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Smart Construction: How AI and Machine Learning Will Change the Construction Industry

These days, seemingly everyone is applying Artificial Intelligence (AI) and machine learning. I have written about disruptions in the manufacturing industry, such as Industry 4.0, while illustrating the Hard Trends that indicate where improvements will be made in the future.

The construction industry, which makes up 7% of the global workforce, should already have applied these technologies to improve productivity and revolutionize the industry. However, it has actually progressed quite slowly.

Growth in the construction industry has only been 1% over a few decades while manufacturing is growing at a rate of 3.6%. With the total worker output in construction at a standstill, it is no surprise that the areas where machine learning and AI could improve such statistics were minimal. Yet, those technologies are finally starting to emerge in the industry.

Artificial Intelligence (AI) is when a computer mimics specific attributes of human cognitive function, while machine learning gives the computer the ability to learn from data, as opposed to being specifically programmed by a human. Here are ten ways that AI and machine learning will transform the construction and engineering industries into what we’ll call “smart construction.”

  1. Cost Overrun Prevention and Improvement

Even efficient construction teams are plagued by cost overruns on larger-scale projects. AI can utilize machine learning to better schedule realistic timelines from the start, learning from data such as project or contract type, and implement elements of real-time training in order to enhance skills and improve team leadership.

  1. Generative Design for Better Design

When a building is constructed, the sequence of architectural, engineering, mechanical, electrical, and plumbing tasks must be accounted for in order to prevent these specific teams from stepping out of sequence or clashing. Generative design is accomplished through a process called “building information modeling.” Construction companies can utilize generative design to plot out alternative designs and processes, preventing rework.

  1. Risk Mitigation

The construction process involves risk, including quality and safety risks. AI machine learning programs process large amounts of data, including the size of the project, to identify the size of each risk and help the project team pay closer attention to bigger risk factors.

  1. More Productive Project Planning

A recent startup utilized 3D scanning, AI and neural networks to scan a project site and determine the progress of specific sub-projects in order to prevent late and over-budget work. This approach allowed management to jump in and solve problems before they got out of control. Similarly, “reinforcement learning” (machine learning based on trial and error) can help to collate small issues and improve the preparation phase of project planning.

  1. More Productive Job Sites

Professionals often fear machines will replace them. While intelligent machines will take over first repetitive and eventually more cognitively complex positions, this does not mean a lack of jobs for people. Instead, workers will transition to new, more fulfilling and highly productive roles to save time and stay on budget, and AI will monitor human productivity on job sites to provide real-time guidance on improving each operation.

  1. Safety First

Manual labor not only has the potential to be taxing on the body, but also to be incredibly dangerous. Presently, a general contractor is developing an algorithm that analyzes safety hazards seen in imagery taken from a job site, making it possible to hold safety briefings to eliminate elevated danger and improve overall safety on construction sites.

  1. Addressing Job Shortages

AI and machine learning have the capacity to plot out accurate distribution of labor and machinery across different job sites, again preventing budget overruns. One evaluation might reveal where a construction site has adequate coverage while another reveals where it is short staffed, thereby allowing for an efficient and cost-effective repositioning of workers.

  1. Remote Construction

When structures can be partially assembled off-site and then completed on-site, construction goes faster. The concept of using advanced robots and AI to accomplish this remote assembly is new. Assembly line production of something like a wall can be completed while the human workforce focuses on the finish work.

  1. Construction Sites as Data Sources

The data gathered from construction sites and the digital lessons learned by AI and advanced machines are all tools for improving the productivity of the next project. In this way, each construction site can contribute to a virtual textbook of information helpful to the entire industry.

  1. The Finishing Touches

Structures are always settling and shifting slightly. It would be beneficial to be able to dive back into data collated by a computer to track in real time the changes and potential problems faced by a structure — and AI and machine learning make this possible.

Given the inevitable changes on the horizon, and the potential for costs to drop up to 20% or more with increased productivity, professionals in the construction industry must pay attention to Hard Trends, become more anticipatory, and ultimately learn to turn disruption and change into opportunity and advantage.

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Best Practices Culture Entrepreneurship Industries Leadership Skills Technology

Will A.I. Disrupt Your Profession?

Artificial intelligence (A.I.) is a technological advance for humankind that has some people excited and others terrified of what is to come. The main concern is rooted in what A.I. will do to jobs, and how we as human beings will be affected by changes in digital and mechanical techniques.

