How AI Works: The Basics You Need to Know

Although AI is not exactly fool-proof, it is pretty close to it. There are many benefits to using AI in your workflow and processes. Here are just a few examples of its benefits.

1. It reduces human error.

Let’s face it. Sometimes people make mistakes. We are only humans, after all. The thing about making a mistake is that we can usually learn from it, process what we have learned, and attempt not to make the same mistake again.

Artificial intelligence operates in the same way. While AI acts and performs like a human, it can vastly reduce human error by helping us understand all possible outcomes and choosing the most appropriate one.

AI uses real-time data to predict alternative outcomes. Using data and predictions, we can better understand our options, the results, and the impacts of those outcomes.

This is particularly helpful in business. Decision-makers can consider all possibilities before moving forward.

2. It helps with research and data analysis.

Another benefit of AI is using technology for research and data analysis. AI technologies are smart and can gather necessary information and make predictions in minutes.

What would usually take a human months of research can now be done in significantly less time.

The data collected by AI and the analysis performed are invaluable. With the information collected by AI, your data analysts are better able to make smarter, more informed decisions in less time.

Use the data collected by AI alongside your data analysts' work.

3. It can makes unbiased, smart decisions.

With the appropriate data, AI removes bias from decision-making. To get the best, unbiased results using AI technologies, you need to ensure you input the most accurate information and data set.

When AI is given the best data, it can accurately predict outcomes, solve problems, and properly perform its functions without human favor of a particular desired result.

However, if the data you feed your AI programs is flawed, you will likely have a biased outcome.

Be sure to check your data for accuracy to maximize this benefit of AI.

4. It performs repetitive tasks.

Although automation and AI are not the same technologies, AI can act like an advanced version of automation, meaning it can be used to perform repetitive tasks and suggest alternative outcomes.

Using AI to perform repetitive tasks gives your employees more time to work on other more complex matters, like closing a sale or checking in with current clients on your roster to retain customers.

AI can be used to perform a multitude of repetitive tasks. AI can perform tasks in HR, like employee onboarding.

AI can also integrate with a chatbot into your website. Although a chatbot might not provide a human touch when interacting with potential customers, using AI to automate interactions between your company and your clients can jump-start processes and move your clients through your pipeline.

For example, AI can help a would-be customer start a new inquiry and gather important customer information and behavior data. Then, that data can be entered into your CRM for later review.

How does AI work?

AI technology is a complex and extremely useful for businesses. HubSpot has incorporated AI right into its software to augment already existing workflows.

HubSpot’s AI can uncover team performance by monitoring sales calls and providing insight to the team. It can also optimize content or create transcripts of recordings and calls.

If AI is a complex but necessary technology, how does it work?

To put it simply, AI works by combining large data sets with intuitive processing algorithms. AI can manipulate these algorithms by learning behavior patterns within the data set.

It’s important to understand that AI is not just one algorithm. Instead, it is an entire machine learning system that can solve problems and suggest outcomes.

Let’s look at how AI works step-by-step.

Input

The first step of AI is input. In this step, an engineer must collect the data needed for AI to perform properly.

Data does not necessarily have to be a text input; it can also be images or speech. However, it’s important to ensure the algorithms can read inputted data.

It’s also necessary to clearly define the context of the data and the desired outcomes in this step.

Processing

The processing step is when AI takes the data and decides what to do with it. While processing, AI interprets the pre-programmed data and uses the behaviors it has learned to recognize the same or similar behavior patterns in real-time data, depending upon the particular AI technology.

Data Outcomes

After the AI technology has processed the data, it predicts the outcomes. This step determines if the data and its given predictions are a failure or a success.

Adjustments

If the data set produces a failure, AI technology can learn from the mistake and repeat the process differently. The algorithms’ rules may need to be adjusted or changed to fit the data set.

Outcomes may also shift during the adjustment phase to reflect a more desired or appropriate outcome.

Assessments

Once AI has finished its assigned task, the last step is assessment. The assessment phase allows the technology to analyze the data and make inferences and predictions. It can also provide necessary, helpful feedback before running the algorithms again.

AI is extremely beneficial in business. However, choosing the right AI technology for your business needs is important.

The Four Concepts of AI

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As previously mentioned, not every type of AI will be appropriate for your business, your processes, or your data set. In fact, there are four main concepts of AI that you should consider.

1. Reactive Machine

Reactive machines live up to their concept name. This type of AI can respond or react to real-time data. However, this AI is limited and can’t store information or build a memory bank.

Because it can’t store memories, the AI can’t use past experience to analyze data based on new data behavior.

Reactive machine technologies are best used for repetitive tasks designed for simple outcomes. Consider using reactive machines to organize new client information or filter spam from your inbox.

2. Limited Memory

Unlike reactive machines, limited memory technologies can store and use information to learn new tasks. A limited memory machine will need pre-programmed data to be set in motion.

Once it has processed that information, it can analyze real-time data to make predictions and observations.

Limited memory technology is the most common AI technology used in business. In fact, this is the technology that makes self-driving cars work.

A chatbot is an example of limited memory technology. Chatbots use pre-programmed data to interact with customers and predict their needs based on their actions and inquiries.

3. Theory of Mind

Theory of mind technology is more advanced than limited memory. Like limited memory, theory of mind technology can store information and make observations based on the real-time data it observes.

This technology is more advanced, though, meaning it can respond to human emotions.

