Digitalization has long since taken hold of the engineering and planning sector—but with the integration of artificial intelligence (AI), the industry now faces nothing short of a quantum leap. What seemed futuristic only a few years ago is rapidly becoming everyday reality in mid-sized engineering and planning firms: AI systems performing complex calculations, automatically optimizing design variants, and precisely forecasting project workflows.
According to a 2024 survey by the German Mechanical Engineering Industry Association (VDMA), 47% of mid-sized engineering service providers are already using AI solutions in at least one business process—a figure that is rising rapidly. The economic impact is impressive: companies with successful AI integration report, on average, 23% higher project margins and are able to speed up planning processes by up to 35%.
But what does this transformation actually mean for your business? Which use cases are already proven in practice, and where do potential pitfalls lurk? This article highlights the most relevant AI applications specifically for engineering and planning firms, and shows how you can strategically leverage this technological shift for your company.
Table of Contents
- The Status Quo: AI Adoption in Engineering and Planning Firms in 2025
- AI Use Cases in Technical Planning and Design
- AI-Driven Project Planning and Management
- Simulations and Digital Twins
- Document and Knowledge Management with AI
- AI in Customer Service and Technical Support
- Integrating AI into Existing IT Structures
- Success Factors for AI Implementation
- Looking Ahead: AI Trends for Engineering and Planning Firms up to 2030
- Conclusion
- Frequently Asked Questions
The Status Quo: AI Adoption in Engineering and Planning Firms in 2025
The technological landscape in engineering and planning firms has fundamentally changed over recent years. According to current data from the Boston Consulting Group (2024), technical service providers are now investing an average of 8.7% of their IT budgets in AI technologies—a jump of more than 200% since 2021.
The industry’s leading AI applications are clearly identifiable: 63% of firms employ AI for simulations and predictive analytics, 57% for document management and text analysis, 49% for generative design, and 38% for automated project planning (Source: Accenture Industry Report 2024).
“Engineering companies that deploy AI strategically can increase their innovation speed by up to 40%. The decisive factor isn’t the technology itself—but rather, precisely aligning it with the company’s core processes.”
– Dr. Michael Brandt, Board Member, Federation of German Industries (BDI), 2024
However, core challenges remain for AI implementation. In a 2024 survey by the Fraunhofer Institute of 320 mid-sized planning companies, 68% of respondents cited a lack of expertise as the greatest obstacle, followed by uncertainties around ROI calculation (54%) and concerns about data security (51%).
Implementation gaps are particularly striking: while 72% of large engineering firms with more than 250 employees are already using AI in at least three business areas, this applies to only 29% of firms with fewer than 50 employees.
This gap is becoming a real challenge, as AI-powered efficiency gains are increasingly reshaping competition in the industry. The good news: It is now much easier for companies to get started with AI than it was just a few years ago.
AI Use Cases in Technical Planning and Design
Computer-Aided Design Optimization (CAD/CAE)
The integration of AI into CAD/CAE systems is fundamentally transforming the design process. Modern AI systems analyze design data in real time and suggest optimizations to reduce material usage, weight, or production costs.
Autodesk Fusion 360, equipped with the “Generative Design” AI add-on, for example, can generate and evaluate hundreds of design variants for a specific component requirement—a process that would otherwise take weeks by hand. Studies by TU Munich (2024) show that AI-supported design shortens development time by 47% on average and boosts material efficiency by up to 32%.
A real-world example: The engineering firm Schmidt & Partner from Stuttgart used AI-driven topology optimization on an automotive supplier project to reduce weight by 28% while increasing part stability by 15%. The payback period for the AI software was less than six months.
Automated Error Analysis and Quality Assurance
According to a 2024 study by the German Institute for Standardization (DIN), design errors are responsible for around 44% of all project cost overruns. AI-supported error detection systems can make a significant difference here.
Solutions like Siemens NX with the Validation Assistant use deep learning to predict design errors or tolerance problems before they lead to costly changes. The software compares new designs to a database of successful projects, flagging any deviations from established standards.
Especially valuable: these systems’ ability to learn continuously. At engineering firm Müller & Weise in Dortmund, AI-powered error analysis reduced the number of design revisions by 63% within one year, with the system becoming more accurate with each iteration.
