Enhancing Value Engineering through Human-AI Collaboration in Federal Projects

Value Engineering (VE), also known as Value Methodology (VM), is resurging as a critical project management tool across the federal government as projects face increasing scrutiny over cost, schedule, and outcomes. Recent U.S. Department of Energy (DOE) directives require value studies on all capital projects.

In the past, VE has too often been applied reactively when projects encounter overruns or delays, leading to scope reductions that undermine project delivery rather than enhance it. To realize VE’s full potential, it must be used proactively and consistently throughout the conceptual design and development phases of a project.

Now, digital tools employing data analytics, artificial intelligence (AI), and building information modeling (BIM) are revolutionizing how VE can be conducted. The application of these tools in what is known as digital VE allows for better handling of interdependent systems and diverse and often competing stakeholder requirements in value studies.

While the introduction of AI-driven tools is reimagining VE, the importance of the human aspect in VE remains. Utilizing a framework to implement human-AI collaboration in a value study ensures that VE is applied proactively to coincide with project design and development stages and provides more value to project performance rather than just reactive cost-cutting. This framework demonstrates that the potential for VE enhancement lies not just in the digital tools alone, but in VE teams knowing how to effectively harness them to deliver optimal value.

The Resurgence of Value Engineering in Federal Projects

The DOE and other federal agencies have renewed their commitment to VE, recognizing it as a proven method for controlling costs and enhancing project outcomes. For decades, VE has helped agencies identify opportunities to improve function, reduce waste, and achieve better value.

VE has too often been applied reactively after projects face cost overruns or schedule delays. In these circumstances, VE tends to devolve into scope reduction or cost-cutting, undermining performance and the original goal of value studies.

To fulfill its true potential, VE must be applied proactively and consistently. By utilizing industry-accepted standards and practices, such as SAVE International’s VM Job Plan (SAVE International, 2020), to initiate VE during project planning and maintain it across design, development, and implementation, teams can shape projects to realize improvements in cost and other goals identified by the stakeholders such as safety, sustainability, and resilience.

The complexity of today’s project environment makes this shift more urgent than ever. AI-driven digital tools provide the analytical speed and modeling power needed to make proactive VE both practical and effective.

Human–AI Collaboration: A Balanced Approach

Neither humans nor AI alone can deliver the entirety of this new VE potential. Success comes from a structured collaboration. Humans contribute creativity, judgement, stakeholder engagement, and ethical oversight. AI provides rapid data aggregation, multivariable scenario modeling, and accelerated analysis. Together, these complementary strengths contribute to a transparent, auditable, and stakeholder-aligned project outcome.

Establishing digital VE as a continuous process guided by the VE team and facilitator and supported by AI tools increases the potential of VE to add value.

Human–AI Collaboration Across the VM Job Plan

The following framework breaks down SAVE International’s VM Job Plan into its eight phases, which align with the early-stage conceptual design and development of a project. Mapping the capabilities of both the VE team and AI-driven tools across these phases demonstrates a proactive approach to integrating digital solutions tools into a value study.

Best Practices for Embedding Digital VE

To implement digital VE effectively, organizations should maintain human-in-the-loop (HITL) safeguards, ensuring that AI augments rather than replaces human decision-making. To do so, standard operating procedures (SOPs) should be identified upfront to maintain transparent and ethical AI use. Teams should also invest in cultural readiness by training VE facilitators and team members to collaborate effectively with digital tools. Finally, VE should be treated as a process of continuous improvement. Lessons learned should be captured systematically and used to retrain AI models and refine study outcomes over time.

Human-AI Collaboration at Management Solutions

At Management Solutions, we consistently apply this collaborative framework to enhance project delivery for the DOE and other federal clients. Our teams work with stakeholders to define project objectives extending beyond cost to metrics such as sustainability, resilience, and risk. Digital tools are used to accelerate modeling and analysis, providing teams with quicker, more comprehensive insights.

Most importantly, our project teams remain central to the process. They define project objectives based on stakeholder requirements, generate ideas, interpret results from digital tools, and provide final judgement to ensure that AI outputs align with project goals and our clients’ missions. To ensure transparency, all AI interactions and contributions to the project outcome are documented.

Ultimately, value engineering is essential to the DOE and it must evolve. Applied proactively and supported by digital tools, VE ensures that projects meet budget, schedule, and any variety of additional stakeholder requirements. By adopting a framework that combines the capabilities of staffed teams and digital tools, Management Solutions and the next generation of value engineering will move beyond cost-cutting to help deliver greater value to federal projects.

References

SAVE International. 2020. A Guide to the Value Methodology Body of Knowledge. Mount Royal, NJ, United States.