Published on 28 May 2025
min read
Remember “Houston, we have a problem?”
An iconic moment from the docudrama Apollo 13, what followed was the mission control team ultimately saving the astronauts using only the materials available aboard the spacecraft. Their ability to innovate under pressure and work collaboratively underlined an essential truth – success, often, is really about the team behind them.
I feel that this principle holds especially true when implementing and operating digital twins across modern industrial use cases. To harness the power of the digital twin, organizations need multidisciplinary teams capable of understanding the business process value chain, driving innovation, ensuring alignment, and delivering tangible value.
Structuring Teams for Digital Twin Success
Building a team for digital twin operations resembles assembling a chessboard where each piece serves a distinct role but also works toward a unified objective. From technical specialists to frontline operators, the collective expertise of the project group directly influences the effectiveness of your digital twin.
Organizations adopting to the digital twin ecosystem, therefore, need to prioritize a balanced mix of technical know-how and operational insight when assembling their teams. We need team building foundational systems and platforms to capture static and real-time data, systems engineers responsible for building and adapting models, domain experts who provide industry-specific knowledge and data scientists who manage and interpret the influx of data powering the digital twin. In tandem, cybersecurity specialists protect sensitive information, while product and program managers ensure that initiatives remain aligned with business objectives.
For instance, an O&G major integrating digital twins to optimize plant operations would need first the connected systems and platforms for static data (Engineering, Plant, Process, Asset, etc.) and real-time data (IIoT, ERP, MRP, CRMs), and then a group of data analysts to clean and process equipment health data, engineers to design and update the models, and floor managers to apply insights in their scheduling and maintenance routines. When these roles collaborate effectively, organizations can make data-driven decisions that translate into efficiency and cost savings.
ROI is surely a big measure of success. However, ROI realization is result of a sustained effort over a period of time. It cannot be measured within quarters by just looking at graphs upon graphs of trends and analysis, of no use to businesses for real decisions or to implementing a problem-solving workflow. Executing the projects in the most frugal way is, therefore, the way to go.
Again, there have been enough instances of digital twin initiatives kickstarted by organizations and eventually shut down when the project does not go in the defined direction. It all starts with great funding, excitement, consultants working to create nice dashboards, graphs and finally garbage data inputs producing garbage output of no real value or ROI for the business. The main challenge here is that the company did not focus on the right foundations while building the data platforms and systems.
What needs to be remembered, therefore, is that the implementation of a digital twin is rarely confined to one department. It often requires integration across manufacturing, IT, operations, and even customer-facing roles. The complexity of these projects demands a culture of collaboration and frugality that transcends functional boundaries.
Cross-Functional Excellence in Action
Consider the effect of digital twins in energy grid management, where data streams from generation points, substations, and customer meters feed into a unified simulation. Engineers maintain the physical infrastructure, data scientists refine the computational models, and financial analysts assess cost implications based on predictive maintenance schedules. Each of these perspectives add crucial depth to the digital twin’s value.
But the real breakthrough comes when their expertise converges.
Companies can facilitate such collaboration by establishing cross-functional teams with clear mandates and shared KPIs. This alignment ensures that insights generated by the digital twin are actionable and drive coherent strategies across the organization.
Frequent communication helps teams synchronize their efforts and fosters trust across departments. Digital tools that support shared dashboards, virtual whiteboards, and simulation walkthroughs can make complex findings more tangible for non-technical stakeholders. Regular meetings, complemented by interactive demonstrations of the digital twin’s outputs, can give everyone involved a chance to contribute to and challenge the underlying assumptions.
One of the greatest challenges to unlocking the potential of digital twins is ensuring that the participants are equipped with the necessary skills. Upskilling is no longer optional, but rather, a strategic necessity. Training programs that focus on artificial intelligence, data analysis, and digital modeling techniques can prepare teams to collaborate effectively with digital twins.
Organizations should also pair structured training modules with practical applications tailored to their operations. For instance, a manufacturing enterprise might host workshops that teach staff how to interpret predictive analytics from their digital twin or run simulations based on real-world data. Partnering with technology providers or academic institutions can extend the depth and breadth of training available, ensuring that the teams stay at the forefront of emerging capabilities.
A Culture of Experimentation
Beyond formal training, organizations adopting digital twins need an environment where experimentation is encouraged and failures are seen as the incentive to iterate. Innovation labs, hackathons, or pilot projects provide opportunities for the teams to test new ideas in a controlled setting. By empowering employees to explore the full range of what digital twins can achieve, businesses can unlock applications far beyond their initial expectations.
The effectiveness of digital twin operations, therefore, hinges on the ability of the project teams to deliver measurable outcomes. Collaboration and innovation need to align seamlessly with overarching business goals. Key performance indicators should also reflect the most pressing challenges an organization faces. To cite an example, a healthcare company adopting digital twins to optimize patient flow might track metrics such as reduced waiting times or improved utilization of medical equipment. By tying success to concrete, data-driven goals, teams can demonstrate value to stakeholders and secure ongoing support for their initiatives.
The Need for Executive Sponsorship
Leadership buy-in is a non-negotiable element of successful adoption. Executive sponsors set the tone for organizational priorities, giving the teams the confidence and resources needed to execute bold strategies. They act as the bridge between technical specialists and business decision-makers, ensuring alignment and accountability across the board.
Looking Forward
Much like Frodo’s quest in The Lord of the Rings, where diverse personalities unite to carry out a monumental mission, implementing digital twins is a collective endeavor requiring collaboration, trust, and resilience. Each member — from the analytical ‘wizards’ to the operational ‘warriors’ — bring a unique strength that is crucial for taking the project forward.
Success depends on the people behind the solution — those who share a common vision, collaborate across boundaries, and relentlessly pursue innovation. With the right teams in place, businesses can turn the complex world of digital twins into actionable insights, delivering efficiencies and opportunities that transform their industries.
For like the group working to bring Apollo 13 home, the teams that rise successfully to the challenge of digital twin operations will testify that ingenuity and teamwork remain the ultimate drivers for the future.
