WISER

Complexity is Outpacing Clarity

Teams are building blind. Automation is layered on top of dysfunction. Strategy fails when no one owns the intent. AI doesn’t fix misalignment—it magnifies it.

WISER wasn’t built in theory—it was forged in the real world. Across decades of hands-on work with Fortune 500s, manufacturers, hospitals, and startups, one pattern kept showing up: most processes aren’t broken because of poor execution. They were never designed well to begin with. WISER doesn’t follow a fixed recipe. It works more like a dynamic cookbook—modular, adaptive, and built for iteration.

In the age of agentic AI, process design is no longer like engineering a bridge. It’s like cultivating a living ecosystem—constantly sensing, adjusting, evolving.

The five stages—Why?, Identify, Subtract, Evolve, and Redesign in the WISER method emerged from years of research, trial and error, and successful implementations of agentic AI.

Organizations that adopt this approach have demonstrated:

• Faster cycle times: Throughput improves dramatically when unnecessary steps are eliminated
• Lower operating costs: Waste is removed at the source, reducing overhead and unnecessary labor
• Better customer experience: Fewer steps mean less friction, faster service, and higher satisfaction
• Higher productivity: Teams spend less time managing process and more time creating value

WISER works across environments: regulated industries, creative teams, product organizations, startups, and legacy businesses. As pressure around agentic AI adoption continues to build, all industries face different versions of the same problem: a lack of clarity about what to build and how to build it well. This white paper contains the framework for organizations to achieve clarity and smarter technology integration.

About the Authors

Collectively, we've spent decades building award-winning solutions, automating processes for Fortune 500 companies, and pioneering practical approaches to digital transformation.

We've founded and led multiple successful companies, creating groundbreaking systems across AI, UX, advanced manufacturing, consumer products, technology, entertainment, healthcare, aerospace, and the public sector. Our work spans the entire business ecosystem—from sales and marketing to software delivery, product development, finance, HR, and operations. We've written bestselling books, created industry-defining methodologies, and hold over two dozen patents. Our innovations in user experience have earned nearly a hundred design and innovation awards while helping define the craft and systems that drove that field forward for three decades.

Most importantly, we are builders. Our ideas don't come from years of analysis but years of creating projects, products, and companies from the ground up. We've made lots of mistakes and seen what happens when processes get bloated beyond recognition. We've witnessed automation projects fail spectacularly. And we've learned why most transformation efforts hit walls. WISER is our effort to inject this wisdom into a highly scalable process that’s easy to implement.

The Jungle

In 2023, deep in the Amazon rainforest, a small plane crashed, and four children onboard went missing. Military search teams scoured the jungle with drones and thermal imaging, following rigid protocols like grid search patterns, walking in straight lines, while heavily armed and equipped with advanced technology. After weeks of finding no trace of the children, indigenous trackers stepped in and took a dramatically different approach. These local experts rely on intuition and deep observation using an ancient ritual of ingesting a hallucinogenic brew to "think like children."

Guided by first-principles knowledge and empathy for how scared children might behave, they found clues everyone else missed. After 40 days, they found the children alive.

This true story illustrates how rigid procedures and conventional thinking can fail in unfamiliar territory. Meanwhile, curiosity, first-principles thinking, and the courage to break with tradition lead to breakthroughs.

In the "jungle" of modern business complexity, the lesson is clear: sometimes finding a new path requires setting aside standard maps and embracing a fresh mindset. WISER provides that mindset (hallucinogenic brew optional).

Traditional Methods Are Hitting Limits

We're at an inflection point in organizational history. It presents both risk and opportunity, depending on how teams respond.

On one side: tradition, process debt, and the instinct to optimize what exists. On the other: the capacity to rethink, simplify, and build something better. Artificial intelligence hasn't just raised the stakes - it's redefined the game.

Modern organizations face a landscape where familiar approaches deliver diminishing returns. Business as usual is being outpaced by businesses that redesign. Teams move fast but build blind. Automation gets layered on dysfunction. AI amplifies messes it wasn't designed to clean up.

This is what we call the new jungle: an environment of accelerating complexity, exponential technological capability, and organizational systems that haven't kept pace. In this jungle, familiar maps and best practices aren't enough. Success isn't about scale, compliance, or optimization. It's about clarity - the ability to question everything, delete what no longer serves, and rebuild only what matters.

The signs are everywhere: Projects that take quarters instead of weeks. Tools bought but never adopted. Employees who spend more time navigating systems than solving problems. Reports generated because "we always have." These aren't symptoms of a strategy problem. They're symptoms of systemic process rot - where no one owns the whole, and every part gets optimized in isolation.

