The third installment of the HR and Finance in the AI era series gets practical about leveraging AI as a catalyst for breaking down silos, unlocking human capacity, and orchestrating the powerful partnership between human potential and AI-driven scale.
Everyone is talking about AI, but not enough people are talking about the groundwork it takes to implement it successfully and get the most out of their investment.
According to our research, 75 percent of decision-makers expect moderate AI proficiency to become standard across most non-technical roles by 2028, but many organizations are still in a fragmented phase of experimentation. However, the biggest AI challenges aren’t about adopting and deploying tools. They’re about redesigning the systems, skills, and behaviors that help our people use those tools effectively.
AI works best when it’s given clean, cross-functional data and clear decision rules. With that shared context, organizations can transform fragmented data into organizational intelligence, connecting AI to the data, definitions, and decisions it needs to support better workforce planning.
Key takeaways: AI data readiness and workforce productivity
- AI data readiness starts with HR–Finance alignment. AI depends on clean, connected people and financial data to support useful decisions.
- Managers want AI with guardrails. AI companions can support people decisions when they include human review, explainability, and auditability.
- Governance creates trust. Reviewing output quality and documenting workflow decisions help organizations scale AI more responsibly.
- Capacity creates value. AI supports productivity when saved time becomes better planning, stronger analysis, and higher-value work.
- Upskilling is a shared investment. HR and Finance can connect AI skills, role design, performance, promotion, and budget planning.
AI transformation starts with the operating model
The companies getting the most out of AI built their HR and Finance data foundations before scaling the technology. That unglamorous groundwork—aligning governance, definitions, and shared metrics—is what this article is about.
Drawing on insights from global leaders, we explore how HR and Finance can work together to leverage AI to build organizational intelligence. We’ll also look at why that starts with shared groundwork and why rapid upskilling becomes just as critical once that foundation exists.
Organizations are already moving in this direction. In fact, our research shows that 67 percent are already actively linking AI skills to promotion criteria, and 50 percent are tying them to performance ratings.
The workforce transformation is underway. The question is whether your operating model is ready to support it.
The AI-forward operating model
| Clean data → | Governed AI → | Strategic capacity → | Rapid upskilling |
Step 1: Use AI as a catalyst for convergence
Turn AI interest into momentum for cleaner, more connected HR and Finance data.
- Identify where HR and Finance data still sits in separate systems
- Define the data managers need to make AI-supported people decisions
- Build human override, explainability, and decision logs into AI workflows
- Use AI readiness to accelerate cross-functional data alignment
AI is encouraging HR and Finance to work from the same foundation. But building a truly human-driven, AI-fueled organization starts by moving from fragmented adoption to governed capability.
To support strong decision-making, AI needs context from across the business, such as headcount, compensation, budgets, capacity, performance, and skills. When those inputs sit in separate systems, teams have a clear opportunity to bring their data, definitions, and planning processes closer together.
Brett Ungashick, founder and CEO of OutSail, sees this already happening. As he explains, organizations are starting to understand “the limitations of data in silos … and the rise of AI is helping to … build demand for increased convergence for Finance and HR.”
Give managers AI support they can trust
Demand also shows up in the research where 87 percent of managers say they would welcome an AI companion that unifies HR and Finance data and suggests people decisions.
Managers also know what they need to use AI with confidence. They need human override, explainability, and auditable decision logs. Those guardrails help organizations use AI responsibly, securely, consistently, and at scale.
This helps explain why 34 percent of companies still prohibit or restrict AI usage. Many leaders are taking a cautious approach while they build the governance, policies, and cross-functional alignment they need to move forward.
For HR and Finance, this creates an opportunity. AI gives both teams a shared reason to connect data, clarify ownership, and support people managers within clearly defined policy constraints. It positions technology as the connective tissue between workforce strategy and business execution, providing managers with better context, clearer recommendations, and stronger support for the decisions they already make.
Step 2: Build the “unglamorous” AI foundation
Build the governance foundation that AI-ready decisions depend on.
- Align people, cost, performance, and capacity metrics
- Define how HR and Finance data is governed, accessed, and updated
- Review where AI outputs require human checks and documentation
- Strengthen people analytics capability before scaling AI use
Good AI always starts with the data, definitions, and governance that make it useful. The challenge organizations face today is governed adoption, not just access to tools.
Malvika Jethmalani, founder of Atvis Group and former CHRO and interim CFO, describes this as the practical work behind AI transformation. As she explains, “I think this work is kind of unglamorous, but it’s the foundation upon which the AI house needs to be built.”
Treat AI transformation as change management
Malvika’s Zero-Based Budgeting example, which we explored in Part I of this series, shows what that looks like in practice.
Rolled out across 21 countries, the initiative only worked because HR and Finance treated it as change management. That meant combining rigorous cost discipline with clear communication, leader education, and genuine empathy for how the changes would land with people.
The result was tens of millions saved, while preserving engagement and trust. That’s what HR–Finance alignment delivers when teams lay the groundwork properly.
The numbers back up why that foundation matters so much for AI specifically. When leaders are asked which everyday AI behaviors they value most, they don’t point to technical expertise: 52 percent point to reviewing output quality, and 52 percent also point to documenting workflow decisions for reliability.
These priorities show why AI readiness depends on governance, not just adoption. Especially for HR and Finance, governance starts with trusted data, shared definitions, and clear ownership.
Step 3: Reallocate capacity for strategic impact
Measure AI success by the value people create with the capacity it unlocks.
- Identify repetitive work that limits strategic contribution
- Redirect saved time into planning, analysis, and higher-value work
- Use AI to expand capability and improve decision-making
- Track how AI supports productivity, collaboration, and business impact
AI can save time, but the bigger opportunity is what organizations do with that capacity.
