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Digital workers are a concept that’s been around for quite some time now. But, in recent years they’ve sprung back to the fore, taking on a whole new meaning in the context of the AI revolution.

The idea of replacing human activities with computerized systems isn’t new. What is changing, though, is the kind of work that can be handled without requiring a real-life human being.

Of course, this also represents a huge opportunity for early adopters.

Today, we’re covering everything you need to know about implementing digital workers within real-world business processes.

Specifically, we’re diving into:

• What are digital workers?
• Types of digital workers
• Top internal functions for digital workers
• Digital worker architecture, integrations, and design patterns
• Real-world applications and examples

Let’s start with the basics.

What are digital workers?

As we said earlier, this is a term that has evolved somewhat in recent years.

At the highest level, a digital worker is any software system that replicates, either entirely or in large part, a role within a business process that would otherwise be carried out by a human colleague.

Naturally, this is highly tied to task automation more generally.

Previously, digital workers were largely limited to more repetitious job functions, where the same actions are applied on the same inputs, to create the same outputs. For example, data entry or basic processing.

More recently, though, most definitions of digital workers have come to include some degree of autonomy.

This means that systems can interact with their environment, including assessing contextual information and connected data in order to independently decide which available actions to take in a given scenario.

In other words, choosing from a set of available actions in order to meet a defined goal. As we’ll see shortly, this is closely tied to the idea of AI agentic workflows .

Why are digital workers gaining popularity?

As we said earlier, interest in digital workers has risen greatly over the past couple of years. This has been driven by two distinct forces.

Firstly, the scale and complexity of IT operations in most organizations have massively increased. This means managing cloud infrastructure, SaaS tools, on-prem systems, and devices across global user bases.

As such, maintaining service levels without sacrificing efficiency or security requires advanced automation solutions, removing the burden of routine tasks from service delivery colleagues.

At the same time, changes in technology have rapidly transformed what’s possible here.

The way we build automated workflows has changed greatly over the past few years - first with the rise of no/low-code solutions, and more recently with agentic AI.

In turn, this means that a larger number of job functions can more easily be replicated with automation tools. This simultaneously expands the reach of digital workers, and positions them as a more accessible option for a range of teams.

Digital workers vs other automation tools

Before we go any further, it’s useful to outline the relationship between digital workers and automation solutions more clearly. Essentially, the issue here is that there is a decent amount of room for interpretation in our high-level definition of a digital worker.

Terms like automations, scripts, or bots often overlap, but there are a few key characteristics that separate digital workers from these.

One way of looking at this is that traditional automation tools typically focus on discrete tasks. They operate within defined scopes, executing predefined actions, without adapting to context or interacting intelligently across multiple systems.

By contrast, digital workers execute entire workflows or functions that would otherwise be performed by human colleagues.

This requires:

• Multi-system interaction: Pulling and pushing data across different platforms via APIs, screen scraping, or database queries.
• Decision-making logic: Applying business rules or AI-based reasoning to choose between different actions based on input data.
• End-to-end process ownership: Managing complete workflows rather than isolated tasks. For example, receiving a service request, validating it, routing it, and closing it.

We can take the example of a specific use case, like handling password resets. A traditional automation script might simply reset a user’s password in Active Directory, requiring human input for other associated tasks.

A digital worker could receive and analyze an initial service request, create a ticket, authenticate the user via secondary methods, update Active Directory, notify the requestor, and log the actions take - all using independent reasoning to respond to the specific context of the request.

The challenge is that this distinction can become somewhat messier in the real world.

This is because the question of whether a system replicates an existing job function depends on the use case as much as it does on the nature of the solution.

That is, we can replicate repetitive job functions that require little independent reasoning much more easily than we could more cognate, skilled functions.

Therefore, we could describe certain types of solutions or systems as digital workers in some contexts, but not others, as this depends on the wider process and job function in question.

Benefits

We can also think in more detail about why digital workers are fast becoming a top priority for all kinds of organizations.

In other words, what are the concrete benefits of adoption for businesses?

These include:

• Cost optimization - While requiring upfront investment, digital workers can offer significant cost reductions within workflows by reducing our reliance on human colleagues.
• Scalability - Digital workforce solutions offer a scalable approach, as employee headcounts don’t need to proportionally increase to meet rising demand for services.
• Consistency & accuracy - Including eliminating the scope for human error.
• Enhanced resolution times - Eliminating delays associated with human-centric workflows.
• Security & compliance - Helping to ensure that critical processes are executed consistently and according to policies.
• Availability - Digital workers can act continuously, meaning we don’t need to account for core business hours or other resourcing issues.
• Freeing up human resources - Enabling skilled colleagues to focus on more strategic projects.

In short, digital workers benefit businesses by enabling operational resilience, agility, and innovation, while also reducing their reliance on expensive manual efforts.

