Frequently Asked Questions About AI in Industry

Artificial intelligence brings basic human-level reasoning skills to machines. Those abilities will absolutely have an impact on every industry. But the type of effects they have will be different from industry to industry and business to business… and even from year to year.

Anyone thinking strategically about business planning today, or about careers in business tomorrow, had better be asking some questions about artificial intelligence in their industry right now.

There are important strategic consequences that will come with the answers to those questions. Titans of industry will fall in the next decade because they have gotten the answers wrong. New businesses will climb to dominate their fields through adept adoption of artificial intelligence.

Although today much of the employment in AI is in academic areas and private R&D, the vast majority of artificial intelligence engineering and other kinds of jobs in the future will come in the business world. So anyone considering those types of careers should be intensely interested in the impacts and effects of AI on industry.

You can start your quest for answers right here, with some of the most frequently asked questions about AI in the business world today.

Are there any specific industries that have seen significant advancements with AI? How is AI being used in various industries?

Many industries are experiencing significant advances through applied artificial intelligence.

According to a Forbes survey from 2024, industries that are seeing major impacts from AI tools include:

There are also non-traditional kinds of industries that are being changed even more rapidly by advances in AI.

In some sense, the fields of mathematics and statistics have been the ones that have seen the greatest changes. Although these are mostly realms of pure theory and experimentation, their applications in other fields have been dramatically accelerated by AI. Looking in the rear view mirror, you can largely lay the explosion of success of data science at the feet of machine learning, a specific AI tool that matured ahead of the rest of the industry.

By the time you read this, there will almost certainly be even more industries experiencing profound changes driven by artificial intelligence.

That has bled over into science in general, busting through roadblocks in materials science, pharmaceutical development, and more. Even our understanding of the most crucial challenge of our times, global climate change, is being accelerated by AI in monitoring satellites mapping ice sheet breakdown and in machine learning routines crunching millions of environmental datapoints both from history and in real time.

Can AI technology create new job opportunities in different industries?

Artificial intelligence has already created new jobs in various industries.

Not only is the introduction of AI creating new listings for current roles like software developers, consultants, project managers, and software architects, but it’s leading to entirely new types of jobs that never existed before.

These are just a taste of a wave of new AI hires in almost every industry. You can read more about industries where an AI degree will future-proof your career.

Can you explain how AI can save businesses money?

Yes, we can! How long do you have to read about it?

That’s because the financial savings coming from AI in different industries is nearly endless. When you look at the opportunities to create efficiencies, they can vary depending on the business model.

On the big picture level, the way that AI saves businesses money is through automation.  In almost every business, labor is the largest cost center. Any machine that can perform that work automatically has the potential to save the salaries being paid to people. Even high one-time costs for expensive machines tend to be minor compared to salaries over time.

The World Economic Forum estimates that more than a third of all business-related tasks in the world are already performed by machines.

Heavily repetitive tasks have long been farmed out to machines as soon as machines became capable of performing them. Up till now, those tasks had to be pretty simple and predictable… spinning cotton, operating telephone switches, calculating spreadsheet columns.

When machines gain reasoning skills, though, there is a whole new category of work that can be automated.

Humans bring creativity and reasoning abilities to the roles they fill. That’s essential for flexibility in non-linear processes. The human mind is adaptable; computers, historically, have not been. In other cases, people are necessary for the visual perception, fine motor skills, and complex articulation that the human body is capable of.

AI brings those reasoning skills to machines, allowing them to come up with creative and innovative solutions to complex tasks. Machine learning advances are improving computer vision and their ability to perceive the environment. That leads to more abilities to interact with the real world, and more flexibility in dealing with the unexpected.

Put it all together, and it means humans are less and less uniquely suited to all kinds of different positions. Everyone from paralegals to managers may be on the chopping block.

Eliminating those salaries, of course, saves money. On the other hand, the development costs of AI aren’t cheap; Forbes estimated that each training run behind the GPT-3 model cost around $5 million in compute time. So the savings aren’t going to be one-to-one. But OpenAI announced in 2023 that 80 percent of Fortune 500 companies were using its ChatGPT system in some way, and by some estimates nearly half of American companies were able to save between $25,000 and $70,000 in the process.

How does AI improve customer experiences?

AI is expected to improve customer experience for many businesses in at least three different ways.

As artificial intelligence capabilities grow, and as the public gets more used to dealing with AI, it’s likely that more and more openings will appear for AI to improve every kind of customer interaction.

How is AI changing the way industries collect, analyze, and use data?

Depending on how exactly you define it, the current wave of artificial intelligence really got its start as a key piece of a closely related field: data science.

