AI is becoming a mass trend in business year by year. 2 out of 3 companies already employ intelligent technologies or explore the opportunities they bring. But AI is not a single tech; it is a family of several branches that deal with specific tasks and applications. So which one fits your business goals?

If you are lost, we feel you. It is challenging to keep up with every opportunity intelligent technologies may offer. We gathered AI technologies and tasks they can solve in one practical cheatsheet to give you some hints and ideas. 

AI Expertise in Action: Tailoring Technologies to Business Needs

Every AI domain can do a lot for business. As technologies develop rapidly, intelligent innovations can contribute to any business operation and add remarkable value. Let us show what we mean!

Natural Language Processing (NLP)

NLP fills the gap between people speaking their languages and computers interpreting the code. It helps machines understand, manipulate, and generate human language. 

The tech can do the following for your business aspirations:

• Enable personalized chatbots in sales, marketing, and client service to understand natural speech and respond to queries; 
• Translate text for easy content localization;
• Group information by keywords, titles, topics, and queries; also;
• Detect spam by recognizing “triggering” words in messages that might signal about unwanted content;
• Classify emotions:  NLP makes machines catch the tone of messages in product reviews or social media comments;
• Generate text content for automated email communication, product descriptions on online marketplaces, and social media content;

• Provide content recommendations based on the user’s previous requests in the media & entertainment industry;
• Strengthen the functionality of search engines by understanding the user’s intent and context behind the query;
• Edit and improve texts to make your business communication, social media, and client conversations look more professional;
• Help your employees access the required corporate document by simply entering their query to the system;
• Summarize texts: The technology can save time by condensing user reviews and shortening important information minutes before meetings and events. 

Our case: GPT-Powered Virtual Companion

In 2020, when people were isolated from others in their homes, our client decided to create a new communication platform that mimicked the relaxed atmosphere of a nightclub. Later, they decided to add a new GPT-based virtual companion with different personal profiles. Users can choose from entertaining, teaching, chatty, or supportive modes. Such an innovative approach to fighting loneliness immediately resonated with the app’s audience and boosted user engagement.

What potential is behind this solution? Our client expects the following metrics:

• The 5%-7% increase in customer retention;
• User engagement improvement by more than 10%;
• Acquisition rates increase by 8%-10%;
• Boost in customer satisfaction by 10% or more;
• Sales & revenue growth by up to 12%;
• At least a 10% market share growth.

Computer Vision

Computer vision makes machines ‘see’ and ‘understand’ what is in front of them.

The tech does a lot in business:

• Detects, recognizes, and tracks objects: With computer vision, machines can find, name, and follow items in images and videos. It’s helpful for security systems, sports analysis, geomarketing, and logistics optimization; 
• Recognizes faces for better personalization in retail, improved security in smart home systems, and easy access to personal devices and apps;
• Adds to augmented reality experiences in virtual fitting rooms and experiments with makeup and styles;
• Supports visual search to help customers quickly find an item (clothes or decor) using a similar picture instead of word description;
• Infuses robots, machinery, and self-driving cars with AI-powered cameras and lidars to navigate the environment, plan paths, detect obstacles, road markings, and street signs;
• Detects anomalies in healthcare to find tumors on medical scans, in agriculture to identify plant diseases or parasites as timely as possible, and in manufacturing to inspect products and equipment for defects; 
• Capture and process data from text documents to, for example, check test answers or the driver’s ID, provide real-time translations, recognize street signs, process loans and invoices, etc. All these use cases help automate manual labor and optimize your workflow with that; 
• Creates and edits visuals, for example, logos, product mockups, illustrations, and promotion materials;
• Builds 3D models for better product visualization.

Market Example: 3D Head Reconstruction Solution

A producer of translucent souvenir glass cubes with 3D headshots wanted to automate their workflow. We developed a solution that scans a two-dimensional photo, detects a face and its landmarks, estimates rotation, and constructs a new model. Now, the construction process takes only ten minutes. 

Machine Learning

Machine learning (ML) enables computers to make decisions without direct instructions. ML algorithms can process large data sets, learn from them, divide information into groups, find patterns, and generate predictions. The technology supports strategic moves and daily operations of many departments, like sales, marketing, finance, and human resources.

You need machine learning expertise if you think about the following:

• Recommendations: ML algorithms are good at making suggestions based on data. With them, Netflix shows its viewers more exciting series, Amazon offers relevant goods, and social media feeds users with more engaging content;
• Predictive analysis: Machine learning models are gurus in analyzing data and foreseeing events before they happen. You can use them to:
• predict customer churn behavior;
• build a pricing strategy;
• forecast demand fluctuations;
• count credit scoring;
• plan your production schedule with equipment maintenance in mind;
• Segmentation: Algorithms can divide your clients, markets, and products by specific criteria (demography, geography, usage patterns, etc.) to help you target your efforts more efficiently;
• Data tagging: ML tools can categorize your corporate data based on its sensitivity and business value. For example, organizations can provide reliable security measures by continuously labeling datasets with tags like “strictly confidential”, “public”, or “for the X department”;
• Finding anomalies: You can use ML solutions to find unusual patterns in your data. For example, anomaly detection is commonly used in equipment monitoring, identifying network intrusion, health tracking, financial fraud detection, customer behavior analysis, and quality control.