Generative Design and Parametric Modeling
Perhaps the most spectacular application of AI in engineering is in generative design. This technology reverses the traditional design process: instead of creating and optimizing a solution, engineers define requirements and constraints—and the AI generates hundreds of possible solutions.
PTC Creo’s Generative Design extension, for example, uses evolutionary algorithms to generate optimal part geometries for specific load cases, taking manufacturing methods, material constraints, and cost targets into account.
According to a McKinsey analysis (2024), such tools reduce the development costs of complex components by 25-40% and unlock lightweight construction potential that conventional methods could never realize.
| Metric | Improvement | Source |
|---|---|---|
| Development Time | -47% | TU Munich |
| Material Efficiency | +32% | TU Munich |
| Design Revisions | -63% | Müller & Weise Case Study |
| Development Costs | -25% to -40% | McKinsey |
AI-Driven Project Planning and Management
Resource Planning and Capacity Optimization
Resource planning is one of the most complex challenges in engineering projects. AI systems can make a decisive difference here by analyzing historical project data and proposing optimal resource allocations.
Planning software like Deltek Acumen Risk leverages machine learning to forecast resource bottlenecks and automatically optimize planning scenarios. It takes into account factors such as employee availability, skill profiles, and project dependencies.
A case study from project management consultancy CapGemini (2024) shows that AI-supported resource planning increases engineering team utilization by an average of 23% and reduces idle times by over 30%.
Risk Assessment and Predictive Project Control
Spotting project risks early—before they become issues—is where AI excels in project management. Modern systems like Oracle’s Primavera P6 with AI extensions continuously analyze project data to identify patterns that flag potential troubles.
These technologies use algorithms trained on thousands of historical projects to detect, for example, at-risk milestones, ballooning costs, or probable quality issues.
According to a Forrester Research analysis (2024), AI-powered early warning systems reduce schedule overruns by 38% and average budget overruns by 24% in complex engineering projects.
Automated Documentation and Knowledge Management
Documenting engineering projects consumes significant resources. AI has the potential to revolutionize this process by capturing, structuring, and preparing project data automatically.
For example, Microsoft Project with Power Automate integration can generate status reports, create meeting minutes, and prepare decision documentation at the push of a button. The software extracts key information from emails, chats, and project management tools.
A 2024 benchmark study by the Project Management Institute (PMI) estimates that automated documentation saves project managers 8-12 hours per week—time that can be devoted to higher-value activities.
“The true value of AI in project management is not just the time savings, but the quality of decision-making. Today, we make choices based on data and predictions that simply weren’t available in the past.”
– Carsten Weber, Head of Project Management at Bosch Engineering GmbH, 2024
Simulations and Digital Twins
Real-Time Modeling of Complex Systems
Modeling complex technical systems often pushes conventional methods to their limits. AI-powered simulation tools, however, can tackle systems once considered too complex to model.
ANSYS with the AI platform Discovery Live, for example, uses GPU-accelerated algorithms and deep learning to perform complex flow simulations in real time—a process that would otherwise take hours or days with conventional CFD methods.
According to the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), AI-based simulations reduce computation time by a factor of 50 to 500—while maintaining accuracy sufficient for most use cases.
Predictive Maintenance and Condition Monitoring
For plant manufacturers and operators, AI-driven predictive maintenance opens up significant savings potential. The technology analyzes sensor data in real time, detecting anomalies long before they lead to downtime.
Siemens MindSphere with the Predictive Service Suite, for instance, utilizes machine learning to monitor the condition of machines and equipment continuously. By learning from historical failure patterns, it can predict with high precision when components should be replaced.
The economic benefits are striking: a 2024 study by RWTH Aachen estimates that AI-supported predictive maintenance in machinery and plant engineering cuts costs by 30-40% compared to conventional maintenance. System availability increases by an average of 25%.
Energy Efficiency and Sustainability Analyses
With rising energy prices and stricter environmental regulations, efficiency and sustainability analyses have become critical for engineering firms. Here, AI systems can analyze complex interdependencies and identify optimization opportunities.
Schneider Electric’s EcoStruxure software, for example, uses AI algorithms to optimize building technology, production lines, or infrastructure systems for energy—analyzing thousands of operational parameters simultaneously and uncovering savings potential invisible to human experts.
