Artificial intelligence (AI) is increasingly becoming part of our lives, even in ways we don’t realize. As a business, this has a wide range of positive implications. But first, let’s take a step back and explore AI at its core.

The team at Pew Research asked people how often they think they engage with AI. The majority thinks they interact with it a few times a week. In reality, most people engage with AI throughout each and every day. From checking spam folders to allowing an app to set the music playlist, we are using AI.

Before implementing the newest AI tool in your workplace, use this guide to learn what is AI and what its applications are. Also, discover how it can potentially change society in the coming years.

What is AI technology?

Artificial intelligence is the process of teaching computers to make decisions. The goal is to simulate human intelligence to promote problem-solving and critical thinking when faced with a challenge.

Before you look at AI, it helps to take a step back to consider what a machine is. At its core, it is a system designed to perform tasks through mechanical power. For example, a lawnmower or a vending machine. AI takes the concepts of machinery one step forward. The machine determines the best way to complete its assignment.

Consider the difference between a traditional vacuum cleaner and a robot one. A human has to turn on the vacuum, move it across the room, avoid obstacles, and decide when the job is done. With AI, the robot maps out the room, calculates the best route and makes sure to vacuum as much of the space as possible. Its algorithms allow it to think and solve problems, like going around the leg of a chair.

To gain a fundamental understanding of AI, it’s important to recognize that computers use various types of reasoning to accomplish their tasks. Engineers continue to create new AI tools that leverage these different methods of thinking. As a result, there is an increasing number of AI applications emerging in the market. This guide will explore these distinct types of reasoning and explain how they are put into practice.

Is machine learning AI?

As you learn more about artificial intelligence you will also come across the term machine learning. These two concepts are not the same, but they are related.

Machine learning is the process for computers to gather information that can be applied to their decision-making. The goal of machine learning is to replicate the neural patterns of humans so computers can think for themselves.

On the other hand, basic AI works a little differently. Programmers give information to computers and these use it to complete their tasks. For example, a computer might be taught to identify an octopus. They would show the computer hundreds of pictures of octopi to give it information about what these animals look like.

In other words, with machine learning, the computer is taught to learn about sea life and then identify an octopus based on the information it gathers. Programmers can spend less time teaching computers because the AI systems can learn new information on their own.

Despite their similarities, in the world of technology, it’s not artificial intelligence vs. machine learning. One is a feature that supports the other. The better the machine is at learning, the more it can apply that information and skills to the tasks at hand.

The four types of artificial intelligence

Now that you have a basic understanding of how machines learn and use that information, you can explore the different types of AI that developers use. While it is widely recognized that there are four types of AI, researchers have yet to develop all of them. There are still forms of computer processing that haven’t been created yet but will likely develop in the future. Here are the four types to know about.

Reactive artificial intelligence

This is the most basic form of AI. The computer is only able to recognize simple patterns and cannot process information outside of its rules. For example, a reactive AI machine that is trained to fold towels won’t be able to fold a sock. Even the fastest laundry-folding robot still takes more than a minute to fold a single towel.

Reactive AI systems have the lowest level of machine learning. Programmers can give them rules, but they are rarely able to make their own.

Limited memory AI

This is a slightly more advanced AI. With this option, the robots can learn from their past mistakes. They can also make predictions based on historical data, which improves their decision-making.

Learning from experience will be essential for self-driving cars that encounter foreign objects. If an autonomous vehicle encounters a dog on the road, it should use its previous experience to treat it like a pedestrian and wait for it to cross.

These AI systems need machine learning so they can solve problems when they encounter them.

Theory of mind AI

This AI level understands human reasoning and can empathize with people. The robot steps away from being a purely logical machine and can understand how different events affect people.

Emotionally intelligent robots will be helpful with customer service chatbots. People are emotional when snow storms cancel flights and cause people to miss the holidays. Robots can benefit from knowing the humans they are speaking to are upset so they can empathize while also solving the problem. Exactly like a human hopefully would.

Until now, this level of AI remains science fiction, as current systems lack true emotional understanding. And while theory of mind AI aims to mimic human empathy, the next AI level is a purely theoretical concept.

Self-aware AI

This would be the most advanced artificial intelligence system that has not been developed yet.

Just imagine this. The robot is aware that it is a machine programmed for specific tasks. It thinks like a human and has complex problem-solving and neural processing skills. However, this too is science fiction and we simply have no clue when, or if, it will ever be possible.

What is generative AI?

As you explore the artificial intelligence basics, you might come across the term generative AI (GenAI).

GenAI is one of the most consumer-friendly ways for people to engage with artificial intelligence technology. In late 2022, seemingly everyone was sharing AI selfies on their social media pages as generative AI tools made art based on their images. Ever since then, all GenAI flavors available have been used to generate all sorts of content, from sitcom episodes to podcast scripts.

