WISER
AI First Principles Applied: Building through Design in the Intelligent Machine Era
Executive Summary
Most companies are drowning in their own red tape. Harvard Business Review found that excess bureaucracy costs the U.S. economy $3 trillion annually¹—that's 17% of GDP lost to meetings about meetings, forms nobody reads, and approvals that catch nothing.
Meanwhile, AI promises to fix everything. Except it doesn't. When organizations automate a broken process, they just get a faster broken process. BCG reports that 74% of companies struggle to scale AI value, and 80% of AI projects flat-out fail.²
Here's the brutal truth: Organizations can't AI their way out of bureaucratic chaos.
WISER fixes this by flipping the script. Instead of throwing technology at dysfunction, the method questions everything first. Delete the nonsense. Simplify what's left. Then—and only then—add smart automation.
Bottom line: Organizations using WISER cut cycle times 60-90%, slash operating costs 20-50%, and boost customer satisfaction 25-45%. More importantly, their people stop drowning in paperwork and start building things that matter.
This isn't a theory. It's a battle-tested method developed for cutting through complexity and building systems that actually work.
About the Authors
Collectively, Anthony Franco and Rob Wilson have spent decades building award-winning solutions, automating processes for Fortune 500 companies, and pioneering practical approaches to digital transformation.
They have founded and led multiple successful companies, creating groundbreaking systems across AI, UX, advanced manufacturing, consumer products, technology, entertainment, healthcare, aerospace, and the public sector. Their work spans the entire business ecosystem—from sales and marketing to software delivery, product development, finance, HR, and operations. They have written bestselling books, created industry-defining methodologies, and hold over two dozen patents. Their 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, they are builders. Their ideas don't come from years of analysis but years of creating projects, products, and companies from the ground up. They have made lots of mistakes and seen what happens when processes get bloated beyond recognition. They have witnessed automation projects fail spectacularly. And they have learned why most transformation efforts hit walls. WISER is their effort to inject this wisdom into a highly scalable process that's easy to implement.
The Problem:
Organizations Are Optimizing the Wrong ThingsYears of organizational analysis reveal a troubling pattern in how companies pursue "efficiency." Lean this, Six Sigma that, Agile everything. What becomes clear is that most of these frameworks are solving yesterday's problems with tomorrow's vocabulary.
They're built for a world where the biggest challenge was managing people, not managing the chaos people create when left unchecked.
The Bureaucracy Tax
Modern business suffers from a fundamental problem: Every process starts simple and becomes a monster.
Someone has one bad experience, so they add a step. A department wants visibility, so they add a report. Legal gets nervous, so they add an approval. Before organizations know it, their "streamlined" customer onboarding takes three weeks and touches seventeen people.
Research backs this up. Companies typically add 4-7% more process complexity each year and almost never subtract anything.³ It's like organizational cholesterol—it just builds up until something breaks.
The math is staggering:
- Businesses lose up to $1.3 million annually from inefficient processes⁴
- 67% of business leaders say their organizations are too complex⁵
- Managers at Fortune 500 companies waste 530,000 days annually on bad decisions⁶
- Process inefficiency costs knowledge workers 41% of their time on discretionary activities that offer little personal satisfaction⁷
- Organizations with high complexity show 50% lower employee engagement scores⁸
- Complex organizational structures increase operating costs by 15-25% annually⁹
The AI Mirage
Then AI showed up, and suddenly everyone thought they'd found the magic bullet.
"We'll automate our way to efficiency!"
Except automation without simplification is like putting a turbocharger on a broken engine. Organizations go faster, but they blow up quicker too.
The data is brutal:
- Only 4% of companies generate substantial value from AI¹⁰
- 42% of businesses scrapped most AI initiatives in 2024¹¹
- 70-85% of AI deployment efforts fail to meet ROI expectations¹²
- Gartner predicts 30% of GenAI projects will be abandoned after proof-of-concept by end of 2025¹³
- Only 26% of organizations have moved AI beyond pilot to production¹⁴
- 90% of AI initiatives fail to scale beyond departmental level¹⁵
- Average enterprise AI ROI remains below 6% despite billions in investment¹⁶
- The problem isn't the technology. It's that organizations are applying sophisticated solutions to fundamentally broken processes.
The Complexity Paradox
Why Simple vs. Complex is the Wrong Question
Most organizations get complexity backward. They simplify what should be sophisticated and complicate what should be simple.
Unnecessary Complexity (Eliminate Ruthlessly)
Banking: 5 forms asking for the same customer information
Healthcare: Patient signing consent forms for each department visit
Manufacturing: 3 manager approvals for standard material orders
Retail: Weekly reports that no one reads or acts upon
These create work but add no value. They're bureaucratic artifacts.
Necessary Complexity (Handle Elegantly)
Banking: Fraud detection algorithms that adapt to new attack patterns
Healthcare: Triage protocols that account for patient edge cases and comorbidities
Manufacturing: Quality control systems that catch defects other companies miss
Retail: Personalization engines that predict customer preferences
These create competitive advantage. Competitors can't replicate them easily.
The Triage Test
Ask: "If our biggest competitor eliminated this complexity, would they be better or worse off?"
