You probably interact with artificial intelligence (AI) on a daily basis and don’t even realize it.
Many people still associate AI with science-fiction dystopias, but that characterization is waning as AI develops and becomes more commonplace in our daily lives. Today, artificial intelligence is a household name – and sometimes even a household presence (hi, Alexa!).
While acceptance of AI in mainstream society is a new phenomenon, it is not a new concept. The modern field of AI came into existence in 1956, but it took decades of work to make significant progress toward developing an AI system and making it a technological reality.
In business, artificial intelligence has a wide range of uses. In fact, most of us interact with AI in some form or another on a daily basis. From the mundane to the breathtaking, artificial intelligence is already disrupting virtually every business process in every industry. As AI technologies proliferate, they are becoming imperative to maintain a competitive edge.
What is AI?
Before examining how AI technologies are impacting the business world, it’s important to define the term. “Artificial intelligence” is a broad term that refers to any type of computer software that engages in humanlike activities – including learning, planning and problem-solving. Calling specific applications “artificial intelligence” is like calling a car a “vehicle” – it’s technically correct, but it doesn’t cover any of the specifics. To understand what type of AI is predominant in business, we have to dig deeper.
Machine learning
Machine learning is one of the most common types of AI in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of AIs are algorithms that appear to “learn” over time.
If you feed a machine-learning algorithm more data its modeling should improve. Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.
For example, if you manage a manufacturing plant, your machinery is likely hooked up to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it’s too much data for a human to ever sift through; and even if they could, they would likely miss most of the patterns. [Related: Artificial Insurance? How Machine Learning Is Transforming Underwriting]
Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine-learning algorithm can catch it and notify decision-makers that it’s time to dispatch a preventive maintenance team.
But machine learning is also a relatively broad category. The development of artificial neural networks – an interconnected web of artificial intelligence “nodes” – has given rise to what is known as deep learning.
Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.
Deep learning
Deep learning is an even more specific version of machine learning that relies on neural networks to engage in what is known as nonlinear reasoning. Deep learning is critical to performing more advanced functions – such as fraud detection. It can do this by analyzing a wide range of factors at once.
For instance, for self-driving cars to work, several factors must be identified, analyzed and responded to simultaneously. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5-10 seconds. All this information is calculated at once to help a self-driving car make decisions like when to change lanes.
Deep learning has a great deal of promise in business and is likely to be used more often. Older machine-learning algorithms tend to plateau in their capability once a certain amount of data has been captured, but deep learning models continue to improve their performance as more data is received. This makes deep learning models far more scalable and detailed; you could even say deep learning models are more independent.
AI and business today
Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although AI currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data much faster than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, we can use AI to help game out pfossible consequences of each action and streamline the decision-making process.
“Artificial intelligence is kind of the second coming of software,” said Amir Husain, founder and CEO of machine-learning company SparkCognition. “It’s a form of software that makes decisions on its own, that’s able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software.”
Those traits make AI highly valuable throughout many industries – whether it’s simply helping visitors and staff make their way around a corporate campus efficiently, or performing a task as complex as monitoring a wind turbine to predict when it will need repairs.
Common uses of AI
Some of the most standard uses of AI are machine learning, cybersecurity, customer relationship management, internet searches and personal assistants.
Machine learning
Machine learning is used often in systems that capture vast amounts of data. For example, smart energy management systems collect data from sensors affixed to various assets. The troves of data are then contextualized by machine-learning algorithms and delivered to your company’s decision-makers to better understand energy usage and maintenance demands.
Cybersecurity
Artificial intelligence is even an indispensable ally when it comes to looking for holes in computer network defenses, Husain said. Believe it or not, AI systems can recognize a cyberattack, as well as other cyberthreats, by monitoring patterns from data input. Once it detects a threat, it can backtrack through your data to find the source and help to prevent a future threat. That extra set of eyes – one that is as diligent and continuous as AI – will serve as a great benefit in preserving your infrastructure.
“You really can’t have enough cybersecurity experts to look at these problems, because of scale and increasing complexity,” Husain added. “Artificial intelligence is playing an increasing role here as well.”
Customer relationship management
Artificial intelligence is also changing customer relationship management (CRM) systems. Software programs like Salesforce and Zoho require heavy human intervention to remain current and accurate. But when you apply AI to these platforms, a normal CRM system transforms into a self-updating, auto-correcting system that stays on top of your relationship management for you.
For those in brand-new companies, read our Zoho CRM review, or our review of Salesforce to learn about one of the most popular CRMs.
A great example of how AI can help with customer relationships is demonstrated in the financial sector. Dr. Hossein Rahnama, founder and CEO of AI concierge company Flybits and visiting professor at the Massachusetts Institute of Technology, worked with TD Bank to integrate AI with regular banking operations.
“Using this technology, if you have a mortgage with the bank and it’s up for renewal in 90 days or less … if you’re walking by a branch, you get a personalized message inviting you to go to the branch and renew purchase,” Rahnama said. “If you’re looking at a property for sale and you spend more than 10 minutes there, it will send you a possible mortgage offer. [Related: CRM vs. Marketing Automation: What’s the Difference?]
Internet and data research
Artificial intelligence uses a vast amount of data to identify patterns in people’s search behaviors and provide them with more relevant information regarding their circumstances. As people use their devices more, and as the AI technology becomes even more advanced, users will have a more customizable experience. This means the world for your small businesses, because you will have an easier time targeting a very specific audience.
“We’re no longer expecting the user to constantly be on a search box Googling what they need,” Rahnama added. “The paradigm is shifting as to how the right information finds the right user at the right time.”