Data scientists tapped into machine learning as a way to analyze and process data long before AI became cool again. In turn, it has been breakthroughs in collecting and analyzing large amounts of information that have powered new and amazing generative AI systems.

This is creating a kind of feedback loop, where AI can collect and independently analyze huge collections of loosely related and often noisy data, and in the process of doing so get even better at the entire process.

Already, AI is being seen as the umbrella science in which data science belongs.

Data science has revolutionized industries like:

AI may be the only thing that allows a useful approach to ingesting the vast amounts of digital data generated by the Internet of Things. With everything from dishwashers to copy machines spitting out gigabytes of data every day, only AI has the ability to sift through and identify useful information from the flow.

That feedback loop will make every industry even more hungry for detailed data, and more effective at using it to create efficient processes and new products and services tailored to consumers.

How is AI contributing to the automation of routine tasks in various sectors? What are some everyday examples of AI in business?

AI today is popping up in almost every industry and sector of the economy to handle tasks that are both routine and extraordinary.

Just a few examples include:

  • Scheduling – Humans staring at colorful blocks on a calendar to find openings according to mutual availability is a problem that plagues ever industry. But it’s a straightforward rules-based process that has been one of the first to fall to AI schedulers. Allstate Insurance put their agent appointment scheduling entirely in the hands of AI through text messaging, using natural language processing (NLP) to parse even typo-filled requests and automatically fit clients into the slots that worked best.
  • Customer service systems – A Florida car dealership recently took AI one step further than just scheduling appointments; AI also handles all inbound service calls for the company, using NLP to engage in conversation with callers to either offer answers to routine questions or book a service appointment if needed.
  • Autonomous vehicles – While the headlines all go to crazy behavior by Waymo and Cruze robotaxis on bustling city streets, a little company called May Mobility has been successfully deploying driverless transit service in more controlled circumstances around the country. Autonomous shuttles in retirement communities and on college campuses have been rolling since 2018.
  • Automated check-out – Few things are as routine as grocery shopping, but not the way Amazon Go does it. Using advanced imaging and tracking systems fused with deep learning routines, customers can wander around stores, pick up their groceries, and wander out again with no human interaction… and have their items accurately and automatically billed to a credit card in the background.
  • AI-powered pair programming – One of the runaway successes in generative AI has been its ability to instantly generate snippets of computer code when prompted. Github’s Copilot, the GPT-3-powered code completion tool from Microsoft, is reported by the Economist to be in use by more than 1.3 million subscribers. The tool translates natural language prompts into code, reviews and corrects human-written code, and even allows non-native English speakers to write English code through real-time translation. No one knows how much production code has been produced by the tool so far, but the KLOCs (thousands of lines of code) it generates are increasing daily.

Like other kinds of new information technology, AI automation is likely to bubble up through many organizations rather than launching in big initiatives from the top down. So it’s impossible to say what innovative uses small teams or business units are already adapting ChatGPT and other commercially available AI platforms for. The odds are good that plenty of jobs are being automated without a lot of fanfare or even the awareness of senior leadership.

What are some real-world examples of companies that have successfully implemented AI in their operations?

AI is already making a difference in businesses large and small. But there are big differences between putting AI to work, and actually successfully integrating it into daily operations.

Search engines might be the most obvious example of companies that have implemented AI in their daily operations… although it’s an open question what success looks like, after some high-profile flubs. But Google, Bing, Kagi, and other search providers have started to return AI-driven answers automatically and at the top of search results for billions of queries every day.

A less obvious but more clearly successful example of AI implementation in daily operations comes from other tech companies: businesses like Amazon and Netflix have slowly but completely integrated their various recommendation systems with AI-driven analysis and recommendation routines. These power the viewing selections and product purchases of millions of consumers around the world.

Other behind-the-scenes but still very real AI operational deployments are happening in the industrial world. For example, Vistra, a power generation company based in Texas, has developed an AI-driven heat rate optimization system to maximize thermal efficiency at their coal-burning Martin Lake Power Plant. Taking in hundreds of inputs and adjusting fuel consumption every half hour, the system operates about two percent more efficiently. But that two percent results in some big numbers: $4.5 million per year in savings and a carbon output reduction of 340,000 tons annually.

What are the key advantages of implementing AI in business operations?

Efficiency is the number one advantage in using AI to help with or run business operations. Even accounting for AI model training costs, over time a computer that can perform a function inevitably costs less than a human. AI offers the kind of inferential and reasoning ability that only humans were previously able to bring to bear on many tasks. That will allow even more jobs to be efficiently automated than by previous software systems.

Efficiency combines both cheaper and faster task completion in business operations.

