Pay-for-performance models are widely adopted yet struggle to capture value
Companies are adopting pay-for-performance models at scale. Around 77% already use some version of it. It works, for roles where output is clear. If you build more units, answer more calls, or hit a quota, your compensation reflects that. The model is simple, which makes it appealing.
But here’s the problem: Not all roles produce clearly measured outcomes. If you’re evaluating a maintenance programmer, counting resolved tickets doesn’t always reflect the actual value delivered. A business analyst might spend weeks realigning a broken relationship between departments, critical work that won’t show up in a dashboard of KPIs. These contributions aren’t easily quantified, but their impact moves the business forward. That’s where the pay-for-performance model begins to show cracks.
What senior leaders need to recognize is this: if you only reward what’s directly measurable, you risk missing the full picture. Projects stall, teams become transactional, and long-term strategy becomes compromised. Incentives drive behavior. So, if you’re incentivizing only the countable, you could easily be disincentivizing what actually creates lasting value.
This is not an argument against performance-based compensation, far from it. It’s a call to rethink how performance is defined. Tangible output is one dimension. But equally important are communication, problem-solving, and context-specific execution that only becomes obvious in hindsight. Evaluating performance in today’s technical enterprise space requires recognizing both what can be measured, and what must be observed.
Traditional pay-for-performance environments excel when applied with quantifiable outputs
In some environments, pay-for-performance is straightforward and effective. Think manufacturing lines, call centers, or frontline banking roles. These are structured systems. You can count how many components someone assembles. You can track how many calls they handle or how many customers they sign up for a service. Good performance is visible, immediate, and largely individual.
This structure makes it easy to link performance with compensation. The upside is clarity. People know what they’re being measured on. Managers can see at a glance who’s delivering and who isn’t. That works well in environments where volume is the priority.
But there’s a catch. When quantity becomes the primary performance driver, quality often takes a hit. A teller pushing credit card sign-ups may meet goals, but if two-thirds of customers never activate their cards, the business sees very little return. An agent rushing to meet a per-call metric might push longer calls onto someone else, not solving the problem, just redistributing it. A factory worker chasing quota can cut corners, leading to rework or product failures down the line.
The flaw isn’t in measuring performance. It’s in measuring only part of it. Counting output without considering purpose, impact, or execution outside the narrow metric leaves room for manipulation and misalignment.
For executives, the insight here is simple: Measurable doesn’t always mean valuable. You want a compensation model that encourages performance aligned with long-term goals, not just short-term output. It’s about setting the right metrics, not the most convenient ones.
Blending quantitative and qualitative evaluation criteria
In IT operations, support and maintenance are often measured by basic metrics, number of tickets closed, uptime percentage, resolution time. These are useful baselines. They tell you whether teams are moving. But they don’t tell you where they’re going.
A help desk technician closing the most tickets isn’t always the person delivering the most value. What matters more is whether the resolution sticks, whether users walk away with confidence in the support, and whether known issues are communicated to others who could be affected. The same applies to maintenance programmers. It’s not about how many fixes were shipped. It’s about whether they solved the core problems facing the business, and whether they were properly documented and communicated downstream.
Relying on hard metrics alone leads to surface-level insights. Real performance comes from layering those numbers with feedback from the user side. When a global issue is resolved, did the technician follow through to prevent recurrence? Did the programmer go beyond the standard fix to optimize the function for long-term use?
Effort, communication, execution quality, these aren’t guesswork. They just require the right systems to track and observe them. Department surveys and direct user feedback offer visibility that raw output metrics can’t. Once you have that visibility, it’s up to leadership to decide the right mix of hard data and soft inputs to evaluate each role. Transparency is key, employees need to know what outcomes and behaviors the organization values. The more aligned they are with that understanding, the cleaner the feedback loop becomes.
For executives, this isn’t about adding complexity for complexity’s sake. It’s about optimizing the measurement strategy to reflect the reality of modern IT work. Closing out tasks quickly is one signal. Making those tasks matter to people and systems is another. You need both, weighted properly.
Business analysts and trainers require multifaceted performance evaluation
Some roles in IT aren’t about direct output. They’re about connecting moving parts and making information useful to others. Business analysts and trainers fit this model. They don’t manufacture deliverables in isolation. Their success depends on coordination, communication, and ongoing contribution to larger teams and initiatives.
