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Picture this: It’s Saturday morning, and you’ve been waiting for this moment all week. A crush of deadlines had you working late hours every night, and it feels like you’ve barely left the office. But your projects are finished and you’re thrilled to have 48 hours of downtime in your home. 

Your house isn’t just any old house. It’s a smart home. Before you woke, smart blinds rose over your windows, letting in just the right amount of warm morning sunlight. As you step out of bed, you’re not too hot or too cold because your thermostat monitors your house to maintain an optimal temperature at all times. Stopping at the bathroom to brush your teeth, your toothbrush gathers data about how well you target those hard-to-reach molars, sending feedback through a mobile app letting you know if you’re brushing too hard or haven’t spent enough time in a particular quadrant. 

A long time coming, smart home technologies built using Internet of Things (IoT) devices and systems are already widely available, with countless new systems on the horizon. But the old saying “There’s no such thing as a free lunch” is at play here. There’s no such thing as a smart home that doesn’t come with privacy and security trade-offs. 

After all, the convenience of always-on devices is driven by always-on data collection. Each device and component in a smart home is recording data about how you live your life round-the-clock. And as we’ll discuss in this article, many smart home apps and products are alarmingly insecure. 

Confronting the risks of the Internet of Things

IoT promises that in the near future, we’ll be able to move seamlessly from the office to social events to our homes. A network of smart devices and mobile apps will track everything from our locations to our heart rates and will optimize our environments to maximize our comfort. Having a voice-activated assistant goes without saying, but then there’s the security system you can control no matter where you are, the fitness tracker that tells your thermostat what temperature to make the house when you come home from your daily run, and the refrigerator that automatically adds items to your grocery list based on its contents. 

But there are risks. IoT devices collect massive amounts of personal data. In 2016, 83% of consumers surveyed by TransUnion said they worry about identity theft and cyber threats, and the use of IoT devices exacerbates the risk of becoming a victim.  

To grasp the scope of IoT cybersecurity challenges, think for a moment about the Equifax data breach that impacted at least 145 million Americans. Credit agencies collect an incredible amount of personal financial information, so a breach of this magnitude poses grave risks for identity theft. But just imagine the types of data a network of IoT devices in your home can collect. Those apps and platforms will contain not only payment information, but they’ll record information on when you and your family are in your house, when you go to sleep at night, the types of food you keep in the house, and other very personal – and very telling – details. 

That doesn’t mean we should avoid smart home products or shy away from connective technologies. It means it’s imperative to know the risks and how to protect ourselves, especially as smart homes continue to evolve. 

The beauty of the smart home is that, as in so many other areas of our lives, artificial intelligence is taking the drudge work off our hands. It’s making life a little easier, a little healthier, a little more comfortable. And by augmenting our daily routines, it’s giving us more time to enjoy our homes and spend time with our families. After all, the time we spend cooking, locking up at night, and making grocery lists adds up. Stay-at-home mothers, for instance, spend 23 hours per week – nearly 3 full-time work days – just on housework. That doesn’t even include time spent with their children. Saving time by automating chores like laundry, doing dishes, vacuuming and window-washing with the aid of a smart home device allows them to give more time to their kids (not to mention take a few minutes for themselves). 

Although the advantages of smart homes are many, however, we should be clear on what we’re giving up in exchange for those conveniences. Setting aside security concerns, which we’ll talk about in a minute, there’s the question of privacy. The more smart devices we use, the more information about our private lives we give away. 

Take smart water meters. You probably don’t think much about the information your water meter records about your daily activities, if you give it any thought at all. But utility companies, law enforcement, and even appliance companies can gather surprisingly specific information about what you’ve been doing in the privacy of your own home just based on water meter readings. They can tell which day of the week is laundry day in your household or whether you’ve been running the dishwasher more often than usual. Police in Bentonville, Ark. even used data from a smart water meter to solve a murder case. 

Most people aren’t up to such nefarious acts, and their water usage probably doesn’t reveal many interesting insights. Nor do we want information on our daily dishwashing habits to be publicly available. 

Thinking about privacy in smart homes

As we become increasingly reliant on IoT devices and voice-activated assistants, we must consider who has access to data about our private behaviors. When you’re requesting songs, searching for products, and making appointments using these technologies, you become more aware of the digital picture you’re creating about your home life. And while prominent voice assistant platforms claim only to work when activated by a specific phrase, IoT security has proven shaky in the past, leading some experts to recommend that we err on the side of caution when around these devices. 

Consumers became more aware of the consequences of sacrificing privacy for convenience earlier this year when iRobot’s CEO suggested that the company could sell floor plans gathered by its Roomba devices to third-party buyers. Presumably, those third parties could use these maps of users’ homes in advertising and product development campaigns. Though iRobot assured consumers that it respects their privacy, the issue highlighted just how easily IoT devices can expose intimate details of our lives. 

