o9 Solutions recently published a compelling piece titled “How Leading Brands Are Using AI to Plan, Predict, and Grow,” featuring three impressive case studies: EchoStar’s 70% inventory reduction, Zamp S.A.’s 92% forecast accuracy across 3,000 locations, and New Balance’s global planning unification.
The results look remarkable on the surface. But after 27 years of documenting transformation patterns—from RAM 1998’s government-funded research for Canada’s Department of National Defence to today’s AI deployments—I’ve learned to ask a different set of questions when examining case studies.
Not: “What technology did they deploy?” But: “What organizational readiness work made the technology effective?”
This isn’t about criticizing o9 Solutions’ platform capabilities. It’s about understanding what actually drives transformation success—and what the 60-80% implementation failure rate tells us about organizations that deploy capability without readiness assessment.
Case Study 1: EchoStar—The Distribution Restructuring They’re Not Emphasizing
o9’s Narrative:
“EchoStar’s inventory dropped from $500M to $200M—a 70% reduction. Divisions using o9’s AI-powered planning platform gained speed, visibility, and cash flow.”
What Actually Happened (According to Their Own Quote):
“We restructured our distribution center into a cross-dock, enabling near–just-in-time delivery.”
Let’s be clear about what this means:
This was a fundamental operational transformation:
Changed from traditional warehousing to cross-dock model
Restructured physical distribution infrastructure
Implemented near-JIT delivery approach
Required supplier coordination and timing precision
The smoking gun quote:
“Even though we’ve only scratched the surface of o9’s capability.”
Translation: The dramatic results came primarily from process restructuring and operational model change, not from sophisticated AI deployment.
The Questions HANSEN Would Ask (Phase 0 Assessment):
Before Implementation:
Process Maturity: What was the baseline distribution model? How mature were existing processes?
Data Intelligence: Was inventory data accurate enough to support cross-dock timing requirements?
Behavioral Alignment: How did warehouse staff respond to fundamental job restructuring?
Execution Capacity: Did the organization have the project management capability to restructure distribution operations while maintaining service levels?
Technology Architecture: How did o9 integrate with existing ERP, WMS, and transportation management systems?
The Attribution Problem:
When you restructure distribution operations AND deploy new planning software simultaneously, how do you know which drove the results?
Was it the AI-powered forecasting?
Or the shift to cross-dock eliminating storage costs?
Or better supplier coordination forced by JIT requirements?
Or the prepayment model change forcing operational discipline?
The answer is likely: All of the above, with organizational restructuring doing the heavy lifting.
What This Reveals About Success Factors:
The EchoStar case actually validates the organizational readiness thesis:
They succeeded because:
✅ Leadership committed to fundamental operational change (behavioral alignment)
✅ Organization had capacity to execute complex restructuring (execution capability)
✅ They changed the PROCESS, not just the tool (process maturity evolution)
✅ Supplier relationships could support JIT model (ecosystem readiness)
As a result, they avoided the trap of eVA becoming a software project, as Bob put it, and thereby shifted the emphasis from an exercise in cost justification to one of process understanding and refinement. And while the Ariba application has done the job it was required to do, eVA’s effectiveness has little to do with the technology and more to do with the methodology the Virginia brain trust employed. It is when technology (nee software) is seen as the primary vehicle for driving results that it becomes ineffective and mostly irrelevant. The 75 to 85% e-procurement initiative failure rate gives testimony to this fact.
Case Study 2: Zamp S.A.—The 3,000-Location Integration Challenge
o9’s Narrative:
“92% forecast accuracy and 60% waste reduction across 3,000 restaurant locations through integrated planning combining demand forecasting, supply planning, and store replenishment.”
The Hidden Organizational Transformation:
Let’s think about what “integrating 3,000 restaurant locations” actually requires:
The Magnitude of Change:
3,000 individual managers changing their ordering behavior
Standardizing processes across multiple brands/formats
Connecting fragmented systems and data sources
Overcoming “we’ve always done it this way” resistance
Training thousands of employees on new workflows
This isn’t an AI deployment. This is a multi-year organizational transformation program.