With GenAI, computers aren’t thinking for themselves. These systems pull information from across the web and generate content from it. Google initially cautioned against AI-generated content because it was low-quality and overproduced. However, Google later pivoted to a more nuanced response. They introduced tools like Gemini and shifted toward supporting AI as a research and organizational tool.

As you explore AI uses in your business, you will likely come across GenAI tools. Consider how you can use these apps and why they might be valuable to your operations.

Why should companies care about AI?

Before jumping on the bandwagon, it’s important to consider why you should use AI-based tools in the first place. One of the biggest reasons is simply efficiency. By teaching computers to think for themselves, they can complete tasks that humans do not enjoy or take up a lot of time.

Using the robot vacuum again as an example, this eliminates that specific task from your to-do list. This way, you’ll have more time for other chores or for relaxation.

The same applies to artificial intelligence software in the workplace. When you can eliminate tedious tasks from your workload, you can focus on more strategic problems that robots cannot solve. AI is a tool that is used to help companies scale their processes and free up time.

Keep this benefit in mind as you explore different digital intelligence systems. Check that each AI tool you adopt saves time and provides the same (if not better) results than a human. This will help you choose AI tools effectively so you don’t rush into solutions that fail to produce desired results.

What can AI do for your business?

More companies incorporate artificial intelligence into their software systems. This leads to more and more AI applications being released on the market.

Basic apps like ChatGPT can help teams brainstorm blog posts or generate social media content, saving time for the marketing department. Going a step further down the line, companies of all sizes and across all industries began testing (partially) autonomous AI agents, the new kid on the block these days 🙂

Here are a few ways companies can tap into AI technology, based on responses by a Forbes Advisor survey.

Customer service

More than half of business owners see value in artificial intelligence for customer service. One of the most common uses of AI is with chatbots and virtual answering services.

While this technology has been around for decades, AI tools make them more advanced. The virtual answering system won’t rely only on a series of prompts and keywords, like before. Instead, it will use natural language processing to better understand customers and their needs.

When AI tools solve basic problems without human intervention, representatives devote time to more complicated problems. This cuts down on the amount of time it takes to resolve issues as more customers are helped simultaneous due to AI. Check out these case studies on how AI has impacted customer service for multiple companies.

While AI chatbots enhance customer service, businesses must guard against algorithmic bias. For instance, chatbots trained on biased historical data might provide unequal support to certain groups. To mitigate this, companies should regularly audit AI systems for fairness and ensure diverse datasets.

Additionally, human oversight is essential for complex or sensitive interactions. AI should augment, not replace, empathy and judgment in customer-facing roles.

Cybersecurity

Even basic artificial intelligence tools are trained to identify patterns. And AI will continue to be valuable for cybersecurity and fraud detection. Through limited memory programming, AI tools can learn from their experience and grow their knowledge of how hackers and fraudsters try to attack companies. This allows them to stay caught up on new scams, even before human IT departments know they are a threat.

One report found that companies that didn’t use AI in cybersecurity lost $5.36 million on average in data breaches. Those that used AI extensively only lost around $3.60 million according to IBM Security Cost of Data Breach Report 2023.

Inventory management

Operational teams can use AI to track inventory and make sure it isn’t running low. Teams will either receive alerts that they need to replenish items or, in the future, autonomous AI agents will order them automatically. The right systems will make inventory management smarter. It’s clear that overordering causes waste. By reducing it, companies will save money in the long run.

Traditional auto-replenish systems operate on fixed rules, but AI inventory management takes this further. It analyzes demand patterns, seasonality, and external factors like weather for example. Through this analysis, AI enables organizations to optimize their orders dynamically.

Marketing

This article has already touched on a few ways that AI supports content marketing efforts. Generative AI tools can list out dozens of ideas within a few minutes. They can create outlines and generate rough drafts that human copywriters can then improve upon. These tools can also create social media content, including images and posts with hashtags, within a few minutes. Just keep in mind that AI tools complement human creativity by generating drafts, freeing up time for strategic editing and ideation.

Outside of content creation, AI can also be used for advanced segmentation. Marketing teams can save money by targeting specific audiences instead of mass messaging their customers. An AI tool can process data and identify patterns much faster than a human can. Furthermore, companies can obtain better marketing results without spending hours on campaign development.

Accounting

Depending on the need, AI technology can be reactive or predictive. With reactive AI systems, accountants can present large groups of data for the technology to organize and review. These tools can sort through complicated financial documents and audit countless receipts, invoices, and checks. A process that would take a human bookkeeper weeks could be done much faster through a programmatic workflow.