Even highly personal products such as baby monitors pose security risks. Researchers found serious vulnerabilities in nine prominent baby monitor models, all of which could be exploited to access videos and images from within the home. Perhaps more frightening, Internet-connected children’s toys were also identified as being vulnerable to hackers. Few things are more chilling to a parent than the idea that an ill-intentioned stranger could reach their children through something as innocent as their toys.  

Internet of Things security is a real concern across many industries. In fields such as medical care, there are many challenges to data security in part because old systems simply aren’t up to modern challenges. Developers and engineers will need to build new systems to appropriately address increasingly sophisticated cyber threats. 

The fact that we know that data-gathering devices increase our security risks is actually to our advantage. The baby monitor research was predictive rather than reactive, as was the smart toy hacking tests. White hat hackers are also testing the technology used in computer-assisted vehicles to identify vulnerabilities so engineers can close those gaps before cyber attackers can exploit them. Such preventive and diagnostic measures can help mitigate existing risks, and current research will help companies design more secure products in the future. 

Embracing IoT with an ounce of caution 

Smart homes offer many exciting benefits – and with our increasing reliance on smartphones and other devices, they’re an inevitability. We know there will be growing pains as we transition to this ever-more-connected existence. But if we understand what we’re giving up in exchange for convenience and how we can protect ourselves against cyber threats, we can enjoy the growing range of IoT products with minimal headaches. 

Here’s a fun experiment: Make a mental list of everyone you know. OK, maybe not everyone. But at least the core group of people with whom you interact every day. Friends, college roommates, romantic partner – anyone who qualifies as a favorite in your smartphone contact list. 

Now think about what they do for a living. Chances are there’s a programmer, or a social media manager, or a UX designer. Throw in a cloud security engineer, and you could form your own startup. 

While these job titles are the norm today, many of them didn’t exist 15 years ago. The Internet created a wave of new industries that transformed the way we think about work. As more and more companies transitioned online, the demand for capable software engineers and web designers swelled with no end in sight. Programming remains one of the most desired job skills in today’s economy, and digitally-savvy creatives are in demand as well. One report found that 7 million U.S. job openings in 2015 required some coding ability and that the programming field is growing 12% faster than the market average. 

But the Internet didn’t just open the door to new career opportunities. It set in motion a trend toward a skills-based, value-driven job market. For decades, conventional wisdom said that earning a college degree all but guaranteed you a well-paying job. While a diploma is still a valued commodity, some employers have begun emphasizing computer science skills over degrees in their hiring decisions. Coding skills can also boost your chances of landing non-tech jobs such as customer support, content marketing, and technical writing. 

That trend may soon extend to other fields. Just as the Internet created new professional roles, so, too, will artificial intelligence. The demand for experts in machine learning, natural language processing, and complex mechanical skills will rise as AI and robotics technologies become more prolific. But opportunities in other areas will as well. For instance, companies will increasingly employ humans to tag data used to train machine learning systems. 

Accenture researchers have identified three categories of new jobs AI will create: trainers, explainers, and sustainers. These will include responsibilities such as training an algorithm to respond empathetically to customer queries and explaining the technology’s impact on a company’s overall business outcomes. 

While formal computer science training will be necessary for some of these roles, such as those in AI ethics compliance, the researchers suggested that others may not require a degree. Empathy trainers, for instance, will be valued for their intuition and keen understanding of human interactions than for their prestigious academic backgrounds. “The effect of many of these new positions may be the rise of a ‘no-collar’ workforce that slowly replaces traditional blue-collar jobs in manufacturing and other professions,” the researchers wrote in the MIT Sloan Management Review. 

The no-collar workforce goes back to the future

Conversations are growing around how best to educate students for the evolving workforce, and many experts agree that countries such as the U.S. and the U.K. must do more to prepare for the coming changes. With AI handling routine tasks, people will need to develop additional qualities like creativity, adaptability, and interpersonal skills. 

This has implications in everything from what’s taught at the elementary and high school levels, to how recent college graduates and seasoned professionals embrace lifelong learning. Many of the most in-demand skills will be those that are uniquely human. And to stay competitive, we’ll need to amplify the emotional and intuitive traits that distinguish us from increasingly competent machines. 

“The new smart will be determined not by what or how you know but by the quality of your thinking, listening, relating, collaborating, and learning,” one professor wrote in Harvard Business Review. “Quantity is replaced by quality. And that shift will enable us to focus on the hard work of taking our cognitive and emotional skills to a much higher level.”

On that note, let’s look at the core skills you’ll need to compete in the no-collar workforce of the (near) future: 

1. Communication 

If you’ve ever been involved in a misunderstanding with a boss or co-worker, you know how valuable workplace communication is. Not only do outstanding problems create tense, uncomfortable office environments, it also costs American businesses nearly $360 billion in paid hours, or 385 million workdays. Employees who possess strong communication abilities and can de-escalate conflicts will be increasingly valuable as we transition to the new workforce. 

