AI Knowledge Center

All-in-one encyclopedia for business users

What is AI?

Artificial Intelligence (AI) can be defined as giving a machine the ability to mimic human intelligence, or the ability to react to changes in its environment with the goal of accomplishing some task. In developing technol- ogies that are considered AI, many fields are involved – from the well-known (computer science) to the lesser-known (cognitive neuroscience) to the unknown (linguistics) – and all contribute to the development of new automated and intelligent systems increasingly permeating our world.

As AI technology advances, more business applications are being developed. For example, simple applications like chatbots are being integrated into many online service providers to speed up customer communication and increase satisfaction. Moreover, more complex applications like autonomous driving are being continu- ously tested and updated to be fully commercialized.

But the real question is, “What do I need to know to find the right AI for my business?” Let’s dive in to take a closer look at Artificial Intelligence and how different types of AI can be applied in solving various business problems.

Types of AI

AI can be divided into different categories according to the method it uses. The simplest diagram would be: AI > ML > NN > DL

Machine Learning

Machine Learning is an application of AI that enables machines and systems to learn on their own to predict real-life outcomes. The keywords to remember are “self-learning,” “access to more data,” and “iterative learning on-the-go.” The machine learns on its own without explicit programming. It utilizes data to learn patterns within and develop self-learning algorithms to maximize the performance. Naturally, more data will enable the machine to find more accurate patterns and iterative experiments lead to better results. Within Machine Learning, there are Supervised or Unsupervised Machine Learning, based on the availability of a target variable, or the output.

Neural Networks

Neural Networks use a system of mathematical connections that mimic neurons in the human brain. Unlike the human brain in which neurons communicate with other neurons simultaneously in different directions, Neural Networks learn from the data with clearly defined stages of observation. The networks are made up of stacked layers of simple computational nodes that work in unison to find patterns within your data. Compared to general Machine Learning algorithms, Neural Networks are better at learning complex and diverse datasets, especially text and images. Neural Networks can be used in both Supervised and Unsupervised Learning.

Deep Learning

Deep Learning is another application of Machine Learning that is particularly useful when working with unstructured and unlabeled data. By adding more layers of complexity to what you’re asking the machine to learn, Deep Learning uses many layers of Neural Networks to find complex patterns in the data. By going “deeper” than a single-layer neural network would achieve on its own, Deep Learning learns from its mistakes and delivers probability with a degree of accuracy.

What's right for me?

Whether you are in charge of implementing AI in your organization or a subject matter expert looking to enhance productivity with AI, you need to pick the right AI because each business case is unique and requires a different strategic approach.

Supervised Learning

When we have the output values, we can teach the machine to learn the relationship, or find the pattern, between the input and the output values, thus allowing it to find the best way to build a model that will predict an output when it’s given a new set of input values.

For example, you can ask a machine to predict the chances of a person developing diabetes. If you look at different key parameters – such as height, weight, BMI, age, blood sugar levels, genetic markers, etc. – you can see that manually creating and testing various combinations is next to impossible. However, if we ask a machine to digest the data, learn the rules, and find patterns, it can very quickly build a machine learning model that can output the probability of a person developing diabetes when a new input, or a patient’s information, is provided.

Unsupervised Learning

Unsupervised Machine Learning is for when there isn’t a specific set of output labels, with a goal of learning from the structure of the data. A common use of clustering would be grouping data points that are similar to each other without a label. Let’s use the example of predicting diabetes; we do not know of the disease: “diabetes,” or its symptoms. Imagine a room full of people with an unknown illness and varied symptoms. Doctors could spend weeks finding commonalities, attempting to group patients with the similar conditions. We can ask a machine to find clusters of patients that are alike. The machine would group patients with similar symptoms, though not directly identifying the patients with diabetes because we ourselves would not know, thus making it easier to identify who needs assistance.

Deep Learning

Deep Learning is commonly used in analyzing images, videos, and speech recognition.

Natural Language Processing (NLP) is when a machine interacts with your words as they come out naturally – processing grammar, semantics, and phonetics in text and audio formats. For example, a banking chatbot will address your queries, make recommendations, search through your previous conversations or customer history with the bank, and help you to resolve your problem.

Computer Vision is a field of AI where a machine learns to understand the context of images and videos. A common example is the Autonomous Vehicles because the vehicle is constantly analyzing the visual data collected to move safely.

Marketing

Increase Lead Conversion Rate to Optimize Resources for ROI

Business Challenge
With various leads generated from the website, the marketing team is tasked with building a lead scoring algorithm to prioritize leads for the sales team to pursue.

AI Solution
By looking at the historical data of converted customers, the marketing team can identify the segment of customers that are more likely to convert.

Data Can Include

  • Industry
  • Department
  • Annual Revenue
  • Customer Title
  • Region
  • Landing Page

Target
Conversion Rate

Sales

Better Sales Pipeline Forecasting for Improved Budget Planning

Business Challenge
Each company has a unique lifecycle and requires different attention. The sales team is asked to optimize resource allocation.

AI Solution
From the historical data of the sales pipeline, the sales team can calculate how many days the team needs to engage in order for the deal to close.

Data Can Include

  • Downloaded Content Type
  • Number of Calls Made
  • Number of On-site Meetings
  • Number of Virtual Meetings
  • Region
  • Deal Size

Target
Number of Days to Close

Customer Service

Increase Customer Lifetime Value by Predicting Churn

Business Challenge
Customers leave for a variety of reasons. The Customer Service (CS) team wants to reduce customer churn and improve customer satisfaction by understanding the reasons and engaging with customers with a high risk of churn.