With predictive AI, technology tools forecast trends and guide companies to make decisions. They might use historical data to make estimates and then adjust them based on recent patterns. AI could be used by financial advisors who want to help their clients make strategic decisions for the future.

Human resources management

You can already notice AI rapidly shaping HR teams. Its role starts with recruitment. AI software systems can draft job listings based on a list of tasks provided by the managers. They can sort through hundreds of resumes to choose the best ones.

Once a candidate is chosen, AI can step in again with useful onboarding tools and personalized training materials. The company’s internal knowledge base can become more useful this way. New employees could use it better to identify resources and receive answers to their questions.

By asking AI to perform repetitive tasks, HR teams can focus on more meaningful work. They can get to know job applicants better and have better conversations with people who need to resolve conflicts. They can focus more on the human element in human resources.

Ethical considerations in AI implementation

While AI offers benefits, businesses must address ethical considerations. They should mitigate risks such as algorithmic bias, data privacy violations, and unintended consequences of automation. Furthermore, responsible AI adoption requires transparency and accountability.

In general, regular third-party audits can help verify that AI systems align with such ethical standards and regulatory requirements.

Bias mitigation

The first thing that comes to mind is bias mitigation. Because AI systems can unintentionally perpetuate bias if trained on flawed data. A well-known example is hiring tools favoring certain demographics over others.

While AI streamlines HR tasks like resume screening, it’s vital to address bias. Over-reliance on AI could lead to biased hiring decisions. This happens if algorithms favor candidates who match historical patterns rather than qualifications. Therefore, companies should pair AI tools with human review to ensure fairness.

Privacy compliance

AI-driven customer interactions must respect privacy. Generative AI tools sometimes process personal information from users. Thus, also such tools must comply with regulations like GDPR to avoid legal and reputational risks.

Cybersecurity best practices

While AI strengthens cybersecurity, it also introduces new risks. For example, generative AI tools might inadvertently expose sensitive data if trained on unsecured internal documents.

Companies must enforce strict access control policies and anonymize datasets whenever possible. AI systems handling customer or employee data must comply with privacy laws. Additionally, they must ensure transparency about how information is used. Prioritizing ethical data practices builds trust and avoids costly compliance failures.

Human impact of AI automation

Companies should also consider the societal impact of automation. While AI boosts efficiency, it may displace certain roles.

Organizations could invest in upskilling employees rather than sidelining them. Let’s say a factory automates assembly lines with robotics. But instead of laying off manual laborers, they create a training program. This way, workers learn to operate, maintain, and troubleshoot automated systems.

By prioritizing upskilling, companies turn potential disruptions into opportunities for growth. And everybody wins. Employees gain new skills and feel valued. Productivity skyrockets, fueling business success.

Transparency

Lastly, transparency is crucial. Stakeholders need to understand how AI systems make decisions. And this necessity is amplified in high-stakes scenarios like financial advising or medical diagnostics. In such cases, decision-making clarity is paramount.

Ethical AI adoption demands proactive measures. These may include regular audits of algorithms and clear communication about AI’s role in your workflows.

How to make an AI tool work for your business

As you might assume, there is no single best way to use AI. While it has many benefits, it’s important to be careful with how you incorporate AI software into your workplace. You need to approach it mindfully and track how it impacts your operations as a whole. Remember that the first tool or software you implement might not work. So be willing to pivot as you test what does and doesn’t fit for your needs.

Here are a few things to ask as you try to improve existing workflows with AI.

• What specific pain point will this AI tool address? Avoid tools that have to make up problems to sell your solutions.
• How will our team measure the impact of the AI software? Set key metrics for output, time-saved, and other capabilities.
• What human oversight does this tool require? At this point in its development, AI still requires human management.
• Can one software tool provide multiple services? As more tools use AI, you might be better off with one software system instead of several apps.
• How will humans be impacted by the tool? Consider how your customers, employees, and vendors will be affected by your new systems.

These questions can guide your discussions as you explore different AI-based apps. For example, you might love the idea of an AI chatbot, but your customers might not. While an employee might want to use a specific app, another option might be better if it has additional functionality. Move forward strategically with AI and track how it changes your operations.

Understand how AI can impact your team

Before you start shopping for products and solutions using AI, you need to understand what it is. For sure, artificial intelligence is a tool for saving time. It empowers computers to use logical reasoning to solve problems and make decisions.

The next step is to decide how to apply AI to your company’s workflows. As you embrace it, remember that technology alone isn’t the answer. Ethical AI adoption requires balancing innovation with responsibility. Ensure your tools empower people, protect privacy, and uphold fairness at every step.

AI won’t solve all of your problems, but it can be used to improve your operations. And it will help you move your business forward. You just have to know how to use it and keep flexible as you learn.