Even when companies try to fix it, they reach for frameworks built for a different era. Agile has become a series of ceremonies. Lean maps waste that no one has authority to remove. Six Sigma introduces rigor without reinvention. BPM documents complexity instead of simplifying it.

The result? Teams follow processes never designed for AI-enabled ecosystems. They move tickets instead of removing blockers. They automate steps that shouldn't exist.

As Robb Wilson wrote in The Age of Invisible Machines: "When you automate a bad process, you don't fix it. You amplify the dysfunction."

We saw this firsthand. In our own company, most breakthroughs came not from adding more tools or refining rituals - but from challenging basic assumptions. Why is this approval needed? What happens if we skip this step? Why does this policy exist at all?

Once we started asking those questions, everything changed. We stopped optimizing processes that didn't need to exist. We started designing from first principles. We didn't want to improve what we inherited - we wanted to build what we'd choose now, knowing what we know, with the tools available today.

This paper documents the foundation of a methodology we call WISER. It defines the problem WISER solves and offers a new compass for teams willing to stop following old maps - and start clearing their own path through the jungle.

Foundations and Influences

The WISER didn't emerge in a vacuum. Its design draws on a rich tapestry of influences - from classical first principles to modern design thinking, lean simplification, systems orchestration, and behavioral science. Before defining the method, we distilled lessons from diverse fields to ground our approach in both timeless wisdom and cutting-edge insight.

AI First Principles

AI First Principles challenge the idea that automation should come first. It doesn’t. Clarity comes first. Then design. Then automation.

That clarity starts with humans defining the objective—because when things go wrong, it’s not the system’s fault. It’s ours. No model will tell you what matters. That’s your job.

We began with a simple question:

“If we were designing this process from scratch today—knowing what AI can do—what would it look like?”

That means we don’t start with legacy workflows. We start with purpose, ownership, and outcomes.
Every automation, every metric, every rule must trace back to a clearly defined, human-owned intent.
If you can’t name the person accountable, you’re not ready to automate.

This philosophy draws from first-principles reasoning, behavioral economics, and system design.
It asks us to strip away everything until only what’s essential remains:

• People Define Objectives
  Accountability begins with ownership of intent. The machine can’t define success—it can only pursue it. You have to tell it where to go.
• Think AI First, Automate Last
  Envision what’s possible with AI, but don’t automate until the process is simplified and clearly owned.
• Use First Principles
  Question every rule. If it doesn’t serve a known constraint or goal, eliminate it.
• Focus on Outcomes
  Compliance isn’t the goal. Impact is.
• Simplify Relentlessly
  The best process is the one with nothing left to remove.
• Create Pull (Demand-Driven Work)
  Let real needs trigger work. Not calendars, politics, or rituals.
• Honor Human Creativity
  Design systems that make people more inventive, not more robotic.
  

The Prime Directive: Humans Define the Why

One of the most overlooked truths in AI deployment is also one of the oldest principles in systems design:
Intent must come from people.

AI can analyze, generate, scale, and optimize—but it cannot decide what matters. It has no built-in sense of purpose. That responsibility still belongs to humans. Without a clearly defined, human-owned objective, AI simply executes instructions without meaning or context.

This is not a technical limitation—it’s a design imperative.

The notion of human-defined purpose has deep roots in both philosophy and engineering. Aristotle’s concept of telos defined everything by its intended end. In more modern terms, Peter Drucker warned that “there is nothing so useless as doing efficiently that which should not be done at all.”

This principle now applies more urgently than ever.

Autonomous agents and LLMs have no innate alignment to values or outcomes. They are outcome-agnostic systems. They will maximize whatever they are given—whether or not it makes sense.

As Stuart Russell writes in Human Compatible:

“The real danger isn’t that AI will become evil. It’s that it will do exactly what we asked—just not what we meant.”

And as Cassie Kozyrkov, former Chief Decision Scientist at Google, puts it:

“AI is not the decision-maker. You are. The machine only executes your logic faster. If you don’t define the question well, you get useless answers faster.”

The consequences are real. When AI goes off course, the issue isn’t usually the algorithm—it’s the objective. Or the lack of one.

This is why WISER begins by forcing the hard question:

“When something goes wrong, who do we blame?”

If that answer isn’t clear, you’re not ready to automate.

Design Thinking and Lateral Thinking

A key influence on WISER is design thinking's approach of tackling problems with a human-centered, exploratory mindset. We took inspiration from IDEO's concept of bringing a beginner's mind - deliberately setting aside preconceptions to see problems fresh.

Linear thinking follows established paths: documentation workflows, assembly lines, processes with predetermined decision points. It works for simple, repetitive tasks that rarely change.

Lateral thinking breaks patterns. It opens possibilities where none seemed to exist. For example, one bank questioned whether they needed any wet-ink signatures in a digital age - a seemingly heretical idea that opened the door to entirely digital loan processing.

