Moving beyond “gut feel” to precision resource allocation
Executive Summary
As a business management consultant for Marq Neasman Consulting, I have noticed a pattern with many organizations that struggle with hiring decisions. Workload and staffing ratios are misaligned due to several factors – capacity, a capability/skills gap, assumption, and lack of understanding what goes into task or process completion. Regardless of organization size, public or private sector, the practice of hiring often can be and is highly reactive. Oftentimes, it is when management or staff begin to feel strain or burnout from current or growing workloads, scream for help, or resign — which leads to high turnover rates.The most expensive mistake a business can make is hiring based on the “feeling” of being busy. According to the U.S. Bureau of Labor Statistics, staffing and human capital costs account for 70% of total operating expenses in many industries. In addition, HR professionals only spend 15% of their time managing the cost of labor. Organizations can make better hiring decisions by not only utilizing industry standard practices, but by implementing workload staffing studies to create benchmarks, justify the need, costs, and optimize their hiring practices. When an organization shifts to data-informed decision making, it allows for accountability and identifies inefficiencies, trends, and root causes in hiring decisions. This leads the organization to greater productivity and contained costs when the workload staffing ratio is optimized.
In this white paper, we discuss the current culture of hiring practices, the principles that govern hiring, the root causes of misalignment, and the data-driven solutions required to build a sustainable, high-performing workforce.
The “Unknown Workload” Epidemic
Before we dive into the unknown workload epidemic, let’s first understand what a workload actually is. Workload is an all-encompassing term that includes any variable reflecting the amount or difficulty of one’s work (Bowling and Kirkendall, 2012 p. 222). A quantitative workload defines the amount of work done versus a qualitative workload describes the difficulty of the work (Bowling & Kirkendall, 2012). Relatedly, role overload is when the “role demands create the perception that available resources are inadequate to deal with them, resulting in distraction and stress” (Kahn et al., 1964, cited in Brown et al. 2005).Across sectors and industries — whether service- or production-based — there is an invisible crisis in the modern workplace – the unknown workload. Organizations scale too fast to the point where current workload demand and requirements cannot be met efficiently or effectively. This results in businesses and employees having unclear definitions of specific tasks, resource requirements, and time commitments necessary to achieve business goals. The cause of this epidemic is straightforward — a lack of structured business processes. (Duwe, 2021) It also results in higher turnover and higher burnout in an organization as no employee can thrive in an unstructured environment.
Additionally, resource capacity is traditionally assessed by simple headcounts or basic output targets. However, with modernized practices, such as digital transformation, organizations fail to account or measure for the hidden tension that automation and technology brings. This includes unnecessary or extra administrative tasks, uneven task distribution, and constant context-switching which ultimately slows productivity and reduces efficiency. If organizations took the time to assess perceived and actual workloads of their employees, they can eliminate burnout, reduce high turnover rates and hiring costs and increase employee retention. In today’s labor market, organizations cannot afford to throw financial resources at an efficiency problem.
The Governing Principle
For organizations to successfully solve a staffing crisis, let’s first look at the law of diminishing returns. The law of diminishing returns states that if one variable increases while keeping other variables fixed, the extra output produced will eventually decrease. For example, a law firm hires additional paralegals to handle one attorney’s fixed caseload. In this example, the excess support staff can and most often lead to reduced efficiency, communication breakdowns, case management bottlenecks, increased client complaints, and increased errors. In this example, the perceived assumption is that the attorney’s workload is too high and the solution is adding additional resources to reduce the workload. In reality, the law firm should consider workload optimization to balance responsibilities and ensure staffing additions enhance productivity.The Solution: Data-Informed Decision Making
In order for organizations to gain insight into how to balance workload and staffing ratios, data-informed decision making must be utilized to justify hiring costs and practices. Specifically, a workload-staffing study should be conducted to achieve optimal resource allocation. Workload staffing studies assist management with determining how many staff and resources are required to adhere to the specified performance standards. These types of analyses help organizations make the best use of their resources and generate data to inform ongoing workload management. Benefits of a workload study reveal the accurate time required for different task types, a clear understanding of the time staff has available to complete tasks, fair work distribution, improved business outcomes, enhanced employee well-being, and an evaluation of current staffing levels versus staffing needs.
