Smart Construction: How AI and Machine Learning Will Change the Construction 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.”

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.

Know What’s Next

Discover proven strategies to accelerate innovation with my latest book The Anticipatory Organization. Follow this link for a special offer.

Shape the Future–Before Someone Else Does It For You!

Artificial Intelligence: Disruption or Opportunity?

AArtificial intelligence (AI), one of twenty core technologies I identified back in 1983 as the drivers of exponential economic value creation, has worked its way into our lives. From Amazon’s Alexa and Facebook’s M to Google’s Now and Apple’s Siri, AI is always growing — so keeping a closer eye on future developments, amazing opportunities, and predictable problems is imperative.

IBM’s Watson is a good example of a fast-developing AI system. Watson is a cognitive computer that learns over time. This cognitive AI technology can process information much more like a smart human than a smart computer. IBM Watson first shot to fame back in 2011 by beating two of Jeopardy’s greatest champions on TV. Thanks to its three unique capabilities — natural language processing; hypothesis generation and evaluation; and dynamic learning — cognitive computing is being applied in an ever-growing list of fields.

Today, cognitive computing is used in a wide variety of applications, including health care, travel, and weather forecasting. When IBM acquired The Weather Company, journalists were quick to voice their amusement. However, IBM soon had the last laugh when people learned that the Weather Company’s cloud-based service could handle over 26 million inquiries every day on the organization’s website and mobile app, all while learning from the daily changes in weather and from the questions being asked. The data gleaned from the fourth most-used mobile app would whet the appetite of the permanently ravenous IBM Watson and enable IBM to increase the level of analytics for its business clients.

Weather is responsible for business losses to the tune of $500 billion a year. Pharmaceutical companies rely on accurate forecasts to predict a rise in the need for allergy medication. Farmers’ livelihoods often depend on the weather as well, not only impacting where crops can be successfully grown but also where the harvest should be sold. Consider the news that IBM followed its Weather Company purchase by snapping up Merge Healthcare Inc. for a cool $1 billion in order to integrate its imaging management platform into Watson, and the dynamic future of AI becomes more than evident.

The accounting industry can benefit from this technology, as well. When I was the keynote speaker at KPMG’s annual partner meeting, I suggested that the company consider partnering with IBM to have Watson learn all of the global accounting regulations so that they could transform their practice and gain a huge advantage. After doing their own research on the subject, the KPMG team proceeded to form an alliance with IBM’s Watson unit to develop high-tech tools for auditing, as well as for KPMG’s other lines of business.

Thanks to the cloud and the virtualization of services, no one has  to own the tools in order to have access to them, allowing even smaller firms to gain an advantage in this space. Success all comes back to us humans and how creatively we use the new tools.

IBM’s Watson, along with advanced AI and analytics from Google, Facebook, and others, will gain cognitive insights mined from the ever-growing mountains of data generated by the Internet of Things (IoT) to revolutionize every industry.

Advanced AI is promising almost limitless possibilities that will enable businesses in every field to make better decisions in far less time. But at what price? Many believe the technology will lead directly to massive job cuts throughout multiple industries. and suggest that this technology is making much of the human race redundant.

It is crucial to recognize how the technological landscape is evolving before our eyes during this digital transformation. Yes, it is true that hundreds of traditional jobs are disappearing, but it’s also important to realize the wealth of new roles and employment opportunities arriving that are needed to help us progress further.

The rise of the machines started with the elimination of repetitive tasks, such as those in the manufacturing environment, and it is now moving more into white-collar jobs. The key for us is not to react to change, but to get ahead of it by paying attention to what I call the “Hard Trends” — the facts that are shaping the future — so that we can all anticipate the problems and new opportunities ahead of us. We would do well to capitalize on the areas that computers have great difficulty understanding, including collaboration, communication, problem solving, and much more. To stay ahead of the curve, we will all need to learn new things on an ongoing basis, as well as unlearn the old ways that are now holding us back. Remember, we live in a human world where relationships are all-important.

We need to be aware of the new tools available to us, and then creatively apply them to transform the impossible into the possible. By acquiring new knowledge, developing creativity and problem-solving skills, and honing our interpersonal, social, and communication skills, we can all thrive in a world of transformational change.

Are you reacting to change or paying attention to the Hard Trend facts that are shaping the future?

If you want to anticipate the problems and opportunities ahead of you, pick up a copy of my latest book, The Anticipatory Organization.

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.

Shaping the Future of A.I.

One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google’s DeepMind beat the world’s best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM’s Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today’s human jobs in the years ahead.

Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more.  

As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics. If we don’t have common definitions for and understanding of what we are talking about, it’s likely we will create an increasing number of problems going forward. With that in mind, I will try to add some clarity to this complex subject.

Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.  

Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.

Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learning and/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.

Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement.  For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.  

Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.   

A.I. advances represent a Hard Trend that will happen and continue to unfold in the years ahead. The benefits of A.I. are too big to ignore and include:

  1. Increasing speed
  2. Increasing accuracy
  3. 24/7 functionality
  4. High economic benefit
  5. Ability to be applied to a large and growing number of tasks
  6. Ability to make invisible patterns and opportunities visible

Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans. Yes, A.I. — like all technology — will take the place of many current jobs. But A.I. will also create many jobs if we are willing to learn new things. There is an old saying “You can’t teach an old dog new tricks.” With that said, it’s a good thing we aren’t dogs!

Start off The New Year by Anticipating disruption and change by reading my latest book The Anticipatory Organization. Click here to claim your copy!