Shah Muhammad, head of AI Innovation at design and engineering firm Sweco, offers his perspective on how artificial intelligence is redefining urban planning.
Have you sat in endless gridlock and thought that city layouts could be smarter? Or glanced at a massive new structure and worried it might drain too much power?
For ages, designing towns and cities took careful estimates and lengthy reviews. Teams often made educated guesses and drew on experience to place roads and buildings. But imagine city planners previewing multiple development scenarios before any ground is broken. Picture them forecasting traffic flows, energy demand and environmental impact months ahead.
Digital twins, or virtual replicas of urban districts, are central to this new process. These digital models merge data from traffic cameras, weather records and utility networks to mirror real urban dynamics. Planners can walk through these virtual zones, test road changes and instantly see how adjustments affect commute times or flood risk.
In planning workshops, architects, engineers and municipal officials gather around displays showing simulation outputs. They can tweak building heights, street configurations or green corridors and watch the effects on sunlight exposure, air quality and travel patterns in real time.
Sensor networks feeding AI engines capture noise levels, air particulates and ground stability. As construction moves forward, fresh readings update the models and fine-tune predictions, keeping the simulations linked to actual conditions.
This is no fantasy. AI is giving planners that level of foresight.
“AI is transforming our approach to city planning at Sweco by speeding up workflows, sharpening decision-making and driving sustainable outcomes,” Shah Muhammad says. “We can process vast data sets, evaluate many scenarios and design neighborhoods that adapt well to future conditions.”
He notes that AI lets teams ask key questions: How can a district be laid out to ease congestion and reduce particle emissions? Can a building be designed to stay cool in heat waves with modest energy needs? The system can calculate thousands of options in seconds, revealing the designs that deliver the best balance.
Actual builds face shifting weather, supply delays and evolving community needs. That unpredictability puts data-driven models to the test.
“The main challenge in fitting these models to real environments is capturing the full range of changing conditions,” Shah Muhammad notes. “Models must reflect on-the-ground complexity and adapt when circumstances shift.”
To meet that, Sweco lays a strong data foundation. Teams verify that all information fed into the AI is accurate and compatible.
“We enforce strict data governance, standardize formats across tasks and choose software that links seamlessly,” he adds. With trusted, uniform data, AI can merge input from design, engineering and environmental experts.
Across projects, Sweco’s AI framework lets teams transfer lessons from one site to another. A bridge model tuned with vibration sensor data in Scandinavia can inform foundation design for a new tunnel in Southern Europe. That kind of cross-project insight cuts analysis time and prevents repeated errors. Also, civil structures and industrial facilities benefit from these shared insights.
AI can also help planners address social equity. By mapping access to parks, schools and healthcare facilities, the systems can highlight underserved neighborhoods. Teams can then propose sidewalk improvements, public transit routes or pocket parks to close service gaps.
Beyond layout and materials, AI also supports environmental protection. By integrating satellite imagery with on-site sensor logs, intelligent systems can flag habitats for vulnerable species before any earthmoving begins.
“In many projects, AI has driven clear gains in stewardship,” Shah Muhammad says. “One example used AI to track endangered birds in a development zone and share that data with conservation teams.”
In that case, technology acted like an extra voice in planning sessions, reminding teams to steer clear of nesting areas and preserve migration corridors. Such insights help route work around fragile ecosystems and maintain local biodiversity.
Looking ahead, predictive analytics and automation are set to shape the next phase of architecture, engineering and construction. Early warnings of cost overruns or equipment faults let teams prevent delays. AI-driven agents can handle routine design checks and progress reports, freeing professionals to explore creative solutions.
This vision could yield safer bridges, longer-lasting roads and timelier schedules. It may also shift expert focus toward innovative urban ideas, rather than repetitive tasks.
As adoption deepens, integrated dashboards could provide live overviews of traffic status, energy flows and environmental health all at once. These control rooms may become as routine for city managers as emergency dispatch centers are today.
Shah Muhammad will speak at the AI & Big Data Expo Europe in Amsterdam on September 24–25, 2025, presenting on “Leveraging Generative and Agentic AI for Intelligent Process Automation.” He plans to discuss case studies and share best practices for weaving AI into every stage of planning, design and execution.