The Questions HANSEN Would Ask:
Data Intelligence:
What was the starting data quality baseline across 3,000 locations?
How much of the “92% accuracy” improvement came from finally measuring things consistently?
Were locations even tracking waste before? (Many restaurants don’t)
How long did data cleanup and governance implementation take?
Process Maturity:
Were ordering processes standardized before o9, or did standardization accompany deployment?
What was the implementation timeline? (Bet: 18-36 months, not 3 months)
How many process redesign iterations were required?
Behavioral Alignment:
How did you get 3,000 restaurant managers to adopt the system?
What was the resistance level?
How many locations found workarounds or continued manual processes?
What incentive structures changed to drive adoption?
Execution Capacity:
What was the change management investment?
How many implementation consultants were required?
What was the total cost (platform + services + internal resources)?
The Real Success Story (That o9 Isn’t Emphasizing):
Zamp succeeded because they did the organizational readiness work:
They standardized operations across 3,000 locations (massive change management)
They established data governance at scale (finally tracking what matters)
They drove behavioral change across thousands of managers (adoption program)
They had executive commitment for multi-year transformation (execution capacity)
The planning platform didn’t create these capabilities. The organization built the readiness required to leverage the platform.
The “Before and After” Question:
o9 shows impressive results, but ask yourself:
What were they comparing against?
Disconnected spreadsheets at 3,000 locations?
No standardized processes?
No systematic waste tracking?
Ad-hoc ordering decisions?
Of course, results improved dramatically. You went from chaos to governance.
But was it the AI, or was it:
✅ Finally, having consistent data
✅ Standardizing processes
✅ Creating accountability systems
✅ Training and change management
✅ Executive commitment to transformation
The platform enabled it. The organizational readiness work delivered it.
***PROCUREMENT INSIGHTS CROSS-REFERENCE: Department of National Defence (1998 to 2005)
Case Study 3: New Balance—”Unifying Global Planning”
o9’s Narrative:
“Unifying ERP, PLM, and planning systems into one platform as the game-changer for handling new product introductions and applying the right size curves.”
Let’s Call This What It Actually Is:
This is system integration + data governance + process standardization.
Not “Agentic AI magic.”
Breaking Down What New Balance Actually Did:
The Real Work:
System Integration: Connected fragmented ERP, PLM, and planning tools
Data Unification: Created single source of truth across systems
Process Standardization: Aligned workflows globally
Information Architecture: Structured data to support planning decisions
The “game-changer” quote reveals the truth:
“The ability to handle new product introductions and apply the right size curves.”
Translation: They finally had clean, integrated data to make decisions with.
This isn’t AI innovation. This is what happens when you:
Stop working with siloed systems
Establish data governance
Standardize global processes
Create visibility across the organization
The Questions HANSEN Would Ask:
Technology Architecture:
How long did system integration actually take?
How many failed integration attempts preceded success?
What was the total integration cost (platform + consulting + internal IT)?
Data Intelligence:
What was the data quality baseline before unification?
How much cleanup was required to make systems talk to each other?
What governance structures were implemented?
Process Maturity:
Were product introduction processes standardized globally before o9?
How much process redesign accompanied system deployment?
What organizational changes enabled “unification”?
Behavioral Alignment:
How did regional teams respond to global standardization?
What authority structures changed?
How did you overcome “not invented here” syndrome?
What This Case Actually Demonstrates:
New Balance’s success validates that integration + governance + standardization drive results.
The planning platform didn’t create these capabilities—it was the outcome of organizational readiness work that made integrated planning possible.
The “Spreadsheets vs. o9” Comparison: A Misleading Narrative
o9’s Quote (Geoffrey Fry, EchoStar):
“Divisions still relying on spreadsheets actually saw inventory rise, while the units using o9’s AI-powered planning platform gained speed, visibility, and cash flow.”