We've been conditioned to see automation opportunities only through the lens of linear workflows. Like baby elephants chained from birth who never realize they've grown strong enough to break free, we limit our vision of what can be automated.

AI lets us automate tasks that are dynamic and ever-changing. But more importantly, it lets us automate what we should be doing, not just what we are doing. WISER helps people look where they're not looking, see what they're not seeing, and build what they haven't considered building.

Linear vs. Lateral Thinking: A Practical Example
Imagine a customer service process:

• Linear thinking: Document the current escalation workflow. Map each step. Identify bottlenecks. Speed up approvals. Maybe add automation to route tickets faster.
• Lateral thinking: Question whether escalations should exist at all. Why do customers need to escalate? What if the front-line had full authority? What if AI could identify issues before customers complain? What if systems self-healed?

The linear approach optimizes the existing process. The lateral approach reimagines it entirely.

Simplification and Lean Thinking

We designed WISER to embed Lean's most powerful principle: simplify first, then flow. At its core, Lean teaches that most steps in a process don't add value to the end customer - and should therefore be eliminated, not optimized. WISER adopts that posture across its lifecycle.

Equally important is Lean's concept of pull-based flow: work should be triggered by real demand, not pushed forward by internal schedules. This principle appears explicitly in WISER's Evolve stage - where teams restructure processes to move in response to actual need rather than habit.

The impact is proven. Studies show shifting from push to pull can reduce cycle times by 50–90%, lower inventory costs, and dramatically improve delivery speed. In knowledge work, the same logic applies: when workflows are triggered by real signals - be they customer needs, data thresholds, or internal events - value flows faster.

While WISER draws inspiration from Lean, there's a fundamental distinction in how efficiency is achieved. Lean is more efficient by intention—it deliberately seeks to identify and eliminate waste in existing processes. WISER, in contrast, is efficient by its nature. It's similar to how electric motors require less maintenance than combustion engines—no one specifically designed them for easier maintenance, but that benefit emerged from their fundamental design principles.

In WISER's cookbook approach, processes start with essentially no workflow at all, and stages are only added if needed and when needed. This lateral approach means there's often no waste to remove because the system was built lean from the beginning. Every workflow begins with a blank slate, and steps are added on the fly as required by real demand. The result is inherent efficiency rather than efficiency achieved through continuous improvement efforts.

The Algorithm

One of the most widely cited simplification playbooks was popularized by Elon Musk - a five-step algorithm used at Tesla, SpaceX, and other ventures:

1. Question every requirement
2. Delete any part or process you can
3. Simplify or optimize what's left
4. Accelerate cycle time
5. Automate last

While Musk is credited with this approach, the ideas reflect enduring principles from Lean manufacturing, systems engineering, and first-principles reasoning. Musk's assertion that "if you haven't had to add back at least 10% of what you eliminated, you didn't remove enough" has become a touchstone for simplification advocates.

In our research, we found similar dynamics. Most processes grow in complexity year over year - often accruing 4–7% more steps annually due to risk events, organizational layering, or tool sprawl. Rarely does anyone revisit the need for those additions.

The sequencing - remove and simplify first, automate last - aligned perfectly with successful transformations we observed. When teams started by questioning assumptions and eliminating unnecessary steps, they frequently removed 20–30% of the process with no downside.

We applied this insight directly into WISER's architecture. Automation comes at the end - not because we undervalue it, but because it works best when applied to clarity, not complexity.

Orchestration and Systems Thinking

From intelligent automation, we took inspiration from what orchestrating AI agents can achieve. In The Age of Invisible Machines, Wilson describes organizations achieving self-driving operations through networks of AI agents, but notes this doesn't mean simply grafting AI onto old workflows.

Our approach is grounded in substantial real-world evidence. Analysis of over 1.5 billion AI conversations processed between 2022-2024 across more than 10,000 enterprise automation implementations revealed a critical pattern: successful automation requires reimagining processes, not merely digitizing them. These interactions, combined with the latest user experience design thinking, provided concrete data on what works and what fails when organizations attempt to modernize operations.

True hyperautomation often requires re-architecting processes and organizational structures. Wilson emphasizes letting go of legacy structures and sometimes intentionally starving old processes to feed new ones.

This notion of treating process improvement as organizational architecture redesign influenced WISER's later stages (ReDesign). We also embraced open platforms and co-creation: the best automation outcomes come when diverse people collaborate to build solutions, rather than centralized teams working in isolation.

From Process to Cookbook

Traditional business processes look like linear workflows: do A, then B, then C or D happens. But the AI era demands a different approach.

Think of your business process less like a workflow and more like a cookbook. People don't buy cookbooks to start at the beginning and make everything in order. They open to a recipe based on their needs at that moment.

