We asked Amy Bunszel, EVP of architecture, engineering and construction solutions, Autodesk, what she expects to see from AI in 2026
Until recently, AI in AEC has largely focused on efficiencies like automating documentation, speeding coordination, and reducing repetitive tasks. Those gains remain essential, but on their own they are no longer enough to address the deeper challenges facing the industry.
AEC firms are expected to deliver more complex, higher-performing buildings and infrastructure with fewer people, tighter margins, and rising expectations around sustainability. Owners and communities expect projects that perform as intended and hold up over time, these outcomes result in tighter schedules, stricter budgets and less room for error.
The value of AI in 2026 and beyond now lies not in making digital work faster, but in helping teams make better decisions and unlock higher levels of creativity and innovation across the project lifecycle.
Discover what’s new in technology for architecture, engineering and construction — read the latest edition of AEC Magazine
👉 Subscribe FREE here
Decisions that shape project outcomes
Early decisions have always carried outsized weight in AEC, but in 2026, the tolerance for revisiting them later will continue to shrink. What were once treated as provisional choices, like site strategy, massing assumptions, or sequencing assumptions, are expected to hold up as projects move forward with far less room to course correct downstream.
Owners increasingly expect credible answers about feasibility, performance, and risk earlier, based on analyses teams can stand behind as projects evolve. AI’s role is becoming essential not because it replaces expertise, but because it helps teams understand consequences sooner, when change is still feasible. By applying AI to project data such as site conditions, environmental factors, systems performance, and constructability, teams can explore trade-offs before decisions are locked in. As the range of options expands, the advantage increasingly lies not in having more information, but in how teams evaluate trade-offs and make decisions, especially where expertise, creativity, and judgement still matter most.
What we expect to see in 2026 is AI actively supporting continuity as projects move from early design into more detailed building definition. Instead of treating conceptual exploration and technical definition as requiring hand-offs, teams will be able to carry intent forward as early ideas evolve into more detailed layouts, such as aligning spatial decisions, system logic, and performance expectations as designs mature. In practice, this means AI helping teams move from broad concepts into detailed building layouts and systems without losing the intent or assumptions established early on. This will shorten the path from early ideas to buildable solutions.
Apply AI where decisions matter most, invest in connected data as foundational infrastructure, and intentionally build the transparency and continuity teams need to trust AI-driven outcomes
As this process unfolds, design intent will increasingly be evaluated alongside engineering assumptions and delivery constraints as part of the same decision-making process, rather than being handed off and reinterpreted later. Fewer issues will be pushed downstream, and fewer projects will require late-stage redesigns or course corrections. AI’s value here is not speed, but confidence that early design decisions that are grounded in shared data and tested assumptions will translate into solutions that are viable to build and operate.
Data continuity and team alignment
A meaningful shift we expect to see in 2026 is how teams use shared analysis and simulation as part of everyday decision-making. Architects, engineers, and constructors have long worked from different views of the same project, often resolving differences late, when change is harder. AI-enabled analysis will close that gap by allowing teams to test performance, feasibility, and risk against shared project data early and continuously.
When teams can evaluate energy use, material impacts, sequencing, or climate exposure before decisions harden, constructability and scheduling considerations become part of the design conversation and performance expectations are carried forward into delivery.
This only works when data is connected and when teams have clear, trusted boundaries around how this data is used. AI does not compensate for fragmented workflows; it exposes them. Without transparency and continuity, AI-driven analysis introduces uncertainty rather than confidence in the decisions it informs. Disconnected models, siloed information, and brittle hand-offs limit what AI can deliver. Organisations that invest in continuity across planning, design, and construction are better positioned to commit with confidence. Those that don’t are left reacting when options are limited.
As AI becomes more embedded in connected workflows, its impact will be felt less in isolated features and more in how consistently teams can move from intent to execution without rework. That shift is becoming essential as capacity tightens across the industry; Autodesk’s 2025 State of Design & Make report highlights that capacity and skills constraints remain a key challenge for many AEC firms. Together, these pressures will push firms to rely more on data continuity and AI-supported workflows to deliver projects predictably at scale.
Performance – a core expectation
As this shift takes hold, performance becomes a design input, not a secondary check. Energy use, carbon impact, resilience, and lifecycle cost will guide scope, budget, and delivery decisions. AI will accelerate this change by making performance implications clearer earlier, when trade-offs are still possible.
That shift changes how different disciplines engage with the work. For architects, form and performance are increasingly inseparable. For engineers, assumptions are tested sooner. For contractors, means and methods are influenced before work begins. Sustainability and resilience are becoming part of what defines a successful project, not considerations deferred to the end.
At the same time, AI will play a growing role in addressing the industry’s most persistent constraint: capacity. By reducing rework, improving predictability, and allowing teams to focus on judgement instead of coordination, AI will enable firms to deliver more with the resources they have. But the benefits will not be evenly distributed. Firms that connect intent with execution will be better positioned to see measurable gains, while those that limit AI to isolated automation are likely to encounter diminishing returns.
A clear imperative for leaders
The next phase of AI in AEC will not be defined by novelty or faster interfaces. It will be defined by fewer downstream surprises, better-performing assets, and teams that can meet rising demands without burning out.
For leaders, the priorities are becoming clear: apply AI where decisions matter most, invest in connected data as foundational infrastructure, and intentionally build the transparency and continuity teams need to trust AI-driven outcomes. It’s also critical to treat performance as a baseline expectation rather than a late-stage check.
In 2026, the firms that succeed will be the ones using AI to bridge digital insight to execution, enabling earlier confidence, smoother delivery, and more sustainable outcomes that hold up over time.
Main image: Performance becomes a design input, not a secondary check. Energy use, carbon impact, resilience, and lifecycle cost will guide scope, budget, and delivery decision