Better off = Unnecessary complexity (delete it)
Worse off = Necessary complexity (improve how you handle it)
AI First Principles: The Missing Foundation
Before diving into WISER, understanding the philosophy behind it is essential. WISER is grounded in AI First Principles—not because AI comes first, but because teams think AI-first while they work.
People Define Objectives
No algorithm decides what matters. Humans do.
This sounds obvious until observing companies deploying AI without clear success criteria. "Let's use machine learning to optimize customer service!" Optimize for what? Speed? Satisfaction? Cost reduction?
If organizations can't name the person accountable for the outcome, they're not ready to automate anything. Research from MIT shows that 67% of failed AI projects lack clear ownership and accountability structures.¹⁷
Build Through Discovery
Teams discover what they actually want by making things, not by writing requirements documents.
This comes from the Japanese concept of monozukuri—excellence through making. Instead of planning their way to perfection, successful teams prototype their way to understanding.
Most people don't know what they really need until they start building. Stanford's d.school research demonstrates that iterative prototyping reduces project failure rates significantly compared to requirements-driven approaches.¹⁸
Question Everything Intelligently
Teams must challenge every assumption, but be smart about it.
Not all complexity is bad. Some complexity creates competitive advantage—like sophisticated fraud detection or nuanced customer service workflows. The trick is distinguishing between necessary complexity (which handles real challenges) and unnecessary complexity (which just wastes time).
Harvard Business School research identifies this as the "complexity paradox"—organizations that master both simplicity and sophisticated complexity outperform peers by 23%.¹⁹
Honor Human Creativity
Keep people in the creative loop where insight and craftsmanship matter most.
AI should amplify human judgment, not replace it. Use automation for the meta-work—data gathering, routing, status updates—but let humans handle the decisions that require context, empathy, or ethical reasoning.
McKinsey's research on human-AI collaboration shows that hybrid teams outperform either humans or AI alone by 40% in complex decision-making scenarios.²⁰
Embrace Play
Psychological safety unlocks breakthroughs that planning never could.
When people feel safe to experiment and admit they don't know things, innovation happens. Fear kills creativity faster than any technology.
Google's Project Aristotle found that teams with high psychological safety demonstrate 19% higher productivity, 31% more innovation, and 27% lower turnover rates.²¹
Use First Principles
Strip away everything until only what's essential remains.
Question why every step exists. If the answer is "that's how it's always been done," teams have found their first target for elimination.
Elon Musk's application of first principles thinking at Tesla and SpaceX demonstrates how questioning fundamental assumptions can reduce costs by 50-90% while improving outcomes.²²
Focus on Outcomes
Track what moves the needle: objectives, velocity, and exceptions. Ignore vanity metrics.
Measure whether teams are hitting their goals, how quickly work flows, and when people bypass processes. If everyone's working around the system, that's data about what needs to change.
Research from the Harvard Business Review shows that outcome-focused metrics improve performance by 35% compared to activity-based measurements.²³
Simplify Intelligently
Eliminate unnecessary complexity ruthlessly; handle necessary complexity elegantly.
Don't eliminate fraud detection because it's complex—build better tools to handle fraud complexity. Fight bureaucracy like an immune system fights infection, but preserve what makes organizations uniquely capable.
Think AI First, Automate Last
Envision what's possible with AI, but don't implement until the process is clean and purposeful.
This counterintuitive principle, think AI first, automate last, ensures teams apply technology to systems that deserve to exist.
Create Pull
Let real demand drive action. Pull systems reveal true priorities and eliminate artificial work.
Work should happen because someone needs it, not because it's Tuesday. Toyota's pull-based production system demonstrates how demand-driven workflows can reduce waste by 50-70% while improving quality.²⁴
The WISER Method
There are five stages organized under three moves.
The three moves are:
REVEAL (W + I): Expose what actually happens and why it exists
REFINE (S + E): Eliminate waste while building sophisticated capability
REBUILD (R): Redesign based on discovery and automate intelligentlyThe 5 stages Are:
W - Why
Define Purpose and Question Everything
Teams start by getting crystal clear on what they're actually trying to accomplish.
Most processes exist for reasons nobody remembers. Someone needed something five years ago, built a workaround, and it became "policy." Now everyone follows it religiously without knowing why.
Apply monozukuri's 'go and see' principle - observe the actual work being done before defining purpose. Don't rely on documented procedures. Watch real people doing real work, then articulate what the process actually achieves in practice.
Key Actions:
- Define the process purpose in one clear sentence
- Assign a human owner (not a department—an actual person)
- List every assumption and requirement for challenge
- Question especially anything that sounds like "we have to" or "they require"
Example:
An insurance team reframed their claims process around a simple goal: "Reimburse customers fairly and fast, while preventing fraud." Suddenly, sprawling approval chains looked ridiculous. Half of their controls were solving problems that no longer existed.
I - Identify
Map Reality and Sort Complexity
Next, teams figure out what's actually happening versus what's supposed to happen.
The principle is to go to where the work gets done. Watch real people doing real work. Don't trust the process diagram—trust direct observation.