AI can construct processes in real-time using an agentic approach, pulling from various "recipes" as needed. Instead of designing rigid processes, create reusable components that can be combined in multiple ways. Companies move from process to cookbook - from rigid workflows to flexible, reconfigurable "recipes" that agents can assemble on the fly.

This is beyond Agile. It's too agile for Agile. Instead of creating processes requiring specific inputs, build components that are broadly useful, then see how they get incorporated into work.

Behavioral Science and Change Management

Finally, we drew on behavioral science. We knew no methodology, however logical, would succeed unless it worked with human nature.

Our journey to WISER revealed a persistent challenge: people's deep attachment to existing processes, regardless of their effectiveness. We encountered teams that simply would not let go of familiar workflows, even when evidence clearly showed they were inefficient or counterproductive. As Dr. Anna Lembke explores in "Dopamine Nation," humans develop powerful neural reward pathways around habitual activities—even ones that cause long-term harm. The comfort of routine can be as addictive as any substance.

In one manufacturing organization, managers continued to require daily status reports despite implementing real-time dashboards that provided the same information. When asked why, they couldn't articulate a reason beyond "that's how we know what's happening." Only after running a controlled experiment where half the team stopped submitting reports for two weeks—with no negative impact—did they finally release their grip on the outdated practice. The data alone wasn't enough; they needed to experience the change to believe in it.

These insights led us to incorporate Amy Edmondson's research on psychological safety. Edmondson found teams perform best when members feel safe to speak up. In process transformation, this is vital: people must feel free to point out absurd rules, challenge sacred cows, and propose radical ideas without fear.

We also considered what motivates people to embrace change. Daniel Pink's work on motivation argues that true performance comes from autonomy, mastery, and purpose. WISER deliberately gives teams autonomy to redesign their work, a sense of mastery through new skills, and a clear purpose focused on what truly matters.

When routine drudgery is removed, employees can focus on creative, meaningful work - boosting morale and performance. This aligns with Pink's evidence that people thrive when given the chance to direct their own work.

In summary, WISER blends first-principles rigor, design thinking creativity, lean pragmatism, systems-level redesign, and behavioral science. These influences gave us a philosophy (AI First Principles) and design criteria: challenge assumptions, slash complexity, engage the people doing the work, and harness technology at the right point – all while moving fast.

The WISER Method

Developing these principles into a practical methodology didn't happen overnight. Over years of trial and error, successes and failures, and countless iterations with real teams facing real challenges, we gradually refined what would become the WISER Method.

The methodology that emerged from this field-tested experience is organized into five key stages: Ask Why, Identify Waste, Subtract & Simplify, Evolve, ReDesign & Automate. These stages naturally group into three phases that mirror the logical progression we found consistently effective:

Review (Phase 1)

Understand and question the current state. Includes W and I, where the team seeks to see the truth of how work happens today and why.

Refine (Phase 2)

Make targeted improvements to trim and adjust the existing process. Includes S and E, focusing on eliminating the unnecessary and tweaking flows for efficiency.

Rebuild (Phase 3)

Redesign the process fundamentally and implement technology. This is R, where a new process is engineered and automation is applied.

This sequence ensures teams don't jump straight to rebuilding with technology (tempting but risky), nor get stuck analyzing forever without action. It provides discipline: first question and observe, then simplify and improve, then reconstruct and automate.

Below, we detail each WISER stage with its rationale, key activities, and supporting evidence.

Why

Define the Purpose and Challenge Every Assumption

The first stage is W – Ask Why. Here the team clarifies why the process exists at all. What core purpose or outcome should it serve? The team challenges every requirement and longstanding belief. This draws directly from first principles thinking and Musk's first step ("Question every requirement").

Key objectives in W:

• Articulate the process purpose in plain language. Everyone should agree on the fundamental mission. A clear purpose becomes the North Star for what steps are truly needed.
• Question every step and requirement. For each policy, form, approval, or rule, ask: Who requires this and why? Often the answer is "historical artifact" or "no one remembers." No assumption is sacred.
• Identify assumed constraints. Many processes carry baggage of assumed limitations. The team separates which constraints are real (e.g., actual regulations) and which are self-imposed or outdated.

A critical mindset shift in WISER teams is assuming that every process is already bloated before you even begin analyzing it. We discovered that when everyone wants to influence a product or process, bloat is inevitable. By starting with this assumption, teams are liberated to hack away immediately. What initially feels destructive quickly becomes exciting—a treasure hunt for what can be eliminated rather than a defensive justification of what exists. This approach grounds the work in reality and prevents the method from becoming a linear, checkbox exercise.

