Beyond the Hour: New Research Examines the Future of Engineering Pricing 

As artificial intelligence changes how engineering firms work, new research from the ACEC Research Institute explores how firms can better capture the value they deliver to clients. 

Engineering firms have spent decades investing in technologies that allow them to work more efficiently and deliver increasingly sophisticated solutions. But the industry’s traditional pricing models remain largely tied to labor hours and the cost of delivering services. Now, artificial intelligence (AI) is creating an opportunity to rethink that approach. If engineers can deliver better solutions in less time, how should their work be compensated? 

A new ACEC Research Institute study, Beyond the Hour: Making the Case for Value-Based Pricing, examines how engineering firms can move beyond traditional cost-based pricing toward compensation that better reflects the value they create for clients.  

The study, led by researcher Paul S. Chinowsky, PhD, draws on 19 executive interviews, a survey of 488 ACEC member firm leaders, and a literature review examining engineering and other professional services industries. The findings reveal an industry exploring new approaches to pricing but still grappling with how to translate its expertise, innovation, and efficiency into compensation that reflects the value delivered. 

One of the research’s central findings is that engineering firms often confuse lump-sum contracting with value-based pricing. While lump-sum contracts establish a fixed fee, that fee may still be calculated using estimated labor hours and internal costs. True value-based pricing begins with the economic or strategic value of the outcome to the client.  

Survey respondents defined value in several ways, including innovation (22%), risk reduction (21%), specialized capabilities (20%), financial return (19%), and speed (18%). These findings underscore that value is not defined by a single measure. It depends on the specific needs of the client and the benefits an engineering firm can demonstrate. 

AI is making this distinction increasingly important. Among survey respondents using AI, firms reported average labor savings of approximately 15% in areas where the technology has been implemented. Proposal and bid assembly accounted for 26% of reported AI applications, followed by information synthesis at 22%. However, most firms remain in the early stages of AI adoption, and pricing models have generally not changed.  

The research suggests that AI’s greatest long-term opportunity may extend beyond completing existing tasks faster. By enabling firms to analyze more alternatives, identify risks earlier, and develop new advisory services, AI could help engineers deliver greater value to clients.  

Without corresponding changes in compensation, firms may struggle to recover their technology investments even as clients benefit from improved productivity and expanded capabilities. 

The study also identifies significant differences between public- and private-sector opportunities. On a five-point scale ranging from no opportunity to a great deal of opportunity, respondents rated private developers highest for value-based pricing opportunities at 3.10, compared with 2.14 for municipal agencies and 1.92 for state and federal agencies. Additionally, 36% of respondents identified agency policy as the largest source of resistance to implementing value-based pricing. These findings suggest that firms may need different approaches depending on their clients. Private-sector projects may offer greater flexibility to test alternative pricing arrangements, while public-sector opportunities may require client education, measurable demonstrations of value, procurement flexibility, and policy changes. 

To help firms navigate this transition, the report introduces a practical framework called the Value Ladder. The framework guides firms through four stages: value creation, value demonstration, value capture, and value-based pricing. The process begins with identifying what additional value a firm brings to a project, demonstrating that value through credible evidence, determining how much of it can reasonably be captured, and establishing an appropriate fee.  

The report also outlines four implementation pathways:  

• enhancing traditional deliverables with additional insights,  

• differentiating through specialized expertise,  

• developing new services and advisory relationships,  

• and building data-focused partnerships that enable proactive solutions.  

Value-based pricing can take several forms, including premium fixed fees, milestone-based arrangements, and advisory agreements. The defining factor is how the fee is determined, not the contract structure itself. 

The research makes clear that value-based pricing is not simply about charging more. It requires firms to identify what matters most to their clients, demonstrate measurable benefits, and establish compensation that reflects those outcomes. As AI changes the economics of engineering services, firms have an opportunity to rethink not only how they deliver projects, but how they communicate and capture the value of their expertise. The central takeaway is that engineering firms must be able to demonstrate the value they create before they can expect to be compensated for it. Successfully making that transition could help firms sustain investment in the people, technology, and innovation that deliver better results for clients and communities.