It’s easy to get sucked into the hype around artificial intelligence, but it’s just as easy to get duped into thinking it’s all hype. The truth is somewhere in the middle. AI’s uses come in many forms, from simple tools that respond to customer chat to complex machine learning systems that predict the trajectory of an entire organization. Despite years of overpromising, AI is not sentient machines that reason like humans but rather more narrowly-focused pattern matching at scale to complement human reasoning.
In order to help business leaders understand what AI is capable of, how it can be used and where to begin an AI journey, it is essential to first dispel the myths surrounding this huge leap in computing technology. Learn more in this AI cheat sheet.
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What is artificial intelligence?
It’s easy to imagine AI functioning like science-fiction robots or, closer to reality, fully autonomous self-driving cars. Neither is a reality today, nor will either be a reality anytime soon. Note that while we use AI throughout this cheat sheet, most enterprises actually engage with a subset of AI called machine learning or deep learning. We’ll use AI here as a shorthand that includes these subsets.
The truth of AI today is much more limited, though it’s still incredibly powerful. The key to appreciating AI is to recognize that it’s largely a pattern-recognition tool that can run at a scale that is dramatically beyond any human, yet never quite replaces humans. Even at its best, AI delivers acceptable though not perfect results, giving people the ability to step in, observe the data and reason therefrom.
With such pattern recognition in mind, modern AI can perform image recognition, understand the natural language and writing patterns of humans, make connections between different types of data, identify abnormalities in patterns, strategize, predict and more. However complicated these may seem in practice, at the core of these and other AI-driven applications is the simple ability to identify patterns and make inferences based on those patterns.
AI isn’t truly intelligent in the way we define intelligence: It can’t think and lacks reasoning skills, it doesn’t show preferences or have opinions, and it’s not able to do anything outside of the very narrow scope of its training. Note, however, that AI can and is just as biased as the data we choose to feed into our ML models. In turn, though we rely on ever-increasing quantities of data to make decisions, that data is just as increasingly mediated by machines that try to spoon-feed it to us in ways that make it easier to consume.
SEE: Artificial intelligence: A business leader’s guide (free PDF) (TechRepublic)
That doesn’t mean AI isn’t useful for businesses and consumers trying to solve real-world problems, it just means that we’re nowhere close to machines that can actually make independent decisions or arrive at conclusions without being given the proper data first. And it’s also true that AI can tend to confirm our biases, rather than eliminate them.
What can artificial intelligence do?
Artificial intelligence, as mentioned, is essentially pattern matching at scale. No human can comb through gargantuan piles of data to uncover patterns in that data — machines can. By contrast, machines struggle when presented with an outlier that might be easy for a human to spot but contradicts the data the machines have been trained with. Machines can’t reason, but people can. The best AI applications, therefore, are highly focused and combine human reasoning with the brute power of ML.
SEE: All of TechRepublic’s cheat sheets and smart person’s guides
Since the COVID-19 pandemic began in early 2020, artificial intelligence and machine learning has seen a surge of activity as businesses rush to fill holes left by employees forced to work remotely or those who’ve lost jobs due to the financial strain of the pandemic.
The artificial intelligence rich definitely got richer in 2021, according to the 2022 Stanford AI Index report. Private venture investment in AI exploded to $93.5 billion in 2021, more than doubling the 2020 tally. At the same time, the nature of where organizations are focusing their AI investments has changed. The global pandemic shifted AI priorities and applications: Instead of solely focusing on financial analysis and consumer insight, post-pandemic AI projects have trended toward customer experience and cost optimization, Algorithmia found.
Like other AI applications, customer experience and cost optimization are based on pattern recognition. In the case of the former, AI bots can perform many basic customer service tasks, freeing employees up to only address cases that need human intervention. AI like this has been particularly widespread during the pandemic, when workers forced out of call centers put stress on the customer service end of business.
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What are the business applications of AI?
Modern AI systems are capable of amazing things, and it’s not hard to imagine what kind of business tasks and problem solving exercises they could be suited to. Think of any routine task, even incredibly complicated ones, and there’s a possibility an AI can do it more accurately and quickly than a human — just don’t expect it to do science fiction-level reasoning.
