Enterprise technology has always been viewed as a tool that helps employees work more efficiently. Software automated repetitive tasks, stored information and gave people access to the systems they needed to do their jobs.
In this environment, the employee is the ultimate decision-maker. The technology simply provided the tools, and people determined how and when to use them appropriately. That relationship is changing fast.
Software now does more than support workplace decisions. It increasingly recommends what employees should do, determines which tasks require the spotlight and influences how work progresses through an organisation.
Some examples include a sales platform recommending which customer to contact next or an automated workflow determining whether a request requires human review or can proceed without intervention.
This shift is increasingly being described as algorithmic management, which is the use of algorithms and AI-powered systems to organise, allocate, monitor and influence work.
Recent research from the OECD shows that algorithmic management is already widespread, with organisations using these tools to instruct, monitor and evaluate workers.
The term is usually associated with managing employees through technology but its implications can go beyond traditional management functions. Increasingly enterprise technology is also influencing how employees prioritise tasks, respond to requests and make decisions.
As algorithms increasingly shape workplace decisions, are employees still in control of how work gets done?
The Evolving Relationship Between Employees and Technology
Algorithmic management is far from a new concept. Organisations have long used automated systems to allocate resources, monitor performance and coordinate workflows. But the thing that’s changing is the ever-increasing scope of these systems.
Advances in AI and enterprise automation now allow technology to analyse information, identify patterns, make recommendations and initiate actions across more and more complex business processes.
The software is no longer waiting for an employee to provide instructions but can now influence what happens next.
Consider a customer service team. Instead of working through requests in the order they arrive, an AI-powered platform might categorise them based on urgency, customer value or potential risk. The employee thus receives a prioritised queue rather than an unfiltered list.
This can help teams respond to important issues more quickly. However, it also changes the employee's role. Rather than independently deciding which request deserves attention, they are working in a system that has already made a decision based on an initial assessment.
Technology has not necessarily replaced human judgement but has rather influenced where that judgement is applied.
From Simply Executing Instructions to Shaping Choices
Algorithms aren’t what issue the instructions that eventually influence behaviour. They shape workplace decisions through:
- Recommendations: Suggesting which customers, cases or tasks deserve attention.
- Prioritisation: Ranking work according to rules, predicted outcomes or risk assessments.
- Task allocation: Assigning responsibilities based on availability, skills or system-generated predictions.
- Workflow automation: Moving work between departments when certain conditions are met.
- Performance monitoring: Tracking activity against predefined targets or system-generated expectations.
An employee might be free to ignore a recommendation or change a task priority based on their own assessment, but the system still shapes the starting point. NIST’s AI Risk Management Framework highlights the importance of meaningful human oversight when AI influences decisions.
Over time, employees may become increasingly reliant on these system-generated recommendations, especially when technology processes more information than an individual can reasonably evaluate.
This is where algorithmic management goes beyond traditional management. Technology is increasingly shaping how employees make decisions, even when people still make the final call.
How Algorithms Are Influencing Enterprises
The algorithmic workplace is visible in many of the systems organisations already use, such as:
1. Sales teams and customer prioritisation
Customer relationship management platforms can recommend which prospects or existing customers sales representatives should contact.
These recommendations may come from data on customer engagement, previous interactions and conversion potential.
Instead of independently deciding which customer to approach next, employees begin with a system-generated list. This can reduce administrative work, but it also influences which customers receive attention and which opportunities are deprioritised.
If the underlying data is incomplete or inaccurate, or the model favours certain customer characteristics, the system shapes sales behaviour in ways employees don’t fully understand.
One needs to know not only if the recommendation is useful, but how it’s determined which customer is worth prioritising.
2. AI-driven task allocation
AI-powered enterprise platforms can automatically distribute work based on employee availability, workload, skills and predefined rules. This can help managers coordinate teams and reduce overhead costs. However, automated task allocation also affects how employees experience their roles and even job satisfaction.
If a system continually assigns certain types of work to particular employees over and over, it may influence their opportunities to develop new skills or discourage them from taking on different responsibilities. Automation also limits employee visibility into why a task was assigned to them in the first place.
Algorithmic management therefore raises questions about how much freedom of choice employees are able to keep over their workload and how automated allocation affects professional development.
3. Automated approval and request workflows
Automated workflows can route procurement, expense, access and operational requests based on value, risk or other criteria. Some requests may be automatically approved, while others are directed to employees for review.
Naturally, this reduces manual coordination, but on the flip side it means the system determines how requests move through the organisation and not the employees. A request that falls outside a predefined rule may be escalated, while another proceeds without the same level of human scrutiny.