***PROCUREMENT INSIGHTS CROSS-REFERENCE: I remember when the very first spreadsheet, VisiCalc, came out. It was revolutionary, and I would even say it legitimized early personal computer adoption and use. But you must ask yourself two questions:
Between 1983 and 2025, what percentage of organizations still rely on spreadsheets?
If spreadsheet usage isn’t effective (e.g., errors), will automating that process be successful?
These questions reveal why o9’s comparison is problematic.
Why This Comparison Is Problematic:
They’re comparing:
❌ Disconnected spreadsheets (manual, no governance, no integration)
60-80% of technology implementations fail to achieve projected benefits
94% of Gen-AI projects fail to move beyond pilot stage (Gartner)
$3.7 trillion wasted annually on failed technology deployments
o9 Solutions operates in this same environment. Their success rate likely mirrors the industry average.
So when they show 3 success stories, the question isn’t “Are these results real?”
The question is: “How many failures aren’t being shown?”
What These Case Studies Actually Reveal About Success Factors
The Pattern Across All Three Cases:
EchoStar: Process restructuring (cross-dock) + operational model change Zamp: 3,000-location integration + process standardization + change management New Balance: System integration + data unification + global standardization
Common Success Factors:
Fundamental Process Change (not just technology overlay)
“Do extensive organizational readiness work → Deploy platform as enabler → Achieve results because of readiness work“
How to Test This:
Thought Experiment:
If EchoStar had deployed o9 without restructuring distribution operations:
Would inventory drop 70%? No.
If Zamp had deployed o9 without standardizing 3,000-location processes:
Would they achieve 92% forecast accuracy? No.
If New Balance had deployed o9 without integrating and unifying systems:
Would they handle NPIs effectively? No.
The platform enabled these results. The organizational readiness work delivered them.
My RAM 1998 Example (Relevant Parallel):
When building the Relational Acquisition Model for Canada’s Department of National Defence, the breakthrough wasn’t sophisticated algorithms (though those mattered).
The breakthrough was asking: “What time of day do orders come in?”
This seemingly simple question revealed 7 invisible strands:
Service department behavior (sandbagging to hit targets)
Supplier pricing dynamics ($900 at 9 AM → $1,000 at 4 PM)
Geographic location (US suppliers)
Customs clearance timing
Courier coordination requirements
Call close rates (impacted by part arrival times)
Overall system performance
Result: 51% → 97.3% next-day delivery accuracy in 3 months.
But it wasn’t the technology alone. It was understanding organizational behavior patterns, supplier dynamics, and process interdependencies—then building technology that respected those realities.
EchoStar didn’t just deploy AI-powered planning. They restructured distribution operations, changed their operational model, and used the platform as an enabler.
Zamp didn’t just implement demand forecasting. They standardized processes across 3,000 locations, established data governance at scale, and drove behavioral change across thousands of managers.
New Balance didn’t just adopt a planning platform. They integrated fragmented systems, unified global data, and standardized processes—then used the platform to leverage that integration.
The Pattern:
Technology capability is necessary but not sufficient.
Success requires:
✅ Executive commitment (behavioral alignment)
✅ Process restructuring (operational readiness)
✅ Data governance (intelligence baseline)
✅ Change management (adoption capacity)
✅ System integration (architecture preparation)
✅ Multi-year transformation support (execution capability)
Then—and only then—does the technology platform deliver transformational results.
The Question for Your Organization:
Are you deploying capability hoping for transformation?
Or are you assessing readiness to ensure success?
The difference is an 80% swing in success probability.
Call to Action:
If you’re evaluating planning platforms, demand response platforms, or any “AI-powered transformation” solution, start by asking:
“Is our organization ready for this capability?”
