Why AI Value in AEC Starts with Standardization

One of the biggest obstacles is the lack of integration and standardization across platforms. Data and workflows often live in disconnected systems, limiting the value firms can extract from their data and hindering their ability to unlock the full ROI of AI and BIM analytics investments.

This challenge was the central theme of Confluence Chicago 2026, where AEC leaders came together to share ideas, strategies, and lessons learned for how to get the most out of their technology platforms in an AI-enabled future.

HED’s Jason Rostar and Roan Isaku hosted a session titled “Building a Smarter Practice: Co-Intelligence in Practice,” addressing the power of standardization, governance, systematic deployment, and strategic intent in delivering business outcomes for AI adoption.

We asked Roan to share key takeaways from the event and explain how firms can turn technology investment into measurable performance gains.

Q: Why is process standardization and structured data so critical for AEC firms?

Firms are racing to adopt new technologies, but many of these solutions are fragmented and don’t integrate well. Project data exists in BIM, business software, project management tools, financial platforms, and other sources, but if that data is inconsistent or disconnected, it’s extremely hard to use at scale. At best, it creates more work for teams to homogenize it. At worst, it creates errors that aren’t caught until after decisions are made.

Standardization solves that problem by ensuring data is accessible, consistent, and accurate from the source. When workflows, templates, naming conventions, documentation practices, and data structures are aligned, teams can spend less time reconciling and double-checking data and more time using it to make decisions. It reduces errors, improves coordination, and allows teams to act confidently.

Q: How does AI raise the stakes on data standardization and governance?

The quality, consistency, and governance of BIM and business data dictate your ability to leverage AI. Having “clean” data is essential for generating meaningful insights you can trust.

It’s the old adage of “garbage in, garbage out.” If the underlying data is inconsistent, incomplete, or poorly managed, the outputs will reflect that. With AI processing and replicating information so quickly, foundational errors can compound fast.

Standardization and governance allow firms to build a solid foundation of reliable data they can use across the business to move from isolated, tentative AI experimentation to scalable, confident operational AI.

Q: One of the major takeaways from Confluence was that project data should be treated as a strategic asset, rather than a project byproduct. What does that mean in practice?

Every project generates significant data: design decisions, model data, coordination history, performance specs and predictions, cost considerations, and client-specific demands and insights. Historically, that information has been treated as relevant only to the project at hand.

But if project data is structured and governed appropriately, the lessons from one project can be leveraged more broadly to inform and improve future work across the firm, supporting faster decision-making, improved quality control and forecasting accuracy, and more predictable outcomes. Proper governance can also enhance coordination across disciplines because teams are working from the same complete, shared data set.

That translates into measurable gains: faster project starts, fewer hours spent on rework, streamlined reporting, better model quality, and the ability to identify and remediate risks earlier.

Viewing data as a strategic asset means firms can better understand how their work gets done, where bottlenecks occur, and where they can improve.

Q: With so much potential, how should firms decide which AI use cases to pursue?

At HED, our process begins with identifying pain points. What business or project obstacles or opportunities can we address?

The strongest use cases usually start with reducing fragmented or repetitive, low-value work that slows teams down, or areas where eliminating inconsistencies could streamline a process or decision.

We also evaluate use cases through a commercial and operational lens. There must be clear, measurable value, a reasonable ROI, and opportunities to scale. We take a “prove, then move” approach: incremental adoption and pilot deployment to validate that it works before rolling it out firm-wide. You also need to consider the organization’s readiness to adopt, along with whether there is a business owner who is accountable for implementation and results and can champion the cause.

This discipline is critical because the buzz around AI makes it tempting to try and tackle everything at once. But sustainable success requires focus. Our framework prioritizes use cases with the greatest potential to deliver meaningful business value: shorter timelines, less rework, increased margins, and more control over variables. And it helps us avoid mistaking novelty for value.

Q: What roles do leadership and culture play in successful AI adoption?

They’re essential. Technology alone does not drive transformation. Lasting adoption requires leadership, communication, and alignment across people, processes, and technology.

To get people to buy into change, they need to know why it matters. Leadership has to connect technology adoption to business performance, efficiency, and outcomes. Teams need to see how new systems will make their work better or easier, not simply add layers of complexity.

That’s where governance and communication are critical. Buying a platform does not create a strong data culture. Leaders must listen to the teams doing the work, understand the practical implications, and clearly articulate the “why” to instill trust and confidence.

Ultimately, the firms that achieve the greatest value from AI will be those that combine strong data foundations with a culture that embraces evolution.

Q: Looking ahead, what factors will determine success for AEC firms in the age of AI?

It’s not a race to see who can deploy the newest tools first or make AI the center of their practice. The leaders will be the firms that build the strongest foundation for intelligent application where it creates measurable value.

The volume of data and the number of sources will continue to grow exponentially. AI will continue to expand what’s possible. But without strong data governance, strategic platform integration, and standardized, repeatable processes, any investment in AI tools will fall short. Success must start with thoughtful investment in quality, consistency, reliability, and trust.

At HED, we’re committed to building a smarter practice. One that uses advanced technology with a clear purpose: to improve the way our teams work and the value we deliver for clients through better coordination, smarter decisions, more predictable performance, and the ability to build on what we’ve learned through continuous improvement.

What this means for our clients:

At HED, our integrated design approach depends on seamless coordination, consistent delivery, and the effective use of our collective intelligence throughout the project lifecycle.

Our investment in standardized workflows, structured data, and disciplined AI governance means our clients benefit from tighter systems-level integration, faster starts, and our ability to deliver on time, on spec, and on budget.