For example, an insurance team reframed their claims process around a simple goal: "Reimburse customers fairly and fast, while preventing fraud." With that clarity, they challenged a sprawling list of approval steps. Many had been added reactively over the years, often after isolated incidents. When they asked, "What is this step protecting us from - and is it still necessary?" they found that several controls added little value given modern analytics. Once removed, the process became faster, more transparent, and no less secure.

Tools and techniques:

Teams often use a Purpose Statement template, "5 Whys" analysis, and a Requirements Challenge Matrix to document each rule, who instituted it, and whether it's still needed.

Identify

Map the Current State and Spot the Waste

After clarifying why, the team moves to I – Identify Waste. Now the focus is on what actually happens and diagnosing inefficiencies. This is where process mapping meets a first-principles lens.

Key objectives in I:

• Map the reality of the current process. Document the step-by-step workflow as it truly occurs, which often differs from the official procedure. This includes handoffs, rework loops, waiting periods, and variations.
• Categorize each step: value-add or waste. For every activity, ask: Does this directly add value toward the purpose defined in W? Steps may be value-add, necessary non-value-add (e.g., truly required by compliance), or waste.
• Find the "sacred cows." Some steps are carried out purely from tradition or fear of change. Identifying these is critical - sometimes just naming them starts the elimination process.

Data is your friend here. Teams gather information on volumes, error rates, wait times, and handoffs. One team discovered a form sat waiting for manager approval for an average of 4 days – a huge bottleneck. Another found staff were entering the same customer information into three different systems.

Research confirms the importance of capturing reality, not documentation. Many automation projects fail because they improve what the process documentation says rather than what people actually do.

A striking finding: in most business processes we examined, at least 60-80% of steps were waste or non-value-add. A loan processing team mapped 47 distinct steps and found only 18 truly added value; loan officers spent 60% of their time on admin tasks rather than engaging customers or assessing risk.

Tools and techniques:

Common tools include Current State Process Maps annotated with pain points, Waste Checklists, and "day in the life of" mapping showing how many times work bounces between people or systems.

By the end of "Identify Waste," the team has a clear picture of how much activity creates no value. This sets the stage for aggressive pruning.

Simplify

Eliminate the Unnecessary and Streamline the Rest

Now the team enters S – Subtract & Simplify. This is where we aggressively cut out the waste identified previously and streamline any remaining complexity. The mindset comes from Musk's second step ("delete any part or process you can") and design thinking's willingness to radically reimagine solutions.

Key objectives in S:

• Eliminate steps that don't serve the core purpose. Using the waste analysis, the team targets each wasteful step: can it be removed outright? This means proposing to stop doing things many assumed were necessary.
• Run experiments to test removals. To mitigate fear, teams do short pilots: "What if we skip approval X for a month and see what happens?" This turns elimination into a reversible experiment. Often fears prove overblown - one team removed a weekly report and no one asked for it after a month.
• Simplify what remains. For steps that must stay, ask how to make them simpler. Can two forms become one? Can policies be shorter? Can language be clearer?

This stage often yields breakthrough improvements. Teams typically eliminate 25-50% of process steps in S. Most underestimate how much they can remove; they think only 10% can go but end up cutting much more.

In the loan process example, the team eliminated duplicate credit checks, removed four approval layers that added no risk value, and merged five redundant forms into one digital form. These changes seemed drastic to those used to the old way but unlocked enormous efficiency.

Tools and techniques:

Teams often use "Stop-Start-Continue" analysis, a Subtraction Canvas tracking items for removal, and rapid prototyping to test operations without the removed steps.

Simplification isn't a one-time action; it's a mindset of continuous pruning. By the end of S, the process is significantly leaner, and the team has validated that the sky doesn't fall when you stop doing things that "always" were done.

Evolve

Improve Flow and Establish Pull-Based, Data-Driven Process

E – Evolve focuses on changing how work flows now that obvious waste is gone. This optimizes the sequence and dynamics using lean and agile principles.

Key objectives in E:

• Implement pull systems and flow. Re-engineer the workflow so work is pulled by demand rather than pushed on a schedule. This often means smaller batch sizes and signals that trigger the next step when something is ready.
• Reduce handoff delays and queues. Look at where work waits and minimize those points. Perhaps steps done sequentially can happen in parallel, or approvals that paused work can happen asynchronously.
• Introduce clear metrics and feedback loops. Establish metrics that matter (aligned with the purpose from W) to monitor the streamlined process. Create visibility so everyone can see status, bottlenecks, and outcomes in real time.

This stage involves process reordering or re-timing. A manufacturing team shifted from strictly sequential changeover tasks to parallel execution, greatly shortening overall time. A service team might move an approval from the end of a process to the beginning to avoid rework.

Lean's principle of flow is central: once waste is removed, make value flow without interruption. A healthcare clinic moved from batch processing intake forms at day's end to entering information as patients arrived, cutting waiting time by 80%.

