Boh Bros. Construction Co., LLC

Adam Krob

Adam Krob is a technology strategist focused on transforming IT into a business advantage for mid-sized companies. He specializes in practical AI adoption, digital transformation and aligning technology with real-world operations to drive efficiency, resilience and measurable growth.

The construction industry is moving rapidly from using AI tools as experiments to sources of real organizational value. From RFC creation to submittal reviews, firms are actively investing in AI-enabled solutions with the expectation of driving efficiency, improving margins and reducing risk.

However, a growing number of organizations are encountering a new set of challenges with rising costs, fragmented tools and applications that are simply abandoned. In many cases, the issue is not the technology, but the approach companies are taking to it. Early use cases often lack a direct connection between the costs and the tangible benefits.

As the pace of change in AI continues to accelerate with new models, reduced token pricing, and a growing list of vendor capabilities, organizations must rethink how they structure AI investments to ensure long-term value. Three principles should guide construction companies’ approach to AI tools so that they can realize the benefits while maintaining cost discipline.

Anchor AI Initiatives in Measurable Value

The most successful AI deployments begin with friction points in existing processes, not on the capabilities that the tools can bring to bear. General contractors have found effective use cases for AI in areas like:

• Reporting and project analytics

• Document and submittal management

• Cost tracking and reconciliation

• Repetitive administrative workflows

The reason that AI is a good fit is that these processes represent ongoing, measurable costs. These processes are recurring cost centers where time is traded off for risk mitigation, making an AI solution an ideal candidate for measurable efficiency gains and risk mitigation, for example, reducing submittal review time by 20% while uncovering 3-5 additional material risks. In addition, focusing AI deployment on a component of an existing process reduces the effort required to implement these tools (retraining is not required) and improves overall adoption (the process is not foreign).

Adopt an API-First Architecture

The second principle of AI adoption is to manage the pace of AI change by building applications that are model and lab-independent. Model capabilities, pricing structures and performance benchmarks are shifting rapidly, so a model that was effective for a task this week might be eclipsed by a new (and perhaps less costly) model the next.

"The most successful AI deployments begin with friction points in existing processes, not on the capabilities that the tools can bring to bear."

Firms should not build solutions that are tightly coupled with a specific model or frontier lab; they should build applications that call AI functions with an API (application programming interface). This creates an independence between the model and the solution. This approach allows organizations to:

• Maintain flexibility across multiple AI providers

• Optimize for cost as pricing models evolve

• Select models based on task-specific performance

• Rapidly adapt to advancements in the technology landscape

Creating independence does not require adding significant complexity. The API architecture separates the business logic of a solution from the underlying model, allowing companies to choose a more efficient or cost-effective model that can deliver the same outcome without a complete redesign.

The AI market is defined by volatility, making an API-first strategy a long-term winning approach, particularly as models improve in their capabilities and costs trend downwards.

Use AI to Build a Bridge to Automation

One of the best lessons of the early wins with AI is that the solutions do not always require ongoing use of an AI model. When a process is designed and implemented, it may be better to move from an AI tool to automation, using scripts, rules-based workflows, or robotic process automation (RPA) to achieve the same results.

This approach offers several cost advantages:

• Elimination of recurring token or usage-based costs

• Improved processing speed and reliability

• Reduced system complexity

As AI tools become automated, the business value remains the same (whether it is time savings, cost reduction, or risk reduction), but the ongoing cost declines. AI serves as a bridge to automation, reducing the time and effort required, but it does not need to be a recurring commitment of time and spending.

Construction firms that treat AI solutions as a managed investment will be in the best position to realize the potential of these rapidly changing solutions. They prioritize measurable value, design modular applications, and evaluate when prompts and agents can be replaced by automation. AI is just another resource in their business process, one that needs to be held to the same standard as an investment in labor, material, or equipment.

As the pace of AI evolution accelerates, companies that are taking a disciplined approach to the adoption, development, and ongoing evaluation of their solutions will find themselves in a strong position to obtain a real competitive advantage.