Digital transformation is redefining how infrastructure projects are delivered: from the way teams plan and collaborate to how decisions are made in real-time. What was once a support function has evolved into a core driver of project performance, automation and transparency – and at STV, an essential part of project delivery.
According to an FMI/Autodesk report, more than 80% respondents reported an increase in the project data their organizations are creating from construction technology over the past three years – with nearly half the respondents saying they experience a more than 50% increase in data creation.
In this roundtable, KiSeok Jeon, vice president, digital advisory group leads a conversation with Bilal Assaad, senior data scientist; Shashanka Mallikarjuna, project controls specialist; John Morton, project controls manager; Pierce Thysell, assistant project manager; and William White, lead software engineer. Together, the team provides insights on how technology has moved from a tool of convenience to an indispensable force driving smarter, faster and more connected infrastructure projects
KiSeok Jeon: Many firms are just beginning their digital transformation journeys. What advice and advantages from STV’s early investment have you seen?
John Morton: People still make the difference. Technology enables and empowers, but it doesn’t replace them. With centralized, integrated platforms, we have a single source of truth with timestamped workflows and transparent data, which has made collaboration faster and more accountable.
Shashanka Mallikarjuna: Exactly. Cloud-based platforms and dashboards have replaced static spreadsheets, providing project leaders with instant insight into cost, schedule and risk, making data-driven management a part of everyday operations.
Bilal Assaad: Beyond that, STV’s culture incorporates digital transformation into our project delivery, recognizing data scientists and data engineers as essential parts of the delivery team.
KiSeok Jeon: With that in mind, the construction industry can collect more data than ever before. How can project teams transform that information into meaningful and actionable insights?
Bilal Assaad: It starts with contextualizing the data. Analytics and visualization tools make that context useful for decision-making, but interpretation still depends on people.
John Morton: To add to that, accurate input is essential. If the data entered in the field isn’t consistent or complete, even the best analytics tools can’t deliver reliable insights. It’s about building disciplined data practices from the ground up.
William White: Our goal is to simplify data delivery. By integrating multiple data sets, we filter out noise and bridge the gap between raw information and meaningful insight.
KiSeok Jeon: In short, technology is only as effective as the people who use it. In your experience, what cultural or organizational changes are needed to help project teams successfully adopt new digital tools?
Pierce Thysell: Successful adoption happens in small, manageable steps. Teams need training, time and space to adjust to new tools. With that, the tools themselves need to be intuitive. Simple interfaces and phased rollouts reduce friction, helping people build genuine confidence in the system.
From a field-to-office perspective, consistency is just as important. When expectations and workflows are clearly defined, it becomes much easier for teams to trust the process and use the technology in a reliable way. Ultimately, incorporating users into the process through feedback loops makes a significant difference. It surfaces what’s working, what isn’t and builds ownership. When people feel the tool supports their day-to-day responsibilities rather than creating extra steps, adoption follows naturally.
Bilal Assaad: We involve users early, interviewing them and iterating on design until the tool fits their workflow.
William White: Users need to believe in the accuracy of what they see. That’s why we rely on feedback sessions and validation before scaling new tools.
KiSeok Jeon: Looking ahead, how will automation, AI and predictive analytics reshape project planning, controls, and decision-making in the next decade?
Shashanka Mallikarjuna: At the most basic level, routine tasks such as AI-driven planning, compliance checks, and document summarization will be automated, with predictive project controls that anticipate risks before they occur. Our data scientists and data engineers can transition from analyzing historical data to forecasting future trends.
John Morton: AI-driven planning and integrations will make scheduling faster, smarter and more proactive, but trust and validation are essential before applying insights at scale.
William White: We can utilize those insights, along with safe spaces for experimentation such as beta testing environments, to innovate while minimizing risk.
Pierce Thysell: In the next decade, I think the biggest shift of AI and automation will bring is speed and clarity in how we make decisions. Much of what slows teams down today isn’t the work itself; it’s sorting through information, reconciling different versions of the truth, or trying to understand where a project stands. As routine tasks become automated and predictive tools mature, project teams will spend less time gathering data and more time acting on it. For project managers and field staff, this means earlier visibility into risks, faster alignment on priorities and more proactive planning, rather than reacting to issues after they appear on site.
