How (Decision) Process Engineering Activates Your Decision Intelligence Designs

Alright, Decision Intelligence warriors – your decision model is ready to go! It’s meticulous, precise, and (seemingly) well aligned with your business objectives. You’ve harnessed game-changing tools like the Causal Decision Diagram (refer to Pratt & Malcolm’s Decision Intelligence Handbook) to map out the elements that shape the decision-making processes you aim to influence or automate. This isn't just a sophisticated blueprint; it's context-aware and designed to directly advance your organizational goals.

Decision Intelligence (DI) represents a revolutionary paradigm, sharpening the precision of how decisions are made by pinpointing critical processes, unraveling their logic, and embedding insights enriched by data and refined through psychological principles to boost your organizational success. Yet, even the most meticulously designed DI model risks fading into irrelevance if it remains largely theoretical. Without robust infrastructure to operationalize these designs, you might manage to force through decisions manually, but at what scale and cost? This approach is unsustainable, failing to integrate effectively with the systems, workflows, and stakeholders they are intended to benefit.

This is where Decision Process Engineering (DPE) enters the picture. Unlike standard process engineering, which primarily streamlines workflows and reduces inefficiencies, DPE takes a more comprehensive approach. It integrates Decision Intelligence designs into your existing workflows, bringing the strategic vision and detailed plans of the blueprints to life within your organization. This ensures that decisions not only transition smoothly into action but also enable continuous learning and adaptation.

A quick note: You might be wondering how Process Engineering (PE) differs from Business Process Management (BPM). PE focuses on building or overhauling processes with an emphasis on innovation and technical redesign. BPM, on the other hand, is about managing, monitoring, and incrementally improving processes over time. While BPM is valuable for ongoing maintenance, Process Engineering takes center stage here. This is where operationalizing your DI design and making its insights actionable becomes the priority.

Bridging Design and Action in Supply Chain Planning

Let’s make this real with a supply chain planning example. Imagine your DI model looks at demand forecasts, inventory levels, and supplier performance to recommend optimal stock levels and reorder schedules. On paper, it’s a masterpiece. In practice, if those insights remain stuck in dashboards or isolated systems, they might as well be gathering dust on the shelf.

DPE ensures these recommendations go beyond the design. Automated triggers reorder inventory when thresholds are breached (thank you, agentic AI). Alerts notify planners of exceptions needing human review, while feedback loops continually refine contributing models. Behavioral strategies (like Nudge Theory) guide planners to act confidently by highlighting cost savings for acting now or flagging risks of stockouts with clear, actionable prompts.

This isn’t just operational efficiency. It’s Decision Process Engineering in action.

Three Core Elements of Process Engineering

1. Map the Current Workflow(s)

Begin by examining the business processes that support and surround the decision-making processes you're looking to influence. Mapping these workflows helps identify inefficiencies, delays, and manual steps that disrupt the smooth execution of decisions. It highlights how decisions (and data!) move through your systems, uncovering bottlenecks or disconnects between insights, actions, and outcomes. This step also sheds light on critical factors like data pipelines, IT dependencies, data refresh cadences, and system integrations that are essential for ensuring decisions transition seamlessly from insights to action.

How DPE Goes Further: Traditional process mapping doubles down on identifying bottlenecks. DPE digs deeper by evaluating how human behaviors, biases, and misunderstandings affect decision quality and how DI design can address these gaps.

Expert Tip: Involve the people who live the process. Planners and IT teams know the shortcuts and workarounds that can reveal where workflows break down. Engaging an enterprise architect early is also critical, as they bring a holistic view of your systems, data flows, and integrations, helping to identify underlying technical dependencies and structural gaps that could impact decision execution.

Example: Mapping shows that planners use spreadsheets and email to manage inventory, leading to inconsistent updates and delays. It also reveals that planners frequently overorder to avoid perceived risks of stockouts, a behavioral quirk DPE can address.

2. Mine for Insights

Process mining tools analyze your company’s actual data flows to uncover bottlenecks, deviations, and inefficiencies in how workflows operate. These tools watch, trace, and monitor how transactions flow through your systems, using timestamps, volumes, and other various metrics to pinpoint opportunity areas for improvement. By providing a detailed view of your processes in action, they help identify where workflows break down or fail to align with desired outcomes.