A meta-analysis by the Technical University of Berlin (2024) found that AI-based energy optimization delivers average energy savings of 17-23% in technical systems—a remarkable achievement in view of current energy price trends.
| Parameter | Conventional Methods | AI-Powered Methods | Improvement |
|---|---|---|---|
| Computation Time | Hours/Days | Seconds/Minutes | Factor 50-500 |
| Resource Requirement | High | Moderate | Factor 5-10 |
| Number of Variants | 5-10 | 100-1000 | Factor 20-100 |
| Optimization Potential | Moderate | High | +40-60% |
Document and Knowledge Management with AI
Intelligent Document Analysis and Classification
Engineering and planning firms generate huge amounts of technical documentation. Organizing, searching, and managing this information is an increasing challenge—and AI is fundamentally transforming this area.
For example, Microsoft SharePoint with the Syntex AI extension can automatically categorize technical documents, tag them, and extract relevant information. The system can independently recognize document types such as specifications, test reports, or technical drawings and categorize them accordingly.
According to a 2024 study by the Information Management Research Center, AI-powered document classification saves companies an average of 6.5 hours per employee per week—time previously spent searching for information.
Automated Creation of Technical Documentation
Preparing technical documentation ties up significant resources at engineering firms. AI-based systems can largely automate this process.
Adobe FrameMaker, for instance, with its AI assistant, can automatically generate technical manuals, user guides, or maintenance documentation from CAD models, simulation results, and project data. The system extracts relevant information and creates structured documents according to predefined standards.
A Deloitte case study (2024) for a mid-sized machinery manufacturer found that AI reduced the creation time for technical documentation by 68%, while significantly improving the quality and consistency of the documents.
AI-Powered Knowledge Bases and RAG Systems
Engineering firms possess an enormous wealth of expertise—often residing only in the minds of experienced employees. Retrieval Augmented Generation (RAG) systems help to capture and make this knowledge usable.
IBM Watson Discovery, for example, uses natural language processing and machine learning to unlock corporate resources. Employees can ask questions in natural language and receive precise answers sourced from internal documents, project reports, and best practices.
A 2024 survey by the German Institute for Economic Research (DIW) found that AI-driven knowledge management systems reduce onboarding times for new employees by an average of 42% and improve solution quality for complex technical issues by 37%.
“Our AI-powered knowledge management system has not only improved efficiency, but has also elevated our collaboration to a new level. Today, young engineers have access to all the company’s collective expertise—a huge competitive advantage.”
– Dr. Sabine Müller, CTO at Heidelberger Druckmaschinen AG, 2024
AI in Customer Service and Technical Support
Technical Chatbots and Virtual Assistants
Technical support often ties up highly qualified specialists in engineering companies. AI-powered chatbots and virtual assistants can provide significant relief here.
Zendesk’s Answer Bot component, for example, can automatically analyze technical inquiries and suggest solutions. The system continuously learns from previous support cases, constantly improving its responses.
According to a Gartner study (2024), technical chatbots can now resolve 62% of standard inquiries unaided. Average response times have fallen from 4.2 hours to less than 2 minutes.
Remote Diagnostics and AI-Assisted Error Resolution
For companies with widely distributed technical assets, AI-driven remote diagnostics deliver significant benefits. The technology analyzes sensor data and error logs in real time, helping technicians target issues quickly and efficiently.
PTC’s ThingWorx, integrated with ServiceMax, leverages machine learning to detect changes in the state of technical systems from sensor data and generate precise error messages. The system proactively recommends maintenance measures and supports technicians with interactive 3D guides.
According to the German Association for Electrical, Electronic & Information Technologies (VDE), AI-assisted remote diagnostics reduce average repair times by 47% and cut the number of on-site service visits by 38%.
Predictive Customer Service
The most advanced form of technical support is predictive customer service. AI systems detect potential issues before they occur and proactively initiate countermeasures.
Salesforce Service Cloud, powered by Einstein AI, continuously analyzes customer data, usage patterns, and technical parameters. The system recognizes patterns indicating potential problems and proactively initiates maintenance actions before they escalate.
An IDC (International Data Corporation) analysis (2024) shows that companies employing predictive customer service saw their customer satisfaction increase by an average of 32% and customer retention rates by 24%.
| Metric | Before AI | After AI | Change |
|---|---|---|---|
| Average Response Time | 4.2 hours | 2 minutes | -99% |
| First-Contact Resolution Rate | 47% | 78% | +66% |
| Support Cost per Case | €87 | €23 | -74% |
| Customer Satisfaction (CSAT) | 72% | 92% | +28% |
Integrating AI into Existing IT Structures
Connecting Legacy Systems and Modern AI Solutions
One of the greatest challenges for engineering companies is integrating modern AI solutions into their established IT environments. The good news: hands-on strategies are available to master this challenge.
