What Is Digital Labor? Digital Work in an AI-driven Workforce

Digital labor is work that is performed by digital systems, including AI agents, chatbots, digital assistants, and other applications and systems that perform digital work. Digital labor can significantly improve efficiency and productivity by automating tasks and large volumes of data. Digital labor is transforming how work is organized and performed, with implications for job roles, skill requirements, and the overall structure of organizations.

What is Digital Labor?

Digital labor refers to technologies, such as AI automation and agents that mimic human decision-making and cognitive abilities. It extends human capacity to complete tasks faster than a human-only labor force. Agents help handle unprecedented volumes of data while delivering a better client experience.

In today's economy, a digital organization can truly revolutionize the way work is done and improve revenue and profitability for companies. The adoption of digital systems continues to transform work and social landscapes, with far-reaching and ever-evolving implications. In the evolving digital labor landscape in 2025, AI and digital skills are in high demand. Fields such as AI and big data, networks and cybersecurity, and technology literacy are the fastest-growing skills areas.

Why is digital labor important?

When talking about digital labor, even though digital labor platforms are synonymous with digital labor, this article will discuss the use of automation and AI agents in organizations. AI agents help in turning data into actionable insights. With vast data and technological advancements, agents can now transform the workplace. AI agents can perform complex tasks and autonomously make decisions.

The rise of digital labor augments human labor and enhances organizational growth. Digital platforms are also on the rise, where digital labor is of prime importance. Growth in high-playing fields like information and communication technologies is a major overall driver of market growth. In 2024, the digital labor market saw significant growth with the global digital job market projected to increase from 72 to 93 million, driven by AI and high-paying fields.

The platform economy and agentic digital labor represent a shift in how work is organized, with AI agents taking on repetitive tasks, allowing humans to focus on higher-level tasks, and potentially increasing productivity and global GDP. With globalization, AI systems are now being adopted more than ever. Fluctuations in job markets increase the need for digital employees.

Types of Digital Labor

Aspects of labor and digital mechanisms can vary with organizations or even in digital media, like social networking. Nevertheless, digital labor can be classified into three categories based on automation processes.

1. Basic process automation

Basic process automation includes technologies such as:

Macros - It is a simple set of commands that automatically perform a repetitive task.

Scripts - A small piece of code written in a programming language that automatically performs a repetitive task, usually by mimicking human actions. It streamlines a manual process to save time and improve efficiency.

Screen-scraping - Screen scraping involves using software to automatically extract data from a user interface (UI) on a screen by reading visual elements, which allows data transfer between systems.

Business workflow technologies - This includes workflow management systems, robotic process automation (RPA), etc., which essentially allows businesses to mechanize repetitive tasks within a process by defining clear steps and conditions, often through user-friendly interfaces to streamline workflows across different systems.

Robotic process automation (RPA) involves using software robotics to mechanize rule-based tasks, which is particularly useful in industries like finance and healthcare, where repetitive tasks are common. RPA solutions streamline operations and reduce costs.

2. Enhanced process automation

Enhanced process automation involves technologies that use AI and Natural Language Processing (NLP), including chatbots and digital assistants.

AI-powered chatbots - These digital agents interact with customers in real time, providing support and information. They can handle multiple enquiries simultaneously, improving response times. Chatbot solutions are designed to enhance customer interactions and drive satisfaction.

Virtual assistants - Virtual assistants help users manage tasks through voice commands, scheduling appointments, setting reminders, and providing information. Virtual assistant solutions can be customized to meet specific business needs.

3. Cognitive platforms

Generative AI has ushered in the age of cognitive platforms. These include application software that can analyze context and implications. Cognitive platforms use AI to mimic human thought and improve decision-making. These are self-learning systems and improve workers' output and productivity. Cognitive platforms include:

Machine Learning Models - These models analyze data patterns to make predictions or decisions, commonly used in marketing, finance, and healthcare for tasks like fraud detection and customer segmentation. Machine learning is a foundational technology with cognitive platforms.