To move beyond perception, a 90-day workload staffing study was conducted within the firm’s case management system. All billable and non-billable activities were tracked and categorized, including attorney-only tasks, paralegal responsibilities, administrative work, client communications, and intake functions.
The data revealed significant misalignment:
Evidence in Action
A boutique personal injury law firm with approximately 100 active cases across pre-litigation and litigation faced a common leadership question: Do we need to hire more staff? The firm consisted of one attorney and one part-time paralegal, while signing one to two new cases per week. Although strain was evident, leadership lacked clarity on whether the issue was capacity, capability, or process-related.To move beyond perception, a 90-day workload staffing study was conducted within the firm’s case management system. All billable and non-billable activities were tracked and categorized, including attorney-only tasks, paralegal responsibilities, administrative work, client communications, and intake functions.
The data revealed significant misalignment:
- 50% of the attorney’s time was spent on non-attorney tasks, primarily intake.
- 25% was dedicated to routine client updates.
- Only 25% was spent on substantive legal work.
The part-time paralegal was not operating at full capacity, with measurable bandwidth available to absorb additional responsibilities.
The issue was not insufficient staffing — it was inefficient task allocation.
Rather than hiring another paralegal reactively, the firm implemented a targeted restructuring strategy: a part-time intake specialist was hired, client updates were reassigned to the paralegal, and the paralegal role was transitioned to full-time.
As a result, the firm restored 50% of the attorney’s capacity to high-value legal work, improved client communication, and created a scalable staffing model aligned with growth — without disproportionate payroll expansion.
This case illustrates a core principle of workload optimization: data clarifies whether an organization has a headcount problem or an allocation problem.
The issue was not insufficient staffing — it was inefficient task allocation.
Rather than hiring another paralegal reactively, the firm implemented a targeted restructuring strategy: a part-time intake specialist was hired, client updates were reassigned to the paralegal, and the paralegal role was transitioned to full-time.
As a result, the firm restored 50% of the attorney’s capacity to high-value legal work, improved client communication, and created a scalable staffing model aligned with growth — without disproportionate payroll expansion.
This case illustrates a core principle of workload optimization: data clarifies whether an organization has a headcount problem or an allocation problem.
The Marq Neasman Competitive Advantage
Sustainable workforce optimization requires more than data collection. It demands disciplined analysis, executive-level judgment, and structured implementation. Marq Neasman Advisory differentiates itself by integrating workload analytics, leadership strategy, and operational design into a unified performance model.Where many advisory firms deliver recommendations in isolation, Marq Neasman applies a structured methodology grounded in measurable outcomes. Its framework emphasizes the alignment of people, processes, performance standards, and profitability. Rather than relying on assumptions or industry averages, the firm conducts empirical workload and staffing analyses tailored to each organization’s operational environment. This ensures hiring decisions are justified, scalable, and financially sound.
A key differentiator is the firm’s focus on performance-based impact. Recommendations are not theoretical; they are designed to produce measurable improvements in productivity, efficiency, and cost containment. By combining data-informed diagnostics with leadership accountability, organizations are equipped not only to identify inefficiencies but to correct them at the structural level.
Additionally, Marq Neasman Advisory bridges the gap between strategy and execution. Workforce optimization initiatives often fail because leadership lacks the tools to operationalize findings. Through executive advisory, structured implementation planning, and leadership development, the firm ensures that insights translate into sustained organizational performance.
In an environment where labor represents the largest operating expense for most organizations, hiring decisions cannot be reactive. They must be analytical, strategic, and aligned with long-term growth objectives. Marq Neasman provides that rigor — transforming workload ambiguity into measurable clarity and turning staffing uncertainty into a competitive advantage.