through a single, human-driven, AI-fueled source of truth.
Getting it right is an equally technical and organizational (read: human) challenge. In the new operating model, skipping this fundamental step could mean your integrated systems simply process bad data faster. That’s why trust and collaboration between HR and Finance are imperative, and they begin with working together on:
- Data governance
- Standard definitions
- Shared success metrics
Do the unglamorous work that makes AI valuable
Malvika Jethmalani frames this as the foundation for AI transformation.
She explains that “[s]o many organizations … are eager to adopt AI, but they lack AI-ready data. But … when HR and Finance collaborate on things like data governance, standard definitions, shared metrics across BUs, they can start to create the infrastructure that makes AI adoption and AI transformation possible. So, I think this work is kind of unglamorous, but it’s the foundation upon which the AI house needs to be built.”
That separates AI readiness from AI ambition, and the numbers back it up.
The AI behaviors leaders value most
According to HiBob’s research on AI skills and readiness, 52 percent of respondents said they prioritize reviewing output quality, and 52 percent say documenting workflow decisions for reliability are the most important everyday AI behaviors. Those priorities show that organizations are prioritizing structured, governed systems over raw tech adoption.
Why definitions matter for fairness
Data governance also protects consistency in people decisions. Our research found that 63 percent of managers worry that similar roles are evaluated with different metrics across teams. Without shared definitions, fairness can depend too heavily on local interpretation.
Governance gives teams the shared language they need before dashboards or AI can add real value. But quality governance first requires data you can trust and teams that trust the data. The distinction here is critical.
Trusting the data is the crucial behavioral shift that can change the game. John Brownhill describes the beneficial outcome organizations can enjoy once they make it: “As Finance starts to trust the data presented, I expect both teams will work more closely to discuss the impact rather than the quality or reliability of that data.”
That’s the real goal of a single source of truth. Fewer debates about accuracy and more focus on impact.
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Step 3: Deploy unified dashboards
Bring HR and Finance data into shared dashboards so teams can move from gut feel to shared evidence.
- Show budget, actuals, workforce trends, and people impact side by side
- Give HR and Finance the same context before decisions move forward
- Ground conversations in real-time data rather than disconnected reports or manual checks
- Help people managers make faster, fairer, and more defensible decisions
To finalize this phase of the new operating model, unify HR and Finance data in dashboards that show budget, actuals, and people metrics side by side.
Once HR and Finance audit the Stitching Tax and do the governance work, the payoff becomes visible at the point of decision. Unified dashboards bring budget, actuals, workforce context, and people metrics into the same conversation.
Karen Longest, people operations manager at Support Partners, describes what that looks like in practice: “We have that single source of truth, and we’ve got the dashboards and the data that both let us see exactly what’s happening in real time. And so those conversations are grounded in reality rather than that gut feel.”
With unified dashboards, HR and Finance don’t have to start every conversation by reconciling numbers. They can look at the same data, discuss the same context, and move more quickly to the decisions themselves.
What changes when teams work from the same evidence
The same principle also helps teams make decisions under pressure.
As Tom Pearson, director of people operations for Twenty7Tec, puts it: “The financial decisions were supported by strong people data from HR, and that meant that we could make the decisions that were right for the business but also minimize that impact on our people.”
The alternative is clear in the research. When data is hard to access:
- 68 percent say missing or conflicting information leads to slower, less fair, or less cost-effective decisions at least half the time
- 35 percent say real-time budget versus actuals is the most critical feature they need in a shared dashboard
Clean, connected people data is a competitive advantage. It powers faster decisions, stronger forecasting, and real adaptability today and into the future.
Ground decisions in reality
The organizations shaping what’s next for the world of work aren’t just collecting more data. They’re aligning around an entirely new, integrated operating model where managers have real-time budget-versus-actuals in the same screen as their people data—and they’re building it from the ground up.
Managers are asking for this visibility (79 percent agree that a shared HR–Finance dashboard would help them manage fairly and effectively, but only 2 percent report having access to one today).
In a human-driven, AI-fueled operating model, unified data isn’t just a reporting enhancement. It’s the key to pulling ahead and winning the future of work.