Automations as Primitives and Digital Employees are two powerful discovery tools that tap into existing, proven processes to identify high-value AI agent use cases. By harnessing established RPA workflows and analyzing employee tasks, organizations can focus on AI projects that address real business needs and yield tangible benefits.
From manufacturing floors to customer service desks, artificial intelligence (AI) is transforming how organizations automate tasks, provide better customer experiences, and create entirely new products and services. One of the biggest challenges in rolling out AI is finding the right use cases—ones that are clearly valuable and address critical business needs. By leveraging existing automations and organizational functions, we can create an effective “discovery toolkit” that shines a light on opportunities where AI agents can have the greatest impact. In this post, we’ll introduce two such discovery tools—Automations as Primitives and Digital Employees—and explain how to use them to identify game-changing AI agent use cases.
With all new technologies, the first challenge is finding the most valuable use cases.
What Are “Automations as Primitives”?
Automations—especially those built with Robotic Process Automation (RPA)—are often the first step organizations take toward digital transformation. Over the years, companies have automated repetitive tasks to reduce costs, streamline processes, and improve quality. These RPA workflows or “bots” have already proven their business value, making them perfect candidates for AI-driven enhancements.
Treating each existing automation as a “primitive” means looking at it as a building block. A business workflow (like extracting invoices, validating data, or automating approvals) can be taken apart, enhanced with advanced AI capabilities such as natural language processing (NLP) or decision-making logic, and reassembled into a powerful AI agent use case.
How to Use “Automations as Primitives”
1. Identify Core RPA Automations
Start by listing your most critical or frequently used RPA automations. Focus on processes that have high-volume transactions, require advanced logic, or are prone to exceptions that can be handled with AI.
2. Evaluate Automation Gaps
Look for areas where the current automation could benefit from better decision-making, deeper data insights, or more sophisticated understanding of language and context. For instance, you might have an existing process that can’t handle exceptions or requires a human to step in for judgment calls.
3. Brainstorm AI Enhancements
Ask yourself, “If this process had human-like understanding or advanced analytical capabilities, how could it deliver more value?” Some ideas include:
• AI-based document understanding for reading and categorizing more complex information.
• Predictive analytics for making proactive decisions in real time.
• Machine learning models that can learn from historical data and optimize ongoing workflows.
4. Prioritize Use Cases
Focus on automations that already provide clear business value, as transforming them into AI-driven solutions will more reliably yield ROI. Document the potential impact (e.g., time saved, quality improved, cost reduced) to prioritize which automations to enhance first.
Why “Automations as Primitives” Are Important
By starting from automations that are already proven and valuable, you reduce the risk of investing in AI that doesn’t address a real need. This approach ensures there is an immediate business impact and helps secure buy-in from stakeholders, since the benefits are rooted in an existing, measurable process.
What Are “Digital Employees”?
In many organizations, certain employee roles revolve around repetitive or structured tasks—everything from data entry and recordkeeping to form processing and routine validations. These roles (or specific parts of them) can serve as “templates” for AI agents. Instead of thinking of a single software solution, imagine assigning work to an AI-enabled digital employee. This digital entity can learn to perform the tasks a human might do, freeing up your people for more creative or strategic work.
How to Discover Opportunities for Digital Employees
1. Map Out Role Responsibilities
Look at how employees in various divisions spend their time. Identify tasks that are highly repetitive, rule-based, or structured in nature—these are prime spots for an AI agent.
2. Look for Human Bottlenecks
Find the tasks that slow down operations because they rely on human intervention or manual input. If an AI agent can handle even 60-70% of these tasks, it can greatly improve efficiency and throughput.
3. Identify the Skills Gap
An AI-powered digital employee can handle tasks that require speed, consistency, or pattern recognition. If the task requires creativity or deep empathy, it may not be a good candidate for full automation. Instead, you might look for ways to augment the human role with AI insights rather than replace it entirely.
4. Iterate and Evolve
Once you have deployed a digital employee for a specific function—say, triaging customer inquiries—monitor its performance and look for opportunities to refine the model. AI thrives on feedback loops, so continuously gather data on how your digital employee is performing, and train it on new scenarios or data sources.
Why “Digital Employees” Are Important
Embracing the digital employee concept ensures that your AI roadmap aligns with real human tasks and business needs. It helps you focus on where AI can solve genuinely time-consuming or expensive problems. By training AI agents to act as digital employees, you anchor your investment in workflows that already exist—and that have proven necessary to organizational success.
Putting It All Together: Controlling for the “Need” Factor
One of the most critical steps in planning any AI initiative is proving there is a real need. It’s easy to get pulled into the hype around AI and end up creating a shiny new solution that no one actually uses. By starting with Automations as Primitives and Digital Employees, you:
• Root each potential AI project in existing, proven processes (ensuring that your investment addresses a real business requirement).
• Shorten your AI discovery phase by focusing on tasks and automations that already have metrics associated with them.
• Align with stakeholders by showcasing clear value from the get-go, which is key for securing budget and organizational support.
Conclusion
Finding the right AI agent use cases doesn’t have to be a guessing game. By turning to your existing RPA workflows (Automations as Primitives) and examining real-life employee tasks (Digital Employees), you can pinpoint AI opportunities that are not only technologically feasible but also aligned with genuine business needs. This dual approach helps you avoid the pitfall of “solution in search of a problem” and accelerates time to value, ensuring that your AI investments make a measurable impact on your organization.
Ready to get started?
1. Catalog your current automations to see where AI could provide deeper insight or more sophisticated decision-making.
2. Map employee tasks to identify opportunities to create AI-powered digital employees.
3. Prioritize these opportunities by estimating potential impact and complexity.
By following these steps and strategies, you’ll build a solid foundation for discovering and deploying AI agents that matter—transforming your business and moving toward the future of work, one valuable use case at a time. To learn more about other tools and techniques for finding valuable AI agent use cases go to the Agent design sprint community here.