Being “new smart” will mean being able to walk back your own ego and help others do the same. Change can be difficult, and those who offer calm, rational, and clear communication will be those whose contributions are most valued as we navigate this brave new world. 

As entire departments adapt to automated or AI-assisted programs, a strong communicator might be tapped to oversee the transition. This person would need to be familiar with the programs, but more importantly, they would need to be able to explain why the company had opted to implement these platforms. Not only would they guide people toward using the software, they’d offer empathetic reassurance when colleagues felt frustrated or threatened by the new technology. Workers can become very set in their ways, and good communicators can serve as champions for both the people and the technology. 

2. Adaptability 

AI platforms such as machine learning and NLP improve over time based on inputs from humans. However, they’re not able to respond effectively to unpredictability and volatility. That’s why adaptability will remain a key professional asset. In an analysis of AI’s likely impact across 30 different industries, Quartz found that the more unpredictable a field is, the more valuable and competitive a skill human adaptability will remain. 

Positions such as conflict mediators or workplace counselors demand a readiness to respond to unpredictability at all times. One can imagine that an AI algorithm could analyze employee personality assessments and responsibilities to optimize team dynamics and working environments. But if conflicts break out within a team and tempers flare, a mediator may need to defuse the situation. They’ll need to bring the conflicted team members together, quickly understand what caused the problem, and respond in real-time to the shifting dynamics to ensure that the company doesn’t lose money or progress over interpersonal challenges. 

3. Emotional intelligence

AI’s strengths lie in data collection and processing, and smart computers are increasingly able to offer recommendations for business strategies. However, AI platforms cannot offer comfort after a dire medical diagnosis, or inspire people to rally around new ideas, or deftly defuse a tense business meeting. People who can reassure and persuade will find that their skills are highly desired across a range of industries. 

Although most people accept that AI and automation will impact their jobs, they’re not entirely clear on how and are understandably nervous about what those changes mean. Emotionally intelligent super-managers will be able to steer their companies through uncharted waters without seeing their entire employee base jump ship. They can reassure employees about their places within the organization, guide them toward skills development programs that suit their abilities, and help them adapt to the new economy. 

4. Complex problem solving 

The World Economic Forum ranks complex problem solving as the top skill that employers will seek by 2020. The term refers to the ability to solve novel, open-ended problems in complex, settings, a key cognitive asset in a rapidly changing workforce. As new technologies and AI platforms are introduced, employers will seek workers who can embrace new ways to solve problems rather than clinging to old processes. 

Complex problem solving may be especially useful in areas such as product development. An employee who can shift easily from technical discussions about software or product functionality to debates about human psychology will be able to make valuable contributions to those conversations. By combining both of these aspects, they’ll leverage technology to better serve their customers’ needs. 

5. Critical thinking 

As AI helps companies gather more data than they’ve ever had access to before, humans will need to extract insights from that information and determine what those mean for the company. Being charismatic and communicative will be important as millions of workers enter the age of AI. But the ability to critically assess data sets and make difficult decisions will be equally essential. 

That ability will be a top priority across all industries, because every company and organization is dealing with massive quantities of information. The key to critical thinking is looking beyond the numbers and seeing the human story behind the data. 

The core “no-collar” skills workers will need in the coming years center around some of our most human traits. Although we’ll all likely learn to code at some point and will be called upon to use technology in ways we never imagined, our top priority must be honing the emotional and cognitive skills that come from self-reflection and humble introspection. 

Northern California is famous for many things, but two in particular stand out: health-conscious lifestyles and Silicon Valley, the global epicenter of technology and innovation. To learn more about both of these topics, a group of European innovators visited the Bay Area on a “design for life” tour. Entefy was one of the destinations on their tour.

The inspiration for the tour was the need to bridge today’s rising awareness of healthy living with novel new products and services that support and enrich people’s lives. On the itinerary were stops at local universities and farmers markets, including a stop at Stanford University’s d.school to attend a lecture by Barry Katz, an Entefy advisor and Fellow at IDEO.

The lively and friendly meeting at Entefy kicked off with a warm welcome from our team. Our CEO Alston Ghafourifar then led a discussion of Entefy’s philosophy of life-compatible technology. He shared some behind-the-scenes insights about starting and growing a technology company in Silicon Valley’s hyper-competitive environment. 

The Q&A session covered issues tied to the guests’ backgrounds in industries including food & beverage, public health, education, and sports. One theme was the importance of quality and value in engineering people-centric products—a core belief at Entefy. 

These days, threats to your digital privacy and security can come from practically anywhere. We put together a set of slides highlighting 7 examples of data trackers—apps, web services, and devices that collect your private data, sometimes without your permission or awareness. 