IBM Cloud Pak for Data, for example, provides dedicated connectors for legacy systems, allowing gradual integration of AI functionalities into existing applications. The platform leverages container technology to connect AI services without changing core systems.
According to a Digital Association Bitkom study (2024), 72% of successful AI implementations are based on phased integration concepts that augment, rather than replace, existing systems.
Data Security and Compliance in AI Implementations
AI use raises specific questions about data security and compliance. For engineering firms working with sensitive customer data, this is a key issue.
Microsoft Azure, with its Security Center, offers dedicated compliance tools for AI applications that continuously monitor and document compliance with data protection regulations. The platform supports industry standards such as ISO 27001 and TISAX.
According to a KPMG analysis (2024), concerns over data security remain the main reason for caution with AI investments among 63% of mid-sized companies. Transparent security concepts are becoming a critical success factor.
Hybrid Cloud Solutions for AI Applications
Modern AI applications require considerable computing power and storage. Hybrid cloud solutions provide the optimal balance between performance, flexibility, and data security.
AWS Outposts, for example, allows compute-intensive AI applications to run in the cloud while keeping sensitive data on-premises. This solution combines the scalability of the cloud with the security of local systems.
A 2024 study by Technical University Darmstadt found that hybrid cloud architectures can shorten AI project implementation times by an average of 43% and reduce operating costs by 37%.
“The key success factor in AI integration is not the technology itself, but a well-designed architecture. We’ve learned that step-by-step approaches with clear interfaces to existing systems achieve the highest success rates.”
– Prof. Dr. Thomas Schmidt, Director, Institute for Digitalization in SMEs, 2024
Success Factors for AI Implementation
Change Management and Employee Buy-In
The technical implementation of AI systems is often easier than cultural integration. Successful companies therefore rely on thoughtful change management.
A 2024 study by the University of St. Gallen reveals that 68% of all failed AI projects in engineering were derailed by organizational, not technical, obstacles. The number one factor: lack of employee buy-in.
Most successful are approaches involving employees early and positioning AI as a complement—not a replacement—for human expertise. At Bosch Engineering, every AI project involves subject-matter experts as “AI mentors,” who serve as a bridge between technology and business.
ROI Analysis and Business Case Development
Evaluating the business impact of AI projects is a challenge for many organizations. Traditional ROI calculations often fall short, as they don’t account for indirect effects like quality improvements or innovation potential.
According to a PwC analysis (2024), an expanded ROI model for AI projects is necessary—one that translates direct cost savings as well as factors such as risk reduction, quality enhancement, and strategic competitive advantages into monetary terms.
ThyssenKrupp Engineering, for example, developed a multidimensional assessment model for AI projects that, alongside classic KPIs, incorporates “innovation readiness” and “future capability building.” This, according to the company, increased the success rate of AI projects by 47%.
Agile Implementation Strategies for Fast Results
Successful AI implementations rarely follow the classic waterfall model. Instead, leading companies rely on agile, iterative approaches with fast feedback cycles.
An Accenture study (2024) of 150 mid-sized engineering companies shows agile AI projects have a 3.4 times higher success rate than traditional methods. The key: early minimum viable products (MVPs) with measurable business impact.
For example, the engineering firm Fichtner in Stuttgart has shifted its AI strategy to 12-week sprints, each ending with a functional solution. This, the company says, reduced AI project time-to-value from an average of 18 months to under 3 months.
| Success Factor | Impact on Project Success | Key Measures |
|---|---|---|
| Employee Acceptance | 68% | Early Involvement, Training, AI Mentors |
| Clear Business Case | 57% | Multidimensional ROI, Focus on Measurable Results |
| Agile Approach | 53% | Short Iterations, MVPs, Continuous Feedback |
| Data Quality | 49% | Data Cleaning, Metadata Management, Quality Assurance |
| Executive Sponsorship | 41% | Clear Leadership Commitment, Adequate Resources |
Looking Ahead: AI Trends for Engineering and Planning Firms up to 2030
The AI landscape is evolving at breakneck speed. For engineering and planning offices, several key developments are expected by 2030.
