The AI Revolution in Engineering: Celebrating progress and eyeing future potential | New Civil Engineer LOGIN / FREE TRIAL Menu Menu LOGIN / FREE TRIAL Subscribe --> New Civil Engineer Civil engineering and construction news and jobs from New Civil Engineer Latest Airports News Business News Bridges News Energy Rail News Roads News Tunnels News Water & Floods News Sectors Airports Bridges Energy Floods Ports Rail Roads Stadiums Tunnels Water Projects HS2 Lower Thames Crossing Great Grid Upgrade Hinkley Point C Sizewell C Transpennine Route Upgrade Topics Ageing assets Nature-based solutions Resilience Skills Technology Magazine Podcast Ask NCE Events ICE news Careers Newsletters NCE Examines Subscribe --> You are here: Opinion The AI Revolution in Engineering: Celebrating progress and eyeing future potential 07 Oct, 2024 By Thomas Topolski , Elliot Sander and Saar Dickman The engineering sector is experiencing a transformative era, propelled by the strategic integration of artificial intelligence (AI). Leading industry players such as Jacobs, Aecom, Bechtel, WSP, HDR, Stantec, Arcadis and others are at the forefront of this revolution. Their AI initiatives have led to efficiency and expanded capabilities. Nevertheless, there remains an untapped potential to drive direct revenue growth through more focused and innovative AI implementations. These advancements could fundamentally change the way these companies generate profit, unlocking new avenues for financial success and major operational improvements. Thomas Topolski is founder and president of FuturEdge Consulting Current AI Achievements in Engineering Leading firms focused on critical infrastructure have already begun to reap the benefits of AI, gaining improved operational efficiencies and enhanced predictive analytics. AI-powered models now facilitate superior decision-making in project planning, risk management, and asset management. Examples of AI Implementations by Major Firms Jacobs integrates AI for digital twins and predictive maintenance, focusing on real-time monitoring and optimisation in large-scale transportation and water infrastructure projects. Elliott Sander is president of Sander Consulting Aecom applies AI and machine learning within their Building Information Modeling (BIM) workflows to improve design precision, clash detection and resolution, reduce lifecycle costs, and conduct more accurate environmental impact assessments in urban infrastructure. Bechtel applies AI for risk management and complex project logistics, utilising data analysis to predict challenges and streamline supply chain management particularly in their energy and industrial infrastructure business units. WSP employs AI in smart city and transportation infrastructure projects where it predicts traffic patterns, manages urban mobility systems and simulates climate impact scenarios to design resilient infrastructure. Loading… Saar Dickman is CEO of Dynamic Infrastructure Stantec focuses on water management systems, using AI to model flood risks and create more robust management plans for improving infrastructure safety and resilience in vulnerable regions. The Need for Revenue-Driven AI Implementations Despite these successes, the revenue model for most engineering firms remains heavily dependent on billable hours and margins on materials, typically yielding a profit margin of 8-10%. While effective, this model is increasingly constrained by labor and materials costs. AI adoption has become essential in the critical infrastructure space due to market positioning, digital transformation and innovation pressures, though its direct impact on earnings remains tenuous. AI presents an opportunity to diversify revenue streams, akin to the high-tech software industry where profits are driven by leveraging large data sets to deliver advanced analytics and knowledge creation. Shifting Toward Data-Driven Revenue Models The real game-changer lies in AI’s potential to diversify the existing financial model used by most engineering firms into one that to includes data as a meaningful and more profitable revenue source. By leveraging AI for data processing and advanced analytics, companies can evolve from simply delivering projects to offering long term high-value, data-driven insights. These insights can be monetised through subscriptions, licensing models, or data-as-a-service (DaaS) offerings much like how tech giants generate revenue. For instance, predictive maintenance models or city-wide mobility analytics could be sold as recurring services, providing a more scalable and higher-margin revenue stream than traditional billable hours. AI provides opportunities to factor a life-cycle approach to design and implementation leading to innovative solutions that are practical, fundable, buildable, and sustainable. Two examples of potential paradigm shift that could result in a direct revenue increase: Expanding Market Horizons with AI AI’s ability to analyse data points and identify patterns within complex datasets surpasses human capacity, revealing