expressed opinion entrepreneur Contributors are themselves.
Advances in artificial intelligence, the development of the “gig economy,” virtual reality, robotics, autonomous driving, and the blockchain have all revolutionized the way we work. But that’s just the tip of the iceberg, and the changes and penetration rates of these technological advancements will create a reconfigured workforce — not just a new way of working.
Algorithmic Workforce Using computer algorithms rather than cellular organisms, for example, to manage or enhance them through a data-driven approach.Algorithms are penetrating rapidly; exact numbers unknown, but 40% of corporate human resources Departments use AI applications. Some prominent use cases include deploying HireVue for first-level video interviews, assessing facial expression, tone of voice, and verbal fluency. Adopters of algorithmic labor argue for productivity metrics, while critics shy away from the technology, citing bias and opacity.
Related: 5 Ways to Prepare for the Future of Work
current state Algorithmic Workforce
Today, companies deploy algorithmic workers across multiple domains. For example, my customer support organization is run by digital humans, and my executive assistant is an algorithm named “Julie”. I also use voicebots extensively to help replace typing and blogging. In recruiting, many companies use AI to screen large volumes of resumes and automate interviews at the first level.
This can be extended to management functions in many industries, i.e. assigning tasks and shifts. Hospitals, retail, gaming and tourism sectors see these deployments, while manufacturing companies deploy algorithmic managers to track movement, actions and production rhythms.
Many industries, such as Uber and Airbnb, have also adopted performance reviews using ratings, behaviors, incentives, and penalties. The results of these reviews are used to determine wages, allowances, promotions and dismissals.
During the pandemic, kiosk-style holographic workers were deployed, and essential human communications had to be sent multiple times on a sustained basis.
Related: 4 Things Employers Need to Consider About the Future of Work
Some Use Cases for Algorithmic Workforce
workers in Amazon warehouse in Melbourne, Australia, managed by an algorithmic workforce that accurately measures pick, move, and ship rates. The food delivery sector shares its KPIs in the following ways: Monthly Personalized Report About their performance: time to take orders, response rates, and travel times. AI is constantly recalibrating KPIs based on various factors.
Algorithms, which act as pricing managers on many travel, hospitality, and sports websites, are now more cognitively capable than the rules-based engines of previous eras.
Generative AI is already being used to create things like images, sounds, music, videos, and even code. Self-healing code and autonomous bug-finding software are always on the rise, and sooner or later they will mature and scale.
The adoption of algorithmic workforce solutions has increased over the past three years. Algorithmic approaches are now feasible and scalable for many companies and industries.
Related: What is Lensa AI? Does it raise privacy and ethical concerns?
The Future of the Algorithmic Workforce
As algorithms manifest as bots, digital humans, generative AI, and cyborgs augment the workforce; companies assess the impact and trade-offs between human emotion and the uplift that comes with deploying an algorithmic workforce. Some considerations include two-way information flow (not just from algorithms to humans like Uber), involving humans in the loop for key decisions, creating more of a human touch, and ensuring working conditions remain suitable for employee morale.
As AI creates other AI algorithms, companies will need to rethink workforce strategy and organizational design to account for new governance models, incentives, roles for human managers, levels of transparency, and data collection thresholds.
With self-driving cars, DAOs, the gig economy, and robots scaling up, humans won’t be doing the same jobs or making the same decisions as they did a decade ago. For adopters and detractors alike, these shifts are already here and will only have far-reaching consequences.
pros and cons Leverage Algorithms in Your Workforce
The expansion of the algorithmic workforce has both advocates and detractors. Whether someone likes it or not, the phenomenon has taken root and will only have greater impact. Let’s examine some of its pros and cons.
Advantages include lower costs, such as mundane activities required to deploy humans, and algorithms can process specific workloads in seconds that might take hours or days for humans. Greater efficiency leads to higher productivity and less waste. Algorithms can also make less emotional and more data-driven decisions — and while bias will creep into AI, many decisions and actions are less prone to bias than humans.
There are also controversial and detrimental parts to the implementation of algorithmic labor like surveillance, for example, Uber drivers who report to the algorithm know they are constantly being watched, including location, speed, acceptance rate, rating, actions, etc. Multiple wrong actions can lead to them being banned from the platform. While data is necessary for data-driven decision-making, it can affect employee morale, reduce trust, and ultimately lead to employee turnover. The opacity of these algorithms is associated with a workforce that quickly adapts to new conditions and makes decisions outside of human comfort zones, creating a sense of dehumanization.
The growth of the “gig economy” Revolutionizing the way millions of people work. Both adopters and detractors have made their case and agree that this is an inevitable trend, but it has not yet scaled up.
All of these jobs as typist, traffic light operator, shift manager, and even driver were decent jobs in the last century; this decade will see more of these types of departures.
The question now is how companies manage the dual workforce that balances human emotion and algorithmic efficiency. I was an early adopter of many of these algorithmic colleagues and can imagine the scalability of the workforce in the future. As unknown risks emerge, new approaches to governance and management need to be implemented.
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