Market Example: AI Monitoring Software for Vehicles

Any machinery needs maintenance. Monitoring systems usually react to troubles that have already happened. But our task was to peer into the future and build an ML-based tool that analyzes data from car sensors and predicts potential failures.

Our solution can detect slight changes in sensor signals that indicate equipment failures. It helps to reduce vehicle downtime and positively influences our client’s business results. 

Market Example: RPA Logistics Solution

Every logistics operation comes with a solid package of 10-25 documents that need processing, which means more working time and human efforts. Or not? Our client faced understaffing after COVID-19 and decided to introduce an AI business automation solution for documentation management.

CHI engineers developed a custom tool to recognize a document type, extract the required information, and fill in relevant fields in the system. The tool can also learn new types of documents and fields. 

Audio ML

Audio machine learning is an AI subfield that detects, processes and generates sounds. In business, it can do many things, for example:

• Recognize a person by voice as a part of the user authentication process in many apps and services;
• Monitor health: ML-powered health apps can detect and analyze sounds of snoring or coughing;
• Analyze sentiments in call center records to understand customer emotions, gender, language, or keywords pointing at a certain issue; 
• Enable personal recommendations for music and podcast platforms;
• Change voice characteristics like tone, speed, pitch, or accent without changing what was said. The technology is used in media, entertainment, and assistive technologies to personalize customer interactions, localize audio content for a new market, and help people with disabilities; 
• Find anomalies and predict maintenance: In production, audio machine learning can find existing or future equipment failures by detecting slight changes in production noises a human ear cannot catch;
• Convert speech to text and vice versa: Voice assistants, dictation tools, messengers, and automated transcription services transform spoken words into written pieces. Reading assistants, tools for the visually impaired, and multimedia content voiceovers do the opposite and convert text to speech. 

Market Example: Voice Assistant for a Geosocial App

Voice assistants have become a trendy feature in the mobile industry. So our client, who launched a new geosocial networking app, decided not to stand aside and add a voice-based intelligent virtual assistant that is capable of the following:

• recognizing a user’s voice for identification;
• speaking English and Vietnamese;
• translating street names and signs for user convenience;
• communicating recommendations for places and events;
• responding to user queries.

As soon as the project was over, our client expected to achieve the following results:

• Increasing the number of active app participants by up to 20%;
• Boosting conversions by up to 8%;
• Increasing response times to clients by up to 5%;
• Improving retention by 6% or more.

Signal Processing

Signal processing is a technology that monitors, analyzes, and manipulates audio, visual, electromagnetic, electrical, and other types of signals. All the signals bring information the algorithms use for different purposes. What purpose could it be? Let’s take a closer look at the most popular use cases:

• Predictive maintenance & quality monitoring: Signal processing analyzes data from sensors and cameras to predict equipment failure and find product defects;
• Communications: The tech adds to the quality of signals in phone calls and video conferencing by strengthening good parts of signals and suppressing noises. It also improves data transmission speed by reducing file sizes without compromising quality;
• Content production: Signal processing improves audio & video record quality as well as sharpens and brightens colors on photos;
• Patient care & medical diagnostics: The tech improves the quality of medical images and enables monitoring devices to react to dangerous changes in patient conditions.

Market Example: Driving Pattern Analysis Tool

Everybody knows that aggressive driving leads to road accidents and higher technical service costs. Therefore, harsh drivers need expensive insurance, while careful drivers may pick a cheaper option. But how can you know for sure the person’s driving style?

Our client, a taxi fleet owner, needed a solution to determine this individual style based on car sensor information about speed, acceleration, engine temperature, geolocation, etc.

To meet the requirements, our team developed a software that does the following:

• obtains car sensor data through OBD II connector;
• calculates average speed, car acceleration, and harsh breaking indexes;
• defines driving performance indicators for a certain period of time;
• detects indicators that fall out of the normal range;
• analyzes driving performance.

As a result, the client gets the solution that would help them effectively negotiate with insurance companies, reduce the number of road accidents, and guarantee transparent control of vehicle conditions.

Attitude and Heading Reference System (AHRS) and Inertial Positioning

AHRS and inertial positioning will help when you need to find your way in an environment where satellites or visual references are unavailable. The technologies can track people and assets in buildings, mines, and undergrounds. They also can navigate airplanes, ships, and drones to determine position without satellite signals.

