Data lakehouses: The key to scalable AI in construction

Artificial intelligence has the potential to transform the construction industry. From accelerating estimates to improving jobsite safety, AI can enable construction firms to work smarter and faster while reducing risk.

But to move beyond isolated AI tools and toward enterprise-wide use cases, construction leaders must realize that AI is only as good as the data it can access. Connecting data through modern data lakehouses needs to be a strategic priority.

Keep reading to learn how data lakehouses bolster AI initiatives and how you can start using them.

How do data silos hinder AI use in the construction industry?

Construction firms often maintain separate systems for:

• ERP and accounting
• Estimating
• Scheduling
• Project management
• Bidding
• RFIs and change orders
• Time tracking
• Sales and Marketing
• Internal document management

AI tools can’t reach their full potential and deliver measurable ROI if they can only access a partial amount of your business’s data. For example, a tool might have access to SharePoint documents, email conversations and meeting notes but be locked out of ERP data, project schedules, procurement systems or job costing information.

AI typically does not recognize when it’s working with only partial data. It will still produce answers that appear polished and authoritative. But with incomplete data, how valuable are those answers?

What is a data lakehouse?

A data lakehouse is a centralized platform that integrates data from systems used across the business.

By bringing together structured data, such as financial records, schedules and project costs, together with unstructured data, such as emails, RFIs, contracts and project documentation, a data lakehouse allows data to be accessed and analyzed from a single source. This removes the need for people and AI systems to search across multiple systems or manually connect information.

If correctly implemented, a data lakehouse makes your company’s data available in one place, in one language at one time.

How does a data lakehouse benefit construction companies?

The value of a data lakehouse isn’t just centralized data. It’s what becomes possible when data are centralized and organized into logical and informational models.

When information from all your systems and documents can be analyzed together, AI gains the context it needs to deliver more meaningful insights.

Examples of how a data lakehouse can benefit your construction company include:

Budget versus actual performance

When estimates and reality capture are combined with actual project financials, change orders and other project data, leadership teams gain a better understanding of project health and future projections.

Schedule and procurement risk

Project delays rarely occur in isolation. A delayed subcontractor, late material shipments and a compressed schedule can create cascading risks across a project.

In a connected environment, AI can identify these downstream impacts early, helping teams intervene before minor issues become major disruptions.

RFI cost and schedule impact

RFIs frequently affect budgets, schedules and project outcomes. The challenge is that the impacts often surface weeks after the initial issue appears.

When RFIs, schedules, budgets and change orders are connected, AI can highlight the true ripple effects of open issues and help project teams prioritize responses based on business impact.

Enterprise-wide visibility

Many contractors operate projects as independent units with limited visibility across the organization.

A connected data foundation allows firms to identify trends across their entire portfolio, including:

• Subcontractor performance patterns
• Procurement opportunities
• Resource allocation conflicts
• Margin erosion risks
• Emerging project issues

The ability to see across projects creates opportunities for better decisions at both the project and executive levels.

How can AI that uses data lakehouses be used in construction?

When AI leverages a data lakehouse, it delivers value both in the office and onsite for construction companies. AI systems with robust data can improve:

Estimates

AI can analyze previous bids, identify comparable projects, highlight pricing anomalies and help teams evaluate potential risks before a proposal is submitted.

Estimating capacity can be a growth constraint. AI can help reduce bottlenecks by making institutional knowledge more accessible and speeding up routine analysis, allowing experienced estimators to focus on higher-value decision-making.

Engineering

Engineering and project teams are increasingly using AI to review specifications, compare design documents, identify inconsistencies and accelerate review processes. Generative AI tools can also help draft responses, summarize technical information and provide quick access to historical project knowledge.

Safety

Safety was one of the earliest areas where AI gained traction in construction. AI tools can analyze photos, videos and safety documentation and inform field crews of the protocols required for specific job sites.

PDF and project document organization

Construction firms are swimming in PDF documents. RFIs, specifications, contracts, submittals, drawings and other documents exist almost entirely as PDFs.

AI can help extract information from unstructured documents, summarize content and answer questions about project records. It can also connect these documents to project schedules, financial data, procurement records and other operational systems.

How to get started with a data lakehouse

The successful use of a data lakehouse isn’t just about technology. It’s also about having clear business objectives.

When getting started, your business needs to:

Know what is to be accomplished

Before investing in a data lakehouse, define the business problems you are trying to solve.

Do you want more accurate estimates? Better project forecasting? Improved visibility into project profitability? Faster executive reporting?

Many firms find success by starting with a focused pilot project, demonstrating value and then expanding capabilities incrementally.

A clearly defined use case helps prevent costly investments in technology that isn’t needed.

Find the right partner

Your expertise is building, not designing AI architectures or data platforms.

Find a partner with experience in the construction industry that can help your business evaluate technology options, prioritize use cases and avoid costly missteps.

The best partners do more than implement technology. They take the time to understand your business, processes and goals.

Communicate goals and processes

Technology alone cannot fix inconsistent business processes.

Organizations should clearly define how work is performed, what data is required and how success will be measured. Strong processes and governance help ensure AI systems are using reliable and consistent information.

The responsibility of understanding how information is used throughout your company’s business process should fall on your CIO or IT Director.

Understand this isn’t just an IT project

Construction firms should treat this effort as an organizational initiative rather than an IT project. Success depends on aligning business and information leadership, operations and technology around shared objectives and ensuring everyone understands how connected data will support better business decisions.

How can Wipfli help?

Wipfli helps construction firms move beyond isolated AI experiments by building the data foundation necessary for long-term success. Our team can work with you to assess current data and technology environments, break down data silos across systems and design and implement data lakehouse solutions. Start a conversation.

Develop a data lakehouse strategy

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