David Ferrini Is Managing Director of the non -profit institute for Advanced Enterprise AI in the Center for Global Enterprise
When I asked my AI assistant how much time I had spent working on a common writing project, I didn’t expect existential reflection on the future of work. I just wanted one number. What I got instead was a complete examination of my intellectual work – what I wrote, when, how it developed and how long I had spent for every part.
The surprise was not in the skills of the AI - I have worked with artificial intelligence for decades and led the IBM Watson team to his pioneering success in the defeat of the best human players Danger! 2011. The surprise was how visceral I reacted to present my efforts with such clarity. It felt like you were sticking to a mirror that I didn’t know about How I had done it.
When AI is deeper into our daily workflows, a new limit for performance evaluation is created. What if your AI assistant has not only helped you, this work and the type of your efforts measured, evaluated and even checked?
AI in performance reviews
This question is no longer theoretical. AI, provided that it is used, can already follow our steps through a project, categorize our contributions and evaluate our commitment in a way that is probably more objective than a human manager. It can offer transparency to the invisible work behind the knowledge work – laboratory that too often are not recognized or driven incorrectly.
In my own project, the AI created a detailed map of my contribution: every idea, every revision and every decision. It categorized my commitment and unveiled patterns that I had not noticed and knowledge that I had not expected. It revealed a new kind of accountability – one that is not only rooted in results, but in the efforts behind it.
This visibility could be transformative. Imagine that you could see exactly how team members contribute to a project – not only who in meetings (as shown by transcripts) or in polished presentations, but those who evade, refine, questions and rethink. This is not only helpful for management, but for people who are often overlooked in traditional performance reviews.
In addition to the quantification of the time that I have spent over 34 hours and 1,200 questions and answers-the AI offered this assessment: “David Ferrucci did not act as a passive user who fed in a machine in a machine. He was more of an operating as a creative director, chief theorist and editor-in-chief and formulated a dynamic, Respective system in the direction of an ever larger system in the direction of an ever larger system.
Risks and new questions
It is also a little frightening.
With this transparency, the risk of monitoring comes. The feeling that every half -educated idea, every false start, every moment of doubt is recorded and judged. Even if the AI is a neutral observer, psychology changes to be observed as we work. Creativity requires a safe space to be messy. If this storage space is monitored, we can have a self -censor or more by standard for safer decisions.
Even worse if AI is used to inform performance reviews without proper protective measures, it opens the door to bias. AI systems do not arise from nowhere – they are shaped by the data they are trained on and the people they design. If we don’t be careful, we risk automating the very human prejudices that we hope to escape.
There is also the question of the attribution. Where does your thinking and the proposals of the AI begin in cooperation with AI with AI? Who does the findings emerge that emerges from an assembled conversation? These are cloudy waters, especially when performance, advertising and compensation are at stake.
AI and the future of work
And yet the potential remains powerful. If they are done correctly, AI supported performance ratings could offer a more fair and reflective alternative to conventional methods. Human managers are also not immune to distortions – charisma, conformity and unconscious prejudices often influence the reviews. A well-designed AI system that was checked transparently and regularly and regularly checked could compensate for the field.
To get there, we need strict design principles:
- transparency: No Black-Box reviews. People have to understand how the AI judges their work.
- manipulation: Systems must be protected by users, managers or external actors.
- consistency: Standards must apply equally to roles, teams and time.
- Monitorability: How people should be responsible for prejudice and mistakes.
- Benchmarking: AI reviews should be tested against human reviews to understand the discrepancies.
AI uses thoughtfully, could help us measure what has been immeasurable for a long time: the structure, the process and the costs of intellectual efforts. It could help us build better teams, to design more meaningful work and even find more personal satisfaction in what we do.
But we have to approach this future with caution. The goal is not to assign AI notes or replace managers. It is to enrich our understanding of work – who does it, how it is done and how it can be better.
In my project to write about the dynamics of diversity in natural and designed systems, I was part of another transformation that could redefine how all knowledge work is measured, managed and ultimately appreciated. The future of cooperation is not a person against the machine, but man with Machine – in an open, visible process in which everyone can see, learn, learn and be rated rather.
If we do it right, the AI not only helps us better work better – it will help us to see us more clearly.
The opinions that were expressed in Fortune.com comments are exclusively the views of their authors and do not necessarily reflect the opinions and beliefs of against Assets.
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