You can read an in-depth look at these data trackers in Entefy’s article, Data trackers are watching your every (digital) move.

\We’ve been hard at work hiring the company’s next generation of superstars. One question we get asked a lot during interviews is, “What’s it like to work at Entefy?” 

So we’re happy to share that we’ve added a group of new videos to our website that answer that question—and more. You’ll find them in the Careers section. 

Once you finish with the videos, get to know us better by checking out these resources:

• We shared our views on the idea that “Choose a job you love, and you will never have to work a day in your life.”
• Entefy is solving a technology challenge that we compared to climbing Mt. Everest.
• There have been several newsworthy announcements about our success patenting Entefy’s core artificial intelligence, communications, search, and cyber security technology. 
• We’ve also covered “behind-the-scenes” aspects of life at Entefy, like one Entefyer’s experience bringing his daughter to work and the reasons behind our unique way of greeting guests. 

Think you’ve got what it takes to re-write the rules of digital interaction? Make your first stop Entefy’s career opportunities page. We look forward to meeting you.

Take a look at the device you’re using—it could be argued that there is not one person on earth that could make it from scratch. The process necessitates cooperation between many different people wielding knowledge that has been accumulated over many generations. 

A lot of what we see around us these days is in the same boat. How many people can build a car, house, or even a pen for that matter, from scratch? By putting our heads together, we’ve been able to go so far as building cities, rockets, and supercomputers. 

The business world is heavily reliant upon collaboration. Staying ahead of the curb requires the sharing of ideas and information among potentially hundreds of individuals, each with their own knowledge and experience. 

Adding more people to the mix should, then, logically lead to more creativity and better decisions. While that’s sometimes correct, as the group grows larger the overall composition becomes more complex, making cohesion between all the members more difficult to achieve. 

Collaboration is, at times, a game of chance, dependent on having the right people together at the right time and under the right circumstances. If we want to get the most from working with others, we need to take care in selecting when, where, and with whom this work takes place. If not, a number of things can go wrong. 

Collaboration overload

Understanding how a network of people functions can be a demanding task. Psychologist and anthropologist Robin Dunbar suggested that there is a limit to how many people we can know while also understanding how they relate to the others in the group. As a group increases in size, the number of different relationships between individuals increases by orders of magnitude, eventually becoming too complex for one person to handle. Dunbar’s number, as it’s now known, is 150.  

As organizations expand and take on new projects, there becomes a greater need for effective communication between people and departments. Everyone needs to be kept in the loop regarding new ideas, setbacks, or developments. Information needs to travel through the organization fluidly, to the right people and with minimal disruption. 

When a company outgrows the number of people their structure can handle, we often find ourselves spending an inordinate amount of time dealing with others. Collaboration isn’t effective when people spend all their time in meetings or chatting through text, email, or message boards. At some point, we have to put our heads down and do something constructive. 

Collaboration overload is where we see the usual benefits of a “group effort” decay into a state of stagnation—where we spend more time spreading information than we do creating something. Research into the “collaboration curse” found that knowledge workers spend around 70-85% of their time in meetings, tending to emails, or talking on the phone. Research into the cost of email found that a mid-sized firm could be spending more than $1 million a year processing emails, as each email averaged out to about 95 cents in labor costs. 

This culture of collaboration is found not only in the way we communicate but also in how offices are designed. The rise in open office plans has certainly made it easier for you to share ideas and ask questions of your fellow coworkers, but it’s also just as easy for them to come to you—even when you’re busy trying to focus on something else. Couple this with the general noise of other groups that form around you, and you have a recipe for a great deal of distraction. 

While some of those distractions will be important, when they come often and unexpectedly they can take us out of our frame of mind. Working on complex tasks requires remembering and manipulating information in your mind, every time you are distracted this information is lost, and must be regathered before you can pick up where you left off—research into the cost of interrupted work suggests that it can take up to 25 minutes for people to re-engage themselves in a task after being distracted. 

How groups think

Part of the allure of collaboration is the idea that groups will arrive at better conclusions and make better decisions. The combination of many experts should help capitalize on their strengths while minimizing weaknesses. When one person makes a mistake, others are likely to pick it up; when others show a bias, the others should help to offset it.

But this is not how things always work out. Under certain conditions groups can compound issues and biases. People within groups can behave differently than they would alone or one-on-one—take social loafing, the phenomena in which people tend to exert less effort when they’re part of a group. 

Anchoring is one effect that can undermine creativity. It occurs when a meeting or brainstorming session begins with one person presenting an idea or piece of information. The rest of the group can then too easily fixate on that initial information, expanding and iterating on it as opposed to coming up with unique and varied ideas of their own. 

It is also possible for people to bite their tongue when they believe that their opinion or idea goes against what the others think. When several members of the group do this—effectively conforming to one opinion—it can cause the group to go in a direction counter to the preferences of most of the individuals. This is referred to as the Abilene Paradox.