A.I. and other new forms of autonomous machine function are in the process of transforming our personal and professional lives, and this represents a Hard Trend that will happen and a subject I’ve discussed for decades now. We are just starting to see some incredible progression in the A.I. space, giving us a chance to pre-solve problems involved in real-world applications of A.I.

But while function is one thing, the newfound transformation we’ve watched come to fruition is coming from machine learning, a subset of A.I. that enables machines to become better at tasks that were previously dependent on human intelligence. With advances in a machine’s capability to think and learn like people, it’s easier than ever to pre-program physical functions so A.I. can take over menial or mundane tasks. Take, for example, a study conducted by legal tech startup LawGeex, which challenged 20 experienced lawyers to test their skills and knowledge against an A.I.-powered system the company built.

A lawyer is not often considered replaceable by technology or artificial intelligence. In this challenge, the task was to review risks contained in five nondisclosure agreements — a simple undertaking given the group of legal professionals, which included associates and in-house lawyers from Goldman Sachs, Cisco, and Alston & Bird, as well as general counsel and sole practitioners. This lineup should easily have triumphed over an A.I.-powered algorithm, right?

Wrong.

As a matter of fact, the study revealed that the A.I. system actually matched the top-performing lawyer for accuracy, as both achieved 94%. As a group, the lawyers managed an average of 85%, with the worst performer scoring a 67%.

But what about the speed of those decisions? When reviewing the nondisclosure agreements, the A.I. system far outpaced the group, taking just 26 seconds to review all five documents, compared to the lawyers’ average speed of 92 minutes. That is a tremendous spread when compared to the near-perfect accuracy the algorithm performed at in that time! The fastest review time of a single lawyer in the group was 51 minutes — over 100 times slower than the A.I. system! And the slowest time was nearly a standstill pace, as it clocked in at 156 minutes.

While reviewing documents is just one of several parts of the job of a lawyer, this data further proves the Hard Trend that I implore everyone to pay attention to in the years to come. Artificial intelligence is here to stay, and by using machine learning and deep learning techniques, new A.I. systems are learning how to think better and better every day. So the question remains: Are you anticipating how A.I. can be used to automate tasks and do things that might seem impossible today — in other words, disrupt your industry? Are you starting to learn more about A.I. so that you can become a positive disruptor rather than become the disrupted?   

For now, according to consultants, the fact remains that 23% of legal work can be easily performed using artificial intelligence; however, there are many aspects of a lawyer’s job, the obvious example being providing an emotional and compelling closing argument in court, that are currently beyond the capabilities of algorithms. While that may be the case today, what’s next? Using methods that I discuss in my latest book, The Anticipatory Organization, you can learn how to become an anticipatory thinker and be more entrepreneurial in the ways you apply A.I. technology to your profession.

Take the example of Alexa, which is utilized in an ever-growing number of applications, from ordering groceries to playing our favorite song during dinnertime. This device, enabled by A.I., has learned our routines and how to serve us better each day by listening to us ask it questions or give it tasks to accomplish.

Netflix and Spotify media streaming services are using A.I. to learn what we like to listen to or watch, and then, using this knowledge combined with their own databases, they can quickly suggest other songs or shows we may also enjoy. Over time they increasingly learn to understand the dynamics of what we like, recognizing our patterns enough to suggest new things to us we will most likely enjoy — very much like a best friend would introduce us to a new music group.

These are just two examples of many A.I.-enabled services that have been integrated into our lives, yet it was not too long ago that applications like these would have been viewed as an impossibility. In a relatively short amount of time they have become second nature in our lives. If A.I. can quickly accomplish a lawyer’s task today, then it can also learn how to accomplish many tasks in industries once thought untouchable by automation and machine learning, such as medicine, finance and design.

As an entrepreneur, it is increasingly important to understand what A.I. can do to create  business value. A.I. is presently forecast to reach nearly $4 trillion by 2022. Reacting to this opportunity will only keep you behind and disrupted. It’s time to learn to become anticipatory leaders in our fields, solving problems before they happen, and elevating our thinking to actively shape a positive future for ourselves and others.

If you would like to learn more about how you can better anticipate transformation in the professional world and developments in artificial intelligence, then be sure to pick up my latest book, The Anticipatory Organization. Let me help you take your career to the next level and remain indispensable in an ever-changing technological frontier.

