Engineering the Future: How ALLPLAN Is Advancing Design-to-Build in the AI Era

As artificial intelligence becomes embedded in everyday engineering workflows, expectations around transparency, integration, and data control are rising rapidly. Infrastructure owners and engineering firms are no longer asking whether to adopt AI, but how to do so responsibly, securely, and without disrupting established delivery processes.

Under the leadership of Sunil Pandita, CEO of ALLPLAN and Chief Division Officer Planning & Design at the Nemetschek Group, ALLPLAN is advancing its evolution into a Design-to-Build platform rooted in engineering continuity, open standards, and controlled AI integration. In this conversation, Sunil shares his perspective on how the next phase of BIM and digital construction will unfold, and where ALLPLAN and the wider AECO industry are heading.

Our origins are firmly anchored in structural engineering and precision detailing – and that engineering-first mindset still defines us. From the early days, we were trusted on technically demanding, mission-critical projects because we didn’t just produce geometry; we delivered constructible, reliable data.

That foundation hasn’t changed. However, now we’ve expanded beyond single-discipline authoring to enable connected workflows that span design, analysis, detailing, fabrication, and construction. Rather than stepping away from our structural heritage, we’ve used it as a foundation to connect engineering intent across the full lifecycle of a project.

openBIM has proven its strength in collaborative and public-sector environments, where neutrality and transparent data exchange are critical. It enables multidisciplinary teams to work together without forcing alignment around a single vendor stack. That remains a real strength for our users and a principle we continue to stand behind.

At the same time, industry expectations have evolved. File exchange alone is no longer sufficient. Customers now expect parametric intelligence, design intent, and performance data to travel across systems without losing meaning. However, achieving that level of continuity across diverse toolchains is complex.

For us, the next chapter of openBIM is therefore about operational integration – ensuring openness performs under real project delivery conditions. That is where we continue to invest: in deeper interoperability, automation, and APIs that allow connected workflows without sacrificing intent.

Design-to-Build only has real value if customers experience measurable impact in their day-to-day work. So, over the next 18–24 months, we expect to see three clear outcomes.

First, a noticeable reduction in detailing and documentation effort, driven by targeted automation – particularly in reinforcement modeling and steel detailing. Second, stronger model reliability for construction, leading to fewer clashes and RFIs, and reduced rework between engineering, fabrication, and execution teams. Third, greater continuity across the project lifecycle, ensuring that analysis data, detailing decisions, and construction data remain connected rather than being recreated in isolated systems.

To support this, we are concentrating on reinforcement automation, tighter analysis-to-detailing workflows with SCIA and FRILO, steel detailing linked directly to fabrication, and cloud-enabled collaboration that performs under real project pressures.

Cloud environments have demonstrated clear value for collaboration and shared project spaces. Enabling distributed teams to review, validate, and exchange information in real time – while maintaining data ownership – remains a primary focus for us.

When it comes to authoring and high-performance compute, I expect simulation processes and AI-supported automation to increasingly benefit from hybrid architectures. At the same time, certain mission-critical modeling activities will remain desktop-based for reasons of performance, regulatory compliance, or data sovereignty. For many engineering organizations, those considerations are operational realities.

Therefore, rather than forcing a full cloud migration, we see the future as intelligently distributed – leveraging cloud where it enhances scalability and collaboration, while preserving local control where necessary. What’s most important is that the architecture reflects how engineers actually work.

Offsite and DfMA are increasingly practical responses to labor shortages, sustainability targets, and program constraints. In Europe, we are seeing particular growth in infrastructure, industrial, and large public-sector projects, where repeatability, precision, and schedule certainty are critical.

Within a Design-to-Build framework, our responsibility is to ensure that design intent transitions smoothly into manufacturing execution. The acquisition of Manufacton strengthened the downstream stages of the workflow – including production planning, tracking, and handover – without positioning ALLPLAN as a replacement for ERP or factory management systems. That way, we could help ensure that what is designed can move efficiently into controlled production environments without reinterpretation.

Trust is fundamental to the responsible use of AI in engineering. We operate within the strict regulatory framework of the European Union, including the EU AI Act, which establishes clear expectations around ethical data usage. Protecting personal information is essential, but equally important is safeguarding our customers’ intellectual property. We do not monetize our customers’ expertise via AI. At the same time, we’re building partnership-based business models with customers who are interested in joining us in becoming part of a new data economy.

Concerns around data sovereignty, pricing clarity, and long-term control are entirely understandable, particularly for infrastructure owners and public-sector organizations managing assets over decades.

Our goal has always been to be a pioneer in ethical data standards. Interoperability is not an add-on; it is embedded in how our platform is built. We develop our cloud solutions using an API-first approach, providing support for a wide range of file formats – both manufacturer-neutral and vendor specific – that allows customers and partners to integrate ALLPLAN within broader digital ecosystems. Project data must remain portable and accessible independently of vendor relationships or licensing models.

Clients also need confidence that they will be able to access, share and use their data even if their license changes. Predictability is achieved through transparent licensing structures, open interfaces, and guaranteed export capabilities. These foundations reassure customers that their data remains under their control.

We are observing two developments in parallel. The first is the evolution toward intelligent digital twins. BIM data is increasingly being structured within cloud-based environments, enabling granular access to model information through open APIs. For several years, we have supported the migration of both open and native BIM data into database-driven architectures, allowing teams to interact with model intelligence rather than relying solely on file exchange.

The second trend is that ALLPLAN is evolving beyond supporting fragmented planning workflows and toward an AI platform that orchestrates architectural design and infrastructure concepts, translating them into information that can drive autonomous construction execution. In that context, AI provides built-in awareness of economic constraints, local regulatory requirements, and performance criteria – while also accounting for constructability within robotic fabrication and logistics systems.

However, files continue to serve an important contractual function. Formats such as IFC remain essential where legal boundaries and formal delivery milestones are defined through file submissions. That reality will persist for some time.

Europe has taken a proactive approach to AI governance and data regulation, establishing legal and ethical boundaries early in the technology cycle. Frameworks such as the EU AI Act reflect this philosophy.

For infrastructure and public-sector projects, this has practical implications. These environments often involve sensitive information and long asset lifespans, making control over data location, processing, and legal jurisdiction critical considerations.

These factors have directly influenced our cloud and AI architecture. Compliance with European regulatory standards, transparent data handling, and clear separation between customer data and commercial AI models are foundational principles for us.

For organizations seeking to adopt AI without compromising personal rights or intellectual property, we provide a clear alternative to vendors that treat customer and project data as part of their commercial model. As awareness grows around the strategic value of building data and company expertise, this approach becomes a competitive advantage. The construction industry increasingly recognizes that protecting its knowledge assets is essential to maintaining long-term competitiveness.

We expect a gradual reduction of rigid data silos across the industry over the next five to ten years. As structured data exchange and AI-driven workflows mature, the inefficiencies created by isolated systems will become more visible.

However, full consolidation into a single vertically integrated stack is unlikely. The AEC sector is too diverse and highly specialized for that. Instead, we foresee more open project environments where specialized tools remain distinct but operate within connected data frameworks supported by APIs and shared services. These environments will also still allow professionals to control and oversee every decision.

Our strategic position is centered on openness within integration. We believe in connected ecosystems rather than closed systems. The objective is not to control every layer of the software stack, but to ensure that engineering intelligence and lifecycle data can move fluidly across tools without losing meaning.