When industrial companies build intelligent products like digital twins, the idea is to get to optimal outcomes more consistently. An optimal outcome reduces cost, increases output, and otherwise has a positive impact on operations.
Digital twins help organizations achieve optimal outputs more consistently by making the many moving parts and considerations of any one decision more accessible. When everyone can access a tool that incorporates everyone else’s institutional knowledge, for example, every worker is better equipped to do their job.
There’s a big “if” here, though.
Digital twins and other intelligent products drive consistently optimal outcomes IF workers use them consistently. Adoption is essential. So how do you drive adoption in industrial contexts? It starts with building trust.
In this piece, I’ll break down how to build trust and drive adoption so your intelligent products deliver the ROI you budgeted for.
Understand Workers’ Tolerance for Wrong Answers
At the Connected Worker Manufacturing Summit in October, leaders from Rockwell Automation and Georgia Pacific highlighted a crucial point for any industrial team building a digital twin: workers have a very low tolerance for a model giving them wrong answers.
Workers don’t need a model to be perfect, but they do need it to behave consistently and explain itself. When suggestions feel out of step with lived experience and there’s no insight into why, adoption drops.
The solution here is to involve end users from the beginning. Get input from them as you decide what digital twin or intelligent product to build and as you explore how to build it. Explain that developing this tool is a process, and that the intent is to iterate based on how early versions perform in real-world trials.
When workers understand the component pieces of a tool and that those pieces can be adjusted based on its performance, they’re much more tolerant of early failures and, as a result, more likely to adopt the product when it’s been refined.
Prioritize Return on Interaction Over Pure Fidelity
One common mistake in developing digital twins is putting too much emphasis on real-world fidelity, especially visual fidelity. In practice, this often manifests as an attempt to mirror the physical system in unnecessary detail.
In many cases, over-indexing on fidelity can be a mistake. Creating precise visual replicas of real-world systems can take a lot of resources; in many cases, organizations can get better outcomes by funneling those resources toward other things, like ensuring data is updated more frequently or allowing for more parameters to influence a decision.
One guideline that can help determine how “realistic” a digital twin needs to be: how much fidelity do end users need to understand what’s going on and feel comfortable using it?
A digital twin for scheduling rail car maintenance, for example, may not need to exactly replicate the physical system workers currently use to schedule maintenance manually. But to facilitate onboarding, it probably makes sense to borrow from the same universe of visuals that the manual system relies on.
Easier, faster onboarding means more interactions. And more interactions means more opportunity to optimize outcomes. So finding the right level of fidelity helps increase the return on interaction.
A related design consideration: how will workers’ “situational disability” impact their ability to interact with a digital twin (and therefore the tool’s potential return on interaction)?
For example, if workers are in gloves, goggles, or other PPE, will they be able to see and press buttons? Swipe a screen? If they’re using the digital twin on a noisy factory floor, will they be able to hear auditory cues?
Again, the solution is to get user input early and often to create intelligent products that work in their intended contexts, for their intended audience.
Aim to Make the Invisible Visible
When we’re building an intelligent product for a client, one of the most exciting stages is when we test an early prototype by sitting with a group of workers making decisions in real time and run the product in parallel to human decisions to see how it performs.
Usually, when you run an early prototype alongside how people actually make decisions, all the invisible considerations start to show up.
Take a team scheduling maintenance procedures. They might be factoring in how quickly different customers approve work orders, or considering the work schedules of technicians who have the right skills.
If those realities aren’t in the model yet, the twin will make suggestions that differ from what workers would do. If you frame that moment as a miss, trust can drop fast.
But if workers are part of the process and understand that this stage is about surfacing what the model is missing, it can actually build trust. They see how their expertise shapes the tool, and how the model improves with every iteration.
It’s a really valuable part of development.
For those building the digital twin, it’s a moment where the invisible becomes visible – and therefore something that can be incorporated into the model to make it better. For end users, it’s a moment where they get a glimpse into how the model works and is adapted over time.
They get to see it as a dynamic tool that can be refined based on feedback, and they get a better understanding of the variables that inform its outputs. Both of these help build user trust in the model, which helps drive adoption and therefore improved outcomes down the road.
For Better ROI on Digital Twins, Start with End Users
When they’re built well, digital twins and other intelligent products help teams make better decisions more consistently. That’s what leads to stronger performance on the floor and in the business.
That doesn’t happen overnight. ROI comes when adoption is high; adoption happens when users trust the model. Trust comes from understanding how a model works, what informs its outputs, and how it improves when something seems off.
Industrial organizations looking to tap the power of digital twins can maximize their odds of success by looping end users into the development from the start. By centering the experiences of the workers who will actually use the tools, industrial leaders can set themselves up for strong financial ROI alongside a positive return on interaction – meaning more value from every time workers use the tool.
About Jason Hehman
Jason Hehman is the industrials vertical lead at TXI, a boutique digital consultancy for modern industrial leaders. TXI co-creates intelligent products that reduce risk, activate data, and empower the workforce — delivering outcomes that last. Hehman is also the founder of the Modern Industrialist Xchange (MIX), a curated space where leaders in manufacturing, supply chain, and industrial innovation connect through gatherings and shared insights.