The employee's role is therefore shaped by the workflow's design and removes their autonomy, including which decisions require intervention and which are handled automatically.
4. AI-assisted knowledge work
Generative AI has introduced a whole new layer of influence. Employees use AI to summarise documents, analyse information, draft communications and recommend actions. The system isn’t simply processing instructions; it’s actively contributing to the information employees use to make decisions.
An employee reviewing an AI-generated summary may focus on the information the system has selected rather than examining every underlying document. This can save time, but it also creates the risk of automation bias, where employees place trust in system-generated outputs that is unwarranted.
The more deeply AI becomes integrated into workplace applications, the more important it becomes to understand when technology is assisting a decision and when it’s materially influencing it.
The Problem Isn’t That Algorithms Make Decisions
It may seem like the word “algorithm” is a new term due to the advent of social media feeds. Their presence has been a bit contentious over the years because, as one Pew Research study noted, 74 per cent of people feel it doesn’t reflect real life accurately.
But that’s just social media, and what many people don’t realise is that algorithms have long been part of enterprise operations (even if it might not be obvious). Financial systems identify unusual transactions, security platforms detect suspicious activity, and logistics software optimises routes.
It should be noted that the use of algorithms isn’t inherently problematic, but a challenge has now emerged for organisations as a result of their presence. Using these systems influences decisions in environments where consequences aren’t always immediately visible.
A prioritisation system might help teams meet performance targets while overlooking cases that do not fit its assumptions. An automated workflow might accelerate approvals while simultaneously making it harder to identify who was responsible for a decision.
Ultimately, all of this raises questions about how technology is designed, used and governed. The key question to consider is whether organisations understand how their systems influence decisions while still overseeing the results from those conclusions. That becomes much harder when multiple systems are involved.
A customer request might be categorised by one algorithm, prioritised by another and routed through an automated workflow. Each system performs its intended function, but knowing how the outcome was decided may be difficult to explain.
The organisation may know that the request moved through the system and every step in that process but not understand WHY it followed that particular path.
When Are Employees Operators of System-Generated Decisions?
One of the biggest changes that has come along with algorithmic management is the potential shift in employee responsibility.
Employees may still be accountable for completing tasks and meeting performance targets. However, the algorithmic systems they use to do so increasingly influence the decisions that determine how those targets are pursued.
Consider an employee whose work is organised through an automated platform. The system determines which tasks appear first in the queue, how much time is allocated to each task and when completed work moves to the next stage.
Naturally, the employee has the capacity to make adjustments, but the workflow has already predetermined for them the structure within which those adjustments take place. This can improve consistency and reduce cognitive load for the employee.
Ultimately, this leads to asking whether automation gives employees more time to exercise judgement, or does it gradually reduce the range of estimations or appraisals they’re expected to exercise?
The answer to this will depend on how the technology is implemented. A system that handles routine decisions while allowing employees to challenge recommendations has the potential to support autonomy.
A system that measures employees against automated targets without explaining how those targets are generated can create an inflexible working environment.
The difference between these two scenarios lies in whether the technology expands employees' ability to make informed decisions or narrows their range of decision-making ability.
Who Is Accountable When a System Shapes the Decision?
As algorithms become more involved in the making of operational decisions, accountability becomes more complicated.
In a traditional process, an employee or manager may be able to explain why a particular decision was made. In an automated process, the outcome may have been influenced by business rules, historical data, model outputs and workflow configurations, but it’s hidden behind the scenes.
Consider a customer request that is delayed because:
- An automated system categorises it as low priority.
- A workflow routes it to a queue with limited staffing.
- A second system delays escalation.
- The employee follows the assigned priorities.
Who is ultimately responsible for the delay? The employee may have followed the process correctly and the system operated according to how it’s been configured. Yet the combined result may still have created a poor customer outcome.
This is why organisations need to establish accountability before deploying automated decision-making systems that have little to no human oversight.
Some questions to ponder over can include:
- Who owns the system and its decision rules?
- Which decisions can be made automatically?
- When must an employee review an outcome?
- Can employees challenge or override recommendations?
- How are errors identified and corrected?
- Can the organisation retrace the steps taken to reach a decision?
Accountability cannot simply sit with the employee who happens to work with the final output, as there are many phases that come before it. It must also extend to the systems, processes and governance structures that helped shape the decision.
The Visibility Problem: Employees Cannot Challenge What They Cannot Understand
An algorithmic workplace requires employees and managers to have enough visibility into system behaviour to make informed decisions.