Adding more people does not necessarily make for more creative ideas or well-rounded decisions. While collaboration can clearly fall victim to the type of errors that plague individuals, there are other situations in which collaboration can make matters worse. One study found that groups are more optimistic than individuals when estimating the time and resources needed to accomplish a task. Another study found that groups are more likely to overcommit to a failing course of action. 

There is also evidence suggesting groups tend to amplify the initial predisposition of their members. That is, if the average member leans in favor of a risky option, after deliberation the group is likely to be even more in favor of the risk. Seeing others agree with your assessment compounds your certainty in it, creating somewhat of a snowball effect within the group.

Preventing collaboration overload

Effective collaboration relies on the coming together of different skill sets and experiences. Of course, people are unique and complex beings, simply sticking a group into a room won’t ensure they work well together. Like a good band, the best ideas emerge from groups whose members understand and can feed off each other. 

When people feel like essential components of some big and important goal, they are likely to be more motivated and inspired—and when this defines every member of the group, overall productivity and results are likely to be enhanced. Just remember not to over-collaborate and let people do their own thing too.

A report from McKinsey estimated that as much as $12 billion was invested globally in artificial intelligence technologies during 2016, including projects focused on machine learning, natural language processing, computer vision, and autonomous vehicles. That figure cuts across industries, and in real-world terms represents thousands of individual R&D projects. 

Unless you’re closely monitoring developments in artificial intelligence, you probably learn about new AI technologies one headline at a time. “New AI system beats champion Go player” or “Advanced AI improves doctors’ diagnoses.” That sort of thing. 

But you can get a much better sense of the scope and diversity of newly emerging AI technologies by learning about a lot of them at once. In this case, 27 different projects transforming 27 different industries. Which is what our research has produced, as you’ll see below.

News of a single AI technology can be pretty exciting, even inspiring. Understanding the diversity and vision of dozens of them underscores just how powerfully transformative this current generation of AI technologies already is, or will soon be.

Here’s our roundup of disruptive AI technologies being designed, built, or deployed in 27 different industries:

1. Aerospace. AI algorithms enable an entirely new type of self-healing aircraft that makes use of new sensors and other on-board recording devices. Smart systems could use data from these tools to spot problems long before failure occurs, making planes safer, lowering maintenance costs, and reducing the number of delays caused by mechanical issues. 

2. Defense. The military and intelligence worlds are changing in a big way, with AI increasingly important to national defense strategies, on the ground and online. New AI cybersecurity platforms are being fed massive amounts of user-generated data so they can learn to spot anomalies associated with cyberattacks. In addition to using AI for cyber defense, the U.S. military wants to build autonomous weapons, such as drones that can conduct real-time surveillance of enemy territory, jam communications, and fire against enemy combatants. 

3. Automotive. Mass adoption of self-driving cars is still some ways off. But AI is already impacting the auto industry in ways that weren’t possible just a few years ago. A new generation of smart features such as accident avoidance, alerts, and automated braking systems contribute to safer driving conditions for everyone. AI systems are being used to predict mechanical failures as well, employing sensors, apps, and Internet of Things technologies to track performance in individual cars. The data collected triggers alerts to drivers to warn them of potential problems, while manufacturers use the same data to improve production and identify faulty or unreliable parts. 

4. Consumer Goods & Services. Clothing and cosmetics companies—just to name a few—are using smart algorithms to bring shopping experiences out of department stores and into consumers’ homes. A consumer who wants to purchase new make-up need only open an app on her smartphone, and she can “test” a range of product shades to see whether they suit her skin tone. Meanwhile, a person in search of new jeans can pop his measurements and style preferences into a simple interface powered by an online shopping algorithm based on a selected brand’s sizing trends. These experiences are powered by AI technologies such as facial recognition and machine learning systems that use partial or uncertain data to make predictions.  

5. Energy & Utilities. It’s difficult to find a segment of the energy industry not being impacted by the opportunities and challenges of AI, including renewable energy producers using big data to better predict supply and demand as well as traditional utilities rolling out smart electrical grid improvements. AI is helping paint a truer picture of the oil industry as well. Not all oil-producing nations are fully transparent about their supply picture, which creates uncertainties in the commodities markets and volatile oil prices. But a combination of convolutional neural networks (CNN), shadow detection, and satellite imaging is shining an AI-powered spotlight on the situation. By analyzing the shadows on oil storage tanks, AI systems can assess how full the containers are and approximate oil supplies in any part of the world, forcing greater transparency on oil-producing nations.

6. Manufacturing. Factory productivity doesn’t have to come at the cost of workers’ safety. Japanese researchers are studying how AI can not only detect defective products and increase quality output, but how it can monitor workers’ physical conditions as well. Fatigue can cause serious mistakes, particularly when a worker is operating heavy machinery. AI systems can determine when an employee becomes drowsy and needs to be reassigned to a safer task. Greater productivity and better working conditions are a win for everyone. 

