Don't Make These Digital Twin Mistakes: Adopt a JTBD Approach for Strategic Success

Why Focusing on Features Kills ROI, and How Prioritizing User Outcomes with Jobs-to-be-Done Leads to Breakthroughs

Table of Contents

• The Digital Twin Promise and its Stagnation

• Part 1: Understanding the "Job" of a Digital Twin – Beyond Simple Replication

• Part 2: Uncovering Novel Use Cases Through Unmet Needs

  • Working Today (But Few Are Doing Well)

  • Novel Future Concepts (Getting the Job Done Differently, Better, Cheaper, Fewer Features)

• Part 3: Rethinking Development – From Features to Outcomes

• Part 4: A Novel Implementation Strategy – Ensuring Adoption and Value Realization

• Conclusion: The Future is Outcome-Driven Digital Twins

If you’re not sure what digital twins are, I’ve prepared an in-depth industry research report that will get you up to speed. You won’t find a resource like this anywhere else. It even has a 7 minute audio overview for non-subscribers. But you'll miss all the details in a report that’s over 20,000 words!

Digital Twins: Reshaping Industries, Redefining Value 👈

The Digital Twin Promise and its Stagnation

Digital twins – dynamic virtual representations of physical objects, processes, or even entire systems – hold immense promise. We hear about their potential to drive unprecedented efficiency, enable powerful predictive capabilities, and optimize complex operations. The excitement is palpable.

Yet, many organizations find their digital twin initiatives sputtering. Projects become mired in technical complexity, focus heavily on simply replicating existing structures in a digital format, or struggle to demonstrate clear, game-changing ROI. Adoption can be slow, and the breakthrough value remains elusive. Why?

The core issue often lies in a strategy led by technology rather than by purpose. Many digital twin projects are feature-focused, born from a desire to "have a digital twin" rather than a clear understanding of the fundamental problem they are solving or the specific outcomes they are meant to achieve for the end-users.

This is where a Jobs-to-be-Done (JTBD) lens offers a novel and powerful strategic framework. By shifting our focus from the technology itself to the underlying "job" that stakeholders are trying to get done, we can unlock a new level of clarity, identify untapped opportunities, and build digital twins that deliver transformational value.

What's been your biggest challenge or disappointment with digital twin initiatives so far? Share your experiences in the comments below!

Part 1: Understanding the "Job" of a Digital Twin – Beyond Simple Replication

Current digital twin use cases are frequently limited. We see them used for basic monitoring of asset conditions or simple predictive maintenance alerts. While useful, this often only scratches the surface of their potential. These applications are typically defined by what the current technology can easily do rather than what the user ultimately needs to achieve.

The Jobs-to-be-Done perspective forces us to ask a more fundamental question: What are the various stakeholders – from an operations manager on the factory floor to a product developer in R&D, to a city planner shaping urban landscapes – really trying to achieve when they look to a digital twin?

Consider these examples of Job Statements:

• For an Operations Manager: Minimize operational downtime by proactively identifying and resolving potential equipment failures before they impact production.

• For a Product Developer: Accelerate product iteration cycles by accurately simulating performance under diverse conditions to validate design choices faster.

• For a City Planner: Optimize urban resource allocation by visualizing and forecasting the impact of infrastructure changes to improve quality of life for citizens.

This JTBD approach encourages us to elevate the level of abstraction. Instead of thinking about a digital twin as merely "replicating a physical asset," we start to envision it as a tool to "achieve a desired future state with less risk and more certainty." This subtle but profound shift opens the door to entirely new possibilities. The job isn't to have a twin; it's to achieve an outcome like "ensure uninterrupted service" or "maximize resource utilization."

Part 2: Uncovering Novel Use Cases Through Unmet Needs

When we understand the core jobs and the associated desired outcomes that are currently underserved, we can pinpoint truly novel use cases for digital twins – applications that go far beyond mere monitoring.

