If there is one technology that many organizations are incorporating into their strategy, it is artificial intelligence (AI). According to Gartner, “70% of organizations will implement AI architecture due to the rapid maturity of AI orchestration strategies” by 2025. Along the same lines, Forrester predicted that “one in five organizations will double their investment in AI.”
So, what does this mean for business leaders?
Many factors in today’s business environment are driving companies to upgrade their strategies with AI:
- Business applications move to the cloud, allowing companies to securely access the data they need
- Off-the-shelf machine learning (ML) models are used through low-code/no-code platforms, creating an impetus for democratization.
There are also advances in support technology in all areas where AI promises valuable benefits:
5G is disrupting the telecom sector
The Internet of Things (IoT) is affecting the manufacturing, automotive, oil, and gas sectors
Omnichannel experience is driving the retail sector in e-commerce
Blockchain affects financial services, purchases, weapons, etc.
AI is not a magic solution that can be applied in the same way to all industries. Organizations need to understand the key operating points that AI can help drive.
AI applications for common business situations
As the name suggests, in this context, AI helps organizations organize and extract information from unorganized documents. With the onset of ML model maturity, companies can achieve high levels of accuracy and confidence while extracting data from small datasets. For example, using AI Forms, we can drag and drop just two or three invoices to train the ML model properly.
AI computer vision
Computer vision makes it possible to interpret suspicious objects with human-like characteristics. This helps organizations create vision-based automation that can run in multiple virtual desktop environments (VDI), regardless of the system or operating system.
Natural Language Processing (NLP)
NLP capabilities help in language recognition, unstructured data extraction, and sentiment analysis. Communication mining is an application of NLP in business communication. It captures objective data (such as customer terms and reasons for contact), tone, and emotion to improve the automation and understanding of business processes.
One of the main uses of communication mining is for email automation:
- The output of e-mail from the underlying system
- Design based on target scenarios
- Remove messages from group emails (unstructured)
- Processing information as needed
Policy evaluation
Now, to access historical data, ML models allow entrepreneurs to make more informed decisions. Businesses use this ability to increase demand, deliver personalized offers, predict network downtime, prevent fraudulent transactions, and more.