How does the Promise of AI Maturity Hold Up?

Introduction

This blog talks about the impact that AI maturity will have on organizations.

There is a lot of discussion about the effect that intelligent technologies will have across the world. We are already witnessing how these technologies are transforming the healthcare and medicine sectors, and empowering a new age of driverless vehicles. It is a well-known fact that intelligent technologies will shape work and life in ways that we cannot yet imagine.

Most businesses are looking at an intelligent future with equal amounts of anxiety and excitement. At the core of the whole discussion is the term Artificial Intelligence (AI). AI describes the broad field of making machines mimic human-like intelligence. While there is reason to proceed very carefully with AI, there are also so many ways that AI will continue to impact our lives positively.

AI Maturity Model

AI Maturity is a four-stage model. These four stages outline how intelligence will affect organizations.

• Automating: The journey of AI adoption for most organizations starts with automation. The scalability of machine learning systems has allowed automation to be more cost-effective and brought it to places where it was never possible. Automation technologies also provide new possibilities for an employee experience, making it easier to communicate with complex systems through a natural interface.
• Informing: At the next stage, intelligent insights and predictions inform decision-making and planning. Managers, leaders, and frontline employees all become experts at using intelligence to enhance their everyday decision-making, thus, infusing it throughout each thing they do. Also at this stage, machine learning techniques use huge amounts of data to reinforce predictions and create simulations, thus creating entirely new possibilities for DDDM (Data-Driven Decision-making). They make these predictions and simulations so cost-effective and accessible that they can be more widely used than ever before. Natural language technologies also play a crucial role in getting data analytics to each employee by democratizing access to data and authorizing employees to make better decisions.
• Discovering: As automation DDDM becomes widespread across the business, a melting point has reached and the promise of AI is being fully utilized as employees’ productivity missiles due to a move to more high-value tasks. At the discovering stage, one deploys intelligence and the use of smart applications is extensive, and day-to-day operations and decision-making have been made easy.
• Transforming: At the last stage, the business looks quite different as every facet of the organization surrounds intelligence, from the core business model and how employees are working to the arrangement of the workforce itself and the skills that employees own.

Conclusion

Businesses at the initial stages depend mostly on unsupervised learning.  As for business drivers, those at the early (or theory) stages of their AI journey focus on managing large volumes of data and driving innovation. Businesses that have good experience with AI maturity concentrate on scaling up quickly and driving growth. Likewise, these businesses want to enhance these applications to gain a competitive advantage.

Organizations at all levels of maturity can own AI-based systems. However, cone must determine whether an organization can support more  AI technologies. Considering and assessing an organization’s AI maturity provides a clear path to guide AI technology adoption efforts.