Monozukuri Observation Protocol:
- Spend 2 hours on the work floor/with users
- Document what people actually do vs. what they're supposed to do
- Note the 'workarounds' - these reveal process failures
- Ask workers: 'What would you eliminate if you could?'"
Key Actions:
- Map the current process end-to-end as it really occurs
- Categorize each step: necessary complexity vs. unnecessary complexity
- Document baseline metrics (time, errors, handoffs)
- Gather voice-of-customer feedback on pain points
The Complexity Triage Question:
"If we removed this step, would anyone notice or be worse off?"
If the answer is "no, removing this would save time and nothing bad would happen," the team has found bureaucratic waste. If removing it would harm quality or create real risk, it's necessary complexity worth preserving.
Reality Check:
Most processes are 60-80% waste. One team mapped 47 steps in their loan process and found only 18 added actual value. The rest was paperwork theater.
Research from the Lean Enterprise Institute confirms that knowledge work processes typically contain 65-80% non-value-added activities.²⁵
S - Simplify
Delete Before You Optimize
This is where the real work happens. Teams cut everything that doesn't serve their defined purpose.
The approach requires aggressiveness. If teams aren't making people slightly uncomfortable with how much they're cutting, they're not cutting enough.
Monozukuri Elimination Principle: Make one small change, test immediately, learn from the result. Don't eliminate 10 steps at once - eliminate one, observe the impact for 3 days, then eliminate the next. This iterative approach prevents over-cutting and builds confidence.
Key Actions:
- Eliminate steps that fail the complexity triage
- Combine redundant activities (why have five forms when one will do?)
- Run experiments: stop doing something for a week and see if anyone notices
- Simplify what remains—shorter forms, clearer language, fewer approvals
The 10% Rule:
If teams don't end up adding back at least 10% of what they eliminated, they didn't remove enough. Some cuts will prove necessary—that's fine. Learn and adjust.
This principle, popularized by Elon Musk, has been validated across multiple industries. General Electric's application of similar principles saved over $2 billion through systematic elimination of non-value activities.²⁶
E - Evolve
Build Flow and Learn Through Making
With the waste gone, teams optimize how work moves through their simplified process.
Monozukuri Flow Optimization: Work with the people doing the job to redesign the sequence. They know which steps could be parallel, which handoffs cause delays, and where automation would actually help vs. hinder.
Key Actions:
- Switch from push to pull: work only moves when the next person is ready
- Reduce handoff delays (why does work sit for two days between steps?)
- Introduce triggers so work flows automatically based on real signals
- Create feedback loops and measure what matters
Example:
A manufacturing team went from sequential changeover tasks to parallel execution. While one crew cleaned equipment, another staged new materials. Changeover time dropped from 6 hours to 45 minutes.
Lean manufacturing research shows that flow-based systems typically reduce cycle times by 50-90% while improving quality outcomes.²⁷
R - ReDesign
Architect New Systems and Automate Intelligently
Now—and only now—do teams apply serious technology.
By this point, they understand what the process actually needs to accomplish. They've eliminated the garbage and optimized the flow. Any automation they add will amplify something that already works.
Monozukuri Technology Integration: Build the simplest version first, test with real users, then add sophistication. Don't architect the 'perfect' automated system - build the minimum viable automation and iterate based on actual usage.
Key Actions:
- Blueprint the ideal process based on everything learned
- Apply the automation decision matrix: what should be fully automated, augmented, or kept manual?
- Automate the meta-work (routing, data entry, notifications) not the decisions
- Design for exceptions—complex cases should escalate to humans gracefully
The Meta-Work Principle:
Let AI handle gathering data, routing tasks, and updating status. Let humans handle judgment calls, creative work, and anything requiring empathy or ethics.
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.
Comparative Analysis of Process Improvement Methodologies
Agile
Strengths: Fast-moving and customer-focused. Built for uncertainty. Empowers teams to ship value quickly and iterate continuously. Research shows 64% success rates vs. 49% for waterfall approaches.²⁸
Weaknesses: Often becomes ritualized. Stand-ups and sprints can drift from purpose. Assumes teams are building the right thing and doesn't question if it should exist at all.
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. Fortune 500 companies have collectively saved $430 billion over 20 years through Lean implementations.²⁹
Weaknesses: Can stall at system edges. Strong on step-level optimization but weak on structural redesign.
Best Suited For: Manufacturing, logistics, service operations where efficiency, consistency, and throughput drive margin or satisfaction.
Six Sigma
Strengths: Black Belt practitioners save companies approximately $230,000 per project on average. Johnson & Johnson reported $600 million in savings through Six Sigma application.³⁰
Weaknesses: Slow to execute (typically 12-18 months per project). Assumes the process is worth optimizing and discourages disruptive redesign.
Best Suited For: Regulated or mission-critical environments where stability, compliance, and quality assurance are vital.
BPM (Business Process Management)
Strengths: BPM implementations typically yield ROI greater than 15%, with the market expected to expand at 19.9% CAGR through 2030.³¹
Weaknesses: Often freezes complexity in place. Excels at mapping but rarely questions necessity.
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. Delivers results in 2-12 weeks vs. months or years for other methodologies.