This creates a challenge for performance measurement. For business analysts, having deep domain knowledge isn’t enough. If they can’t engage with stakeholders effectively or navigate resistance inside a user department, their impact is limited, even if their analysis is technically correct. Similarly, a trainer may design and deliver a course perfectly, but consistently delayed project timelines downstream can erode the training’s effectiveness before it’s applied.
What actually drives success? It’s a combination: successful sessions delivered, knowledge transfer achieved, stakeholder relationships improved, and user satisfaction increasing over time. These dimensions are harder to track, but they’re critical.
Hard metrics, number of trainings conducted, development of new materials, or number of business requirements captured, still matter. They create structure. But real performance in these roles also rests on external validation. Following up with stakeholders to measure satisfaction and project success gives you a wider lens into these employees’ effectiveness.
This is not about forcing intangible metrics into spreadsheets. It’s about applying judgment where automation ends. Managers need to observe how analysts and trainers show up in cross-functional environments. Did they smooth out blockers? Did stakeholders begin responding instead of avoiding?
C-suite leaders should take note. These roles are often crucial in early and middle phases of projects. If you overlook their contribution because it’s not behind a code commit or support case ID, you’re missing high-leverage moments that determine whether initiatives get traction, or stall. The model must track what actually moves business outcomes, not just what’s easy to count.
Effort and communication play a critical role in creating long-term value
Not everything that drives business progress can be measured on a scoreboard. Many of the most important contributions, consistent follow-through, trust built with stakeholders, collaborative alignment, don’t show up in productivity logs or performance dashboards. Yet, these are often the actions that open paths for breakthroughs and sustained execution.
Effort matters, even when it doesn’t close a metric gap immediately. In technical environments, much of the real progress comes from persistent iterations: solving tough issues that others avoided, sharing knowledge to prevent repeat failures, holding the line on quality even when deadlines compress. When someone puts in that level of work consistently and proactively, the results often cascade across teams, even when the initial output looks the same.
Communication fits the same pattern. It accelerates alignment, removes redundancy, and reduces time lost to ambiguity. And it can’t be easily faked. Project success can often be traced to a single person who kept departments connected, escalated risks early, or gave clarity when there was none. It rarely gets quantified, yet its ROI becomes clear when the alternative is missed deadlines, misaligned goals, or rework.
Pat Riley, former head coach of the Los Angeles Lakers, made this point years ago when he tracked “effort metrics” to influence team performance, not based on outcomes alone but based on engagement. The business context is similar. Employees who consistently show up with intent to move the mission forward, even if it’s not in their job description, drive momentum that a clean KPI snapshot can’t reflect.
Leaders at OKR International have supported this view by stating, “Intangibles often create or destroy value quietly, until their impact is too big to ignore.” The role of leadership is to recognize these forces before that tipping point. That means explicitly incorporating effort and communication as part of the performance evaluation framework, not as an afterthought.
For the executive audience, here’s the takeaway: Just because something isn’t easily measured doesn’t mean it isn’t real. Long-term performance systems must evolve beyond numerical targets. Include the observable, the repeatable, and the culture-shaping behaviors that give your teams resilience and your strategy velocity. Clear metrics matter, but so does what people do when no one’s measuring.
Key executive takeaways
- Reevaluate how performance is measured in knowledge-based roles: Traditional pay-for-performance models miss key value drivers when applied to roles where outcomes are intangible. Leaders should expand evaluation criteria beyond measurable output to capture impact, influence, and cross-functional contribution.
- Balance clear metrics with meaningful results: While output metrics work well in transactional roles, they risk incentivizing behavior that undermines long-term value. Executives should align measurement systems with outcomes that reflect both quantity and quality.
- Use blended metrics for IT support and maintenance roles: Hard data like ticket closures provide structure, but miss context like issue depth, documentation, and communication quality. Leaders should combine operational metrics with user feedback and observed effort to fairly assess performance.
- Design multifaceted evaluations for analysts and trainers: These roles rely on collaboration and timing, making isolated output measures insufficient. Decision-makers should incorporate stakeholder feedback and team-driven results to gauge effectiveness accurately.
- Prioritize effort and communication as strategic levers: Intangible behaviors drive long-term progress and organizational cohesion. Performance models should explicitly recognize these inputs, not just outcomes, to reward the full spectrum of value employees deliver.