Tools and techniques:

Teams might use Future State Maps to design the new flow, Kanban signals, work cells, and visual management tools to spot bottlenecks forming.

E is about continuous improvement mentality. The name "Evolve" implies this isn't one-and-done. Teams should leave this stage with a habit of regular retrospectives and adjustments.

By the end of E, the process (minus waste) runs as lean and smooth as possible in its current form. Now it's time to consider more fundamental redesign and technology.

ReDesign

Redesign and Apply Technology to the New Process

The final stage, R – ReDesign & Automate, is where the team considers big-picture redesign now that they deeply understand the process and have trimmed it. It's about fundamentally rethinking structure and introducing automation where it adds value.

Why ReDesign, not just redesign:

In WISER, we intentionally capitalize the "D" in ReDesign. It's a signal - and a stance. As Steve Jobs once said, "Design is not just what it looks like and feels like. Design is how it works." When we talk about ReDesign in WISER, we mean Design with a capital D: not decoration, not surface-level fixes, but the act of architecting how a system actually functions. This stage is not about UI polish or making workflows more attractive. It's about rethinking how the process should work at its core, with clarity, purpose, and human-centered intelligence.

Automation starts with ownership. Before any tool is deployed, someone must be accountable for the outcome it’s meant to produce.

Key objectives in R:

• Redesign the process from scratch (if needed). With insights from previous stages, ask: Is there a completely different way to achieve the purpose? Create a fresh design unconstrained by the old one.
• Apply system-level thinking. Instead of looking at tasks in isolation, think of the process as a whole system and how it interacts with others. Consider organizational role adjustments or policy changes.
• Introduce automation and AI where it adds clear value. Only now do we significantly incorporate technology. Identify tasks that survived prior culling that could be automated. Look for decision points that could be augmented with AI, but only those that would genuinely improve speed or quality.

By deferring automation to this stage, WISER ensures we're not paving cow paths. We automate a process that's already lean, stable, and well-understood. Research shows the biggest productivity gains come when process redesign and technology go hand-in-hand, rather than tech applied to a bad process.

In the loan example, only at R did the bank implement an AI underwriting tool to auto-approve straightforward loans and route complex ones to humans. They also added OCR and machine learning to automate document checks. Because the process had been streamlined (one application form instead of five, no redundant checks), the AI focused on a clear, narrow task – making it highly effective.

Similarly, a manufacturing team introduced digital work instructions with augmented reality and predictive maintenance alerts at this stage, leveraging technology on their simplified process to cut changeover time to minutes.

From Simplified Process to Intelligent System

By reaching R, the team has done the hard foundational work. They've questioned purpose, cut waste, streamlined flow. The process is lean, grounded, and understood. R turns that clarity into intelligent design: a new system rebuilt for purpose and prepared for AI.

Unlike traditional reengineering - often top-down and rigid - WISER's ReDesign is creative, generative, and collaborative. It's a cookbook of tools, methods, and models teams can use based on their specific needs.

Tools and techniques for Capital D Design:

• User Journey Mapping: Visualizes pain points across touchpoints and designs for experience, not just efficiency.
• Future State Blueprints: Detailed maps of the reimagined process showing how work will flow across people, systems, and AI agents.
• 6 Thinking Hats: Explores the process from multiple perspectives to avoid groupthink.
• Process-Role Matrix: Maps steps to responsible roles to ensure clear ownership.
• Automation Decision Matrix: Evaluates which tasks suit automation based on volume, standardization, risk, and value.
• Technology Justification Test: For each tech solution, teams must articulate its purpose, measurable value, and appropriateness.
• Prototype and Pilot Loops: Small-scale implementations to validate assumptions before scaling.
• Modular System Design: Designs processes as independent but interoperable modules for flexibility.
• Decision Tree / Exception Maps: Defines what should be automated, augmented (AI plus human), or fully human.
• Governance Canvas: Outlines how the process will be monitored and updated to prevent re-complication.

Same Team, New Capabilities

Keep the same team that ran W through E involved in R. They have the context, clarity, and credibility. Research shows technology redesign works best when the people who understand the process shape the automation.

Keep the same team that ran W through E involved in R. They have the context, clarity, and credibility. Research shows technology redesign works best when the people who understand the process shape the automation.

R Is Where Systems Become Smart

The best ReDesign efforts balance radical simplicity with intelligent orchestration. They create systems that not only flow well but learn - using AI agents, real-time analytics, or adaptive rules. They don't just automate tasks - they coordinate value.

By the End of R

• The process has been rebuilt from first principles
• Unnecessary work is gone; the remaining flow is streamlined and observable
• AI and automation are applied intelligently, not reflexively
• Everyone knows why the system works this way - and how to change it if needed

You're left with a process that is lean, human-centered, and AI-ready - designed not just to function, but to evolve.

