Why Headcount Planning Fails and How Data Driven Staffing Models Fix It

Headcount planning does not just occasionally break, it systematically fails in the space between forecast and operational reality. Across healthcare and service-driven industries, inaccurate staffing models directly affect cost, access, and reliability. Leaders often plan using averages and static assumptions, while operations must absorb demand spikes, training ramps, escalations, and strict service level commitments that do not slow down because a staffing plan looks reasonable on paper. When workforce planning is treated as a budgeting exercise rather than an operational system, the result is predictable: reactive hiring, inconsistent service levels, and rising labor costs that organizations struggle to explain or control.

Even in companies that say they plan, the planning often stays superficial. McKinsey reports 73% of surveyed organizations conduct full operational workforce planning, yet only a small share link their strategies to future skill needs. This gap between planning and execution is exactly the problem Alicia He’s work addresses

Alicia He is a Strategic Finance, Business Insights, and Operations Leader at a prominent San Francisco based health tech company that coordinates access to specialty medications through a software driven pharmacy platform connecting patients, prescribers, manufacturers, insurers, and pharmacies. In her prior role, she was Chief of Staff at Butter, where she helped build the company’s operating and workforce infrastructure from the ground up during its earliest growth stage. Alicia designs and operationalizes staffing intelligence for the script processing and customer experience organization, turning workforce decisions into a system leaders can explain and defend. She is also an editor for the Sarcouncil Journal of Entrepreneurship and Business Management, which shapes how she thinks about evidence, assumptions, and what it takes for workforce decisions to hold up under scrutiny.

Alicia, thanks for joining us. From your point of view, why does headcount planning so often break down?”
Because most headcount planning starts with a number someone wants to believe, not with the system the team is committing to run. People pick a headcount, then try to justify it after the fact. When demand shifts or workflows change, the plan turns into a recurring argument instead of a tool.

Why do staffing decisions become so reactive in fast moving operations?
Because the cost of being wrong shows up immediately. Backlogs build, quality slips, customer experience gets noisy, and leaders feel pressure to act fast. If the organization does not have a shared model that ties demand to service levels, it defaults to intuition and urgency.

That is how you get late hiring, overcorrection, and constant debate about whether the team is understaffed or overspending.

What did you build to replace intuition with a repeatable staffing system?
I designed a staffing optimization model and built the first algorithmic labor planning system at the company. The goal is not complexity. The goal is consistency and explainability.

I spearhead capacity management for the script processing and customer experience team, then coordinate with team leads to translate analysis into headcount planning and workflow improvements. The measurable outcome is a 25%+ reduction in unit labor cost per script because staffing and responsibilities are built around real workload, real throughput, and real constraints.

What inputs does your staffing model combine, and why those three?
Volume forecasts, productivity metrics, and SLA requirements. You need volume forecasts because staffing without demand is guessing. You need productivity metrics because capacity depends on what work actually takes, not what you hope it takes. You need SLAs because “enough staffing” only has meaning relative to the response times and quality standards you are committing to deliver.

When those three sit in the same system, tradeoffs become explicit, and decisions stop being personal?
How do you keep productivity assumptions honest as workflows change?
By treating productivity as a living signal, not a fixed number. I run a performance scorecard framework that evaluates productivity and quality by agent and workflow. That scorecard becomes the feedback loop for staffing. When tooling changes, when work is specialized, when steps are automated, the scorecard shows what actually moved so the staffing model stays tied to reality instead of becoming a stale spreadsheet.

What do most leaders miss when they try to make staffing “data driven”?
They underestimate how much of it is definition and governance. If teams do not agree on what a unit of work is, what “done” means, and where time is actually spent, the model becomes a debate generator. Leaders also miss the time horizon. Many organizations talk about planning, but very few do it strategically. Gartner notes that as of 2024, only 15% of organizations are engaging in strategic workforce planning. That gap matters because staffing models only work when the organization commits to consistent inputs, consistent definitions, and a rhythm of review.

I apply that same discipline in my editorial work as well. As an editor for the Sarcouncil Journal of Economics and Business Management , I look for clarity around assumptions and limits. Workforce models need that same discipline or they will be overridden the moment things get stressful.

You were the Chief of Strategy, and first employee at Butter. How did you rapidly scale and onboard dozens of employees without staffing and execution falling apart?
Because growth breaks teams when work expands faster than clarity. As the first employee, I built the operating structure that lets headcount scale without turning into chaos: a clear execution rhythm, explicit ownership across functions, and a way to translate workload into roles instead of constantly improvising.

Practically, that meant setting a planning cadence with short and long term KPIs, building repeatable intake and prioritization so teams did not thrash, and putting lightweight processes around hiring and onboarding so new people could contribute quickly without every answer living in one person’s head. The lesson maps directly to staffing models: if you do not define the work, define the constraints, and update assumptions as reality changes, headcount becomes reactive and expensive.

Why does labor efficiency matter beyond one company, especially in health care operations?
Because in health care operations, labor efficiency is access. It has fewer avoidable backlogs, fewer handoffs that break, and more consistent service levels even when demand shifts.

The constraint is not theoretical. WHO estimates a projected shortfall of 11 million health workers by 2030. When labor is scarce, waste hurts more. Data driven staffing models help protect service levels without relying on burnout or endless hiring.

What is your advice to leaders trying to fix staffing decisions for good?
Stop asking, “How many people do we want?” and start asking, “What system are we committing to run?” Tie demand, productivity, and service levels together. Make assumptions visible. Build a weekly operating rhythm where the model learns and updates, instead of being revisited only when something breaks. I also peer review manuscripts submitted to the Sarcouncil Journal of Entrepreneurship and Business Management , and that discipline carries into this work: if you cannot show your reasoning, you do not have a decision. You have a guess.

Alicia’s point is simple: staffing stops being political when it becomes explainable. When demand, productivity, and service levels sit in one model, leaders can adjust quickly without panicking, and teams can scale without burning out. In a labor constrained healthcare environment, that discipline is not just an internal efficiency win. It is a reliability advantage.