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Best Practices Growth Management Skills Technology

Cognitive Performance — The Human Side of Cognitive Computing

By Daniel Burrus and Neil Smith

(In this blog series on how elevating cognitive performance is a game changer for organizations, I’ve invited Neil Smith, CTO at Think Outcomes, to join me in writing on this important topic due to his expertise and the cognitive performance software his firm has created.)

As a leader, your responsibilities exist on the cognitive side of your business, where you think critically, make complex decisions, collaborate among your network, communicate with your stakeholders, comply with regulations and monitor uncertainties, to name a few. These activities represent your cognitive work. Given today’s rapid growth in organizations using AI, you are most likely exploring the current state of cognitive computing and how it can help you with your responsibilities beyond the collection, storage and retrieval of data through computers as data appliances. You are also considering how the highly marketed definitions of cognitive computing from IBM Watson can help you?

How cognitive performance computing can help you as a critical thinker in business  

As we meet with leaders in business, management consulting and government around the globe, they, too, are wondering how they can leverage cognitive computing for their work.

These professionals occupy roles in the cognitive operations of their organizations, where there is a constant set of unknowns. Senior executives are responsible for closing their gaps in knowledge about the future state of their businesses. Trusted advisors must do the same for their clients. As standard setters must set guidelines as best practices, regulators create the laws that require cognitive compliance from organizational leaders. The next evolution of cognitive computing addresses their cognitive responsibilities — i.e., helping executives and management consultants work through their risk-reward trade-offs in context to situational context and criteria, while standard setters and regulators build the required thresholds into the thinking of organizational leaders. This segment of cognitive computing is known as cognitive performance. Enhancing cognitive performance improves critical thinking, stakeholder communications, decision making, advisory collaboration, monitoring of uncertainties and cognitive compliance. Cognitive performance software extends the human mind with computing to help humans learn, compete and grow the impact of their own intellects.

Leverage cognitive computing for what your mind can’t do

Where machines continue to learn role-based tasks, it’s necessary for humans to work in harmony with machines to better navigate through areas of complexity and uncertainty. For business to advance, enhancing human performance needs to be a strategic imperative for business executives rather than accepting the status quo. With stronger human performance, culture will transform with faster cognitive insights and foresights to create an environment of deeper human learning. Without higher performance from our minds, we’re all operating the same way — figuring out situations on our own in a sequential order and learning from hindsight. Computers, available data and applying the four pillars of the Anticipatory Model will help us learn with foresight. To compete in industry today, we need cognitive computing to do more for our minds than provide data-driven insights. We need cognitive computing to assist the cerebral processing in our minds, help us gain perspective and put us in a position to make high-fidelity decisions. The human computers on top of our shoulders need turbochargers. As visual learners, wouldn’t it be great if we made our thoughts visual through thinking patterns to make the most of our risk-reward trade-offs! That would change the velocity of decision making and stakeholder communications! To move the needle much further in business requires a focus on the performance of human minds across teams — from senior executives and mid-level managers in business to those in their supply chains, consulting circles, insurance relationships, investor partnerships and professionals across the sciences, among others. Enhancing cognitive performance needs to be a strategic imperative to gain a competitive advantage.

Improve reasoning and judgment

Professionals know they rely on instinct and gut reactions all too often. Their ability to process information in their minds or to understand someone else’s viewpoint is often challenging. It’s a human limitation, and that’s where responsibility-based computing can help. The batting averages of professionals in reasoning and judgment must improve in today’s business world. The cognitive era is the time to break through these limitations and leverage computing to extend the capabilities of our minds.

Digital advancement in cognitive performance

Many teams responsible for operating processes on the transactional side of their businesses are now involved in digital transformation. They are automating their manual activities using AI, question-answering systems, big data and other software. Within the cognitive side of their businesses, the cognitive operating processes are ready to digitally advance as well. Those who spend time prioritizing and enhancing the cognitive performance of their teams will leapfrog their competitors as they will strengthen human performance.

The benefits of exponential advances in computing must now be applied to human performance. There’s a long, bright road ahead for the performance of the human mind. We’re just at the beginning.

Learn how to elevate your planning, accelerate innovation and transform results with The Anticipatory Learning System and how to maximize the cognitive performance of your team with Cognitive Performance Software.