How do you determine where AI will have the most impact?
Start with high impact decisions and work backward, overcovering opportunities for AI-assisted decisions.
Determining where to begin with AI can be the hardest decision. Conceptually, anything is possible, but pragmatically, you must align actual technical capabilities with measurable business impact. The temptation is to be more technology-driven, taking a set of new capabilities and finding a use for them. Unfortunately, this often leads to a misalignment with actual end user and business needs that results in low adoption rates, a common problem with advanced analytics.
A better approach is more process-driven, one that first examines the underlying processes and people that support decisions across the enterprise and determines how to improve these decision processes. This process-first approach helps set the context for determining the people strategies and technical solutions that will create the greatest value. Value, in this case, means faster and higher quality decisions enterprise-wide.
Read this before starting your next analytics project.
Check out the Decision Process Improvement Guide to find out how to align your analytics efforts across the enterprise for speed and scale.
Decision Process Improvement (DPI) is about improving operational performance by increasing the speed and quality of decisions.
To improve decision processes, teams must first understand how the process currently works, then make assumptions about what might improve it, align stakeholders around the goals for improvement, develop use cases that drive technical decisions, and embed the new behaviors in the decision processes.
How does it work?
Using DPI, teams analyze a decision process (e.g., loan processing, sales forecasting, or financial forecasting), looking at all the stages, activities, people, data, and tools. The DPI analysis identifies areas for improvement across these elements that will impact the speed and quality of decision-making. These recommendations provide the context for changes in roles, responsibilities, technical solutions, and behaviors.
People & Process
Data & Analysis
It starts with mapping the major stages of the current decision process, clarifying and documenting the process to level set for all stakeholders how the process currently works, and establishing a framework for redesign and improvement recommendations.
By listing the task and activities occurring at each stage, teams can better uncover bottlenecks, blockers, and gaps in capabilities and tools that contribute to slower, poorer-quality decisions. This is the first step in documenting the "from/to" that clarifies the knowledge worker tasks and potential use cases for AI solutions.
By identifying the people involved and their roles and relationships, teams can document who is involved at what point and the type of decision processes used (e.g., consensus-driven). Additionally, DPI analysis categorizes key stakeholders like the internal “suppliers” of information and “customers” of the output to help reframe roles to enable required behavioral change.
At this stage, teams document the inputs (data) feeding the decision process as well as the outputs (analysis) produced at each stage. The objective is to highlight gaps in the availability and quality of the data supporting the process, as well as the robustness of analysis produced.
DPI Roadmap: Future State Improvements
The output from the DPI analysis is a comprehensive roadmap that includes recommendations for:
Decision Process Improvement (DPI): Better, Faster Decisions
In this post, we examine why you need to systematize your decision-making processes to understand better and provide context for your people strategies and AI solution requirements.
Find out more about how DPI can accelerate you analytics adoption.