How DPE Goes Further: Beyond process inefficiencies, DPE identifies where workflows fail to align with DI recommendations and examines behavioral patterns.

Expert Tip: Focus on patterns that keep showing up. Systemic inefficiencies and behaviors that repeat across teams or processes often highlight the best opportunities to make lasting and meaningful improvements. Process mining tools can also be used to monitor these workflows over time, transitioning into the realm BPM. In fact, changes identified by these tools can even be used to trigger incremental Decision Intelligence workflows, ensuring your systems adapt dynamically to evolving needs.

Example: Process mining uncovers delays caused by manual approvals for routine reorders, particularly in regions with varied time zones and inconsistent approval practices. The analysis also highlights redundant layers of approval for low-risk orders, compounding the inefficiencies.

3. Reengineer for Action

Reengineering takes the insights from mapping and mining to redesign workflows, removing barriers and inefficiencies that hinder decision execution. This step focuses on aligning processes with your DI blueprint, ensuring they reflect the decision logic, objectives, and data flows required for optimal performance. Core activities include streamlining or removing redundant steps, automating repetitive tasks, and addressing technical dependencies like data pipelines or system integrations.

How DPE Goes Further: Traditional reengineering focuses on efficiency. DPE goes beyond this by embedding behavioral psychology into workflows, using nudges to guide users toward confident, consistent decisions. It also ensures processes are meticulously aligned with the DI blueprint, mirroring the decision logic, objectives, and insights outlined in the design. This alignment bridges the gap between theoretical models and practical execution, creating workflows that not only work efficiently but also deliver on the strategic intent of your design.

Expert Tip: Use design thinking to center your workflow redesign around the user. Build trust with clear, actionable suggestions, intuitive interfaces, and reduced cognitive load. Collaborate with your organization’s product team to create new applications that support decision-making and capture feedback effectively.

Example: DI insights are seamlessly embedded into your inventory management system. Automated reorders are triggered when inventory levels fall below predefined thresholds, ensuring timely replenishment. Real-time alerts highlight exceptions that require human attention, while carefully designed nudges, such as potential cost savings or risk warnings, guide planners toward informed actions. Feedback loops capture data from these decisions, driving continuous improvement and refining the system over time.

The Risks of Skipping Decision Process Engineering

OK, you may be nodding along, thinking, "This all sounds great, but I’m all set." You’ve got a DI model, maybe even a slick BI dashboard, and you’re ready to roll. Here’s the reality check: not embracing DPE can quickly turn that promising design into a frustrating missed opportunity.

Picture this: A company deploys a DI model for supply chain planning, embedding its recommendations into a (pick your favorite) BI dashboard that planners are supposed to check every morning. The reports are beautifully designed, refreshed at 8 a.m. local time, and packed with insights and recommended actions. Optimism is high, but soon, the cracks start to show:

• Planners struggle to act because the dashboard is disconnected from the tools they need to execute decisions, forcing them to manually bridge the gap
• Manual workflows slow down critical actions, creating bottlenecks that lead to stockouts or overstocking
• Behavioral tendencies, like a fear of stockouts, cause planners to overorder, dismissing the DI model’s recommendations
• Feedback loops are limited/nonexistent, leaving the DI model static and unable to adapt to evolving needs or user behavior
• Trust begins to erode as planners see the system as disconnected from the realities of their day-to-day responsibilities

Without DPE, even the sharpest DI design risks becoming a well-intentioned artifact rather than the transformative tool it’s meant to be. DPE bridges this gap by bringing your design to life within the relevant workflows. It creates an ecosystem that not only functions seamlessly but also aligns with how people naturally think and act.

Getting Started with Process Engineering

If process engineering is new to you, start by learning the basics. Books like Process Mapping, Process Improvement, and Process Management by Dan Madison and The Basics of Process Improvement by Tristan Boutros and Jennifer Cardella are excellent resources. For hands-on experience, explore process mining tools like Celonis, ARIS from Software AG, or SAP Signavio, which offer powerful capabilities to analyze and redesign workflows.

End of the day, I employ you to begin small. Choose one process to map, analyze, and redesign. Collaborate with your team to capture behavioral insights and integrate DI designs into the workflow. Each iteration will demonstrate how DPE transforms potential into measurable impact.

#DecisionProcessEngineering is how you turn your DI designs into something real. Are you ready to drive real, measurable change?