According to a Delphi study by the Fraunhofer Institute (2024), AI systems will be able to produce complete technical designs solely from functional requirements by 2027. The role of engineers will shift more towards defining requirements and validating results, rather than detailed design work.
Multimodal AI systems that can simultaneously process text, images, and 3D data will become standard. Integrating computer vision into engineering workflows will make it possible to automatically record construction progress, scan physical prototypes, and compare them with digital models.
Particularly noteworthy: the rise of domain-specific AI models. Unlike today’s general-purpose systems, these models—trained specifically for engineering applications—will deliver more precise results and account for industry-specific norms and standards.
McKinsey forecasts (2024) that by 2030, about 60% of all engineering tasks will be supported or partially automated by AI. Rather than eliminating jobs, this will shift roles: routine tasks will be automated, while complex conceptual and strategic work will become more important and more highly valued.
Another trend: collaborative AI systems serving as active team members in engineering projects. These systems will not just provide information on request, but proactively offer suggestions, identify inconsistencies, and highlight optimization opportunities.
“By 2030, AI will no longer be considered a separate technology, but a natural part of every engineering tool. The question won’t be if you use AI—but how you use it most effectively to achieve your business goals.”
– Dr. Andreas Meier, Board Member, Association of German Engineers (VDI), 2024
Conclusion
The integration of AI in engineering and planning firms is no longer a vision of the future—it’s already reality, delivering tangible economic benefits for companies of all sizes.
From automated design optimization to intelligent project management right through to predictive maintenance: AI technologies are driving considerable improvements in efficiency and quality across nearly every area of technical services.
Three key takeaways stand out:
- AI changes ways of working—not just the tools. Successful implementation requires not only technical integration, but also thorough change management and a strategic realignment of processes.
- Getting started doesn’t have to be complex. The most successful companies begin with clearly defined, manageable use cases and scale step by step—a pragmatic approach that enables rapid wins.
- AI complements human expertise—it doesn’t replace it. The most valuable AI deployments combine the analytical power of algorithms with the creative problem-solving ability and experience of human specialists.
Now is the perfect time for decision-makers in engineering and planning firms to develop an AI strategy and launch their first pilot projects. The technology has matured, implementation hurdles are lower than ever, and the competitive advantages for early adopters are substantial.
Investing in AI skills today not only secures your company’s short-term competitiveness but also lays the foundation for long-term success in an increasingly data-driven industry.
Frequently Asked Questions
Which AI applications deliver the fastest ROI for engineering offices?
According to current data from the Fraunhofer Institute (2024), AI applications in document management and project planning deliver the fastest return on investment—typically within 3–6 months. These areas are characterized by relatively simple implementation, low investment costs, and immediately measurable time savings. AI-driven document analysis and classification can reduce the time spent searching for information by up to 70%, while AI in project management improves planning accuracy by an average of 35% and significantly reduces project risks. For optimal ROI, a phased approach is recommended, starting with these “low-hanging fruits” and gradually moving towards more complex use cases such as generative design or simulations.
How does AI change the role of engineers and technical planners?
AI shifts the focus of engineers and technical planners from routine tasks to more strategic and creative work. A study by RWTH Aachen (2024) shows that AI can take over up to 47% of repetitive engineering activities, freeing up specialists for conceptual work, problem-solving, and client communication. In practical terms, this means less time on standard calculations, data preparation, and documentation, and more time for requirements analysis, concept development, and innovation. Contrary to some fears, this won’t devalue engineering careers—instead, it will elevate them: AI handles the routine, while value-adding responsibilities that require human creativity and judgement gain importance. The ideal is a human–machine team, with AI acting as a powerful assistant that complements and amplifies human expertise.
What data protection and security aspects must be considered during AI implementation?
AI implementation in engineering and planning firms comes with several critical data privacy and security considerations. First: GDPR compliance, especially with personal data that may be included in project documents. Second: protecting trade secrets and intellectual property—especially when using external AI services or cloud solutions. A 2024 analysis by the German Federal Office for Information Security (BSI) recommends three main measures: 1) implement granular rights management with the principle of least privilege, 2) rigorously mask or pseudonymize sensitive data before AI processing, and 3) prefer on-premises or European cloud services for critical applications. Additionally, AI systems should undergo regular security audits, as their high connectivity and data processing open up new risk vectors.
