Arcadis is expanding its collaboration with Autodesk to bring artificial intelligence, connected project data and digital workflows deeper into architecture, engineering and construction. The partnership combines Arcadis’ engineering and sustainability expertise with Autodesk’s Design and Make platform, Autodesk AI and emerging Model Context Protocol capabilities, with the companies targeting more practical AI adoption while keeping human review at the center of project decisions.
Artificial intelligence is moving deeper into architecture, engineering and construction, but the biggest challenge is no longer whether AI can generate an answer. It is whether that answer can be trusted inside a complex project where design decisions, engineering standards, regulations and physical constraints all intersect.
Arcadis and Autodesk are expanding their collaboration around that problem, combining Arcadis’ domain expertise with Autodesk’s Design and Make platform and Autodesk AI. The companies say the goal is to embed AI and connected data into the workflows where engineers, designers and project teams already make decisions, rather than treating AI as a separate productivity tool.
The partnership also reflects a broader shift in enterprise AI applications toward domain-specific systems. Gartner forecasts worldwide spending on AI models and platforms will reach $64.3 billion in 2026, up 63.4% from 2025. Spending on specialized generative AI models is forecast to grow 210% during the same period.
For the architecture, engineering and construction (AEC) industry, that specialization is particularly important. Generic large language models can summarize documents or generate text, but project teams need AI that understands geometry, engineering intent, project history, specifications and asset information.
Autodesk is increasingly positioning its platform around that context. Autodesk says its Assistant is designed to work with design and make information, while its MCP servers are intended to connect AI systems with Autodesk data and capabilities across tools and workflows.
Arcadis is connecting its own MCP capabilities with Autodesk Assistant, allowing the engineering firm to bring its knowledge, standards and guidance into AI-enabled workflows while retaining control over what it develops.
That approach matters because Model Context Protocol is becoming an important mechanism for connecting AI agents with enterprise tools and data. Rather than building isolated AI applications, companies can use standardized connections to allow models and agents to interact with existing systems.
Autodesk has been developing MCP functionality across its portfolio as part of its broader strategy to make its Design and Make ecosystem more accessible to AI systems. The company describes MCP servers as connectors that allow AI systems to take actions while emphasizing security, quality and reliability.
One of the more tangible examples of the Arcadis-Autodesk collaboration is See Through Walls, an AI-based project designed to infer hidden structural elements inside existing buildings using building data, sensors and predictive modeling. Autodesk describes the technology as a form of digital “X-ray vision” that can help teams assess existing structures without destructive investigation.
That could have implications beyond productivity. Understanding what is already inside a building can support adaptive reuse and renovation, potentially reducing the need to demolish structures and helping teams evaluate material reuse and circular construction strategies.
The collaboration is therefore less about replacing architects or engineers and more about changing how their expertise is applied. Arcadis says its people remain responsible for reviewing and validating AI-enabled outputs, with domain knowledge providing the context needed to determine whether an AI-generated result is suitable for a real project.
That human-in-the-loop model is becoming increasingly important as AI moves from experimentation into operational workflows. Gartner’s 2026 research identifies domain-specific models, agentic AI and multimodal capabilities as major forces behind the next phase of generative AI adoption.
For Autodesk, the relationship strengthens its strategy of turning the Design and Make platform into an AI-enabled environment rather than simply adding a chatbot to existing software. At Autodesk University 2026, the company previewed a more agentic version of Assistant intended to connect capabilities across products, projects and teams and allow customers to bring their own agents and trusted services into the experience.
For Arcadis, the Autodesk collaboration fits into a larger effort to turn decades of engineering knowledge and project experience into reusable digital capabilities. The company has also recently invested in Nomic, a domain-specific AI platform focused on AEC workflows, and said its strategy is to integrate AI agents into tools including Autodesk Forma and Bentley ProjectWise.
The commercial opportunity is straightforward: engineering organizations hold enormous volumes of specifications, designs, project records and institutional knowledge, but much of that information is fragmented across systems. AI can potentially make that knowledge easier to retrieve and apply—provided the underlying data is reliable and the outputs remain subject to professional judgment.
Arcadis says one existing automation initiative has reduced a quality-review process from approximately five days to half a day. That is a company-reported result rather than an independently verified benchmark, but it illustrates the type of measurable workflow improvement engineering firms are now seeking from AI.
The competitive landscape is consequently shifting. Autodesk, Bentley and other AEC technology providers are increasingly competing not just on design software, but on the data, APIs, AI agents and workflow connections surrounding those platforms.
The next phase of AI in engineering and construction may therefore be determined less by who offers the most impressive general-purpose model and more by who can connect AI to trusted project context. Arcadis and Autodesk are betting that combining domain expertise, connected data, AI development frameworks and human validation can make that transition practical at enterprise scale.
Market Landscape
AI adoption in AEC is moving toward domain-specific AI, multimodal systems and agentic workflows rather than standalone generative tools. Gartner identifies domain-specific models and agentic AI as key technologies for scaling GenAI over the next two years.
Autodesk is responding by making its Design and Make ecosystem increasingly AI-native, including Assistant, MCP servers and APIs for developers.
The competitive opportunity extends beyond design automation. Engineering firms can use AI to connect institutional knowledge, project data and digital twins with professional workflows, potentially improving productivity while reducing repetitive work. The challenge will be maintaining data quality, security, explainability and human accountability as those systems become more autonomous.
Top Insights
- Arcadis and Autodesk are embedding AI into engineering workflows rather than treating generative AI as a standalone productivity application.
- MCP connections could allow Arcadis knowledge and standards to interact directly with Autodesk AI-powered workflows and project context.
- The See Through Walls project demonstrates how AI can support adaptive reuse by revealing information about existing building structures.
- AI adoption in AEC increasingly depends on specialized domain data, trusted workflows and professional validation rather than model capability alone.
- Competition among AEC technology providers is expanding toward AI agents, APIs, connected data and workflow intelligence.
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