Enterprise AI involves leveraging advanced machine learning and cognitive capabilities to deliver organizational data, knowledge, and information in a manner that matches how humans find and analyze information. A Gartner study reveals that artificial intelligence is helping enterprises gain a competitive advantage across different industries. The study reveals that AI technologies will create 6.2 billion hours’ worth of worker productivity and $2.9 trillion in business value by the end of 2021. It is no wonder then that businesses are quickly adopting AI into their operations.
However, a big challenge for these early adopters is a lack of foundation for building AI capabilities due to the absence of relevant use cases.
This piece will answer relevant questions regarding enterprise AI to help you leverage the power of the technology to transform your operations. We will also look at the common mistakes to avoid when incorporating AI and machine learning into your enterprise operations.
Some ideal enterprise use cases for advanced capabilities such as AI and machine learning include:
Advanced analytics differ from basic analytics. The former leverages machine learning and vast quantitative data sets to empower businesses to mine information, identify patterns, and discover hidden facts effectively. Such capabilities enable you to understand operations through insights from large and disparate data sources. Ideally, you will be empowered to make relevant and timely decisions and predict future outcomes with precision.
Enterprise AI also enables you to auto-tag and automatically route and organize content and data to the right channels. This will not only enable findability but also enhance content discoverability. You can use this application to conduct data categorization solutions that enable consistent follow-up processes and enable your enterprise to organize data based on the laid down compliance purposes.
Augmented categorization uses machine logic to organize data based on the similarities between contents, context, and users. The machine ideally learns to define the organization and management concepts that may not be mentioned in a specific document, such as emails and helpdesk requests.
Semantic search strives to understand the context and meaning behind search terms. The process goes beyond the execution of queries against keywords. For enterprises, AI’s semantic technologies and enterprise knowledge graphs make it possible to discover data from multiple sources. It also provides you with the flexibility to quickly add, modify, and improve data flows. The technology makes it easier to add new data sources that support future questions currently unknown to you.
AI enterprise applications feature a recommendation system that works by defining a relationship between contents. This ideally provides an exceptional understanding of how things can work together to provide a better user experience for users looking for new facts and knowledge that would typically have remained hidden in usual standards.
To maximize on Enterprise AI solutions, you should first identify your current enterprise information and data management issues that are ideal for an AI solution. Once you have picked the appropriate use cases, you must then develop foundational competencies that ensure the information is structured to be machine-readable.
Most businesses expect instant success once they incorporate AI into their operations. However, this is not always the case. Although several organizations in different industries are enjoying remarkable results with some form of AI technologies, many more have yet to gain any value from their investments due to the following common mistakes:
If you don’t develop precise business applications and relevant use cases, you will likely enjoy little success from your AI investments. You can avoid this mistake by first striving to understand how the technology can impact your business by solving relevant problems. Only deploy AI once you have determined the business focus and specific use cases.
Some business owners and decision-makers have an assumption that AI is a single technology solution for all business problems. Enterprise AI is a combination of related technologies designed to address several specific requirements like prediction, perception, automation, and analysis. Rather than striving to plug-and-play the AI to achieve a one-size-fits-all solution, you should instead plan for a multi-phase design, development, and integration.
There is no doubt that automation is helping reduce repetitive organizational tasks like classification, tagging, and categorization. However, you should never forget that AI is a new technology that is still evolving. As such, your AI needs human validation so that it scales effectively. This is especially true for the cases that need the highest degree of accuracy.
For the machines to provide the ideal solutions, they must first learn the human way of thinking and how your organization operates. You should therefore ensure the information and knowledge that you work on daily is machine-readable. If you fail to provide high quality and well-organized data, your AI applications may not give you the designed outcomes.
If you need help supporting your automation efforts, Capacity has a revolutionary helpdesk powered by artificial intelligence and designed to offer automated support for all your customers and employees.
We understand that teams are experiencing challenges dealing with myriads of emails, phone calls, and tickets. Capacity is designed to answer over 84%of all customer inquiries without a need for human intervention. If Capacity doesn’t have an answer, the question is seamlessly escalated to a trained internal team member through a ticket or live chat. The answer will then be added to the Capacity knowledge base. When you choose Capacity, you stand to gain from the following: