Using Artificial Intelligence to Boost Employee Performance

Compensation & Benefits Trends for the year 2021
In the post-pandemic scenario, emerging trends around compensation and benefits may be bucketed into three distinct categories – (a) Cost, (b) Talent Retention, and (c) Compensation Structure, with cost considerations underlining almost every decision about compensation in 2021.

One process which is universally applicable but unanimously disliked is perhaps the performance management process. Everyone might vote for an objective, unbiased and pro-employee process instead of a subjective, bureaucratic, time consuming and templated process. However, not many companies have been able to crack the code.

Perhaps it is required that we look at the issue with a different lens and shift the discussion from “managing performance of people” to “enhancing performance of people”.

Therein lies an opportunity where AI can assist employees to continuously improve their performance levels. It has the power to help an organization shift from experience-based, manager-driven decision making to data-driven decision making.

Transfer the Ownership to the Employee to Enhance his/her Performance

A football/soccer/cricket/tennis player analyses a multitude of data on multiple variables made available to him/her to analyze own performance (run rate, speed of service, etc.), to understand opponents’ capability (pace, ball control, etc) or to understand owns reaction. Imagine the same being replicated in your company where employees have a rich and intelligent data available to them to enhance their performance levels – an AI platform which analyses all aspects of one’s performance and guides them on which one area they should focus to have a meaningful impact at the workplace.

Agile Platforms Vs Traditional Platforms

Shifting the frequency of appraisal may not be the answer if the discussion remains one-way, subjective, prejudiced and biased.  A well-thought-of AI-based tool that augments the discussion points with structured and objective 360-degree pointers would help the manager and the employee to take precise and decisive steps for course correction. So, primarily what AI does is take the BI data and augments it to make it more intuitive and interactive. Another powerful impact of an AI enabler is that neither the employee nor the manager feels that they are mere rubber-stamps who are dependent on each other’s perceptions or biases.

Augmented Reality Used to Visualise the Work, the Workplace and the Worker

3D modelling and visualization tools will allow employees, their managers and the organization to visualize scenarios/options that are most suited to maximize productivity. Imagine being able to “craft” your job based on intelligent data gathered and analyzed instead of only relying on your own or your manager’s “gut instinct”. One would be able to visualize a career path based on the future impact of the current choices.

Forming Teams Based on AI instead of Perceptions

Michael Arena explains in his book “Adaptive Space” how “structurally teams at GM are positively disrupting themselves and transforming into Agile Organizations by enabling individual employees to connect and create across networks”. It is an interesting premise wherein intelligent data gathered to understand social networks in an organization enables the company to have a commercially disruptive enhanced performance of the teams by leveraging “social capital” (as against human capital alone).

AI-based Customised Learning Framework

Nothing at this point in time compares to the learning options curated for individuals commensurate with their learning agility and interest. This has a huge impact on how one can enhance their skill / strengthen the desired competency and in turn better their performance levels.

I would refrain from saying that AI will remove biases altogether from the performance management process. It will, however, definitely mitigate the biases (risks of “contrast bias” that might still continue to exist). The inherent capability of AI’s predictive modelling will give managers tools to immediately identify changes in performance, thus helping them to model their discussion points for desirable outcomes. Imagine, the predictive tool indicating that 73.8% of all the mistakes Mr. X does is between 12.30 to 1 pm and of that 69 % are done when the cafeteria didn’t serve any fried items such predictions which are transparent, accurate and are self-learning would help the company design its processes, projects, ecosystem, work scenarios, demand volumes. One can build as much complexity into the algorithm as one would want to.


  • How much is too much / the privacy factor, AI can go where no one has been before. It can analyze the response time by tracking keystroke counts, Auto-analyse response time to emails; can assess skill and competency (e.g., Interpersonal skill) by auto-analyzing sentiments in office emails, social media posts, applying machine learning to your LinkedIn updates.
  • AI cannot be treated as a plug-and-play technology but can be used across the hierarchy to augment their performance by getting decisive, specific, data-driven nudges.

Human judgment will still remain critical, although it may no longer remain central. It is akin to Spiderman being able to predict till the nano-second level when to extend his arm and save MJ from the falling debris but would need his own human intuitions to ask her to go for a stroll in the streets of Berlin!

Performance management “needs improvement” had been an oft-repeated phrase of the previous decade. In this new decade let us try to bring AI and try to “enhance performance”.

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