Natural Language Processing (NLP) - NLP enables machines to understand and respond to human language, which is crucial for applications like sentiment analysis and translation. NLP solutions enhance communication and understanding in various applications.

Image and video recognition - AI agents can analyze visual data to identify objects, people, or actions. This is used in security, retail, and healthcare for monitoring and analysis.

Cognitive platforms power digital workers to perform certain tasks, such as task automation within a business process.

What Is a Digital Worker?

The digital age has seen a significant rise in digital workers and digital work. The future of work is determined by the use and applications of digital workers. A digital worker is an application that can execute a complex workflow, which includes many tasks. Digital labor is a broad category that includes RPAs, chatbots, digital assistants, and other applications such as digital workers.

AI is used here, providing decision-making capability so that a digital worker doesn't get stuck. This has a significant impact on labor markets and will influence hiring and firing decisions in the future.

Benefits of a digital worker

• Digital workers are self-learning systems
• They free employees from time-consuming, repetitive tasks
• They are available 24/7 for queries and support
• Help in reducing human errors in data management
• Digital workers improve the overall adaptability of the organization

Digital Labor vs Digital Worker

Digital labor reshapes traditional work models and processes. A digital worker is a specific entity within digital labor, an advanced software application that mimics human capabilities and deploys the execution of complex tasks.

Digital workers act as virtual employees, handling diverse roles. Digital workers can analyze data, make decisions, and interact with customers. The introduction of digital workers has created a hybrid labor force where the human workers and AI seamlessly collaborate. Around digital labor conversations, agentic labor is now in the forefront and AI-based systems are more popular than ever.

Digital labor is work facilitated by AI and digital technologies, whereas a digital worker mimics human capabilities. The tasks done by digital workers are specific, focused AI-agent tasks.

Examples of digital workers are AI-powered customer service agents or data analysts. Digital labor helps improve operational efficiency and enhance job satisfaction.

Digital workers are powered by AI agents and personalize customer experience. They automate complex decision-making to guide strategic business outcomes.

A complementary relationship between the human labor force and digital workers needs to exist to create an adaptable, efficient, and productive work environment. This can also lead to the outsourcing of certain functions in an organization, where the digital workforce will be able to execute and handle tasks independently.

Digital workers and RPA bots

RPA bots are software robots that are generally restricted to single tasks and simple processes, whereas digital workers are autonomous agents that can execute many tasks and interact intelligently with other systems. With agentic systems business models are also being improved, freeing up employees from low-value work, and digitalization of applications and systems in organizations is becoming more common.

Agentic vs Assistant Style AI

AI agents are generally used for structured and predictable tasks. These are complex multi-step tasks with a clear outcome that use an existing workflow to post data into a system. They are autonomous and capable of carrying out these complex tasks.

In assistant-style AI, agents are geared towards subjective and collaborative tasks. Here the outputs are recommendations assessed by a human employee. AI plays an assistant role and is less likely to be autonomous.

AI agents deliver faster decision-making without human oversight or approval. They also offer a more customized experience with more access to user-generated data. AI agents' scope will expand in the future with agents handling more complex scenarios and high-value tasks, like customer service.

Business Case for Digital Labor

The impact on business is largely felt in organizations with the adoption of digital technologies. With digital labor, companies can achieve more with fewer resources. AI-powered agents help in enhancing human creativity, decision-making, and streamlining workflows. Digital labor increases productivity, efficiency, and scalability. AI agents augment human capabilities, allowing employees to make better decisions and help them to be more creative. Digital labor can reduce operational costs by automating tasks. Agentic labor improves accuracy and reliability by reducing human error and ensuring consistent and accurate task execution.

By leveraging digital labor, companies gain a competitive edge through improved efficiency, faster turn-around times, and better customer service.