AI Solution
The historical data of customers can be used to discover the correlation between a customer leaving and a support ticket type. As a result, the CS team can prioritize contacting the customers who are more likely to churn.

Data Can Include

  • Support Ticket Type
  • Total Customer Tenure
  • Total Customer Spending
  • Customer Last Active
  • Last Customer Satisfaction Score (CSAT) / Rating
  • Assigned Agent

Target
Customer Churn (Y/N)

Human Resources

Predict Employee Turnover to Improve Retention Rate

Business Challenge
Happy employees bring positive energy and success to the company. On the other hand, finding talented candidates and training new hires are costly. To ensure the company’s operation at its maximum efficiency, the HR team hopes to locate the department or team with the highest turnover rate that requires improvement.

AI Solution
With the historical data of employment, the HR team can schedule meetings with department or team members that are more likely to leave.

Data Can Include

  • Department
  • Number of Employees in Department
  • Average Salary
  • Average Stock Options
  • Benefits
  • Management Ratings

Target
Turnover Rate

Finance

Improve Company Cash Flow by Forecasting Delinquent Payments

Business Challenge
Maintaining a healthy financial status is an important responsibility of the finance team. At any point in time, unfortunate financial incidents happen and late payments occur. Therefore, having clear visibility can become a powerful advantage to the financial team to take meaningful actions.

AI Solution
From the historical data of invoices, the finance team can forecast which customers are more likely to miss the payment date so the team can engage in advance.

Data Can Include

  • Billed Amount
  • Industry
  • Department
  • Annual Revenue
  • Company Size
  • Location

Target
Late Payment History (Y/N)

AI Team Structures

In order to efficiently implement AI into the business, some organizations build centralized teams and some build decentralized teams. Each approach has its advantages and disadvantages, but both methods face the same problem – lack of communication. What most people don’t realize is that there is another solution: a platform. Let’s take a look at the pros and cons of each structure and how a platform solves the communication challenge.

Centralized

The centralized team structure is when you have a team of AI experts serving the entire organization.

Pro: When AI experts are working closely as a group, they are able to innovate faster. The team will build standardized processes to solve various business problems using AI. Once established, the processes can be shared and applied across the organization for better monitoring. Moreover, it is potentially easier to hire AI experts under this structure.

Con: Due to the distance from the business teams that own the outcomes of these projects, it requires extra push for the centralized team to understand the nuances of the business problem. In other words, the team needs to set up meetings with subject matter experts to learn more and communicate often to ensure they don’t divert. Moreover, the centralized team is likely to become a bottleneck for completing internal AI projects. As a result, only a selected few departments/projects – those with a higher return on investment – will work with the team.

Decentralized

When AI experts are embedded into each business unit, it is called a decentralized team structure.

Pro: AI experts can easily communicate with the business stakeholders and find the core challenges to solve using AI faster than the centralized team. Moreover, all business units that demand AI will receive support from AI experts.

Con: The overall AI innovation across the company may be slow because each AI team could be using different AI techniques – such as programming languages and applications. Hiring becomes difficult and onboarding time may also increase depending on the complexity of each business unit.

Platform

A shared AI platform is the solution that incorporates advantages from both centralized and decentralized teams with more benefits:

1. Both business teams and AI experts use the AI platform as the core communication tool, sharing datasets and project results easily.

2. The AI platform establishes standardized AI processes across the organization regardless of the team structures.

3. Most technical AI aspects are performed by the platform, enabling business analysts to drive AI projects independently and companies to hire AI experts more easily.

Am I ready?

Many organizations feel they aren’t ready for AI. Even with an advanced AI platform with the innovative Automated Machine Learning 2 (AutoML) technology that democratizes data science and transforms businesses, many companies have yet to embrace AI. Believe it or not, the vast majority of companies are ready. You are too. 

Here are some common misconceptions.

Myth 1

We don't have enough data.

The data you have is enough. AutoML delivers sophisticated data prep and feature engineering. While more data is advantageous, an AI platform fills in the missing data by learning from past projects.

Myth 2

We can't hire AI experts.

AutoML handles technical components for you, reducing the dependency to understand math and statistics behind machine learning. Business analysts or subject matter experts with knowledge in the business and the data can be empowered to carry out AI projects alone.

Myth 3

AI is too complex.

A comprehensive AI platform delivers transparency and explainability for the final output, allowing your team and company to understand how the patterns were uncovered to make machine generated predictions.

Myth 4

The ROI is unclear.

AutoML accelerates the time-to-deliver, allowing companies to quickly test and measure success in a cost-effective way. Test small use cases, iterate to improve results, and implement AI into the actual business to collect ROI. Soon, your entire organization will be equipped with AI solutions.

Myth 5

It’s difficult to deploy AI.

An end-to-end AI platform removes the burden on IT by offering easy deployment options. With one-click AI deployment, business users are empowered to apply AI into the business and start making an impact with ease on the platform or using their favorite applications like Tableau.

The Ople.AI Platform

The easiest way to accelerate decision making and reduce risk with predictive analytics

With the Ople.AI Platform, you are only a few clicks away from building predictive models to derive optimal business recommendations with reduced risk.