7. Transportation. Self-driving cars get a lot of media attention, but the entire transportation industry is evolving rapidly with AI technologies. One interesting development is in environmental improvements. The transportation industry accounts for 27% of greenhouse gas emissions in the U.S. When you add up the impact of a cluster of AI-powered autonomous transportation technologies being rolled out for airplanes, cars, trucks, trains, and ships, the potential environmental benefits are significant.

8. Logistics. Ever wonder how goods get from their point of manufacture to your local store? That’s the business of the logistics industry, and it too is undergoing major changes via AI technologies, allowing more goods to get to more places more quickly. The recipe for the supply chain of the future mixes autonomous delivery vehicles—cars and trucks, but also container ships and drones—with predicative analysis to reduce transport times and fuel costs. Taken together, moving goods from point A to B can happen autonomously with greater speed and efficiency.

9. Agriculture. Automation systems are already relieving humans of dangerous farming jobs like picking lettuce, which can expose workers to potentially toxic chemicals. AI may also hold the key to the use of automated farming to solve the global food crises by ensuring better crop yields through targeted farming strategies. Drones can already collect data from vast swaths of farmland to identify which areas are thriving and which are at risk of failing. Some researchers are even attempting to teach drones to cooperate with one another, converging on areas with significant weed problems so they can unleash pesticides on the afflicted sections. 

10. Banking. Advances in natural language processing (NLP) have made financial industry self-service systems capable of increasingly complex functions, such as onboarding new customers and assisting them with major loan decisions. Also, machine learning and optical character recognition are further simplifying banking by allowing people to submit financial documents through their smartphones. A customer can snap photos of the documents and the system will automatically upload the images, extracting the relevant information. 

11. Education. Blended learning, in which teachers use technology to enhance traditional classroom environments, is gaining prominence in American schools. Technologies such as machine learning and NLP create the potential for AI-based lifelong learning companions. These programs would tailor their content to individual students based on the subject areas a child struggles with and which lessons are most effective. AI already serves as a kind of digital teaching assistant, taking over tasks such as grading homework and papers so that teachers can focus more deeply on lesson planning and student engagement. 

12. Food. A powerhouse combination of machine learning and DNA sequencing could lead to food products that help people manage chronic disease. We’re not talking kale and blueberries here, either – these superfoods would be developed around specific peptides and how they impact diseases such as high blood pressure and Type-2 diabetes. The speed of AI-powered analysis could advance a field of study that has long grappled with slow results and extremely high costs, and could lead to breakthroughs in nutrition. 

13. Government. AI is augmenting government work across the spectrum, from data entry to disease outbreak responses. Cognitive applications based on neural networks now analyze data anomalies that impact terrorist threat levels or signal shifts in the markets, events that require urgent government attention. Real-time tracking is also helping the government improve medical outcomes by identifying clusters of serious disease outbreaks. The military is developing technologies that can assess soldiers’ wounds based on data collected through wearable technology, enabling medics to prioritize treatments and treat urgent cases more swiftly. In more ordinary cases, sensors on street lights collect real-time data about traffic and maintenance needs and give citizens a heads up when their parking meters are about to expire. 

14. Healthcare. Machine learning is helping doctors make faster, more precise diagnoses by studying medical records and contrasting images of healthy versus diseased organs. This technology could be used to solve the global caregiver shortfalls with better medical diagnosis and healthcare. In 2015 alone, China’s 80,000 radiologists saw 700,000 new cases of lung cancer. Fortunately, AI programs that can identify lesions and other disease markers are helping radiologists and doctors make earlier diagnoses and therefore prescribe treatment sooner. 

15. Law. The use of AI in the legal discovery process is becoming more mainstream. Technology is expanding into other areas as well, including predictive analysis and contract reviews. The former could prove especially valuable to companies as they determine whether to go to trial and assess their risks. Knowing the likely outcome of a case could save significant resources and shape better policies down the road. 

Although lawyers must be involved in contract reviews, legal industry machine learning platforms can decrease the time lawyers spend on those tasks by 20% to 60%, allowing them to focus on high-level tasks only humans can perform. Litigation strategist James Yoon said clients are still willing to pay a premium for complex, high-stakes legal services. “For the time being, experience like mine is something people are willing to pay for. What clients don’t want to pay for is any routine work.” 

16. Nonprofit. AI is literally saving lives in the nonprofit world. One suicide prevention hotline uses machine learning for the greater good to identify the phrases most often associated with emergency cases so it can prioritize those messages and respond faster to people in need. Another nonprofit, this one aimed at improving students’ writing, uses natural language processing to address users’ problems with sentence fragmentation. The organization had its system analyze 100,000 grammatically correct sentences, then used an NLP platform to break those down. Once the program learned to distinguish sentence fragments from complete thoughts, it showed an 84% accuracy rate on picking out fragments in students’ writing.  