When AI and smart systems reduce repetitive work, HR and Finance can redirect attention toward work that creates more value: scenario planning, workforce design, forecasting, governance, and better decision support for people managers. This is what it means to move beyond mere administration to proactive, intelligent management.
Karen Longest, people operations manager at Support Partners, describes this as an opportunity. “AI and smart systems allow employees to be free from those manual repetitive tasks,” she says. “It gives them the capacity to branch into new areas, connect dots faster, and make a broader impact across the organization.”
Rob Power, HiBob implementation partner and advisor to HR teams at Lemon Platypus, highlights exactly how AI changes this dynamic. Instead of just reporting the past, he sees AI freeing HR from operational work so they can predict the future.
As he explains, “Instead of just reporting on what has happened, it’s going to be even more easy to look at the future and predict what is going to happen … AI will free HR teams from a lot of the operational work that’s historically taken up their time, which allows them to focus more on strategic workforce planning and work much more alongside Finance.”
Creating value from time saved
| Capacity freed from | Capacity redirected to |
| Manual data assembly | Scenario planning |
| Repetitive reporting | Workforce design |
| Data reconciliation | Forecasting |
| Admin-heavy checks | Decision support |
Manual work still consumes real management capacity. According to HiBob research, 60 percent of managers regularly spend three or more hours assembling data across systems before making a people decision. Reducing that drag gives managers more space to compare options, understand trade-offs, and make decisions with clearer context.
Build the skills to redesign work responsibly
Unlocking capacity also requires specific skills. Because the most valuable AI skills center on judgment and reliability, the hardest skills to recruit for today include AI safety, ethics, and governance at 37 percent, and workflow evaluation and redesign at 33 percent.
These are the skills organizations use to decide where AI belongs within the workflow, how work changes around it, and how to protect quality as workflows evolve.
John Brownhill, managing director of People & Technologies, makes the point that once Finance starts to trust the data presented, HR and Finance can spend more time discussing impact rather than the quality or reliability of the data.
That gives both teams more room to focus on planning, impact, and better business decisions.
Step 4: Co-invest in rapid workforce upskilling
Treat AI upskilling as a shared people and business investment.
- Identify where AI skills affect performance, promotion, and role design
- Prioritize manager enablement so teams can use AI well
- Connect upskilling plans to workforce strategy and investment planning
- Fund capability-building as part of long-term business performance
Upskilling works best when it connects to budget and business planning. It becomes part of how organizations build capability, protect productivity, and prepare people for the roles AI will help reshape.
Once AI creates capacity, organizations can decide how to use it. But right now, managers are carrying an under-supported load.
Prepare managers to lead AI upskilling
Our AI skills research shows that organizations expect people managers to play a major role in turning AI skills into a table-stakes capability across their teams. But only 36 percent of leaders surveyed feel managers are highly prepared to upskill their teams.
On top of that, the research shows that companies are already making AI skills a core tenet of talent decisions, with 67 percent linking AI skills to promotion criteria, and 50 percent tying them to performance ratings.
The gap between companies’ high expectations of AI skills across their teams and the low-bar investment in upskilling makes AI transformation a joint HR–Finance priority. HR brings role design, skills planning, learning paths, and manager enablement. Finance brings budget, prioritization, ROI, and trade-offs.
Make upskilling a shared investment
Adam Weber, executive coach at Adam Weber Coaching and former chief people officer, explains that this transformation “can’t be led by HR or Finance alone. It’s both: HR bringing that people strategy and Finance bringing the investment lens.”
As the HR–Finance alliance solidifies, AI, automation, and new technologies will reshape roles across almost every organization. The companies that keep up with the pace of workforce transformation will have their fingers on the pulse of it, rethinking org structures and rapidly upskilling their people for new kinds of jobs as they emerge.
Recommended For Further Reading
Building lasting AI infrastructure
HR and Finance can close the gap between their teams by working together to connect technology decisions with data governance, workforce planning, and people investment.
You know you’ve completed Phase 3 of the new operating model for the AI era when upskilling becomes a shared business priority that HR and Finance fund and plan together.
Getting there takes governed data, capacity returned from manual work, and people managers who have the support to guide their teams through roles that are still taking shape.
FAQs: AI data readiness and workforce transformation
AI data readiness is an organization’s ability to give AI access to clean, connected, governed data and the context needed to support reliable decisions. For HR and Finance, that means aligning people and financial data, definitions, access, and decision rules before scaling AI across workforce processes.
AI needs context from across the business to support useful workforce decisions. When HR and Finance connect data such as headcount, compensation, budgets, capacity, performance, and skills, they give people and AI a more complete and consistent foundation for planning and decision-making.
Data governance establishes clear standards for how information is defined, accessed, updated, and used. It also helps organizations put human review, explainability, and documentation practices in place so people can use AI more confidently, consistently, and responsibly.
Organizational Intelligence is AI-powered intelligence that turns trusted people context into better decisions, recommendations, and actions across an organization. It depends on connected workforce data becoming meaningful context about people, teams, roles, skills, performance, compensation, and how the organization works.
AI can reduce repetitive work such as manual data assembly, reporting, and reconciliation, giving people more capacity for higher-value work. HR, Finance, and people managers can redirect that time toward workforce planning, forecasting, scenario analysis, and stronger decision support.
AI upskilling affects both workforce capability and business investment. HR can help shape skills, learning paths, role design, and manager support, while Finance can connect those priorities to budgets, investment decisions, ROI, and long-term workforce planning.