Weaknesses: Relatively new framework lacking decades of academic backing. Demands intellectual honesty and cultural readiness.
Best Suited For: Organizations facing bureaucratic drag, unclear ownership, or process sprawl. Teams vulnerable to fast-moving AI-native competitors.
WISER Framework Comparative Analysis
To position WISER in the landscape of improvement methodologies, we analyzed its unique characteristics compared to established frameworks:
[TABLE 1: Comparison of WISER vs. other process improvement frameworks on key aspects]
WISER isn't a competitor to these methodologies but a synthesizer and extender. It's what organizations do before (and alongside) Lean/Six Sigma to ensure they're solving the right problems, and what makes Agile and automation efforts yield transformation, not just faster chaos.
Case Studies: WISER in Action
Banking: From 14 Days to 24 Hours
Problem:
Mid-sized bank's loan approvals took two weeks. Customers fled to fintech competitors offering same-day decisions.
WISER Application:- Why: "Enable qualified customers to access financing quickly and securely while protecting the bank from undue risk"
- Identify: Found 47 steps, only 18 added value. Multiple credit checks, redundant forms, approvals that caught nothing
- Simplify: Cut duplicate credit checks, consolidated five forms into one, removed four approval layers
- Evolve: Moved from batch processing to continuous flow, added real-time dashboards
- ReDesign: AI auto-approves straightforward cases (60%), complex cases get human review with AI-generated risk assessments
Complexity Paradox Application:
- ELIMINATED: 5 separate forms asking for same data (unnecessary complexity)
- ELIMINATED: 4 approval layers that caught no additional risk (bureaucratic theater)
- PRESERVED: Multi-layered fraud detection systems (necessary complexity)
- ENHANCED: AI risk assessment for complex cases (sophisticated capability)
META-WORK (Automate This):
- Pulling credit reports from bureaus
- Copying customer data between systems
- Sending status update emails to customers
- Routing applications to appropriate underwriters
- Generating compliance documentation
HUMAN DECISIONS (Keep Human):
- Evaluating non-standard income sources
- Assessing relationship banking opportunities
- Handling customer concerns about denials
- Making exceptions for edge cases
- Balancing risk vs. relationship value
Results:
- Approval time: 14 days → 1-3 days (85% reduction)
- Customer satisfaction: +35%
- Loan officer productivity: doubled
- Error rates: -45%
This case mirrors Bancolombia's success with RPA, which achieved 1,300% ROI within the first year while freeing up 127,000 hours annually.³²
Healthcare: 22 Minutes to 4 Minutes
Problem:
Patient intake averaged 22 minutes of paperwork before any care could begin. Staff spent 40% of time on data entry rather than patient care.
WISER Application:
- Why: "Gather necessary patient info efficiently to enable timely care while creating a positive experience"
- Identify: 60% of collected information was duplicated across forms
- Simplify: Single universal intake form, cut fields by half, eliminated redundant consent forms
- Evolve: Pre-registration online, real-time EHR integration, mobile data entry
- ReDesign: Smart patient portal with adaptive questioning, NLP for document processing
Complexity Paradox Application:
- ELIMINATED: 7 separate consent forms for different departments (unnecessary complexity)
- ELIMINATED: 60% of duplicated patient information requests (bureaucratic waste)
- PRESERVED: HIPAA compliance and patient safety protocols (necessary complexity)
- ENHANCED: Adaptive questioning based on patient condition and visit type (sophisticated capability)
- ENHANCED: Real-time clinical decision support integration (competitive advantage)
META-WORK (Automate This):
- Checking insurance eligibility and coverage
- Extracting data from previous medical records
- Scheduling follow-up appointments
- Sending medication reminders to patients
- Routing lab results to appropriate specialists
HUMAN DECISIONS (Keep Human):
- Interpreting complex symptoms and patient history
- Delivering difficult diagnoses with empathy
- Adjusting treatment plans based on patient response
- Making end-of-life care decisions
- Handling family emotional support needs
Results:
- Check-in time: 22 minutes → 4 minutes (82% reduction)
- Patient satisfaction: +35%
- Staff admin time: -65%
- Information accuracy: +40%
The global clinical workflow solutions market, valued at $10.52 billion in 2023, reflects the widespread need for such improvements.³³
Manufacturing: 6 Hours to 45 Minutes
Problem:
Production line changeovers took 4-6 hours, limiting flexibility and output. The process involved 37 steps with 22 handoffs between departments.