Theory of mind technology must be designed to understand that humans are complex, with individual thought patterns and past experiences that affect how they respond to certain stimuli. Because of this, theory of mind technologies are not yet fully developed.

As it stands now, AI cannot fully respond to people in a human-like manner.

4. Self-Aware

Self-aware technology takes the theory of mind technology one step further. It can process information, store it, use it to inform decision-making processes, understand human emotions and feelings, and is also self-aware on a human level.

In other words, self-aware machines operate like human consciousness and can have their own thoughts and feelings.

Self-aware technology is still a very long way off from being fully developed. But, scientists and researchers are making small strides in understanding how to implement human emotions into AI technology.

How to Create Basic AI

AI does not have to be overly complicated in order for you to benefit. You can use AI to perform repetitive functions that drain your employees of their valuable time — time that could be spent strengthening client relationships or making a sale.

To use AI, consider the processes and workflows you can remove from your employees’ plates. Specifically, think about processes you can automate and will not have to tweak as AI does its job.

Let’s look at the basics of implementing AI in your workflow.

1. Define the problem.

Before you decide to incorporate AI into your workflow, consider the processes your teams use daily that are time-consuming and repetitive.

Does your team spend significant time sorting through data to find contact information for potential clients? Could they use their time better by speaking to potential clients and onboarding new customers?

Take some time to identify time-consuming workflows and make a list. From this list, pick a process that is straightforward and repetitive.

2. Define the outcomes.

AI should enhance your already established processes. After you have made a list of processes and workflows that can benefit most from AI, define the desired outcomes.

For example, AI can gather and sort customer data. But before AI can sort through your potential customer base, you need to tell it what to look for and how to sort the information.

Be sure to clearly define the outcomes of your AI processes. AI works best if you have an end goal in mind.

3. Organize the data set.

Having an extensive, organized data set to input into AI technologies is critical. If you do not already keep your data in a centralized location, it’s best that you do that before implementing AI. You don’t want your program to miss an essential data set because it was housed in a different system.

Use a CRM, like HubSpot’s, to organize your data. You’ll need clean data that the algorithm can read. That way, AI technology can understand the data set and recognize its patterns and behaviors.

4. Pick the right technology.

There are hundreds of AI algorithms to choose from, each performing a task with varying efficiency and quality. It’s important to understand that not every algorithm will suit your data set, problem, or desired outcome.

Spend time researching the best AI technology and choosing the one that best fits your needs. Once you have selected an AI technology, run the data to create a model.

5. Test, simulate, and solve.

Now that you have the appropriate technology and a model of what the data should do, rerun the data to test it. This will allow you to determine any kinks that need to be worked out. Once you’re ready to deploy AI, embed it into your workflows, and let it do its thing!

Now you and your employees have more time for more pressing and valuable matters.

AI Use Cases for Marketers

AI technologies can significantly enhance marketing teams' performance in various ways.

We already know AI can be used for the chatbots on your customer-facing websites. But there are many other ways to incorporate AI into your marketing game. Here’s how.

Sales Forecasting

Sales forecasting is like looking into a crystal ball. Only this crystal ball predicts the future margins of sales for your company.

Analysts must collect necessary data from various sources to make an appropriate forecast. Then, they’ll sort through the data and customer behaviors, compare it to historical data, and predict future sales.

Data analysts often use automated algorithms to help them sort through historical data and keep track of important new information. This process can take quite a while.

But the good news is it can be sped up significantly with the help of AI technology. AI can store data collected from chatbots, analyze which customers are most likely to make a sale, compare real-time data with historical data, and make predictions and assumptions about future sales.

AI uses predictive analytics and can predict forecasts that are up to 80% accurate.

Targeted Advertisements and Content Personalization

Targeted advertising and content personalization is Marketing 101. Every good marketer knows that to make the most sales, it's necessary to put your brand in front of the eyes of the appropriate audience. AI technologies take targeted advertisements one step further.

You already know your target audience, but do you know exactly what they do after seeing your company’s ad? The reality is you might have a good indicator of customer behavior, but sometimes you may miss the mark. AI can help you make a better inference.

AI can use predictive analytics to determine customer behavior and potential customers' actions after seeing your ad. The massive amount of advertising information and customer behavior data gathered by AI can also display the next appropriate ad to your customers.

Lead Generation

In the past, a marketer would need to run several advertisements, collect potential customer data, create a customer profile, establish a contact list, and begin contacting would-be clients. This process would likely take days to complete, cutting into sales time.

AI drastically reduces the time marketing and sales teams spend on lead generation. AI can gather customer data, create customer profiles, and generate a contact list of potential customers most likely to make a purchase.

With the time saved, salespeople can better use their time by contacting qualified leads, establishing relationships with new clients, and making the all-important sale.

Dynamic Pricing

AI isn’t just about saving time for your employees. AI can help maximize profits and margins by enabling dynamic pricing. Dynamic pricing is a marketing strategy many businesses use to adjust the prices of their products based on the current supply and demand.

AI technologies use dynamic pricing models to help predict customer behavior, supply, and demand to alert salespeople when to increase or decrease the price of a product or service.

Enhance your business with AI.

While AI can be a complicated technology, using it in your business doesn’t have to be. Artificial intelligence technologies can significantly improve your workflows by saving valuable time and making more accurate predictions.

Brainstorm with your team to list potential processes to automate with AI software. Then, find the appropriate AI technology that will work best for you and your employees. Start improving your business through AI today.