AI is redefining the workforce — and most planning models aren’t ready
Our take
The article’s core message – that workforce planning models are woefully unprepared for the AI-driven reshaping of the workforce – resonates deeply. It’s a truth many organizations are beginning to confront, often belatedly. The fragmentation of data and planning across HR, finance, and procurement is a long-standing issue, but the accelerating pace of AI adoption is exposing its fragility with stark clarity. We’ve seen similar challenges emerge in other areas of data management; Perplexity today launched hybrid compute for its agentic platform, Computer, a system that lets a single AI agent split Your files stay put: Perplexity’s hybrid AI keeps confidential data off the cloud, demonstrating a push towards solutions that address data siloing and control. The inability to connect workforce decisions to business outcomes isn’t just an inconvenience; it’s a strategic blind spot that undermines agility and increases the risk of misallocation of resources. The SAP research highlighting the disconnect between AI’s impact on productivity versus job design is particularly telling – it reveals a reactive, rather than proactive, approach to workforce transformation.
The expansion of the workforce definition – encompassing contractors, specialized partners, and increasingly, AI systems themselves – is a fundamental shift that traditional planning models simply haven't accommodated. Thinking of the workforce as a monolithic entity of full-time employees is an outdated concept. Fambot is building an AI “chief of staff” to help families manage the emails, calendars, school updates, sports schedule Fambot introduces an ‘AI chief of staff’ for families, illustrating the broadening scope of AI’s influence on work management and coordination, extending beyond traditional enterprise settings. This necessitates a more dynamic, integrated approach to planning, one that considers the interplay of human, digital, and external labor. The article rightly points to the need to move beyond siloed decisions (hire vs. automate vs. reskill) and instead focus on how work is configured across all these elements, at a clear and understood cost. This requires a fundamental rethinking of planning architectures and the metrics used to assess workforce performance.
The emphasis on CFOs and CHROs needing to collaborate is crucial, and the article’s observation that this isn’t driven by a cultural shift but by the inherent necessity of shared perspectives is astute. Breaking down the barriers between these traditionally distinct functions isn’t just about better communication; it’s about aligning incentives and establishing a shared understanding of how workforce investments drive business value. The move from annual budgeting to continuous workforce steering represents a significant evolution, requiring real-time visibility into skills, capacity, and cost. Waymo goes on offense ahead of Tesla’s Cybercab launch Waymo goes on offense ahead of Tesla’s Cybercab launch underscores the importance of anticipating future skill needs and adapting quickly—a core tenet of continuous workforce planning. This shift demands not only the right technology but also a change in mindset and governance.
Ultimately, the article correctly identifies that the biggest hurdle isn’t the technology itself, but the leadership alignment needed to drive the necessary changes. Implementing integrated data systems is a technical challenge, but ensuring that leaders are willing to share data, agree on metrics, and commit to a shared planning cadence is a far more complex organizational challenge. The organizations that succeed in this endeavor won’t just have better data; they’ll possess a fundamentally clearer understanding of how work generates value, across all dimensions. The question now is: how quickly will organizations recognize the urgency of this transformation, and what level of disruption will they be willing to tolerate in order to achieve it?
Presented by SAP
HR tracks employees and skills. Finance owns headcount targets and cost. Procurement manages contractors and services spend. Together, they leave executives unable to answer basic questions about how workforce decisions actually translate into business outcomes.
Fragmented planning creates workforce blind spots
Each function has its own systems, its own planning cadence, and its own assumptions about how work gets done. Recent SAP research found that 62% of C-suite executives are dissatisfied with their current level of integration between people and business performance data. The same research found that while 50% of organizations are planning for AI’s impact on productivity and capacity, only 21% are planning for AI’s impact on job design and organizational structure.
That gap matters because the two are inseparable. You can’t make a sound decision about where to automate without understanding how it will affect the teams, roles, and skills connected to that work. Most organizations are trying to do exactly that, and discovering, usually too late, that the pieces don’t fit together.
The workforce has quietly expanded — and planning hasn’t caught up
The definition of “workforce” has been expanding for years, but most planning models haven’t registered the change. Employees now work alongside contractors, specialized partners, and AI systems that handle real execution-layer tasks — not just support functions, but actual work. In some delivery models, external and digital labor has moved from supplemental to central.
That shift changes the nature of every significant workforce decision. When a company chooses to automate a process, the ripple effects touch headcount, skills, services spending, and productivity assumptions simultaneously. A reskilling initiative can reduce dependency on contractors. Expanding contractor capacity can close an immediate gap while quietly deepening a long-term capability problem. None of these moves can be evaluated well in isolation, but that’s precisely how most organizations still evaluate them — separately, in sequence, by different teams working from different data.
The real question isn’t “should we hire, automate, or reskill?” It’s how work should be configured across humans and intelligent systems, and at what cost. Most planning architectures weren’t designed to ask that question, let alone answer it.
CFOs and CHROs are being pushed into the same room
CFOs are being asked to connect financial signals to real operational choices, particularly in workforce spending, which dominates most income statements. CHROs are being pulled beyond traditional talent management into harder questions about work design and the balance between human and digital labor. Neither can answer these questions from their current vantage point alone, and historically, they haven’t had to answer them together. That’s changing, not because of some cultural shift toward collaboration, but because the decisions genuinely require both perspectives at the same time.
When that partnership works, organizations can move workforce planning from a periodic budgeting exercise to an ongoing strategic conversation. They can ask harder questions: Where does it make more sense to build critical skills internally than to buy capacity externally? When we automate a workflow, how do we know whether we’re creating capacity or just moving a problem downstream? These aren’t questions finance or HR can answer in sequence. They require shared data, shared governance, and frankly, a shared willingness to operate in territory that neither function fully owns yet.
From annual budgets to continuous workforce steering
The organizations handling this best stopped treating workforce planning as a once-a-year negotiation and started treating it as an ongoing operational discipline. That means finance, HR, and procurement seeing the same picture of workforce capacity, skills, and cost, rather than reconciling three different pictures after the fact. It means modeling scenarios that combine hiring, reskilling, automation, and external labor as connected levers rather than separate conversations.
The metrics are evolving too. Headcount, labor cost, and utilization still matter, but they describe only part of what’s happening. As AI becomes embedded in operations, leaders need visibility into skills and readiness relative to strategic priorities, how work is actually distributed across employees and intelligent systems, and whether automation is unlocking new capacity or quietly eroding the engagement of the people working alongside it. Organizations that track these signals appear to be making structurally different decisions about where to invest. They are not just better-informed, but asking better questions.
The hard part isn’t the technology
Connecting HR, finance, and procurement data creates the conditions for better decisions. It doesn’t make those decisions. The harder challenge is leadership alignment: CFOs and CHROs agreeing on shared metrics, committing to a planning cadence that keeps workforce choices connected to business strategy, and building a working relationship where neither function is simply ratifying what the other has already decided. That’s a governance problem, and it doesn’t come bundled with any platform.
The organizations that move first on this won’t just have better data. They’ll have a fundamentally clearer picture of how work creates value across employees, contractors, and intelligent systems together. The ones that don’t will keep making workforce decisions in the dark. The difference is that those decisions are coming faster now, and the consequences of getting them wrong are larger.
For additional perspectives on workforce planning, continuous planning, and leadership in the age of AI, explore SAP Workforce Planning and the latest SAP SuccessFactors innovations.
David Imbert is Chief Marketing Officer, SAP Financial Management; Lara Albert is Chief Marketing Officer, SAP SuccessFactors.
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