AI may also lead to less biased decision-making in business operations. Although AI is not without bias, the biases it exhibits can be tuned more easily than those of humans. By adopting the proper training data and carefully weighting models, AI can take innate human tendencies out of the equation in everything from important decisions about fairness and equality, and split-second choices in operating dangerous machinery.

AI can also allow businesses to deliver more personalized options and support in their operations. With instant access to reams of customer data and deep training in personalization and natural behavior, AI assistants can create better customer service and more tailored products and services for customer needs.

As artificial intelligence develops more capabilities and as more AI engineers enter the industry, entirely new avenues should open up for taking advantages of smarter computers in business operations.

What future trends should AI students be aware of in the business world?

In large part, AI students ARE the future trend of the business world. But business is where AI will meet social expectations and community standards. So there are several trends that any budding AI engineer should be tracking as they prepare to develop AI tools for tomorrow:

In general, a well-rounded college degree program in AI will come with a built-in education in all of the various trends in business and society that graduates need to be aware of.

What is the impact of AI on job roles and skills required in industry?

The book is still being written on the true impacts of AI on job roles and job skills that will be needed in different industries. While AI is already making a big impact in some industries, it has been gradual and often counterintuitive: AI is likely to reduce skill requirements for many jobs in existence today.

Take data science as an example. Long the province of PhD graduates, diving into Big Data took programming skills, knowledge of data structures and storage, and an understanding of machine learning algorithms.

But new natural language query tools stacked on top of massive datasets allow users to generate similarly insightful reports without all that underlying technical knowledge. Automated Machine Learning, or AutoML, allows users without an ML experience to train custom learning models. Data scientists may still work with executives and staff to understand the questions and figure out how to interpret results. But they may not need to spend years learning how to code in R or develop recursive algorithms for most business purposes.

This may lead to some positions being phased out. Those that existed primarily to offer a particular technical expertise that AI can easily take on will no longer exist. But positions that are about results will find that AI simply offers a more powerful tool to generate those results.

Of course, new roles will emerge. The AI itself will still have to be built and trained. AI prompt engineers will figure out the best ways to get results from the machines. And specialists in various fields will have to figure out the ways that AI can safely and effectively be applied by other users.

These positions, of course, will require entirely new skills and education. Degrees in artificial intelligence are the starter kit to develop skills for such roles.

What role does AI play in decision making and problem solving?

Artificial intelligence is primarily used as a decision support tool for human managers and leaders today.

Using machine learning systems to break down extremely large amounts of disparate data to find valuable insights and trends helps inform leadership decisions at all levels of American industry today. Pricing decisions, bid processes, and vehicle routing are just a few of the many kinds of daily choices that managers and leaders are making with new information from AI-driven systems.

Similarly, generative AI is capable of providing extensive inferential modeling to game out potential courses of action and find the best available option. These can range from AI focus groups that accurately model human behavior and offer decision simulations before they are rolled out, to product merchandising ideas pulled out of decades of sales information.

As a problem-solving tool, AI can be put to work in any problem domain where digital data is available. For example, in New York City, the Metropolitan Transportation Authority (MTA) has had decades of persistent problems with people parking in bus lanes. Drivers could report violations, but the ability to investigate and cite offenders was too slow to have an impact. And it simply distracted drivers to add this to their workflow.

But combined with on-vehicle camera systems, AI is able to automatically spot offending vehicles in transit lanes, identify registered owners, and issue citations. A similar deployment in Los Angeles resulted in a 17 percent increase in bus speeds.

Let off the leash, AI doesn’t just play an advisory role in these areas. It legit solves problems and makes decisions entirely on its own, where allowed. Everything from pulling the trigger on a stock trade to changing lanes on a busy city street are being successfully executed by AI today; it has the potential to do the same thing for decisions like:

As more leaders and industries get comfortable with the abilities of advanced algorithms, AI will make more and more decisions and be trusted to solve more extensive and complex problems entirely on its own.

What skills and knowledge are needed to work with AI in industry, and how can students prepare for AI-related careers?

The core skills and knowledge areas required to work in AI today are straightforward:

Particularly important in industry uses of AI is the relevant kind of domain expertise in that sector. Adapting theoretical AI concepts like deep learning, visual processing, and other techniques to daily use in the field requires in-depth understanding of the tasks and jobs those techniques will support. Any AI engineer will only fill that requirement to the level they understand those businesses.

The best preparation for an AI career is always going to be choosing the right college degree in artificial intelligence. Rather than figuring out on your own what to study and how to meet industry standards, you are better off trusting a university full of experts to:

Of course, pursuing independent opportunities to hone those skills, or to study outside of the classroom is always a good idea. The more you can absorb in each of these critical areas, the better you will perform both through school and on the job as an AI professional.