Implementation challenges

However, as with any other improvement initiative, none of these benefits are guaranteed. Indeed, digital workers represent a significant departure from most businesses’ existing operations.

As such, in order to make an informed decision about whether this approach is right for us, we need to be cognizant of the potential issues that we might encounter here, too. These cut across a range of technical, operational, cultural, and financial considerations.

The key implementation challenges for digital workers include:

• Technical resources - We’ll need the technical skills, capabilities, and resources to implement solutions, whether internally or using third-party services.
• Integrations - Digital workers rely on complex set of integrations, across legacy tools, SaaS platforms, APIs, and more, often with different authentication methods. This can complicate initial set-up and testing, as well as lifecycle management.
• Process standardization - Digital workers rely on consistent, well-documented processes as a prerequisite.
• Governance & oversight - We must put effective measures in place to ensure that automated systems enforce accountability, auditability, visibility, and oversight.
• Change management & cultural resistance - Initiatives to implement digital workers can be met with internal blockers, requiring clear communication, training, and stakeholder management to deal with.
• Maintenance, dependencies & version control - As underlying and connected systems evolve, it’s vital that digital workers are maintained and updated.
• Up-front investment - As with any other transformation initiative, implementing digital workers requires up-front investment, which must be balanced with the potential returns.

The bottom line here is that digital workers require the same level of planning, validation, and cost/benefit analysis as any other kind of enterprise IT initiative in order to maximize value.

Types of digital workers

So far, we’ve discussed digital workers at a relatively high level. But, we haven’t explored what’s happening under the hood in quite as much depth.

To begin to understand how we can build digital workforce solutions of our own, it’s first important to understand the different forms that these can take.

There are two common categories that we must be aware of here.

RPA solutions

Robotic Process Automation (RPA) is one of the original techniques that sparked the concept of digital workers. Essentially, this means mimicking user actions, such as clicks, screen navigation, and form entries across existing tools, without relying on back-end APIs.

This allows us to automate structured, rule-driven tasks for basic functions, including:

• Processing service desk tickets,
• Updating CRM entries,
• Automating user provisioning/de-provisioning,
• Extracting data from legacy applications.

The benefit of this approach is that it’s relatively easy and inexpensive to implement, with a lower development overhead as we don’t need to rely on API requests. This also makes it easy to automate tasks involving legacy systems where an API isn’t available.

We can even implement some degree of logic, using conditionality. But note, this is hard-coded, so it falls short of real autonomy.

However, there are some critical limitations to this approach as well.

For one thing, RPA can struggle with certain tools, including if they feature unstructured data, dynamic UIs, or even where their design has changed.

Similarly, they’re generally less effective for more complex workflows that require contextual reasoning, meaning their use cases are limited to highly repetitive, consistent tasks.

AI agents

In recent years, digital workers have become more and more tightly tied to agentic AI. These are LLM-powered systems that are able to act autonomously, handling unstructured data, responding to context, making probabilistic decisions, and adapting to changing conditions.

In other words, they can act independently in pursuit of a high-level goal, in the same way a human colleague could, rather than only within strictly deterministic workflows.

Agentic solutions feature a few key components, including natural language processing for interpreting requests, LLMs for providing reasoning, tool/function calling for taking actions, and orchestration or observability tools.

Agents can be activated and then decide for themselves which actions to take in pursuit of a goal, with minimal human interaction. This could even include triggering RPA-based actions.

Agentic digital workers facilitate a much wider range of use cases, including:

• Intelligent ticket routing based on context,
• Automated root cause analysis in incident management,
• Knowledge article generation from incident logs,
• Conversational self-service bots that escalate intelligently.

The benefit here is that we can apply digital workers to a much broader range of functions, including ones that require more cognate tasks and awareness of context. Additionally, agentic solutions are able to learn from previous executions and improve over time.

Another element of this is that human colleagues can interact with AI agents in much the same way they could with their real-world co-workers. For instance, simply sending them a DM or email.

On the flip side, this also introduces challenges, as agentic solutions are somewhat more complex and rely on high-quality training data, as well as appropriate governance measures.

Take a look at our guide to low/no-code AI agent builders to learn more.

Top internal functions for digital workers

We’ve already alluded to the fact that certain types of workflows are easier to handle using digital workers than others. Next, we can begin to flesh this out by considering which functions and tasks are best suited to this.

Many of the top functions for digital workers relate to internal services. This includes departments such as IT, HR, finance, and facilities.

Workflows in these departments are characterized by a high level of standardization, clear rules, and high-volume interactions. However, they also require dealing with qualitative information, as well as applying reasoning to handle exceptions, edge cases, and contextual information.

For example, within ITSM workflows, many issues require relatively consistent sequences of actions, such as handling tickets related to password resets.

However, others will require deeper investigation, planning, and more complex actions. For example, handling system outages, bugs, or downtime.