As for business purposes, these technologies can help you with:

• Smart homes in order to automate lighting, heating, and other services based on dweller’s location;
• Patient care by detecting falls and sending alerts when patients enter or leave specific areas;
• Client and employee monitoring to track visitor activities inside offices and detect suspicious behavior patterns (for example, in banks);
• Logistics operations by analyzing routes and machinery performance to optimize the supply management chain.

Market Example: Indoor Positioning System

Our client, a leading construction company from Japan, was looking for a solution to track locations at the building site and determine where exactly a certain footage belongs. Our solution uses computer vision tech, accelerometer, and gyroscope sensor data. 

The tool has the following features:

• Dynamic video processing: The system ensures swift and efficient processing when users upload videos to the server;
• Interactive room layout visualization: The tool displays the filming trajectory on the room layout to reveal the entire route in rich detail;
• Frame-by-frame exploration: This feature enables selecting specific points on the diagram, which offers users an in-depth look at each moment;
• Customizable date & time insights: Users can set specific dates and times on the map to find corresponding videos in the database for comprehensive analysis;
• Room view mode with indoor tracking: The system, integrated with an advanced indoor location tracking tool, offers a switch to room view mode for any chosen date.

Unsupervised and Reinforcement Learning 

AI algorithms need labeled data with special tags that help machine models understand it (imagine a photo of a labrador labeled ‘dog’). The majority of business data is unlabeled, and sometimes it’s impossible to accumulate and label data in advance. In such cases, unsupervised and reinforcement learning algorithms come into play. They can learn by themselves from any data. 

Unsupervised learning models are good at finding hidden patterns in data, and reinforcement learning algorithms learn by receiving feedback about what is right and wrong. These instruments are also very useful for making data-driven decisions in uncertain or changing conditions. Now, how exactly can you use these features in a business realm? Here are a few ideas:

• Customer segmentation based on certain criteria for personalized marketing activities and product recommendations;
• Adjusting product prices according to fluctuations of demand and prices on the market;
• Supply chain management: Models help efficiently allocate resources and plan logistics to meet the demand at minimal cost.

To Sum Up

If you reach this point, congratulations! You passed the course on ‘What AI Expertise Serves Your Business Tasks 101’. As you see, artificial intelligence is multifaceted, and every field has workable applications in business. 

Of course, every case is unique. Modern solutions are complex and often require AI expertise in more than one domain. We at CHI Software gathered a comprehensive team of AI experts to create and deliver the boldest and most challenging business solutions. 

So if you recognize one of your business goals in our piece, just give us a note, and let us discuss what technologies fit your case. 

FAQs

• How can I be sure that a certain AI technology will fit my business needs perfectly?

  Only talking to an expert will help you out. You see, one needs a lot of practical knowledge to be sure about AI usage. Professional AI experts will tell you what next steps to make if you aim to transform your business, so feel free to contact CHI engineers. They provide free consultations where you can share your business needs, plans, and concerns and get comprehensive AI guidance for your business.

• Can I use a mix of AI technologies to solve my business tasks?

  Absolutely! In fact, our projects use several AI elements for the most part. We always say that each AI solution is unique, so we do our best to pick the right combination of tools and techniques that perfectly fit your business needs and infrastructure.
  

• How is machine learning used in predictive analysis?

  ML algorithms are extremely good at analyzing data and seeing patterns. Based on this, they can build forecasts and predict future events. For example, models can foresee client churn behavior, machinery maintenance, and optimal price strategy. The more qualitative data models have, the more reliable predictions they can build.
  

• How can businesses apply audio machine learning?

  Audio ML algorithms can perform many business tasks. For example, in manufacturing, they can predict equipment failure and control product quality. In healthcare, the tech is used to monitor patient conditions. Furthermore, audio ML can recommend music similar to what users listen to or analyze emotions to track customer satisfaction. Another example is iIntelligent audio algorithms which empower devices to transform text into speech and vice versa.

• How does signal processing help in business operations?

  Signal processing adds to many business applications. For example, it tracks equipment and predicts maintenance periods in production facilities. Another case is communications, where the tech improves the quality of video, images, and music.
  

• What applications does computer vision have in business?

  It is employed in face recognition systems, 3D reconstruction solutions, e-commerce recommendations, building automation systems, and more. Algorithms can find a product online with one picture and let people try on a haircut before visiting a stylist. The technology can also create a new logo from scratch and build a 3D model for better project visualization.
  

• How can NLP be used in customer service?

  Natural language processing is the technology that enables automated customer service. Thanks to it, AI chatbots can understand user queries and respond naturally. NLP also helps robots to analyze customer feedback and generate new content for customer interaction.