The Nymbl Take: Construction's Late-Mover Advantage in AI

This is Part 2 of a two-part series on AI in construction. Part 1 Is Construction Ready for AI?examined the state of AI in construction.

In this article, we discuss the most compelling investment opportunities in AI. We examine specific technological trends for investment based on the state of the industry and maturity of each niche rather than listing specific startups. Please refer to our industry map at the bottom of this for a sample of AI startups in construction.

Near-Term (<3 Yrs):

We see immediate investment opportunities in technologies that optimize data collection and management, which are essential for building robust AI models, as well as collaborative robotics that will support the current workforce.

Medium-Term (3 to 7 Yrs):

We see value in AI solutions for MEP/specialty contractors, as they have greater access to project information and have employed better digital data management practices than most other AEC verticals.

Long-Term (7 to 25 Yrs):

The most promising long-term investments will lie in end-to-end construction technologies that enable AI to make autonomous judgment calls and act independently.

Late-Mover Advantage?

The construction sector, which only recently began transitioning from clipboards and spreadsheets to digital cloud infrastructure, may have a late-mover advantage over other, more technologically integrated industries when it comes to the adoption of AI.

While most US companies in other industries adopted companywide ERP (enterprise resource planning) systems a decade or more ago, the AEC space is in the early innings of its own digital transformation. Many of the legacy ERP systems used by corporations today are difficult, if not impossible, to integrate with cutting-edge AI solutions. These clunky legacy systems will either need to be replaced or augmented for the future, and both options are extremely time-intensive and expensive.

This presents a unique opportunity for the unadopted AEC industry to leapfrog other sectors. With no company-wide ERP software to replace, construction leaders can more easily deploy ERP systems and data management frameworks that are purpose-built for the AI era.

Creating an ERP system does not happen overnight. AI-enabling ERP startups focused on the AEC space are beginning as platform plays with open APIs for easy tech stack consolidation (aka middleware), and will gradually grow into fully encompassing enterprise platforms via internal product development and/or strategic M&A.

Nymbl sees compelling investment opportunities in emerging startups that offer a platform to integrate disparate point solutions via open APIs. These startups solve the problem of information silos, and are well-positioned for AI functionality development as a result of their data consolidation approaches.

The convergence of AI-driven economic transformation and construction’s digital evolution presents a pivotal opportunity for the sector to accelerate into the future. But seizing this opportunity requires overcoming a decades-old hurdle in the AEC space: digitally managing and capturing structured data.

Unlocking Legacy Data

Access to diverse, high-quality digital data remains the biggest hurdle for AI innovators in the AEC space. We believe that startups that effectively address these problems present attractive investment opportunities.

Corporate leaders in the construction space have troves of legacy data stored in disparate sources: PDFs, images, spreadsheets, CAD drawings, blueprints, etc, which have the potential to be digitized, "sanitized" and made ready for AI models. New solutions are emerging that attempt to transform these complex documents into usable, structured data ready to train AI.

Startups like MindsDB, Reducto, Unstructured, Multimodal, and others were created to make unstructured data LLM-ready. Most of these solutions were not built specifically for the construction industry, but could be seen as valuable to both AI startups in construction and their corporate clients. However, we believe that these technologies will either be ineffective or too expensive for building AI-ready data sets, and as such, do not view these types of startups as investable.

Improving the way data is collected, revised, stored, and managed moving forward will be more important for the development of AI in this sector than attempting to resurrect old sources.

Investable Opportunities in Real-Time Data Collection

The construction industry is increasingly adopting emerging startups that leverage IoT, sensors, and computer vision for the most comprehensive real-time project information.

Innovators are also enhancing data pools with simulated data, which could be the intermediate fix to this sector's shortage of usable digital inputs for AI enablement.

We see value today in investing in these real-time data-generating startups, which offer the construction industry unprecedented project visibility today, and a consistent stream of quality, structured data that AI technology can use to optimize project performance.

Robotics

3D construction printing and industrialized construction startups (aka prefab/modular building) that are AI-enabled have looked to avoid reasoning concerns by replacing human elements that previously required a level of professional judgment with predetermined robotic processes.

However, these technologies have run into endless issues of their own, including vertical integration issues and economic viability.

Other innovators are looking to introduce humanoid robots to the jobsite, and these are yet to achieve the level of sophistication and reliability to meaningfully address labor shortage.

We believe that robotics is a valuable tool for construction when it automates simple repetitive tasks such as lifting, monitoring, inspecting, painting, measuring, marking, testing, or similar tasks that do not require reasoning or judgment. The same applies to robots that enhance worker safety.

Nymbl believes that robotic technologies focused on lifting, unloading, surveying, inspecting, coating, automated retrofitting, monitoring, offsite construction, and worker safety are areas of opportunity startup investments in the built environment.

The Specialty AEC Players with an Initial AI Edge

AEC players such as HVAC technicians, electricians, mechanical contractors, and BIM managers have a data edge. Many of these specialized contractors have some of the most comprehensive datasets in construction, including schematics, specifications, designs, fabrication and installation procedures, project performance, and financial information.

Simultaneously, the need for specialty contractors is growing rapidly in response to increasing project complexity, driven by surging construction in sectors like hyper-scaling data centers (forecasted to grow 22% in 2025), smart facilities, grid upgrades, and advanced manufacturing facilities. Notably, manufacturing construction alone tripled since 2020, according to AIA Consensus Forecasts.

The accumulation of these structured data points has opened the door to generative AI functionality that reduces errors and allows construction professionals to focus on more creative, important, and engaging aspects of a project.

Startups built around MEP or specialty contractors are better positioned to leverage AI in the medium-term, and represent a good niche for venture capital investments.

Conclusion

There are plenty of investable startup opportunities in the world of ConTech AI, and understanding how this niche could progress is critical to good investment decisions.

The construction sector is well-positioned in the economic shift toward AI-driven productivity enhancements. The foundational data generation and management will be the first step. Aligning the value chain via AI technology will be the ultimate goal.