How Digital Employees Augment Human Productivity

With the introduction of digital labor in companies, digital employees can handle routine, repetitive tasks like data entry, scheduling, and report generation, freeing up humans for higher-value activities.

Decision support - AI algorithms can analyze large datasets to provide valuable insights, assisting employees to make informed decisions.

Improved efficiency - AI can automate tasks and process information faster than humans, increasing efficiency.

Enhanced productivity - By freeing human workers to focus on higher value tasks, AI can increase productivity with digital labor adopted in organizations.

Better customer experience - AI can provide faster and more personalized customer service.

Organizations should not alienate human employees with the onset of digital employees. A hybrid workforce needs to be maintained. Alienation of the human labor force can lead to further problems.

Advantages and Disadvantages of Digital Labor

Advantages of digital labor include identifying trends, predicting outcomes, around-the-clock work, and immediate responses to queries. This positively affects customer satisfaction and response times. Other advantages to enterprises are cost savings, automation, and valuable insights on operations and customer behaviour.

AI agents as a part of the digital labor workforce improve efficiency and scalability, personalize customer experiences, and enhance data analysis and decision making.

Disadvantages of digital labor are inability to respond to human emotions, lack of understanding of the longer-term consequences, technological issues, and disruptions.

Job displacements, lack of human oversight, data security risks, cyberattacks, data breaches, ethical concerns are all factors specific to agentic digital labor. Implementing and maintaining AI agents can be complex and costly, and businesses will become more reliant on technology vendors. If you look at previous workforce disruptions, they created new roles for employees in organizations. The same can be expected with agentic AI also.

Examples of Digital Labor

With the onset of digital labor, many employees will have to be retrained for new roles. Some examples of applications of digital labor are online content creation, writing blog posts, creating social media content, invoice processing, regulating supply chains, translating text, video editing, data entry, and automated data analysis. When there is a shortage of employees in these areas, AI systems can cover the deficit.

AI-powered chatbots are often used for customer service. AI agents can handle initial queries, resolve issues, and proactively offer solutions. Agentic systems generate sales leads, qualify prospects and schedule meetings. They also can create and optimize marketing campaigns, build journey maps, and analyze performance metrics.

Financial data is analyzed using agentic systems to identify deductions, flag compliance risks, and offer financial advice. AI systems can automatically respond to IT issues, integrate with various systems to resolve tickets, and automate complex tasks. They automate repetitive coding tasks, freeing developers to focus on more complex challenges.

AI agents distill critical information from medical data to help doctors make better-informed care decisions, and automate administrative tasks. An agentic system can create a plan to develop to optimize inventory levels based on forecast demand and supplier lead times. Agentic AI can continuously monitor transactions and operations, minimizing non-compliance threshold and improving governance.

AI Agents: Future & Broader Implications

AI agents are seen as an invaluable commodity to an enterprise. They were first introduced in 2022. The full potential of AI agents is yet to be seen. Narrowly speaking, agents execute specialized roles while collaborating on complex workflows. A process identifies how agents work together and how tasks are executed. Task delegation, execution, and completion are overseen.

Frameworks can be used to orchestrate complex workflows for multiagent systems. Different types of workflows are used by agents as per the applications. Depending on the dynamic nature of the applications, workflows may need to be revisited or revised.

AI agents reason, make decisions, take action, and learn. Agents use Large Language Models (LLMs) to figure out what needs to be done and how to do it. LLMs can now work with each other and take real-world actions.

Agents need to get input from users or the environment. An agent reasons by analyzing input, breaking down complex tasks, and generating potential solutions. Planning allows agents to sequence actions over time, ensuring tasks are completed effectively and efficiently. Adaptability allows agents to respond to dynamic environments.

AI agents are and will continue to change organizations and workforce dynamics. With a hybrid workforce of human and digital employees, processes will be faster, outputs higher, and more revenue. AI agents will considerably change the way we work and interact in our workplaces. Human capacity and coordination will be increased with the use of digital labor and will improve efficiency and productivity in companies.