Part 2: Three keys to applying Decision Intelligence in your organization
Part 1: What is Decision Intelligence and why does it matter?
According to a McKinsey Global Survey only 20 percent of respondents say their organizations excel at decision making
Decision Intelligence is a disciplined approach to optimizing decision-making in your organization by applying AI to the decision-making process. It is an outcome focused discipline that organizations implement to deliver on specific business needs. In Part 1, we explored the origins and benefits of the discipline and why organizations should begin to apply Decision Intelligence to achieve their business goals.
In Part 2, we examine three keys to successfully integrating Decision Intelligence.
- Understand what decisions need to be made and current methods
- Start small and build
- Know your data & choose the right tools
“The first step is to formalize a decision-making process in the organization, and only then can you think about adding software to support that process.”
Amaresh Tripathy, global leader of analytics at Genpact
Understand what decisions need to be made and current methods for making them
Organizations make thousands of decisions each day and most have no formal business process when it comes to making choices. Decision Intelligence brings a rigor to the process that makes for better decisions across a range of business needs. Begin your implementation process by taking a high-level inventory and build a hierarchy of decision types in your organization.
What decisions are you making around inventory, resources, finance, customer experience? What tools and resources do you use to make these decisions? Does a formal process exist? What is involved in the process?
Next examine and document what decision model or process is currently being used or can be applied to each decision type.
- Human-based decisions: These decisions are ones where humans make all the decisions. AI systems offer data visualization and insights for humans to make decisions. These processes do not make the final choice or pick.
- Machine-made decisions: AI processes can make independent decisions in this model. AI systems make the decisions while humans are at the end of the decision-making process.
- Hybrid Decisions: This is a model where AI systems and humans come together to reach an outcome. In this type of model, recommendations are made for humans, and the collaboration of AI and humans offers better outputs since human cognitive intelligence is combined with the data of AI.
Source: What Is Decision Intelligence: Best Examples and Business Benefits?
Once an inventory is complete, your Decision Intelligence implementation team can begin to determine a best initial use case.
Start small and build
“You can start small, in fact, many companies are already doing decision intelligence without calling it decision intelligence.”
Erick Brethenoux, Gartner
The goal is to test the viability of Decision Intelligence in your organization and to do so, it is best to begin with a focused use case. Is there a current internal process around a routine decision-making need that is well defined with a long track record of usage? In many cases these low-hanging fruit opportunities have already been automated but can be improved upon by adding additional factors to the decisioning process.
Decision intelligence provides a prescriptive approach to rapidly improving business performance. Examine all areas of your business including finance, marketing, operations, customer experience, and investment. Which decisions could be made faster or better?
In Part 1 we list several common use cases.
Know your data and choose the right tools
Data is critical to the successful implementation of Decision Intelligence as it informs the decision-making process and therefore must be understood and constantly monitored and assessed. The decision intelligence process works by feeding all available data into a central AI-powered application.
Collect information from all departments to include both transactional and behavioral data. The more extensive and diverse this data is, the more accurate and reliable the outcome will be.
When implementing decision intelligence, companies need to be mindful of bias and provide guardrails to ensure decisions are not unduly influenced Next comes an investment in machine learning tools and artificial intelligence models. Certainly, many organizations have made investments in the tools and disciplines required to make better decisions which can speed the journey toward a true Decision Intelligence orientation.
True decision intelligence frameworks blend traditional and advanced techniques to model, execute, design, tune and monitor models and processes.
The four main parts of decision intelligence frameworks are:
- A data warehouse in a centralized accessible location that stores varied business data
- Data management and business analytics tools that analyze and mine data from data warehouses
- (BPM) Business performance management tools that oversee business expectation
- A (UI) User interface that provides ease of access to information through an interactive dashboard
Source: Everything You Need to Know About Decision Intelligence
Decision Intelligence is a new, emerging discipline that many businesses plan to adopt in the coming years. If you want to talk further about this article of machine learning, please contact us.
There are numerous Decision Intelligence articles with varying points of view. Google it and find out. Here are a few, though that focus on emerging implementation best practices.
A Framework for Embedding Decision Intelligence into your Organization
How to Get Started with Decision Intelligence
Introduction to Decision Intelligence
Decision intelligence: The new BI