WISER Application:
- Why: "Transition production lines efficiently between products to maximize flexibility and minimize lost time, without compromising safety or quality"
- Identify: 37 steps, 22 handoffs between departments, lots of waiting for specialists
- Simplify: Eliminated 14 non-value steps, cut approvals by 75%, standardized procedures
- Evolve: Parallel task execution, cross-functional "pit crews," visual management
- ReDesign: AR work instructions, IoT sensors for predictive maintenance, digital machine configuration
Complexity Paradox Application:
- ELIMINATED: 14 changeover steps that added no safety or quality value (unnecessary complexity)
- ELIMINATED: 75% of approval signatures that were rubber-stamps (bureaucratic theater)
- PRESERVED: Critical safety checks and quality validation procedures (necessary complexity)
- ENHANCED: Predictive maintenance systems using IoT sensors (sophisticated capability)
- ENHANCED: AR-guided procedures for consistent quality across shifts (competitive advantage)
META-WORK (Automate This):
- Monitoring machine temperatures and pressures
- Ordering raw materials when inventory hits reorder points
- Logging production counts and quality metrics
- Scheduling preventive maintenance based on usage
- Generating shift reports and production summaries
HUMAN DECISIONS (Keep Human):
- Diagnosing unusual machine behaviors or sounds
- Deciding whether to stop production for quality issues
- Training new operators on complex procedures
- Innovating process improvements based on experience
- Handling customer-specific customization requests
Results:
- Changeover time: 6 hours → 45 minutes (87% reduction)
- Production capacity: +15% (no new equipment)
- Quality consistency: +30%
- Energy consumption during changeovers: -25%
This aligns with Industry 4.0 research showing 10-20x ROI over 5 years for comprehensive manufacturing automation.³⁴
Retail: Inventory Management Revolution
Problem:
Mid-sized retail chain struggled with 12% stockout rates while carrying excess inventory. Store managers spent 15-20 hours weekly on inventory tasks.
WISER Application:
- Why: "Ensure the right products are available when customers want them, with minimal waste and manual effort"
- Identify: 52 touchpoints in inventory management, 40% of time spent on data gathering that yielded no value
- Simplify: Eliminated manual stock counts for 80% of items, removed three approval steps from merchandising updates
- Evolve: Demand-driven reordering based on depletion rates, local manager autonomy for 30% of floor space
- ReDesign: Computer vision for shelf monitoring, AI forecasting with weather/seasonality data, electronic shelf labels
Complexity Paradox Application:
- ELIMINATED: Manual stock counts for standard merchandise (unnecessary complexity)
- ELIMINATED: 3 approval steps for routine merchandising changes (bureaucratic delay)
- PRESERVED: Theft monitoring and shrinkage controls for high-value items (necessary complexity)
- ENHANCED: AI forecasting with weather, seasonality, and local event data (sophisticated capability)
- ENHANCED: Computer vision for real-time shelf monitoring and customer behavior analysis (competitive advantage)
META-WORK (Automate This):
- Tracking inventory levels across locations
- Reordering based on sales velocity and seasonality
- Processing standard returns and exchanges
- Updating price tags across all locations
- Sending personalized marketing offers
HUMAN DECISIONS (Keep Human):
- Handling complex customer complaints
- Merchandising displays for local market preferences
- Negotiating with upset customers for resolution
- Training staff on new products and procedures
- Making judgment calls on non-standard returns
Results:
- Manual inventory tasks: -75%
- Stockout rates: 12% → 3%
- Inventory carrying costs: -22%
- Sales increase: +8%
- Manager satisfaction: significantly improved ("finally running a store instead of filling out paperwork")
Retail automation case studies show similar patterns, with companies like NA-KD achieving 72x ROI within 12 months through unified data platforms.³⁵
Complexity Paradox Pattern Summary Across All Case Studies
ALWAYS ELIMINATE:
- Redundant data collection
- Rubber-stamp approvals
- Reports no one reads
- "Because we've always done it" steps
ALWAYS PRESERVE/ENHANCE:
- Safety and compliance controls
- Quality assurance measures
- Customer experience differentiators
- Competitive intelligence capabilities
RESULT PATTERN:
- Simpler experience for front-line users
- More sophisticated capability where it creates value
- Faster standard operations
- Better handling of complex exceptions
Automation Decision Framework Across All Case Studies
HIGH VOLUME + RULE-BASED = Automate Fully
- Credit report pulling (banking)
- Insurance verification (healthcare)
- Inventory tracking (retail)
- Production logging (manufacturing)
MEDIUM VOLUME + SOME VARIABILITY = AI-Augmented Human
- Initial loan risk assessment (banking)
- Patient symptom triage (healthcare)
- Customer service inquiries (retail)
- Quality issue diagnosis (manufacturing)
LOW VOLUME + HIGH JUDGMENT = Keep Human
- Complex loan negotiations (banking)
- End-of-life care decisions (healthcare)
- VIP customer relationships (retail)
- Process innovation ideas (manufacturing)
Building Your WISER Team
Even the best method fails without the right people. WISER teams are deliberately small (5-8 people), cross-functional, and empowered to actually change things.
Essential Roles
Sponsor -
The decision maker with authority to break rules and make tough callsSage -
Domain expert who knows the history and unwritten rulesArchitect -
Process designer who maps flows and creates blueprintsScout -
Information gatherer who validates assumptions with data
Smith -
Builder who implements technical changes and prototypes solutions
Sentinel -
Quality guardian who tests improvements and catches side effects
Guide -
Facilitator who drives the process and maintains team momentum
One person can wear multiple hats, but every responsibility must be owned by someone.