In the business world, there are plenty of AI applications, but perhaps none is gaining traction as much as business analytics and its end goal: Prescriptive analytics.
Business analytics is a complicated set of processes that aim to model the present state of a business, predict where it will go if kept on its current trajectory and model potential futures with a given set of changes. Prior to the AI age, such analytics work was slow, cumbersome and limited in scope.
When modeling the past of a business, it’s necessary to account for nearly endless variables, sort through tons of data and include all of it in an analysis that builds a complete picture of the up-to-the-present state of an organization. Think about the business you’re in and all the things that need to be considered, and then imagine a human trying to calculate all of it — cumbersome, to say the least.
Predicting the future with an established model of the past can be easy enough, but prescriptive analysis, which aims to find the best possible outcome by tweaking an organization’s current course, can be downright impossible without AI help.
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There are many AI software platforms and AI machines designed to do all that heavy lifting, and the results are transforming businesses: What was once out of reach for smaller organizations is now feasible, and businesses of all sizes can make the most of each resource by using AI to design the perfect future.
Analytics may be the rising star of business AI, but it’s hardly the only application of artificial intelligence in the commercial and industrial worlds. Other AI use cases for businesses include:
Recruiting and employment
Human beings can often overlook qualified candidates, or candidates can fail to make themselves noticed. Artificial intelligence can streamline recruiting by filtering through larger numbers of candidates more quickly and by noticing qualified people who may go overlooked.
Fraud detection
Artificial intelligence is great at picking up on subtle differences and irregular behavior. If trained to monitor financial and banking traffic, AI systems can pick up on subtle indicators of fraud that humans may miss.
Cybersecurity
Just as with financial irregularities, artificial intelligence is great at detecting indicators of hacking and other cybersecurity issues.
Data management
Using AI, you can categorize raw data and find relations between items that were previously unknown.
Customer relations
Modern AI-powered chatbots are incredibly good at carrying on conversations thanks to natural language processing. AI chatbots can be a great first line of customer interaction.
Healthcare
Not only are some AI applications able to detect cancer and other health concerns before doctors, they can also provide feedback on patient care based on long-term records and trends.
Predicting market trends
Much like prescriptive analysis in the business analytics world, AI systems can be trained to predict trends in larger markets, which can lead to businesses getting a jump on emerging trends.
Reducing energy use
Artificial intelligence can streamline energy use in buildings, and even across cities, as well as make better predictions for construction planning, oil and gas drilling, and other energy-centric projects. AI is also being used to minimize corporate water use in the face of climate change.
Marketing
AI systems can be trained to increase the value of marketing both toward individuals and larger markets, helping organizations save money and get better marketing results.
If a problem involves data, there’s a good possibility that AI can help. This list is hardly complete, and new innovations in AI and ML are being made all the time.
What AI platforms are available?
When adopting an AI strategy, it’s important to know what sorts of software are available for business-focused AI. There are a wide variety of platforms available from the usual cloud-hosting suspects like Google, AWS, Microsoft and IBM, and choosing the right one can mean the difference between success and failure.
AWS Machine Learning offers a wide variety of services that run in the AWS cloud. AI services, pre-built frameworks, analytics tools and more are all available, with many designed to take the legwork out of getting started and others like SageMaker for Business Analysts designed to enable corporations to get business insights without writing code. AWS offers pre-built algorithms, one-click ML training and training tools for developers getting started in or expanding their knowledge of AI development.
Google Cloud offers similar AI solutions to AWS, as well as having several pre-built total AI solutions that organizations can ideally plug into their organizations with minimal effort. Google also distinguishes itself by innovating some of the industry standards for AI like TensorFlow, an open source ML library.
Microsoft’s AI platform comes with pre-generated services, ready-to-deploy cloud infrastructure and a variety of additional AI tools that can be plugged into existing models. Its AI Lab also offers a wide range of AI apps that developers can tinker with and learn from what others have done. Microsoft also offers an AI school with educational tracks specifically for business applications.