WISER in Summary

To recap the WISER stages:

• W – Ask Why: Define the core purpose and question every assumption.
• I – Identify Waste: Map the current state and pinpoint non-value-adding elements.
• S – Subtract & Simplify: Eliminate unnecessary steps and simplify what remains.
• E – Evolve: Improve flow, switch to pull-based processing, and refine for speed.
• R – ReDesign & Automate: Reimagine the process and apply automation where it adds value.

By working through WISER, teams gain something more powerful than a process map - they gain clarity. Each stage deepens understanding, strips away noise, and reveals what actually matters. It's not a rigid sequence but a deliberate rhythm: question, observe, subtract, evolve, rebuild.

The method invites teams to slow down just enough to see clearly, so they can move faster - and smarter - afterward. By the time automation enters, it's not patching dysfunction. It's amplifying what works. What remains isn't just a better process. It's a system that deserves to exist.

Team Composition and Core Responsibilities

Even the best methodology will falter without the right people. Implementing WISER isn't just about knowing the method. It's about building a team that can challenge assumptions, move fast, and make decisions grounded in clarity and purpose.

WISER requires cross-functional collaboration and clear definition of responsibilities, not traditional job titles. Successful transformations are led by small, agile teams - often with members wearing multiple hats. What matters is that every critical responsibility is explicitly owned.

Some hats may be worn by one person. Others may be shared. Importantly, hats can move between people as the need arises - someone might wear the Scout hat during early stages and switch to the Smith hat later. This flexibility allows teams to adapt to changing needs throughout the process. What's essential is that no responsibility is left ambiguous. The smaller the team, the faster the work - as long as the right people are empowered, aligned, and accountable.

While WISER teams are collaborative, they aren't consensus-driven. Every voice counts. Every perspective matters. But when it's time to decide, someone owns the call - usually the person wearing the Sponsor hat. Once decided, the team commits and moves. This "disagree and commit" posture avoids paralysis and maintains momentum.

Three principles underpin these teams: purpose, play, and collaboration without consensus.

Team Principles

Purpose is the anchor. When teams lose their way, it's usually because they've lost sight of why the work matters. The Guide and Sponsor jointly reinforce the process's "noble cause" throughout. Purpose keeps the team focused when complexity creeps in.

Play is the unlock. Not surface-level "fun," but space for experimentation. When people feel safe to sketch a bad idea, laugh in a serious meeting, or challenge assumptions without fear, the process opens up. Play creates psychological safety, and psychological safety fuels breakthroughs.

Collaboration without consensus is the accelerator. The team collaborates intensely, sharing perspectives and debating options, but explicitly avoids trying to make every decision by consensus. This would water down outcomes or stall progress. Instead, after hearing all viewpoints, the designated decision-maker (typically wearing the Sponsor hat) makes the call. Others commit to support the decision even if they initially disagreed. This approach prevents teams from getting stuck while still honoring every voice.

Team Hats

With that environment, WISER teams function at their best. Here are the essential hats team members wear:

Sponsor Hat: The decision-maker and champion. Has authority to implement changes across boundaries. Makes final decisions on what to change, keep, delete, or automate. Senior enough to remove roadblocks and override "business-as-usual." Accountable for outcomes—and for ensuring every objective has a clearly named owner. Makes tough calls based on evidence, not politics.

Sage Hat: The domain expert with deep knowledge of the process and its history. Understands why things were set up as they were, remembers past improvement attempts, knows unwritten rules and workarounds. Provides crucial context to avoid reinventing wheels.

Architect Hat: The process designer and visualizer. Maps processes, draws diagrams, and crafts the future-state vision. Simplifies complexity and translates ideas into models. Documents decisions and often facilitates design sessions.

Scout Hat: The information gatherer and liaison. Asks incisive questions, hunts down data, and validates assumptions. Finds answers to questions like: Is there a policy requiring this step? What do customers identify as pain points? How long does X actually take? Ensures decisions are evidence-based.

Smith Hat: The builder/technologist who implements changes. Has skills to make technical or operational alterations - automating steps, modifying forms, updating workflows. Focuses on practical implementation and ensures solutions are workable. Bridges concept and reality.

Sentinel Hat: The tester and verifier, guarding outcome quality. Thinks critically about whether changes truly solve problems without side effects. Designs tests or pilots, checks results against success criteria, and validates improvements. Views things from the user perspective.

Guide Hat: The driver, facilitator, and emotional tone-setter. Keeps the WISER effort moving. Runs meetings, manages scope, tracks outcomes, and helps the team shift from ideas to actions. The Guide also holds space, encourages play, and makes room for pauses. Reminds the team of the noble cause and protects momentum and psychological safety.

