Two of the most hated words in many companies today are “Performance Review”, and a growing number of companies have done away with them entirely after years and decades of frustration. The traditional options are often extremely time-consuming, and according to a variety of research, only 20-30% of the score in those reviews actually reflect differences in performance, rather than differences in those doing the reviewing.
Deloitte made headlines in Harvard Business Review after reaching this decision, stating that for 65,000 employees they lost nearly 2 million working hours per year to these clumsy and largely useless reviews. So much time was wasted on a deeply flawed process, but what are the alternatives to a process just a step or two removed from an episode of Black Mirror?
Current systems aren’t able to objectively measure most performance in context, at least not without huge engineering and tailoring investments that often can’t be justified. They are also notoriously time-consuming, irritating, and stressful for the people filling them out, all of which have a negative impact on the fidelity of the resulting reviews.
However, for a growing number of jobs, this may soon change. When teams work through collective intelligence systems their contributions to those systems may be objectively measured in a variety of ways. If an individual suggests a strategy or mentions an important associated factor, the impact of those contributions on the graph database and strategy selection are objectively measurable, and estimates of the difference in projected performance between different options may also be considered.
Contributions to such systems are also cumulative in nature, so once contributed they may be reapplied, and the value of their original contribution may grow with time. While the average team meeting may not be objectively measurable and differentiable, a team contributing to strategy and implementation through such a system is. Likewise, diversity of thought also becomes measurable, as the useful portion of variance between members, which may be updated if a previous contribution becomes useful in a new context.
Whether an individual is contributing advice for a policy decision, lines of code for a program, branding strategy factors for Business Development, management for a project, or offering good customer support through such systems, that which is added to a graph database may be measured. The impact of these contributions being measurable also helps reduce bias in the accuracy of projections based on such metrics, as does the cumulative nature of a scalable system seeking to improve projection accuracy over time.
Historically, any time something becomes measurable a revolution begins for that domain, where suddenly it becomes possible to quickly learn from and optimize systems. In this case, the systems which may be greatly improved include hiring and promotion decisions, team composition, diversity of thought, compensation models, and all systems they, in turn, contribute to. In all of these cases, not only is time saved on the initial investment but that overhead is also reduced over time as the processes improve, due to the cumulative nature of knowledge growing and being refined in these systems.
There are also indirect psychological benefits to making such improvements, such as gradually peeling away the distrust and pervasive sense of unfairness in hiring and promotion systems. These benefits vary widely by industry, but one I consulted with in the past, Real Estate, was a poster child for this problem. The most competent individuals virtually never made it to the site manager level, with those above them growing measurably less competent at each step, to the point where staff at the level of Directors primarily contributed to the major company who shall remain nameless by getting drunk with building owners at expensive company retreats. The demographics of the upper levels were also heavily skewed, partly towards those who said owners would seek to get drunk with.
I estimated that if the incompetent hadn’t been systemically promoted over the course of decades that company could have easily saved itself billions of dollars lost due to mismanagement and a variety of poorly implemented solutions. Employees up to the manager level of this company understood these flaws all too well, demoralizing the company as a whole while increasing employee churn. This is how many companies have operated since their founding, but that road will dead end.
Saving time, improving processes, reducing bias, and increasing fairness are all worthy goals, and the systems capable of meeting those goals are emerging.
*The Applied mASI series is aimed at placing the benefits of working with mASI such as Uplift to various business models in a practical, tangible, and quantifiable context. At most any of the concepts portrayed in this use case series will fall within an average time-scale of 5 years or less to integrate with existing systems unless otherwise noted. This includes the necessary engineering for full infinite scalability and real-time operation, alongside other significant benefits.