It's Time to Prepare for the Algorithmic Workforce Prepare for an era of working with bots, digital humans, holograms and algorithms as colleagues.
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Artificial intelligence, the growth of the "gig economy," advances in virtual reality, robotics, autonomous driving and the blockchain have all revolutionized our work. But this is only the tip of the iceberg, and the rate of change and penetration of these technological advances will create a reconfigured workforce — not just a new way of working.
Algorithmic workforces use computer algorithms instead of cellular beings, e.g., managing or augmenting them through data-driven approaches. Algorithms are penetrating fast; exact numbers are unknown, but 40% of enterprise human resources departments use AI applications. A few prominent use cases include deploying HireVue for the first level of video interviews, assessing facial expressions, tonal demeanor and language fluency. The adopters of algorithmic workforce argue for productivity metrics, while detractors shun the technology, citing bias and opacity reasons.
The current state of the algorithmic workforce
There are multiple areas where companies have deployed algorithmic employees today. For example, my customer support organization was run by digital humans, and my executive assistant is an algorithm named "Julie." I also use voice bots extensively to help replace typing and blogging. In recruitment, many companies use AI for screening off high volumes of resumes and the first level of automated interviewing.
This can spread to management functions in many industries, i.e., allocating tasks and shifts. Hospitals, retail, gaming, and travel sectors see these deployments while manufacturing companies deploy algorithmic managers to track movement, actions and production rhythm.
Performance reviews using ratings, behaviors, incentives and penalties are also in place across many sectors like Uber and Airbnb. The output of these reviews is used to decide on salary, perks, promotion and termination.
During the pandemic, kiosk-style holographic workers were deployed where basic human-level communication must be sent consistently and multiple times.
A few use cases of the algorithmic workforce
Workers at an Amazon warehouse in Melbourne, Australia, managed by algorithmic workforces measuring pick, move and ship rates accurately. The food delivery sector shares its KPI through monthly personalized reports about their performance: time to accept orders, response rates and travel times. The AI keeps reorienting KPIs based on a variety of factors.
Algorithms act as pricing managers on many travel, hotel and sports sites and are now more cognitive than rule-based engines from the prior era.
Generative AI has already been deployed to create content like images, sounds, music, videos and even code. Self-healing code and autonomous bug-finding software have been rising, and they will mature and scale sooner than later.
In the past three years, there has been an increase in the adoption of algorithmic workforce solutions. The algorithmic approach is now viable and scalable to many companies and industries.
Future of the algorithmic workforce
As algorithms manifest as bots, digital humans, generative AI, and robots increase the workforce; companies evaluate the impact and trade-offs between human emotions and the boosts from deploying an algorithmic workforce. A few considerations include bi-directional information flow (not just from algorithms to humans like Uber), keeping humans in the loop for critical decisions, creating more human touches, and ensuring working conditions remain suitable for workforce morale.
As AI creates other AI algorithms, companies will need to rethink workforce strategy and organization design accounting for new governance models, incentives, the role of human managers, levels of transparency and data collection thresholds.
As autonomous driving, DAOs, the gig economy and robotics scale, humans will not be doing the same jobs from the prior decade nor making decisions the way they were used to. For adopters and detractors, these shifts are here and will only have a more profound impact.
Pros and cons of leveraging algorithms in your workforce
There are advocates and detractors spawned from the scaling of the algorithmic workforce. Whether anyone likes it or not, the phenomenon has taken root and will only make a more significant impact. Let us examine some pros and cons of it.
The pros include lower costs, like mundane activities needed to deploy humans, and algorithms can handle specific workloads in seconds which humans could take hours or days. Greater efficiencies result in higher productivity and less wastage. Algorithms also make less emotional and more data-driven decisions- while bias can creep into AI, many decisions and actions are less prone to bias than humans.
There are also controversial and detrimental parts to implementations of an algorithmic workforce like surveillance, e.g., uber drivers who report to algorithms know that they are under constant watch, including location, speeds, acceptance rates, ratings, actions, etc. Multiple wrong actions can lead to them being barred from using the platform. While data is necessary to collect for data-driven decisions, it can affect employee morale, less trust, and ultimately attrition. The opacity of these algorithms is about for the workforce as it rapidly adjusts to new conditions and decides outside of human comfort zones, and creates feelings of dehumanization.
The growth of the "gig economy" has revolutionized how millions of people work. Adopters and detractors both make their arguments and agree this is an inevitable trend yet to scale.
All those jobs from typists, traffic light supervisors, shift managers and even drivers were decent jobs in the last century; this decade would see the departure of many more such genres.
It is now a question of how companies manage a dual workforce balancing human emotions and algorithmic efficiency. I am an early adopter of many of these algorithmic colleagues and can visualize how scalable the workforce of the future will be. As unknown risks surface, new governance and management styles will need to be implemented.