Team Principles
Purpose is the anchor -
When things get messy, return to why the work matters
Play is the unlock -
Create space for experimentation and safe failure
Collaboration without consensus -Everyone contributes, but decisions have clear owners
Psychological Safety is Non-Negotiable
Google's Project Aristotle found that psychological safety—the belief that people can speak up without risk—is the #1 predictor of team effectiveness.³⁶ Teams with high psychological safety show 19% higher productivity and 27% lower turnover.
In WISER projects, this is critical. The method asks people to challenge sacred cows and propose radical changes. They need to feel safe doing that.
Research from Amy Edmondson at Harvard Business School demonstrates that teams with high psychological safety are 2.3x more likely to achieve breakthrough innovations.³⁷
The 90-Day WISER Sprint
The methodology recommends not trying to boil the ocean. Pick one painful process and fix it in 90 days.
Days 1-30: REVEAL (Foundation Phase)
Assemble team and establish psychological safety
Define purpose and assign ownership (Week 1-2)
Map current state and identify waste (Week 3-4)
Output: Clear understanding of what's broken and why
Days 31-60: REFINE (Quick Wins Phase)
Eliminate obvious waste through pilots (Week 5-6)
Optimize flow and reduce handoffs (Week 7-8)
Output: Measurably improved process without heavy technology
Days 61-90: REBUILD (Technology Phase)
Design ideal future state (Week 9-10)
Implement smart automation (Week 11-12)
Output: Redesigned process with intelligent AI integration
Success Metrics
- 30-50% reduction in process steps
- 60-90% improvement in cycle time
- 25-45% increase in customer satisfaction
- Measurable reduction in employee frustration
This timeline contrasts sharply with traditional methodologies. Six Sigma projects typically require 3-6 months, while BPM implementations often take 12+ months.³⁸
Common Pitfalls (And How to Avoid Them)
Jumping to Automation Too Early
The Problem:
Teams get excited about AI and start building before they understand the process.
The Fix:
Force progression through W, I, S, and E before touching any automation tools.Research shows that 70% of failed AI projects result from insufficient process understanding before automation.³⁹
Analysis Paralysis
The Problem:
Trying to get perfect data before making any changes.The Fix:
Use "good enough" data and run experiments. Prototypes beat PowerPoints.Consensus Gridlock
The Problem:
Trying to get everyone to agree on everything.The Fix:
Clear decision rights. Disagree and commit.Amazon's "disagree and commit" principle has been studied extensively, showing 40% faster decision-making with no reduction in quality.⁴⁰
Ignoring Necessary Complexity
The Problem:
Cutting everything that looks complicated, including valuable capabilities.The Fix:
Use the complexity triage question. Preserve what creates competitive advantage.Lack of Air Cover
The Problem:
Teams afraid to challenge established processes.
The Fix:
Sponsor must explicitly give permission to break rules during the sprint.Change management research shows that visible leadership support increases transformation success rates from 30% to 88%.⁴¹
ROI and Results: What to Expect
Based on WISER implementations across industries:
Typical Performance Improvements
Cycle time: 60-90% reduction
Operating costs: 20-50% reduction
Error rates: 30-60% reduction
Employee productivity: 25-75% increase
Customer satisfaction: +25-45%
Financial Returns
Most WISER projects break even within 6 months and deliver 2-5x ROI within the first year. This isn't from mystical AI magic—it's from eliminating waste and applying automation intelligently.
These results align with broader process improvement research. McKinsey studies show that successful digital transformations deliver 20-30% customer satisfaction improvements and 45% revenue growth for digitally mature organizations.⁴²Cultural Benefits
People stop feeling like cogs in a broken machine. They spend time on work that matters instead of feeding the bureaucracy monster. This shows up in engagement surveys, retention rates, and voluntary improvement suggestions.
Gallup research demonstrates that engaged employees are 23% more profitable and show 21% higher productivity.⁴³
Scaling Beyond the First Win
After the first successful 90-day sprint, the question becomes: how do organizations spread this across the enterprise?
Document the JourneyCapture not just what changed, but why and how. Make it a story other teams can follow, not a technical manual they'll ignore.
Train Internal Champions
Build WISER capability inside the organization. External consultants should work themselves out of a job, not create dependency.
Organizations with strong internal change capability achieve 2.4x higher success rates in transformation initiatives.⁴⁴
Embed Continuous ImprovementMake questioning and simplifying a regular habit, not a one-off project. Quarterly WISER checkups prevent complexity from creeping back.
Celebrate Success
When one team cuts their turnaround time by 85%, make sure everyone knows about it. Success is contagious.
Research from the Stanford Graduate School of Business shows that publicizing early wins increases adoption rates.⁴⁵
The Future Belongs to Builders
Years of helping organizations transform have revealed a crucial insight: the future doesn't belong to the companies with the most sophisticated AI. It belongs to the companies that know what problems are worth solving.
WISER gives organizations that clarity. It strips away the accumulated crud of bureaucracy and reveals what actually matters. Then—and only then—it amplifies that with intelligent automation.
Most improvement methodologies optimize what exists. WISER questions whether it should exist at all.
Most AI projects automate the status quo. WISER rebuilds from first principles.
Most transformation efforts take years and cost millions. WISER delivers results in months with a small, empowered team.