Working Today (But Few Are Doing Well):

While still emerging, some advanced applications highlight this outcome-driven approach:

• Dynamic Process Optimization: Imagine digital twins that don’t just monitor but actively and autonomously optimize complex processes in real-time. For instance, a smart factory twin could adjust production lines not only based on internal sensor data but also by integrating external information like supply chain disruptions, fluctuating energy prices, or even changes in customer demand. The "job" here is to continuously adapt production to maximize efficiency and resilience under dynamic conditions.

• Personalized Customer Experiences: Digital twins can model customer journeys or user interactions with a service. By simulating different scenarios and analyzing outcomes, businesses can refine service delivery to maximize customer satisfaction and operational efficiency simultaneously. This is about proactively designing and delivering the best possible experience.

Novel Future Concepts (Getting the Job Done Differently, Better, Cheaper, Fewer Features):

Looking further ahead, a JTBD strategy points towards transformative digital twin concepts where the innovation lies in getting a higher-context job done in a completely novel way, often resulting in solutions that are better, more cost-effective, and surprisingly, may even have fewer visible features because the complexity is managed at a higher level of abstraction.

• Ecosystem-Level Twins: We can move beyond twinning single assets or isolated processes to creating digital representations of entire interconnected ecosystems. Consider a city's complete mobility twin. This wouldn't just monitor traffic; it would orchestrate seamless movement across public transport, private vehicles, pedestrian flows, and delivery logistics. The core job is to enable fluid and efficient transit for all entities within the urban environment. This could lead to new roles, like an "Ecosystem Orchestrator," who uses the twin to manage the overall health and performance of the system, rather than many individuals managing siloed parts. The individual commuter or logistics manager might see a far simpler interface because the underlying coordination is so sophisticated.

• Predictive Intervention Twins for Complex Systems: The next evolution beyond predicting failure is a twin that initiates and manages preventative actions automatically. Imagine a digital twin for critical national infrastructure, like a power grid. Instead of just alerting an operator to a potential component failure, the twin could, upon detecting subtle precursor signals, automatically reroute power, dispatch robotic maintenance units, or adjust load balances to prevent any service interruption. The job here is to guarantee uninterrupted service delivery. For the human overseer, the number of alarms and manual controls (visible features) could dramatically decrease because the system is self-correcting at a lower level.

• Bio-Digital Twins for Personalized Medicine: A highly personalized digital twin of an individual's unique physiology could simulate responses to various medical treatments before they are administered. This would allow medical professionals to optimize treatment efficacy and minimize adverse effects for each patient. While the physician is still the job executor, the twin takes on the immense task of predictive analysis and scenario modeling, getting the job of "determining the optimal treatment pathway" done far more effectively.

Part 3: Rethinking Development – From Features to Outcomes

The traditional approach to developing digital solutions, including twins, often starts with a list of desired features or is driven by the latest technological capabilities. This can lead to bloated, overly complex systems that don't quite hit the mark on user needs.

A JTBD-driven development process flips this on its head:

• Prioritize by Underserved Outcomes: Development efforts are focused on addressing the most important, currently underserved desired outcomes of the key job executors. This ensures resources are allocated to what creates the most value.

• Deliver a Complete Job Solution: The goal is to enable the user to get their entire job done more successfully. This might mean integrating functionalities that cross traditional boundaries, or it might mean radically simplifying by focusing only on what's essential for that job. This often leads to solutions with fewer, but far more impactful, features.

• Design for the Evolving Job Executor: As solutions evolve and manage more complexity at a higher level of abstraction, the "job executor" might change. For instance, an AI might oversee a process twin's operations, with human operators moving to a role of managing exceptions or strategic oversight. The digital twin's interface and capabilities must be designed for this evolving human-machine collaboration.

The principle of "fewer visible features" becomes a hallmark of elegant, powerful design when a system truly understands and executes a high-context job. Complexity is pushed into the background, automated, or abstracted away, leaving the user with a cleaner, more intuitive experience focused on achieving their ultimate goal.

Part 4: A Novel Implementation Strategy – Ensuring Adoption and Value Realization

Even the most technologically advanced digital twin will fail if it's not adopted and if its value isn't clearly realized by its users. Common implementation pitfalls include a lack of stakeholder buy-in, difficult integration with existing workflows, and an unclear or hard-to-measure ROI.

