We’re way past the point of buzzword with artificial intelligence in engineering and construction. It’s no longer just an idea, but a make-or-break business reality.
The big players in this sector are already hyper-aware of AI as a business imperative. They know all about its benefits, especially the competitive advantages to be gained. A recent survey showed that 84% of enterprises believe investing in AI will give them a competitive edge.
Unfortunately, only a handful of players are ready to introduce AI right now. A 2018 report by McKinsey revealed that despite the proven ROI of AI for engineering and construction firms, only a few are currently in a position to implement them. Most lack the personnel, processes, and/or tools.
At Enstoa, we talk to a lot of companies at various stages of readiness and a few distinct themes have emerged. Here’s my best advice for how to get AI-ready, based on where you are now.
The vast majority of business leaders already understand that AI, machine learning, robotics, and IoT are critical for the future. They also know, however, that they can’t boil the ocean. Resources are limited, so they feel some natural reluctance about taking on a big new project.
If this describes your organization, a good initial step can be just to focus first on moving over to a holistic and integrated data environment. Holistic data is a prerequisite for AI, robotics, AND IoT. So, for companies that aren’t quite ready to implement AI yet, creating a holistic data environment is a savvy first move.
A holistic data environment gets all your company’s information out of documents and into a single source of truth that’s always up to date. Suddenly it’s possible to get real-time visibility into budgets and schedules. It also makes your organization more nimble and future proof because it puts data at the center instead of static documents or expensive software.
Another incredibly strategic thing to do at this stage is to ensure your organizational model is up to date. Intensely hierarchical organizational structures can make it more difficult to introduce change. Decentralizing a bit can help to ensure that when you’re rolling out new models and ways of doing things, team members will embrace new ways of working.
It’s understandable that some traditional firms don’t believe that their core services can be shaken in the short or even medium term by something as new as AI.
The reality we’re seeing, however, is that as with systems integrations, the longer these organizations wait, the more they risk lagging behind and having to play catch up while startups eat into their client base.
A recent Harvard Business Review article warned,
‘“By the time a late adopter has done all the necessary preparation, earlier adopters will have taken considerable market share — they’ll be able to operate at substantially lower costs with better performance. In short, the winners may take all and late adopters may never catch up.”
If your company is waiting for AI to become more mainstream before considering a major implementation, you should still at least test the waters now or you’ll risk missing the boat entirely. Start in an area that’s particularly strategic for your organization or one where there’s some especially low hanging fruit.
For example, if your organization does a high volume of annual building, or if your business model is to compete on price, you could test partial automation of early-stage feasibility assessments, which tend to be a costly, time-consuming part of the construction process. If HR is an unusually expensive area of your business, you might look at testing AI’s ability to automate portions of the recruiting and onboarding process.
Of course, niche applications of AI can only introduce so much competitive advantage, so once you’ve proven there’s ROI there, use that to push for a more comprehensive approach.
If you’re in the small minority of business leaders who don’t yet believe investing in AI will lead to competitive advantages, try having a few high-level discussions with subject matter experts. I haven’t had a single conversation with AI skeptics that didn’t end in at least a few pilot applications of AI technology. You might be surprised!
Some of the niche and pilot applications of AI we’re lately seeing a lot in engineering and construction include schedule optimization, team optimization, cost estimation, and sensor and camera footage analysis and pattern-finding.
Of course, to us, the benefits of AI are already so clear that a more comprehensive approach is generally recommended, but small scale pilots are great because they tend to lead to bigger things down the road.
Again, the best place to start is to focus on the areas that are most strategic for your business. Once you see your costs drop significantly there, who knows? You may just find yourself extolling the virtues of a holistic data environment and running as much AI off of it as possible.
Curious about how AI might be able to drive down costs for your construction or engineering business? Get in touch today.