the potential to reach untapped markets and unmet customer needs with differentiated and enhanced business models. AI can analyse data from sensors and historical records to predict when equipment is likely to fail, allowing for timely maintenance that prevents costly failures and extends asset life, which can be leveraged to help state and local governments better manage their infrastructure without investing millions in expensive asset management software while using more efficiently related consulting services. By optimising maintenance schedules and improving asset utilisation, AI can significantly reduce operational costs and improve overall efficiency. Enhancing Service Offerings There is immense potential in utilising AI to expand the scope of services offered to existing clients as well as new customers. By leveraging AI to analyse existing infrastructure data, firms can deliver customised maintenance plans that are not only preventive but also predictive. This transition can turn standard service contracts into comprehensive, value-added engagements, encouraging clients to invest proactively in maintenance services identified by AI, enhancing client satisfaction and trust resulting in increased customer loyalty and optimised contract values. We asked one of the AI engines to create a radar chart presenting the services associated with AI (based on companies’ annual and quarterly reports) and the relative revenue contributions of these services: Revenue from Infrastructure Activities When comparing the impact of AI on revenue generation in engineering firms such as those mentioned above to other sectors adopting AI, like travel, insurance and real estate, the scale and significance of AI contributions show distinct contrasts. Engineering firms typically use as part of their digital transformation initiatives to support existing processes. It is estimated that less than 10% of revenue increases are directly associated with AI implementation. In these cases, AI primarily serves as a tool to enhance efficiency and competitive positioning rather than a major revenue driver. In contrast, sectors that have strategically implemented AI see more substantial contributions. Companies in other industries use AI for automation, customer personalisation, risk management, and market forecasting. On average, AI-driven services contribute 15-30% of total revenue growth, directly linked to new or improved services and business models. The key takeaway is that while AI adoption in engineering is growing, it is often an enabler of efficiency for existing processes rather than a primary revenue source for itself creating new and valuable knowledge. In contrast, other sectors benefit more significantly from AI implementations, with AI often embedded at the core of their service models, driving a more substantial share of revenue growth. Conclusion Firms in the infrastructure sector must prioritise how new technologies can best serve their clients’ needs. AI-driven technologies can be leveraged to meet the demands of state and local communities as well as private sector clients in managing their infrastructure assets. AI has the potential to permanently alter the fabric of the critical infrastructure industry which has been largely static for decades. Clients and engineering firms transitioning to digitalisation require strategic leadership that understand and appreciate the opportunities and challenges AI presents. This involves adopting the digital business model which treats digital content as assets rather than static documents or files. While the engineering sector’s current use of AI is laudable, there is a strong case for a strategic pivot towards more revenue-centric AI applications. By doing so, engineering firms will not only continue to lead in innovation but also see a direct positive impact on their financial performance. The potential lies in transforming more of these companies’ experience and DNA into data-centric domains, offering high-margin knowledge-based services that could redefine profitability in the sector. The companies that can provide a holistic AI enabled offering built around a core scalable platform, are sufficiently agile to accelerate speed to market, can increase functionality, reduce development costs, standardise quality, promote consistent branding and user experience will win the future. Thomas Topolski is founder and president of FuturEdge Consulting Elliott Sander is president of Sander Consulting Saar Dickman is CEO of Dynamic Infrastructure Like what you've read? To receive New Civil Engineer's daily and weekly newsletters click here. AI artificial intellgience dynamic infrastructure futuredge consulting sander consulting 2024-10-07 Rob Hakimian Share Facebook Twitter Google + LinkedIn Pinterest Email Add to Bookmarks Tagged with: AI artificial intellgience dynamic infrastructure futuredge consulting sander consulting Loading... Have your say Sign in or Register a new account to join the discussion. 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