Watson is IBM’s version of cloud-hosted ML and business AI, but it goes a bit farther with more AI options. IBM offers on-site servers custom built for AI tasks for businesses that don’t want to rely on cloud hosting, and it also has IBM AI OpenScale, an AI platform that can be integrated into other cloud hosting services, which could help to avoid vendor lock-in. In 2021, Watson suffered a media backlash after years of overpromising on what its AI could deliver in healthcare, but many enterprises still turn to it for narrower tasks.
Before choosing an AI platform, it’s important to determine what sorts of skills you have available within your organization, and what skills you’ll want to focus on when hiring new AI team members. The platforms can require specialization in different sorts of development and data science skills, so be sure to plan accordingly.
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What AI skills will businesses need to invest in?
With business AI taking so many forms, it can be tough to determine what skills an organization needs to implement it.
As previously reported by TechRepublic, finding employees with the right set of AI skills is the problem most commonly cited by organizations looking to get started with artificial intelligence. Perhaps the most critical skill, however, is knowing when to skip AI altogether. The reality of AI is that many problems could be solved by applying simple regression analysis or if/then statements. Most AI, in other words, isn’t AI at all: It’s just basic math and common sense.
For more complicated, AI-oriented tasks, the associated data science breaks down into two categories: That which is intended for human consumption and that which is intended for machine consumption.
In the latter case, AI involves complex digital models that apply ML models and algorithms to large amounts of data. These systems then act autonomously to generate a particular ad or customer experience, or make real-time stock trades. Hence, machine-oriented AI professions require “exceptionally strong mathematical, statistical and computational fluency to build models that can quickly make good predictions,” as former Google and Foursquare data scientist Michael Li has noted.
By contrast, the skills needed for more human-oriented data science and AI skew toward storytelling. Given that no data is unbiased, the role of the human-oriented data scientist is to help the data tell clear stories. Such AI storytellers use data visualization to facilitate exploration and insights into that data.
For many in AI, the most sophisticated math they’ll do is power analyses and significance tests. They might write SQL queries to get data, do basic math on that data, graph results and then explain the results. Not gee-whiz data science, but incredibly helpful for breaking down complex data into actionable insights, to use the data science lingo.
With all that in mind, it’s still true that skills needed for an AI project differ based on business needs and the platform being used, though most of the biggest platforms support most, if not all, of the most commonly used programming languages and skills needed for AI.
SEE: Don’t miss our latest coverage about AI (TechRepublic on Flipboard)
TechRepublic covered in March 2018 the 10 most in-demand AI skills, which is an excellent summary of the types of training an organization should look at when building or expanding a business AI team. However, since that time, Python has grown in popularity for AI, and R has been in relative decline.
Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.
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How can businesses start using AI?
Getting started with business AI isn’t as easy as simply spending money on an AI platform provider and spinning up some pre-built models and algorithms. There’s a lot that goes into successfully adding AI to an organization.
At the heart of it all is good project planning. Adding artificial intelligence to a business, no matter how it will be used, is just like any business transformation initiative. Here is an outline of just one way to approach getting started with business AI.
Determine your AI objective
Figure out how AI can be used in your organization and to what end. By focusing on a narrower implementation with a specific goal, you can better allocate resources.
Identify what needs to happen to get there
Once you know where you want to be, you can figure out where you are and how to make the journey. This could include starting to sort existing data, gathering new data, hiring talent and other pre-project steps.
Build a team
With an end goal in sight and a plan to get there, it’s time to assemble the best team to make it happen. This can include current employees, but don’t be afraid to go outside the organization to find the most qualified people. Be sure to allow existing staff to train so they have the opportunity to contribute to the project.
Choose an AI platform
Some AI platforms may be better suited to particular projects, but by and large they all offer similar products in order to compete with each other. Let your team give recommendations on which AI platform to choose — they’re the experts who will be in the trenches.
Begin implementation
With a goal, team and platform, you’re ready to start working in earnest. This won’t be quick: AI machines need to be trained, testing on subsets of data has to be performed and lots of tweaks will need to be made before a business AI is ready to hit the real world. In fact, you should expect that the vast majority of your time won’t be spent in crafting sexy algorithms, but rather in data preparation.
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