This does not mean every employee needs to understand the nitty-gritty technicalities of an AI model. At bare minimum, though, they should be able to understand the practical implications of the systems they use.
They need to be able to answer:
- Why was this task prioritised?
- What information influenced the recommendation?
- What happens if the recommendation is incorrect?
- When should an employee override the system?
- Who can investigate an unexpected outcome?
Without clear answers to these questions, employees may treat system-generated decisions as authoritative or trust it more simply because they come from an enterprise platform. As AI models are updated, workflows change or multiple platforms share data, this issue becomes even more complex.
A process that was understandable when introduced before may become harder to explain as new, more intricate capabilities are layered onto it.
An organisation cannot govern decisions in a meaningful way if it cannot adequately trace, explain or review those decisions.
This not only makes observability and auditability relevant to IT teams, but also to business leaders responsible for operational outcomes.
How to Build Algorithmic Management Around Human Judgement
Just because algorithmic workplaces are emerging, it doesn’t mean organisations have to now reject automation. Instead, it calls for a more deliberate approach to how technology and human decision-making interact.
Organisations should consider several principles when introducing or expanding algorithmic management, including:
1. Draw the line between automation and human judgement
Determine which tasks can be automated, which are subject to human review and which decisions should be under direct human supervision and control.
These limits should be set based on the potential impact that the error could have and not simply according to the convenience of automation.
2. Make recommendations easy to understand
Employees should be able to see the meaningful information behind system-generated recommendations. These could be factors motivating prioritisation and the limitations of AI-generated outputs.
The aim is to give employees enough context to be able to make an informed decision when they need to.
3. The right to challenge decisions
Employees need practical steps they can take to flag errors, override recommendations and escalate concerns to management.
Automated recommendations should not immediately become unquestionable instructions to follow.
4. Measure results, not just system performance
Even if a system performs as expected in technical terms, it may still produce undesirable results.
Organisations need to continuously ask whether automation is creating unintended biases, disadvantageing certain teams or making exceptions difficult to manage.
5. Treat automation as organisational change
Introducing AI isn’t simply a case of implementing software. It has the ability to change roles, responsibilities, workflows and the skills employees need.
To further help manage this implementation, organisations should involve employees in the design and evaluation of automated systems. Especially in cases where those systems influence performance expectations or daily working practices.
Algorithmic Management Is Also About Enterprise Governance
It’s impossible to get away from the fact that almost all, if not all, enterprise systems are interconnected and dependent on one another. Data flows between platforms, automated workflows coordinate activities, and AI tools contribute to decisions across departments.
While this certainly improves organisational responsiveness, it can also make it harder to establish exactly how a particular outcome was produced in the first place.
A manager may know that a performance target was missed or a customer request remained unresolved. Understanding why may require tracing decisions across several systems.
Organisations therefore need to understand how their technology influences work in real-life situations. That includes knowing:
- Which systems influence operational decisions.
- What data those systems rely on.
- How rules and models for automation are maintained.
- Where human intervention is required.
- How exceptions to rules are handled.
- Whether employees are trained and able to identify and challenge system errors.
Without this visibility, organisations risk becoming dependent on processes they cannot fully explain.
The problem isn’t that technology is making decisions. It’s that organisations may not have sufficient oversight of the decisions technology is helping to shape.
Humans Still Make Decisions, but the Environment Is Changing
The algorithmic workplace does not mean accepting a future in which employees surrender ALL decision-making authority to machines. Humans will continue to approve actions, manage exceptions and take responsibility for important outcomes.
However, the context in which those decisions are made is changing. Technology increasingly determines the options employees encounter, which tasks demand attention and when, and how work systematically progresses through an organisation.
This is the broader significance of algorithmic management. Its influence on the workplace isn’t limited to only the systems that directly monitor or assign employee work. It extends to the enterprise platforms that shape how people make decisions daily.
It’s not about whether employees use technology to do their jobs but whether organisations truly understand how technology shapes the real-life decisions employees make.
Capable enterprise systems are great, but organisations need to think beyond just the implementation stage and level of efficiency. They also need to consider how automation affects judgement, accountability and their ability to understand their own operations.
It’s important to recognise the possibility of a workplace appearing to be human-led on a superficial level, but it has slowly become increasingly algorithmically directed in practice. And recognising that difference may be the first step towards governing it.
As AI continues to reshape how enterprises operate, explore the latest developments, insights and trends with EM360Tech to understand where algorithmic decision-making is taking the workplace next.