Digital personal assistants
Artificial intelligence isn’t just available to create a more customized experience for your customers. It can also transform the way your company operates from the inside. AI bots can be used as personal assistants to help manage your emails, maintain your calendar and even provide recommendations for streamlining processes.
You can also program these AI assistants to answer questions for customers who call or chat online. These are all small tasks that make a huge difference by providing you extra time to focus on implementing strategies to grow the business.
Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Humans can use AI to game out possible consequences and streamline the decision-making process.
The future of AI
How might artificial intelligence be used in the future? It’s hard to say how the technology will develop, but most experts see those “commonsense” tasks becoming even easier for computers to process. That means robots will become extremely useful in everyday life.
“AI is starting to make what was once considered impossible possible, like driverless cars,” said Russell Glenister, CEO and founder of Curation Zone. “Driverless cars are only a reality because of access to training data and fast GPUs, which are both key enablers. To train driverless cars, an enormous amount of accurate data is required, and speed is key to undertake the training. Five years ago, the processors were too slow, but the introduction of GPUs made it all possible.”
Glenister added that graphic processing units (GPUs) are only going to get faster, improving the applications of artificial intelligence software across the board.
“Fast processes and lots of clean data are key to the success of AI,” he said.
Dr. Nathan Wilson, co-founder and CTO of Nara Logics, said he sees AI on the cusp of revolutionizing familiar activities like dining. Wilson predicted that AI could be used by a restaurant to decide which music to play based on the interests of the guests in attendance. Artificial intelligence could even alter the appearance of the wallpaper based on what the technology anticipates the aesthetic preferences of the crowd might be.
If that isn’t far out enough for you, Rahnama predicted that AI will take digital technology out of the two-dimensional, screen-imprisoned form to which people have grown accustomed. Instead, he foresees that the primary user interface will become the physical environment surrounding an individual.
“We’ve always relied on a two-dimensional display to play a game or interact with a webpage or read an e-book,” Rahnama said. “What’s going to happen now with artificial intelligence and a combination of [the Internet of Things] is that the display won’t be the main interface – the environment will be. You’ll see people designing experiences around them, whether it’s in connected buildings or connected boardrooms. These will be 3D experiences you can actually feel.” [Interacting with digital overlays in your immediate environment? Sounds like a job for augmented reality.]
AI is predicted to take digital technology out of the two-dimensional screen form and instead become the physical environment surrounding an individual.
What does AI mean for the worker?
With all these new AI uses comes the daunting question of whether machines will force humans out of work. The jury is still out: Some experts vehemently deny that AI will automate so many jobs that millions of people find themselves unemployed, while other experts see it as a pressing problem.
“The structure of the workforce is changing, but I don’t think artificial intelligence is essentially replacing jobs,” Rahnama said. “It allows us to really create a knowledge-based economy and leverage that to create better automation for a better form of life. It might be a little bit theoretical, but I think if you have to worry about artificial intelligence and robots replacing our jobs, it’s probably algorithms replacing white-collar jobs such as business analysts, hedge fund managers and lawyers.”
While there is still some debate on how, exactly, the rise of artificial intelligence will change the workforce, experts agree there are some trends we can expect to see.
Will AI create jobs?
Some experts believe that, as AI is integrated into the workforce, it will actually create more jobs – at least in the short term.
Wilson said the shift toward AI-based systems will likely cause the economy to add jobs that facilitate the transition.
“Artificial intelligence will create more wealth than it destroys,” he said, “but it will not be equitably distributed, especially at first. The changes will be subliminally felt and not overt. A tax accountant won’t one day receive a pink slip and meet the robot that is now going to sit at her desk. Rather, the next time the tax accountant applies for a job, it will be a bit harder to find one.”
Wilson said he anticipates that AI in the workplace will fragment long-standing workflows, creating many human jobs to integrate those workflows.
What about after the transition?
First and foremost, this is a transition that will take years – if not decades – across different sectors of the workforce. So, these projections are harder to identify, but some other experts like Husain are worried that once AI becomes ubiquitous, those additional jobs (and the ones that had already existed) may start to dwindle.
Because of this, Husain said he wonders where those workers will go in the long term. “In the past, there were opportunities to move from farming to manufacturing to services. Now, that’s not the case. Why? Industry has been completely robotized, and we see that automation makes more sense economically.”
Husain pointed to self-driving trucks and AI concierges like Siri and Cortana as examples, stating that as these technologies improve, widespread use could eliminate as many as 8 million jobs in the U.S. alone.
“When all these jobs start going away, we need to ask, ‘What is it that makes us productive? What does productivity mean?’” he added. “Now we’re confronting the changing reality and questioning society’s underlying assumptions. We must really think about this and decide what makes us productive and what is the value of people in society. We need to have this debate and have it quickly, because the technology won’t wait for us.”
A shift to more specialized skills
As AI becomes a more integrated part of the workforce, it’s unlikely that all human jobs will disappear. Instead, many experts have begun to predict that the workforce will become more specialized. These roles will require a higher amount of that which automation can’t (yet) provide – like creativity, problem-solving and qualitative skills.
Essentially, there is likely to always be a need for people in the workforce, but their roles may shift as technology becomes more advanced. The demand for specific skills will shift, and many of these jobs will require a more advanced, technical skill set.
AI is the future
Whether rosy or rocky, the future is coming quickly, and artificial intelligence will certainly be a part of it. As this technology develops, the world will see new startups, numerous business applications and consumer uses, the displacement of certain jobs and the creation of entirely new ones. Along with the Internet of Things, artificial intelligence has the potential to dramatically remake the economy, but its exact impact remains to be seen.
David Cotriss contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article.