How do AI solutions for small and medium-sized engineering firms differ from enterprise-level solutions?
AI solutions for small and medium-sized engineering firms differ from enterprise-grade offerings in several key ways. The most obvious is scale and cost: SME-focused solutions tend to offer modular, pay-as-you-grow models rather than hefty upfront investments. A study by Mittelstand-Digital Zentrum (2024) shows successful AI projects in SMEs typically start with investments between €25,000 and €75,000, while enterprise projects often begin in the millions. SME solutions also tend to focus on preconfigured use cases and industry-specific templates that can be rolled out quickly with minimal customization, whereas enterprise solutions offer more flexibility for fully bespoke development. Another key difference: SME solutions aim for rapid time-to-value (typically 2–3 months to go-live), while enterprise projects can run for 12–24 months but allow for deeper integration with existing IT landscapes.
What qualifications do employees need to work successfully with AI systems?
Successful use of AI in engineering and planning firms requires a specific mix of skills. A joint study by TU Munich and the Stifterverband (2024) identifies three key competency areas: First, technical fundamentals—not every employee needs to program AI, but a basic understanding of how AI works, its capabilities, and its limits is crucial. Second, prompt engineering—the ability to form precise queries and instructions for AI systems will be a core competency. Third, critical evaluation—the skill to professionally validate and interpret AI-generated results. Employees who can serve as “AI translators” between line-of-business and technology are especially valuable. Training programs should cover both technical basics and application-specific modules. According to leading firms, 2–3 days of training for basic users and 5–10 days for key users is optimal for productive adoption.
How can the success of AI implementations in engineering be measured?
Measuring the success of AI implementations in engineering requires a multi-dimensional KPI system. According to a Deloitte analysis (2024), four categories should be tracked: efficiency (e.g., time savings per process, throughput times, resource utilization), quality (e.g., error reduction, improved precision, customer satisfaction), innovation (e.g., new product features, patents, design variants), and business impact (e.g., cost savings, revenue growth, competitive advantages). Leading implementations define 2–3 measurable KPIs with a clear baseline for each category. For example, Siemens measures the success of its AI in requirements engineering using three main metrics: reduced specification time (-67%), improved requirements quality (clarity & testability: +43%), and decreased post-handover amendments (-38%). Crucially, metrics should be defined at the AI project conception stage to enable valid before-and-after analysis.
Which AI tools are best suited for getting started in engineering offices?
The best AI tools for getting started are those offering rapid productivity gains with a moderate learning curve. A 2024 benchmark analysis by Fraunhofer IAO identifies five tool categories especially suitable for engineering firms: 1) Document analysis tools like Microsoft SharePoint Syntex or Adobe Document Cloud AI, which automatically categorize technical docs and extract relevant info; 2) AI-backed project management platforms like Asana (Work Intelligence) or Monday.com (AI Assistants), which optimize schedules and flag risks; 3) Collaboration suites like Microsoft 365 Copilot or Google Workspace Duet AI, which support report, presentation, and client comms creation; 4) CAD assistants like Autodesk Generative Design or Siemens NX with AI features, which suggest design improvements; and 5) low-code/no-code AI platforms like Microsoft Power Platform or IBM Watson, enabling custom AI solutions without programming. Ideally, firms should combine a general office AI tool with a specialized tool aligned to their core business.
How does AI change collaboration with clients and partners in engineering projects?
AI fundamentally transforms collaboration in engineering projects via greater transparency, faster iteration, and enhanced communication. A 2024 Accenture study of 180 project managers found that AI-powered client projects involve 37% more iterations in the design phase yet reach a final design 42% faster. Customer satisfaction measurably rises by 29%. Key changes include: 1) interactive visualizations and digital twins that provide deeper insight into designs; 2) AI-driven translation of technical concepts into accessible language and graphics for non-engineers; 3) real-time feedback systems allowing continuous alignment rather than periodic reviews; and 4) automated documentation of all decisions and their rationale, increasing transparency and legal certainty. Deutsche Bahn, for example, leverages AI-powered collaboration tools in infrastructure projects to visualize planning data in real time and make it accessible to all stakeholders—yielding 47% higher customer satisfaction and 23% fewer subsequent change requests.