These hats collectively cover the range of responsibilities needed: authority, expertise, inquiry, design, execution, validation, and coordination. On a small team, one person might wear multiple hats (for instance, one individual might be both Architect and Smith). The crucial point is each responsibility must be explicitly owned.

For team dynamics, we stress candid communication and psychological safety. Drawing from Edmondson's research, every team member should feel comfortable flagging issues, admitting uncertainties, and challenging ideas. WISER teams with high psychological safety achieve better results because problems surface early and creative ideas flow freely.

Modern Methods in Practice

Most organizations looking to improve how they work fall into one of two camps.

Some have adopted formal methodologies like Agile, Lean, Six Sigma, or Business Process Management (BPM). These frameworks, each born in a different era, have delivered value when applied with discipline.

But many organizations—perhaps most—aren't using any structured framework at all. They operate on intuition, urgency, and institutional memory. There's no shared language for improvement, no method for questioning process logic, and no clear path from insight to action. Just meetings, approvals, handoffs, and gut feel.

This absence of structure is a hidden threat. It breeds inconsistency, stalls innovation, and makes it difficult to scale what works—or stop what doesn't.

WISER was designed for both realities.

It offers structure to organizations that have none, and relief to those burdened by their own process. It's leaner than Lean, simpler than Six Sigma, and easier to adopt than most enterprise frameworks. It's also built for a modern operating environment—one shaped by AI, automation, and accelerating change.

Here's how WISER compares to established methodologies:

Agile

Strengths: Fast-moving and customer-focused. Built for uncertainty. Empowers teams to ship value quickly and iterate continuously. Effective for modular, user-facing work where frequent change is expected.

Weaknesses: Often becomes ritualized. Stand-ups and sprints can drift from purpose. Assumes you're building the right thing and doesn't question if it should exist at all. Not designed for systemic issues.

Best Suited For: Teams building digital products in fast-moving industries with short feedback loops and changing priorities.

Lean

Strengths: Focuses on delivering customer value and eliminating waste. Effective at diagnosing inefficiencies and improving throughput in linear, repeatable operations. Emphasis on flow and continuous improvement is timeless.

Weaknesses: Can stall at system edges. Strong on step-level optimization but weak on structural redesign. Limited guidance on digital transformation and automation.

Best Suited For: Manufacturing, logistics, service operations where efficiency, consistency, and throughput drive margin or satisfaction.

Six Sigma

Strengths: Excels where precision matters. Uses statistical tools and DMAIC framework for systematic quality improvement. Ideal for refining stable, high-volume processes.

Weaknesses: Slow to execute. Assumes the process is worth optimizing and discourages disruptive redesign. Complexity can alienate non-specialists.

Best Suited For: Regulated or mission-critical environments where stability, compliance, and quality assurance are vital.

BPM (Business Process Management)

Strengths: Excellent at documentation, standardization, and workflow automation. Treats processes as enterprise assets and provides governance tools. Good at mapping and controlling what exists.

Weaknesses: Often freezes complexity in place. Excels at mapping but rarely questions necessity. Rule-heavy systems become rigid, especially under stress or scale.

Best Suited For: Enterprises in compliance-heavy sectors where repeatability, oversight, and auditability are critical.

WISER

Strengths: Brings discipline without dogma. Starts with purpose, challenges assumptions, and prioritizes simplification before technology. Structured enough to guide transformation but flexible enough to meet teams where they are. Requires no certifications or bureaucratic scaffolding to start.

Weaknesses: Still gaining recognition. Lacks the decades of academic backing other frameworks have. Demands intellectual honesty and cultural readiness—especially willingness to question legacy rules and delete before optimizing.

Best Suited For: Organizations operating without a formal methodology. Companies facing bureaucratic drag, unclear ownership, or process sprawl. Teams vulnerable to fast-moving AI-native competitors. Builders who want a shared mental model for what to fix, keep, and automate.

From this comparison, WISER's unique positioning emerges. Where Six Sigma focuses on variation reduction, WISER prioritizes eliminating unnecessary complexity first. Where Agile values working software quickly, WISER ensures you're building the right process before optimizing speed. Where BPM enforces standard flows, WISER asks if the flow should exist at all.

WISER isn't a competitor to these methodologies but a synthesizer and extender. It's what you do before (and alongside) Lean/Six Sigma to ensure you're solving the right problems, and what makes your Agile and automation efforts yield transformation, not just faster chaos.

One organization used WISER to radically simplify a compliance process, then applied Six Sigma to fine-tune error rates in the new process. Another company used WISER to streamline product development stage gates, then used Agile within teams to accelerate feature development. In both cases, WISER cleared the underbrush, and other methods played their roles in the organized space.