In the high-stakes world of construction bidding, precision is everything. Yet, many contractors still rely on outdated methods—spreadsheets, fragmented data, or even gut instinct—to prepare bids. This approach leaves too much room for error, and the consequences can be severe. According to Contimod, nine out of ten construction projects exceed their budgets, with average cost overruns of 28 percent. This stark reality underscores the urgent need for stronger estimation and risk analysis to protect project margins and ensure successful outcomes.
Amid rising material costs, labor shortages, and global supply chain disruptions, construction leaders are under unprecedented pressure to stay competitive. The solution? Predictive artificial intelligence (AI). By analyzing historical bid data, identifying hidden risks, and forecasting win rates, AI has the potential to revolutionize the construction industry. It’s not just about securing more contracts; it’s about improving accuracy, profitability, and efficiency across the entire project lifecycle.
At Western Specialty Contractors, we recognized the opportunity to take our bidding process further with AI. We had a solid foundation built over decades of experience, but we knew we could do more. By analyzing years of our own bid history with a custom machine learning model, we achieved over 70 percent accuracy in win-loss forecasting. This allowed us to prioritize high-probability opportunities and allocate resources more effectively.
However, the journey wasn’t without its challenges. Bidding in construction is complex, influenced by inflated costs, shifting labor availability, unpredictable site conditions, and unique client requirements. To navigate this complexity, we partnered with AI specialists who understood our projects, field teams, and data in the real world. This collaboration was crucial to our success.
Yet, our experience also highlighted why many firms still struggle to fully leverage AI. Research from Bluebeam shows that while 74 percent of AEC firms use AI applications, 72 percent still rely on paper-based processes for critical stages like estimating and approvals. Without consistent historical data and connected workflows, AI’s potential remains out of reach.
More accurate bidding impacts far more than just winning work. When our estimates align closely with actual costs and timelines, we anticipate seeing fewer change orders, less rework, and tighter schedules. The connection between smart estimating and predictable project delivery is clear on our job sites every day.
Beyond our organization, we recognize that this idea extends beyond our own work. As highlighted at New York Build (March 2025), contractors generate massive volumes of site data, but too often it is disorganized or siloed. By connecting and sharing that data in ways that inform decisions, we can feed valuable insights back into estimating. Closing the loop between what happens on-site and what gets priced up front helps us continuously refine bids and protect margins.
With the early success we’re seeing in our first foray into artificial intelligence, Western is closely evaluating other potential applications for this technology across the organization. We see AI as a supplemental tool that augments rather than replaces our estimators’ expertise. The real value comes from using AI to enhance our win-loss forecasting, which allows us to focus our resources on the most promising opportunities. That frees up our estimators to focus on what they do best: analyzing risk, shaping pricing strategies, and building trusted relationships with clients.
That said, we’ve also learned that tech alone isn’t enough. Clean, consistent historical data is critical. Without it, even the smartest AI tools won’t deliver results. Just as important is embedding AI insights directly into our day-to-day workflows. If it’s too complicated or separate from how our teams work, they won’t use it.
As we continue to develop our AI capabilities, we’re focused on leveraging a feedback loop between our bidding and project execution to continuously improve our predictive accuracy.
For those just starting out, here’s our advice:
**Partnership is essential.** No single department or contractor holds all the answers for AI transformation. Our IT and finance teams needed to develop the AI bidding tool through extremely close internal collaboration. Plus, we also needed to build a trusting collaboration with our consulting partners. This ensured that everyone who held pieces of the AI transformation puzzle held a seat at the table and contributed to its success.
**Focus on the business problem, not the tech.** Don’t chase AI for the sake of it. Stay clear on the real problems you’re trying to solve, whether that’s improving win rates, reducing rework, or protecting your margins. That’s what makes technology worth the investment.
**Don’t wait, start small and build iteratively.** Begin with a focused pilot project like win-loss prediction before expanding. This allows you to prove value, refine your data processes, and build internal confidence in AI capabilities.
**Prioritize data quality.** Conduct a comprehensive audit of historical bid and project performance data to find gaps and ensure consistency.
**Integrate AI into daily workflows.**