How to Transform AI Insights from Noise to Business Results

How to Transform AI Insights from Noise to Business Results

One goal of artificial intelligence is to extract business insights from enormous quantities of data, but as a relatively new field, this derived knowledge hasn’t always been actionable or capitalizable for businesses. Over time, the sophistication and usefulness of the derived business intelligence (BI) has evolved along with artificial intelligence technologies.

First came online analytical processing (OLAP)-based BI, which defined the descriptive what of an issue, like the number of customers lost in a given time period. Then came machine learning, which added diagnostic and predictive analytics into a situation to explain why customers jumped ship and how it may evolve. Today, we have artificial intelligence (AI) to provide new insights to produce recommendations, like what to do to reduce customer churn. 

So, how do you measure the impact of artificial intelligence against business results? By deploying a Continuous Value Delivery Model (CVDM), you can ensure the production of evermore valuable insights and recommendations, along with the means to make them actionable and to verify their impact on the real world! 

A Brief History of Analytic & AI Maturity Models

Much has already been written about increasing the maturity of an enterprise’s analytic capabilities to realize the true potential of AI in any industry. One of the most famous examples is the Gartner Analytic Maturity Model, which was first published in 2012. Seen as highly aspirational at the time, the model demonstrates the steps necessary to go from descriptive, hindsight-focused analytics to prescriptive, foresight-focused analytics, illustrating how the difficulty and value of data and analytics capabilities grow over time.

Gartner’s Analytic Maturity Model (2012)

In 2017, Gartner released an overview of five levels of maturity in data and analytics to help organizations track how far they’ve come in their journey. According to a subsequent press release announcing the results of a global survey of 196 organizations conducted in 2018, Gartner notes that “91 percent of organizations have not yet reached a “transformational” level of maturity in data and analytics, despite this area being a number one investment priority for CIOs in recent years.” Furthermore, 60% of respondents ranked themselves in the lowest three levels (Basic, Opportunistic, and Systematic).

Gartner’s Overview of the Maturity Model for Data & Analytics (2017)

More recently, in 2019, researchers from Monash University developed the first Artificial Intelligence Maturity Model. The model focuses on an organization’s maturity across four categories and five levels, and is designed to help organizations “assess and ultimately improve their AI capabilities.”

An example of an AI Maturity Model (2019)

How to Deliver Continuous Value with Artificial Intelligence 

Years later, the data and analytics experts at Wizeline have taken another stab at the analytic and AI maturity models of the past. This time, we’ve focused on how the use of AI can deliver continuous value and generate evermore sophisticated intelligence for organizations.

 Wizeline’s Continuous Value Delivery Model (2022)

Our model includes Gartner’s Analytic Maturity Model referenced above, but adds four important additional components:

  1. Data strategy capabilities to ensure the delivery of valuable (strategically-aligned) results
  2. Core data management capabilities to ensure the consistent production of reliable data (analytics materia prima)
  3. Functionality to make the insights actionable
  4. Functionality to measure impact against actual business outcomes

Notice that this solution does not require you to have all your data management issues sorted out first (big-bang approach), since it can be gradually deployed using a domain-driven approach. This way, we focus on solving a particular business issue with the strictly required data to produce relevant insights and Wize recommendations.

Interested in learning more about this model and approach to getting more value from AI investments? Visit our Intelligence Everywhere landing page to learn more about our practice or contact to start the conversation today!

by Heriberto Perez Peñuelas, Technology Director, Intelligence Everywhere
by Heriberto Perez Peñuelas, Technology Director, Intelligence Everywhere

Scott Rayburn

Posted by Scott Rayburn on October 4, 2022