17. Insurance. How much privacy would you trade for cheaper insurance? Artificial intelligence is powered by data. And when it comes to data, often more is better. One distinctive aspect of the insurance industry’s adoption of AI is how these companies intend to collect their data. Insurers are turning to sensors that collect data directly from individuals, including technologies like in-home monitors, automobile transponders, and wearables. These new data sources open the doors to new products and pricing models, but whenever data collection intersects with a real person’s life, privacy questions emerge.

18. Mobile telecommunications. Wireless telecommunication companies have access to volumes of data from their millions of customers. One telecom implemented a real-time customer analytics system that enabled it to track and respond to consumers immediately. The data gathered by the new program facilitated better customer service communication driven by the insights from the custom AI system.

19. Investments. People are not, generally speaking, purely rational investors and their irrationality is what makes markets unpredictable. An artificial intelligence algorithm that can anticipate human behavior while also monitoring economic signals in real-time could be highly disruptive to today’s markets (though some insiders have their doubts). Whether or not an AI “super investor” appears on the scene, the investments industry will require ever-smarter safeguards against exploitation and risk.

20. Travel. Online travel booking is nothing new, but AI-assisted vacation planning? That’s more of a novelty. Beyond aggregating flight times and hotel prices, computer programs now pull data about customers’ online behaviors and use learning systems powered by past preferences to personalize recommendations. When a human agent isn’t in the picture, chatbots can now answer questions and book reservations as well. ‘Nothing will ever replace the expertise and intuitive nature of travel agents,’ said one travel industry veteran. ‘Artificial intelligence brings just another component to their tool kit.’

21. Information Technology. IT professionals in particular find themselves at an exciting turning point in their careers. As more companies integrate AI into their processes, to one extent or another, IT teams are learning how to engage with these new technologies. A 2016 report from Narrative Science and the National Business Research Institute predicted that 62% of enterprises will adopt and use AI by 2018. Given that, IT could soon encompass competencies in machine learning platforms, natural language processing, decision management software, and AI-optimized hardware.

22. News media. The media has been under siege by critics and fake news purveyors during the past several years, but it may find an ally in AI. The Associated Press uses AI software to crank out earnings reports, and data companies are increasingly generating information useful to reporters. The lightning speed at which AI algorithms can gather and process multiple types of data could be a boon to journalists, enabling them to report breaking news as it happens. The Los Angeles Times encountered this firsthand in 2013, when it used a bot to report on an earthquake almost as it was happening.

23. Pharmaceuticals. Pharmaceutical researchers are using machine learning to transform drug creation. These platforms analyze medical histories, chemical databases, and past scientific findings to identify correlations between genetic markers and patient outcomes. This method of drug testing costs 50% less than traditional approaches and provides insight into how a treatment might impact certain types of patients. Pattern-recognition technology can provide a view into how different diseases work as well, allowing researchers to develop drugs that will target them more effectively. Most important, AI deep learning enables doctors to provide more targeted treatment plans based on an individual’s genetics and history.  

24. Online dating. Can autonomous systems make better matches than people? After all, people have been matchmaking practically since there were people to match. Dating itself is ripe for disruption: it is time- and labor-intensive and carries a high failure rate. There’s plenty of room for improvement. So it’s not surprising that the online dating industry is exploring adding AI to the game of love, addressing common online dating complaints like dishonesty in profiles and increasing the relevance of the data underpinning matchmaking algorithms. 

25. Motion pictures. Hollywood loves making movies about AI. Now it’s using AI to make and sell movies. There are AI systems that have been used to create movie preview trailers and even write screenplays. But the movie business might see an even bigger impact from AI systems that predict the likelihood that a given script will be a blockbuster. The system was trained using scripts and box office revenue data going back to the 1980’s. Given that just 20% of movies break even, there is a lot of room to improve the greenlighting process.

26. Publishing. With more than 1 million books published each year—a figure up 400% from just 10 years ago—competition for readers’ attention is fierce. Data can help publishers make decisions about which books to publish, but the best-in-class reader analytics solutions can take up to 4 weeks to process data before providing actionable insights. A new generation of AI publishing systems is rewriting the rules, analyzing the text of books to predict reader engagement and sales performance.

27. Semiconductors. You don’t have to do much more than read business headlines to grasp the impact AI is having on the semiconductor industry. Nvidia, until recently known for its graphics processors used in video games, is emerging as a leader in processors for AI number crunching. Google has launched its own AI-focused chip. The CPU king Intel is making acquisitions to catch up. Winners and losers are to be determined, but clearly the chip industry is being shaken up by the demand for AI processing power.

