2023 was a very special year. It followed the pandemic, a financial crisis, and inflation stemming from how it was all handled.

But that's not all that happened. 2023 will be remembered as the year the general population started interacting directly with AI.

What's New in the field of AI?

Automated systems using integrated algorithms are not new in the business landscape. However, OpenAI made GenAI (e.g., LLM) the most popular form of AI and introduced a new name: ChatGPT.

This wasn't the only development in the field of AI. In parallel, consulting firms like Gartner and IDC are actively working on a “thinking framework” called “Decision Intelligence.” They are trying to define it in the context of what business value it delivers and how a successful “Decision Intelligence Platform” should work, aiming to launch a product category of the same name early next year.

The need for a Thinking Framework

With all the fast-paced research and development in the field of AI, organizations still need a clear path on how these new waves of advanced technologies can fit into their problem spaces. How they address their business objectives such as revenue growth, risk mitigation, customer satisfaction and so on. Additionally, organizations have matured enough to understand data is like fuel. However, if you know your destination, you don't need to acquire all the petrol stations in the country, just in case! The gap between data, AI technology and organizations' need to deliver business value has always been there but not as vivid as it is in 2023. One of the main reasons is the failure of engineering-led AI projects in recent years to meet the business's ROI expectations.

On the other hand, Business Intelligence (BI) that warps around the data management and collection strategy of organizations has failed to deliver business value; it is now very obvious that there are very little insight is used for decision-making in organizations, even though reports, dashboards and etc. are available. Evidence shows that the data-driven approach does not change the organization's behavior in operation and its decision-making approach.

These (a) unclear paths to utilize composite AI techniques to deliver business value whilst avoiding type 3 errors (solving the problem does not exist) in engineering as well as (b) failure of data-driven approach in changing business operation effectiveness in decision-making go hand in hand and highlights the need and importance for a new approach – a “thinking framework”.

The Evolution to Decision Intelligence

This thinking framework and the platforms that enable it evolved from many different spaces, such as data and decision science, operational research, and product categories, such as Business Intelligence (BI) and Decision Management Suite (DMS).

The idea is to enable organizations to (a) define business decisions explicitly then (b) use different techniques to satisfy the requirements of those decisions. Then (c) use orchestration to bring together everything as a single unit of autonomous (e.g. executable) decision-making integrated into the organization's systems and processes.

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So, if you have these three components – decision definition, execution and orchestration – and have FlexRule, then… wait, wait, wait… you are doing it already! This is true, not by accident but by design, since 2010.

If Decision Intelligence is the future there's no need to wait for it. It's already here with our Decision-Centric Approach® and our End-to-End Decision Management Suite. We just didn't call it the same.

Last updated March 25th, 2024 at 01:35 pm Published December 21st, 2023 at 12:18 pm