Similarly, digital workers are highly effective in external-facing workflows, such as customer service or supply chain management.

Like internal services, these kinds of functions feature a mixture of standardized and more irregular processes, requiring an awareness of more complex, contextual information in order to achieve high-level goals, typically derived from natural language inputs.

However, it’s worth noting that this is a fast-moving space, with new technologies and vendors frequently coming to the fore. As such, we can’t overestimate the pace of change in terms of the kinds of job functions that can and will be replicated by digital workers.

Digital worker architecture, integrations, and solutions

With a better understanding of the real-world functions we can target with digital workers, we can think in more detail about their specific makeup, including the structures, systems, and tools required for creating systems.

As we said earlier, modern digital workforces don’t simply execute defined sequences of tasks. Rather, they should be capable of accepting triggers, performing reasoning based on context, and acting autonomously within their environment.

In practice, this requires LLM-powered systems with the following characteristics:

• Input layer - The digital worker must first gather context from structured and unstructured sources. This could include service desk tickets, email threads, knowledge base articles, user chat input, API responses, or business documents. Tools like NLP pipelines, OCR, and vector embeddings help translate these varied formats into usable context.
• Memory and retrieval - To act effectively, digital workers must access both short-term and long-term memory. This is often handled through integration with a vector database or embedding-based search across internal documents, historical tickets, or procedural guides.
• Reasoning and orchestration—Once the context is established, the agent framework takes over, using tools, plans, or chain-of-thought prompts to reason through the task. Depending on the complexity of the problem, this stage might include multi-step workflows, tool selection, or internal debate-style evaluation.
• Action layer - The digital worker then takes action by calling tools, triggering workflows, or interacting with APIs. This might involve submitting a ticket, querying a database, sending a Slack message, or updating a field in a CRM.
• Governance and feedback - Monitoring, human-in-the-loop approval, and audit logging are critical in production environments. Most enterprise-ready frameworks allow for observability, approval gates, fallback logic, and usage analytics.

As you can see, AI-powered digital workers are relatively complex systems, both in terms of their dependencies and the structure of decisions that they might need to make in real-world environments.

Take a look at our guide to the top open-source AI agent platforms to learn more about some of our options here.

Real-world applications and examples

To solidify everything that we’ve learned so far, it’s helpful to think about some real-world examples of digital workers in action.

Earlier we saw some of the key functions that digital workers can target, so let’s think about how we might implement some of these in more concrete, practical terms.

One of the most common examples of a digital worker is handling basic IT service requests. The goal is to classify and prioritize incoming tickets, resolve them if possible, or route them to the appropriate queue if not.

A solution that achieves this might take the following form:

• Inputs - Sources include email inboxes, ticketing portals, and internal chatbots. This is processed using an NLP-powered model to determine the underlying goal of the service request. For instance, the user’s VPN connection has stopped working and they need it restored.
• Context - Connecting to knowledge bases and historical ticket databases to retrieve relevant information. This includes embedding-based retrieval to surface recent relevant resolutions relating to VPN issues.
• Reasoning - LLMs and agent frameworks are used to assess the situation and decide how to proceed, including whether the worker can resolve the request itself, does it need more information, which tools are required, or should it be escalated or routed to a human colleague.
• Action - Once a plan is determined, the worker uses tool calling to take any required actions, such as restarting the server or resetting the user’s password via an API request, as well as logging the outcome and closing the original ticket.

This type of system acts continuously, constantly listening for new service requests and acting accordingly.

Other types of digital workers could be triggered more manually, with a human colleague initiating a specific workflow. A good example of this is HR onboarding.

Whereas resolving an IT service request could potentially require a huge number of different actions, onboarding a new colleague will typically follow a stricter checklist of tasks.

However, the specific order and requirements of these could be influenced by a range of factors, such as the availability of resources or stakeholders, as well as dependencies across tasks.

So, a digital worker which onboards employees could involve:

• Inputs - Triggered by the creation of a new employee record in the HRIS. Information including role, department, location, start date, and manager is used to determine which onboarding tasks apply to the new hire.
• Context - The digital worker retrieves policies, procedures, checklists, and other onboarding documents, using embedding-based retrieval. It also tracks dependencies. For instance, checking whether IT has already provisioned the laptop.
• Reasoning - An agent framework plans and monitors a checklist of onboarding tasks. It adapts sequencing based on resource availability. It can also decide when to remind, escalate, or loop in a human.
• Action - The worker calls external tools to assign tasks, book meetings, trigger provisioning tickets, and notify relevant teams. It logs progress, updates HRIS fields, sends welcome emails, and posts Slack/Teams messages. The system can also request status confirmations from managers or mentors.

So, human-like functions that are being replicated in this case aren’t so much determining which actions to take as applying reasoning in order to perform a defined set of tasks in the most effective order.

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