What is Performance Management and what are KPIs
Performance management is an ongoing process to monitor, assess, and optimize the performance of employees, teams, and the Company, aligning them with strategic objectives through regular feedback, skill development, and corrective actions. The main actors are the Manager and the employee. From their periodic interaction, KPIs are created, measures that allow performance management.
KPIs (Key Performance Indicators) are measurable metrics that clearly define what matters and track progress and results achieved objectively.
They translate strategy into concrete numbers that measure the degree of achievement of objectives by individuals, teams, and organizational structures, monitoring effectiveness, efficiency, and results objectively and in a timely manner, for data-driven decisions.
KPI = Metrics. KPIs should be seen as levers for governance and growth. Defining the right metrics allows for having a dashboard of meaningful and reliable data to make informed decisions at every level.
Therefore, a well-defined KPI (metric) is one that:
- provides a measure that helps describe the state of affairs to be measured
- that description is useful for making a decision
- that description is related to a higher and strategic state of affairs.
Examples:
- Net Promoter Score (NPS) is a KPI that measures customer satisfaction (target >50)
- Employee Turnover Rate is an HR KPI that measures employee retention (<10% annually).
The Evolution of Performance Management
Historically, Performance Management has been applied on an annual basis. At the beginning of the fiscal year, objectives are set at all organizational levels, ensuring that they contribute to achieving the goals and realizing the Company's Strategy.
At every level, the Manager and the Employee meet for the Goal Setting conversation. The Manager proposes the objectives, the employee shares their observations, the objectives are consolidated, and the resources to achieve them are determined. Finally, KPIs are defined to measure the degree of achievement of the objectives. Halfway through the year, the Manager and the Employee meet again for the Mid-term Review, where they check the progress on achieving the objectives using the KPIs and update the action plan to reach them by the end of the year. It is at the end of the year that the Manager and the Employee prepare for the meeting where the achievement of objectives is "certified," and improvement actions are identified that the employee can take to improve their performance in the next cycle.
Today, in modern organizations, this model has shown its limits. It no longer meets the needs for speed and adaptability of organizations, is no longer suitable for evaluating the performance of people who play roles with an increasingly wide space of autonomy and discretion, and who place importance on the meaning of work and the ability to make an impact.
In fact, the traditional Performance Management process is often seen by Managers and Employees as a meaningless and bureaucratic ritual. Consequently, the evaluations expressed are superficial and discriminate little between the performance of employees, often showing a tendency to evaluate almost everyone positively.
For these reasons, organizations are moving from an annual performance management cycle to the so-called "Continuous Feedback," a process where the interaction between the Manager and the Employee is more frequent, and often, the responsibility for initiating these meetings is left to the employee.
New Performance Management models have two important consequences:
- They help update the objectives of individuals, teams, and organizational structures to respond to changes that are increasingly frequent and unpredictable today.
- They offer personalized support to each individual regarding the situation they are facing and their development needs and characteristics.
- They allow for frequent data collection on business performance and improve the responsiveness of the organizational system to external changes or shifts in company strategies and priorities.
The Arrival of AI in Performance Management
Artificial Intelligence is also making its way into the Performance Management process, enabling more frequent and timely feedback. At least, that is the promise.
Artificial Intelligence can enable personalized coaching actions. Machine learning-based systems combine data from different sources to identify the strengths and weaknesses of each employee, selecting contextual "nudges" and optimal intervention times to stimulate desired behaviors (e.g., real-time suggestions during an operational activity).
These "nudge" actions can potentially become a useful tool for the Manager, who can further personalize support for the employee.
However, many issues need to be addressed, the first being the risk of delegating evaluation to Artificial Intelligence. It's easy to think that a Manager with a team of 12 or more direct reports might take AI suggestions uncritically to make their work more efficient (the phenomenon of Cognitive Offloading). This risk is even greater in contexts where the culture of Performance Management is bureaucratic.
The Centrality of KPIs
Another aspect to address is the quality of data and thus the centrality of KPIs.
AI coaching systems supporting Performance Management, to be effective, need high-quality historical data.
Indeed, they aggregate data from heterogeneous sources to create an accurate reading of individual performance, identify behavioral patterns over time, and build their suggestions. Based on this analysis, the system selects the optimal time and content to provide personalized micro-interventions designed to guide behavior towards expected outcomes.
The effectiveness of these systems, however, depends on an enabling condition that is often underestimated: the existence of a solid, consistent, and integrated data foundation of KPIs and metrics at the individual, team, and function levels. Without this information infrastructure — collected with rigor, normalized, and made readable by AI models — even the most sophisticated coaching system produces unreliable output.
For HR Professionals, this means that investing in Artificial Intelligence tools to support Performance Management cannot ignore a preliminary task, often underestimated, of:
- education on the logic of training Artificial Intelligence systems, their potential, and limitations
- training on AI Aptitude, that is, how each of us approaches these tools and integrates them into our work
- definition of the performance data architecture
- quality assurance of Performance Management data
- renewing Manager training on Performance Management, with a specific focus on the quality and certification of the data from their evaluations