The advanced artificial intelligence projects we’ve talked about here were first covered in the following Entefy articles:

• AI disruption is already underway in these 8 industries
• New traditions in traditional industries: 9 disruptive artificial intelligence technologies happening now
• Making smart use of smart systems: AI’s disruptive impact in 10 industries

Focus and productivity are closely linked. The more time you spend focused on something, the less time you’ll lose to distraction. But intending to keep focused and actually doing it are two very different things. Because in today’s world of information overload and constant distraction, your brain sometimes just needs a rest. 

Luckily, there are quite a few simple techniques for helping your mind stay sharp and focused in even the most distracting environments. This video summarizes 23 of those techniques. Start with one and add more over time!

You can check out slides about this topic here.

Remember when Kodak dominated the consumer film industry? When digital came along, Kodak’s leadership made the fateful decision to double down on celluloid film instead of going all-in on digital. With the benefits of hindsight, we know now that that decision was the beginning of the end for the company’s dominance. Think of all the one-off decisions that led up to that grand strategic misfire. 

When you run a business or manage a team, decisionmaking comes with the territory – who to hire, which ideas to pursue, how much money to allocate to each department, which products to greenlight. Difficult choices are par for the course for many professionals. Unfortunately, the right calls are rarely obvious. Even after months of planning and analysis, it’s entirely possible to go wrong when making challenging choices.  

The challenges of decisionmaking today

In one study, 78% of professionals responsible for making decisions said they struggle to find the right answers in the constantly-changing business landscape. Fear of making the wrong decisions can prevent action, which in some cases is just as bad as choosing incorrectly. 

Fortunately, business leaders today have access to unprecedented amounts of data as well as artificial intelligence platforms that enable them to make better decisions about their companies’ futures. If Kodak executives had had a better grasp of changing market trends, they might have chosen a different strategic focus when shaping their business. 

One promise of AI automation technologies is that these systems allow humans to reach their potential more fully. Managers and other decisionmakers in particular will be relieved of administrative duties, empowering them to spend more time on high-level tasks like strategy and talent development. AI platforms will also generate data and provide analyses that help leaders make smarter, more informed decisions. Clearly the better we understand our own industry and market, the better we can serve them. 

But becoming a better decisionmaker takes more than simply hooking up new AI tools and relying on their outputs. As one business expert said, “If one of the potential promises of machine learning is the ability to help make decisions, then we should think of technology as being intended to support [managers].” Many leaders are already coming around to this redefined management style. In fact, nearly 80% of managers surveyed by Accenture “believe that they will trust the advice of intelligent systems in making business decisions in the future,” according to the researchers. 

To truly leverage the potential of these new tools, professionals must hone their judgment skills and view AI as a powerful new complement to existing business processes. 

Big data, big decisions 

Managing the personnel and processes around smart decisions is a complex challenge. In part because our understanding of how people make decisions evolves with new discoveries in neuroscience and psychology. 

In 2007, cognitive and business researchers put forth a business-focused decisionmaking framework based on the concept of contextual responsiveness. They wrote that there are five contexts in which professionals make decisions: simple, complicated, complex, chaotic, and disorder. To effectively steer their companies, leaders need to understand when to apply wisdom from past experiences, when to enlist expert counsel, and when to let patterns reveal themselves before deciding how to address a crisis. 

Such a framework provides a powerful guide to thinking about business challenges and making decisions in a data-rich world. Data is everything, and there’s more of it than we humans can handle on our own. By 2020, 1.7 megabytes of data per person will be generated each second. Even if data analysts worked 24/7, there’s no way they could keep up without the aid of tools like artificial intelligence.  

The massive quantity of data is a good thing for decisionmakers. More data means the potential for more accurate predictions about consumer preferences and market trends. Instead of relying on past market events or customer behaviors, data allows businesses to use real-time information and increasingly accurate forecasts to shape their decisions. 

Machine learning improves business processes

Machine learning will play a major role in business decisionmaking because it can be used to adjust resource allocation based on real-time measurements and demands, and can personalize customer experiences. The more data a system gathers about different users’ preferences, the more accurately it can predict what types of products and services they’ll gravitate toward in the future. 

What better way to keep people engaged on a company’s website than to offer constant promotions on exactly the types of goods and services that interest each individual user? The consumer data analyzed by AI algorithms can help create more effective product strategies and marketing campaigns, not to mention mitigate the fallout from supply chain disruptions and other business crises.  

The implications for improved business processes through machine learning and other forms of AI are far-reaching. With AI algorithms becoming smarter all the time, managers will be able to automate customer personalization, resource allocation, and even fraud detection in cybersecurity. They can then focus on bigger picture questions and complex problems that machines simply cannot solve. 

Making decisions is one of the most challenging aspects of business leadership. AI is making that process both easier and more flexible. By providing deep insights into industries, customers, and markets, AI is giving leaders more options and capabilities than ever before. Now it’s up to us to make smarter decisions.