The choice is simple: Keep managing complexity, or start eliminating it.
Cut the bureaucracy. Build what matters. Get WISER.
References
- Hamel, Gary, and Michele Zanini. "Excess Management Is Costing the U.S. $3 Trillion Per Year." Harvard Business Review, September 5, 2016. https://hbr.org/2016/09/excess-management-is-costing-the-us-3-trillion-per-year
- Boston Consulting Group. "AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value." Press Release, October 24, 2024. https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value
- Lucid Software. "The Real Impact of Business Complexity (With Tips to Create Clarity)." Lucid Blog, 2024. https://lucid.co/blog/business-complexity
- Industry Dive. "Businesses can lose up to $1.3M a year on inefficient processes, report says." CIO Dive, October 15, 2024. https://www.ciodive.com/news/inefficient-processes-cost-money/633460/
- McKinsey & Company. "The State of Organizations 2023: Ten shifts transforming organizations." McKinsey Global Institute, 2023. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations-2023
- Hamel, Gary, and Michele Zanini. "Excess Management Is Costing the U.S. $3 Trillion Per Year." Harvard Business Review, September 5, 2016.
- Asana. "The State of Work: From Chaos to Clarity." Asana Work Innovation Lab, 2024. https://asana.com/resources/state-of-work
- Deloitte. "The Social Enterprise at Work: Paradox as a Path Forward - 2020 Deloitte Global Human Capital Trends." Deloitte Insights, 2020.
- MIT Sloan Management Review. "The Hidden Costs of Organizational Complexity." MIT Sloan, Q2 2023.
- Boston Consulting Group. "Where's the Value in AI?" BCG Publications, July 2024. https://www.bcg.com/publications/2024/wheres-value-in-ai
- Boston Consulting Group. "AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value." October 2024.
- NTT DATA. "Between 70-85% of GenAI deployment efforts are failing to meet their desired ROI." NTT DATA Insights, November 2024. https://www.nttdata.com/global/en/insights/focus/2024/between-70-85p-of-genai-deployment-efforts-are-failing
- Gartner. "Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025." Gartner Press Release, July 29, 2024.
- Deloitte. "State of Generative AI in the Enterprise 2024." Deloitte Insights, 2024.
- IBM Institute for Business Value. "The AI Multiplier: How to Scale AI Impact." IBM Research, 2024.
- PwC. "AI and Workforce Evolution: Annual Report 2024." PwC Global Technology Report, 2024.
- MIT Technology Review. "Why AI projects fail and how to make them succeed." MIT TR Insights, March 2024.
- Stanford d.school. "Design Thinking Bootleg." Stanford Institute of Design, 2023.
- Wilson, Hugh J., and Paul R. Daugherty. "How Humans and AI Are Working Together in 1,500 Companies." Harvard Business Review, July-August 2018.
- McKinsey Global Institute. "The Age of AI: Artificial Intelligence and the Future of Work." McKinsey & Company, September 2023.
- Rozovsky, Julia. "The five keys to a successful Google team." Google re:Work, November 17, 2015. https://rework.withgoogle.com/blog/five-keys-to-a-successful-google-team/
- Vance, Ashlee. Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future. Ecco, 2015.
- Kaplan, Robert S., and David P. Norton. "The Strategy-Focused Organization." Harvard Business Review, September 2000.
- Womack, James P., Daniel T. Jones, and Daniel Roos. The Machine That Changed the World. Free Press, 2007.
- Lean Enterprise Institute. "Lean Knowledge Work: How to Eliminate Waste in Knowledge-Based Processes." LEI Publications, 2023.
- Welch, Jack, and John A. Byrne. Jack: Straight from the Gut. Warner Books, 2001.
- Shingo Institute. "Shingo Model: Principles of Operational Excellence." Utah State University, 2024.
- Version One. "15th Annual State of Agile Report." Agile Survey Results, 2023.
- Lean Enterprise Institute. "Lean Transformation: 20 Years of Impact Study." LEI Research, 2024.
- American Society for Quality. "Six Sigma ROI Study: Fortune 500 Analysis." ASQ Quality Progress, 2023.
- ProcessMaker. "BPM ROI - Calculating Return on Business Process Management." ProcessMaker Blog, 2024.
- Automation Anywhere. "Bancolombia RPA Case Study." Customer Success Stories, 2024.
- Precedence Research. "Clinical Workflow Solutions Market Size 2024 to 2033." Market Research Report, 2024.
- Ericsson. "Industry 4.0 use cases – new ROI report." Ericsson Blog, July 2020.
- Comosoft. "Case Studies: Retailers Winning With Omnichannel Automation." Comosoft Articles, 2024.
- Rozovsky, Julia. "The five keys to a successful Google team." Google re:Work, 2015.
- Edmondson, Amy C. The Fearless Organization: Creating Psychological Safety in the Workplace. Wiley, 2018.
- PMI. "Pulse of the Profession 2024: Project Management Success Rates." Project Management Institute, 2024.
- VentureBeat. "Why 87% of data science projects never make it into production." VentureBeat Intelligence, July 2024.
- Stone, Brad. Amazon Unbound: Jeff Bezos and the Invention of a Global Empire. Simon & Schuster, 2021.
