What Is A Digital Twin & How To Build One

A digital twin is a dynamic, data-driven replica of a real-world system, person, or process. Traditionally used in manufacturing and engineering, digital twins combine real-time data with simulation capabilities to test, monitor, and optimize performance without disrupting the original. As AI capabilities grow and enterprise tools evolve, digital twins are beginning to find their place in areas like leadership development, customer experience, and organizational design. 

• Leadership: Some companies are developing digital twins of executives—capturing a CEO’s communication style, decision patterns, and calendar. These models support leadership coaching, succession planning, and even serve as digital stand-ins for routine interactions.

• Customer Experience: A digital twin of the customer journey helps simulate user interactions with a product or service. Teams can identify friction points and test improvements—without launching a full update.

• Manufacturing: A digital twin of a factory floor can simulate production changes, test "what-if" scenarios, or predict equipment failures.
  

Newer use cases—such as leadership or behavioral digital twins—require more caution. These implementations often rely on large language models (LLMs) and manually uploaded data (emails, calendars, transcripts), rather than the real-time IoT streams that define traditional twins. The result is more akin to a probabilistic behavioral simulator than a full-fledged digital twin.

Digital twins are becoming more intelligent and accessible. Nearly all large companies are investing in AI—92% plan to increase spending—but only around 1% report mature adoption. In manufacturing, over 75% of advanced firms are already experimenting with digital twin technology.

How to Build a Digital Twin for Work

There are a range of digital twins operating in workplaces ranging from the extremely complex to the simple. Here is how I have seen people build simpler versions of digital twins as agents for work using off the shelf tools such as n8n and Flowise.ai. Below is an oversimplification but conveys the key steps. Detailed instructions are on Youtube or I can cover in future posts. These systems have variable levels of security so do your research if you’re considering implementing anything.

1. Design the agent: The platform will start by asking for the purpose and identity of the agent. These are prompts that give it a clear identity, role, and behavioral logic.
   

2. Connect an LLM: The platform will then ask you to link the main instructions to models like Chat GPT or Claude to enable comprehension and response.
   

3. Train with quality data: The next step is to upload data to the platform that shapes an accurate representation. EG: emails, documents, spreadsheets, meeting transcripts.
   

4. Add tools: The platforms allow you to enable capabilities like calendar management, email handling, or spreadsheet analysis so that the AI can act autonomously if that’s a requirement.
   

5. Enable memory: This step means the AI knows about your past interactions with it and can make informed decisions.
   

6. Scale with multiple agents: More complex implementations combine agents to work sequentially, in parallel, or collaboratively similar to how real workers interact together in workplaces.
   

7. Deploy and iterate: Run the twin, gather feedback, and improve continuously.
   

Implications For Management

As AI handles more operational tasks, the role of managers is shifting. They're no longer just supervising people—they’re orchestrating systems made up of both humans and digital agents. AI can already recommend actions, monitor performance, and allocate resources. This shift raises important questions: How much authority should AI have? Most organizations still keep a “human in the loop,” reserving final decisions for people. Yet in low-risk areas like predictive maintenance or scheduling, fully autonomous decisions are becoming more common.

One compelling use case involves startup founders using AI to temporarily fill key roles — like Head of Customer Success or Chief Strategy Officer. The founder chats with each of these AIs continuously to help brainstorm ideas, provide feedback, and accelerate decision-making until they have the budget to hire for the role. An important caveat is that the value of the response often hinges on the quality of the prompt: shallow prompts essentially act as an echo chamber for the founder’s own limitations and biases; thoughtful ones unlock new perspectives—even breakthroughs. 

One way people successfully break out of their biases is using prompts that actively seek to challenge their blind spots or uncover subconscious barriers. Reddit users post these prompts and their success with it. Prompts such as “ChatGPT Prompt of the Day: "Future-self confrontation: The AI mirror that shatters comfortable lies about your path"” and “Subconscious success blocker: Expose the hidden chains holding you back” are have had interesting results in providing the prompter with new understandings of themselves, others, or their situation.

Digital twins are not just tools—they’re new team members. Whether simulating workflows or expanding executive capacity, these virtual agents are reshaping how we design work, make decisions, and develop ourselves. As their capabilities grow, so does the responsibility to use them ethically and intentionally.