How to take advantage of AI and Machine learning for your company

When a computer program can use data to make an informed decision, the result is increased business productivity. The machine completes certain tasks faster than humans could, and people can direct their attention to all the important work that machines can't do. For example, improving customer relationships instead of spending time entering data.

Artificial intelligence and machine learning

Artificial intelligence (AI) and machine learning (ML) are big buzzwords that are making more noise than ever. To appreciate their true value, it is important to know how they differ.

Artificial intelligence is a general term that represents any type of software development solutions that addresses complex questions and simulates the human brain's ability to solve problems. It performs tasks commonly associated with human intelligence and was developed to generalize, reason, discover meaning, and learn from mistakes.

Machine learning , for its part, is a subset of AI technology. ML software automatically takes in and learns new information based on the patterns it identifies. It analyzes the data and then uses this “knowledge” to make the right decision, thus eliminating or minimizing human intervention. Since the machine learns autonomously, it does not need additional programming.

Companies create and receive data from multiple sources and must find the best way to use it. This is where machine learning comes into play: it processes the data, allowing the identification of patterns, red flags and trends so that executives and managers can plan correctly and determine the most efficient way to operate in the future.

Where does big data come into play?

Oracle defines it as a combination of structured (such as contracts and financial records), semi-structured (such as emails and XML), and unstructured data (such as open-ended survey questions and multimedia files) that can be used in machine learning, predictive models, and other applications. analytics. It is characterized by a large data set, which conventional data processing software simply cannot handle.

But even if your company doesn't deal with big data, ML technology can help by manually transforming very complex processes into lighter ones, resulting in less manual and routine work for employees, a better customer experience, and other benefits. For example, we all love those online forms that predict what we're going to write next and automatically fill in the necessary information. What a time-saving luxury it is to not have to press the same keys over and over again, not to mention reducing human error.

Classify, distribute and archive documents without human intervention

Machine learning makes decisions based on knowledge based on previous behavior and the new data it reads. It is a perfect example of custom enterprise software development at the service of the company, its employees and its customers. DocuWare Intelligent Indexing , our auto-indexing solution, is a good example. Extracts information such as sender, recipient, amount, creation date, and other standard elements from a document. It correctly recognizes these data fields whether they appear in the same place in a document type or not. For example, even though each supplier structures their invoices differently, recognize where the relevant information is on each one.

The program uses "few example learning", which trains you with very few examples (typically 1 to 3). Thus, its automated capabilities quickly become very precise. This is a great advantage; the user does not need much sample data to "teach" the software to be accurate and requires almost no configuration.

Training robots to choose and place objects in the right place through repetition and feedback is another of the many uses of ML. It allows robotic systems to adapt to their work environments and participate in workflows without complex programming.

How do you already use AI?

AI functionality is not always obvious to the user. For example, language detection is another behind-the-scenes feature of DocuWare. When a user uploads a document, the AI ​​compares it with the 70 languages ​​that DocuWare supports and determines which language it is written in. This is done by an artificial intelligence model trained with publicly available linguistic data.

Spam filters that separate unsolicited and potentially dangerous emails and send them to spam or trash folders are common and allow us to focus on the emails we want to receive.

Chatbots are artificial intelligence systems that use natural language processing capabilities to maintain a digital conversation. Additionally, many websites now have chatbots that can answer support questions, help guide customers through a sales process, or interact in other ways.

Generative AI is another aspect of artificial intelligence that has come to the fore in the artificial intelligence scene. It not only "understands" the data and makes intelligent decisions based on it, it produces new content. Some of the most notable examples are ChatGPT , which creates content in response to text messages, and similar products like BLOOM, Flamingo, and Jasper. ChatGPT developer, OpenAI , has recently released a new version that can also communicate using spoken words. DALL-E , which synthesizes images from text descriptions, competes with Midjourney, Deep Dream Generator, and Big Sleep. Text-to-speech synthesis also continues to improve, with higher quality artificial readers for ebooks, news and advertising.

Use AI to sell better

When used correctly, artificial intelligence gives us a deeper understanding of our customers, even across different contexts and channels. It is undeniably useful when the website "recommends" items to add to our basket. Every entrepreneur with a product to sell should use AI in this way.

Let's go one step further. Artificial intelligence is capable of reading signals and perceiving the unique purchase, upgrade or cancellation intention of each customer. Powered by real-time data, AI can even guide customer service and sales reps to make the right offer at the right time. The human touch combined with its intelligence and powered by AI is a kind of custom business software development magic that will help your company better understand your customers and personalize their experience in the Amazon style.

AI can help retain top talent

Employee retention is also a key where AI can help businesses thrive. HR professionals can use this technology to help them see who is planning to leave their job. Companies like IBM are using this knowledge to reach valuable employees and negotiate, counteroffer, and retain their services. Using technology to analyze data points and determine who is a flight risk saves all companies valuable time and money.

Using machine learning to transform a manual data entry process, drive automated workflows, or another aspect of AI to accurately predict a sale or retain a valuable employee on a team is what can deliver the greatest return on investment. investment in this technology. As innovations in AI and machine learning by enterprise software development company continue to develop at lightning speed, their contributions to your company's success promise to increase exponentially.

10 terms you should know

1. An algorithm: is a sequence of rules given to an artificial intelligence machine so that it can perform a task or solve a problem. Common algorithms are used for classification, regression and clustering.

2. Deep learning: It is a subset of machine learning that focuses on the formation of abstract concepts. Deep learning systems process large amounts of data and generalize categories and features related to that data using supervised or unsupervised learning. Instead of relying on an algorithm, this subset of machine learning can learn from unstructured data without supervision.

3. Supervised Learning: it is another machine learning model. The computer has a human "teacher" who provides samples of inputs and outputs or possible answers. The machine learns by comparing its answer choice with the "correct" output.

4. Unsupervised Learning : does not involve sample data. Instead, the system is asked to find patterns in the data itself. Learn through the process of trial and error. For example, this technique is useful when searching for hidden insights in big data.

5. Structured data: It is organized in a format or fields, such as in a spreadsheet or database.

6. Unstructured Data : It is not organized in any specific format. Some examples of unstructured data are photos, videos, emails, books, social media posts, or medical reports.

7. Semi-structured data – It doesn't live in a database or spreadsheet, but may have some attributes that make it easier to organize. Examples are XML data and NoSQL databases.

8. Data mining: Searches for patterns in a data set. Identify correlations and trends that might otherwise go unnoticed. For example, if a data mining application were given to a clothing retail company, it might discover that people in the South prefer colors and patterns. Or a coffee chain might confirm that people buy anything with "pumpkin spice" in the product name during the month of October.

9. A neural network: is a computer system modeled after the human brain. It uses nodes that are like biological neurons and perform tasks including computer vision, speech recognition, and board game strategy. A network of firing “neurons” interprets data, makes decisions, and learns from the information over time.

10. Natural Language Processing (NLP): Understands and generates speech the way humans typically use it. Computers have always been able to understand programming languages, but applying these principles to human speech is much more complicated.