A JTBD-driven implementation strategy addresses these directly:

• Communicate Value Through Job Improvement: Clearly articulate and demonstrate how the digital twin helps specific users achieve their desired outcomes more effectively, more efficiently, or with less frustration. The focus is on "What can this do for you in your job?"

• Integrate by Simplifying or Automating Job Steps: Instead of forcing users to adapt to a new tool, design the digital twin to seamlessly integrate into their existing workflows by making specific steps of their job easier, faster, or even entirely automated. Of course, many times workflows need to be re-designed completely (which changes the end users).

• Measure Success with Job-Centric Metrics: Define and track success based on tangible improvements in job completion and outcome achievement. Examples include:

  • Reduction in unplanned operational downtime by X%

  • Increase in the speed of new product design validation by Y days

  • Decrease in citizen complaints regarding urban congestion by Z%

Conclusion: The Future is Outcome-Driven Digital Twins

The journey to unlocking the full potential of digital twins isn't primarily about more data, faster processing, or more complex visualizations. It's about a fundamental strategic shift: from a technology-first approach to an outcome-driven, Jobs-to-be-Done approach.

By rigorously defining the jobs our stakeholders are trying to accomplish, and by focusing our development and implementation efforts on delivering superior outcomes for those jobs, we can transform digital twins from interesting technological artifacts into indispensable business tools.

The vision is one of digital twins that are deeply integrated into how work gets done, are laser-focused on achieving critical outcomes, and continuously evolve to help us tackle ever more complex jobs. These future twins may even be overseen by new roles or intelligent agents, pushing the boundaries of automation and strategic insight.

Before you embark on, or continue, your digital twin initiative, stop and ask: "What job, or jobs, are we really trying to get done here?" Answering that question with clarity is the first and most crucial step towards building a digital twin that doesn't just mirror reality, but actively shapes a better future.

What's one "job" in your industry that you believe a truly novel, outcome-driven digital twin could revolutionize? Share your vision!

If you’d like to take action, I would love to help. Here’s are some steps you can take to make that a reality for us:

1. Join my community and get access to more content and tools

2. Apply for coaching so we can do projects together and build a new business-as-usual with someone who will share the knowledge, and hold you accountable. (I have limited seats so hurry!)

3. I do project work as well. Use the coaching link and we can discuss.

Why Me?

Clients who engage with me early in their innovation journey often see exponential benefits.

I’ve been trained by the best in Outcome-Driven Innovation. Part of that training involved how to understand what the future should look like. As a result, I’ve taken what I’ve learned and begun innovating so I can get you to the outcomes you’re seeking faster, better, and even more predictably. Anyone preaching innovation should be doing the same; regardless of how disruptive it’ll be.

How am I doing this?

• I’ve developed a complete toolset that accelerates qualitative research to mere hours instead of the weeks or months it used to take. It’s been fine-tuned over the past 2+ years and it’s second-to-none (including to humans). That means we can have far more certainty that we’ve properly framed your research before you invest in a basket of road apples. They don’t taste good, even with whipped cream on top.

• I’m also working on a completely new concept for prioritizing market dynamics that predict customer needs (and success) without requiring time-consuming and costly surveys with low quality participants. This is far more powerful and cost effective than the point-in-time surveys that I know you don’t want to do!

I believe that an innovation consultant should eat their own dog food. Therefore, we must always strive to:

• Get more of the job done for our clients

• Get the job done better for our clients

• Get the job done faster for our clients

• Get the job done with with fewer features for our clients

• Get the job done in a completely different and novel way for our clients

• Get the job done in a less costly manner for our clients

But more importantly, I strive to deliver high quality and high availability. That's why I also have to be choosy.

All the links you need are a few paragraphs up. Or set up some time to talk … that link is down below. 👇🏻

Mike Boysen - www.pjtbd.com
Why fail fast when you can succeed the first time?

📆 Book an appointment: https://pjtbd.com/book-mike

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