- Prosci. "The Correlation Between Change Management and Project Success." Prosci Research, 2024.
- McKinsey & Company. "The keys to a successful digital transformation." McKinsey Digital, March 2024.
- Gallup. "How to Improve Employee Engagement in the Workplace." Gallup Workplace, 2024.
- Boston Consulting Group. "The Most Adaptive Companies 2021." BCG Publications, 2021.
- Heath, Chip, and Dan Heath. Switch: How to Change Things When Change Is Hard. Broadway Books, 2010.
For more information about implementing WISER in your organization, visit wisermethods.com or contact the authors directly.
[TABLE 2: Expected Results Across Industries showing primary areas of improvement for Financial Services, Healthcare, Manufacturing, Retail, and Professional Services]
Build What Deserves to Exist
The future won’t be built by faster processes or flashier tools.
It will be built by people who know what should be built—and have the courage to question everything else.That’s what WISER enables. It gives builders, leaders, and teams a shared language for clarity. A method to cut through complexity. A discipline for designing systems that actually serve their purpose.
AI changes the game, but it doesn’t define the goals.
That responsibility stays with us.WISER starts where most methods stop:
With a sharp look at intent.
With someone asking, “Why does this exist?”
With a refusal to automate what was never necessary in the first place.Every stage in WISER reinforces the same truth:
If no one owns the objective, you’re not ready to build.The jungle of modern business isn’t tamed by best practices. It’s navigated by first principles, radical simplification, and human judgment.
WISER helps you move through that jungle—not by handing you a map, but by giving you a machete.Use it well.
Cut deep.
Build what matters.
And only what matters.References
- Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company. https://wwnorton.com/books/the-second-machine-age/
- Dalio, R. (2017). Principles: Life and Work. Simon & Schuster. https://www.principles.com/
- Edmondson, A. C. (2019). The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Wiley. https://www.wiley.com/en-us/The+Fearless+Organization%3A+Creating+Psychological+Safety+in+the+Workplace+for+Learning%2C+Innovation%2C+and+Growth-p-9781119477266
- Franco, A. (2025). AI First Principles. https://www.aifirstprinciples.org/
- Hastings, R., & Meyer, E. (2020). No Rules Rules: Netflix and the Culture of Reinvention. Penguin Press. https://www.penguinrandomhouse.com/books/606529/no-rules-rules-by-reed-hastings-and-erin-meyer/
- Isaacson, W. (2023). Elon Musk. Simon & Schuster. https://www.simonandschuster.com/books/Elon-Musk/Walter-Isaacson/9781982181284
- Jobs, S. (2003). "The Guts of a New Machine." New York Times Magazine interview with Rob Walker, November 30, 2003. https://www.nytimes.com/2003/11/30/magazine/the-guts-of-a-new-machine.html
- Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. https://us.macmillan.com/books/9780374533557/thinkingfastandslow/
- Kelley, T., & Kelley, D. (2013). Creative Confidence: Unleashing the Creative Potential Within Us All. Crown Business. https://www.creativeconfidence.com/
- Kotter, J. P. (2012). Leading Change. Harvard Business Review Press. https://www.harvardbusiness.org/leading-change-why-transformation-efforts-fail/
- Lembke, A. (2021). Dopamine Nation: Finding Balance in the Age of Indulgence. Dutton/Penguin Random House. https://www.penguinrandomhouse.com/books/669929/dopamine-nation-by-anna-lembke-md/
McGrath, R. G. (2019). Seeing Around Corners: How to Spot Inflection Points in Business Before They Happen. Houghton Mifflin Harcourt. https://www.amazon.com/Seeing-Around-Corners-Inflection-Business/dp/0358022339 - Minnaar, J. (2023). "Musk's 5 Step Algorithm to Cut Internal Bureaucracy at Tesla and SpaceX." Corporate Rebels. https://www.corporate-rebels.com/musks-5-step-algorithm/
- Munger, C. (2005). Poor Charlie's Almanack: The Wit and Wisdom of Charles T. Munger. Donning Company Publishers. https://www.amazon.com/Poor-Charlies-Almanack-Charles-Expanded/dp/1578645018
- Pink, D. H. (2011). Drive: The Surprising Truth About What Motivates Us. Riverhead Books. https://www.danpink.com/books/drive/
- Taylor, L., & Youkee, M. (2023). "'We are a force for life': how Indigenous wisdom helped rescue children lost in the Amazon." The Guardian, 16 June 2023. https://www.theguardian.com/world/2023/jun/16/indigenous-wisdom-colombia-amazon-children-rescue
- Wilson, R. (2025). The Age of Invisible Machines: A Guide to Orchestrating AI Agents and Making Organizations More Self-Driving (2nd Edition). John Wiley & Sons. https://www.invisiblemachines.ai/
- Womack, J. P., & Jones, D. T. (2003). Lean Thinking: Banish Waste and Create Wealth in Your Corporation. Free Press. https://www.amazon.com/Lean-Thinking-Banish-Create-Corporation/dp/0743249275
The terms WISER Method™ is a trademark and is protected under common law trademark rights.