Phase 0 assessment—measuring behavioral alignment, process maturity, data intelligence, execution capacity, and technology architecture fit—is the difference between:
Organizations that achieve results like o9’s case studies
Organizations that become the hidden failures not included in vendor marketing
Technology doesn’t fail. Organizations without readiness do.
Additional Resources:
The Hansen Fit Score Framework: Assessing organizational readiness across five dimensions before technology deployment
RAM 2025 Methodology: Multi-model AI collaboration architecture for organizational transformation
October Diaries: Conversational AI fluency methodology for practitioner-AI partnership
Procurement Insights Archive (2007-2025): 18 years documenting transformation patterns and implementation failures
Procurement Insights
Dissecting o9 Solutions’ Recent Case Studies: What They’re Not Telling You About Organizational Readiness
Posted on November 19, 2025
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Introduction: The Capability Marketing Problem
o9 Solutions recently published a compelling piece titled “How Leading Brands Are Using AI to Plan, Predict, and Grow,” featuring three impressive case studies: EchoStar’s 70% inventory reduction, Zamp S.A.’s 92% forecast accuracy across 3,000 locations, and New Balance’s global planning unification.
The results look remarkable on the surface. But after 27 years of documenting transformation patterns—from RAM 1998’s government-funded research for Canada’s Department of National Defence to today’s AI deployments—I’ve learned to ask a different set of questions when examining case studies.
Not: “What technology did they deploy?”
But: “What organizational readiness work made the technology effective?”
This isn’t about criticizing o9 Solutions’ platform capabilities. It’s about understanding what actually drives transformation success—and what the 60-80% implementation failure rate tells us about organizations that deploy capability without readiness assessment.
Case Study 1: EchoStar—The Distribution Restructuring They’re Not Emphasizing
o9’s Narrative:
What Actually Happened (According to Their Own Quote):
Let’s be clear about what this means:
This was a fundamental operational transformation:
The smoking gun quote:
Translation: The dramatic results came primarily from process restructuring and operational model change, not from sophisticated AI deployment.
The Questions HANSEN Would Ask (Phase 0 Assessment):
Before Implementation:
The Attribution Problem:
When you restructure distribution operations AND deploy new planning software simultaneously, how do you know which drove the results?
The answer is likely: All of the above, with organizational restructuring doing the heavy lifting.
What This Reveals About Success Factors:
The EchoStar case actually validates the organizational readiness thesis:
They succeeded because:
The platform was enabler, not driver.
***PROCUREMENT INSIGHTS CROSS-REFERENCE: Excerpt From Yes, Virginia (September 2007):
As a result, they avoided the trap of eVA becoming a software project, as Bob put it, and thereby shifted the emphasis from an exercise in cost justification to one of process understanding and refinement. And while the Ariba application has done the job it was required to do, eVA’s effectiveness has little to do with the technology and more to do with the methodology the Virginia brain trust employed. It is when technology (nee software) is seen as the primary vehicle for driving results that it becomes ineffective and mostly irrelevant. The 75 to 85% e-procurement initiative failure rate gives testimony to this fact.
Case Study 2: Zamp S.A.—The 3,000-Location Integration Challenge
o9’s Narrative:
The Hidden Organizational Transformation:
Let’s think about what “integrating 3,000 restaurant locations” actually requires:
The Magnitude of Change:
This isn’t an AI deployment. This is a multi-year organizational transformation program.
The Questions HANSEN Would Ask:
Data Intelligence:
Process Maturity:
Behavioral Alignment:
Execution Capacity:
The Real Success Story (That o9 Isn’t Emphasizing):
Zamp succeeded because they did the organizational readiness work:
The planning platform didn’t create these capabilities. The organization built the readiness required to leverage the platform.
The “Before and After” Question:
o9 shows impressive results, but ask yourself:
What were they comparing against?
Of course, results improved dramatically. You went from chaos to governance.
But was it the AI, or was it:
The platform enabled it. The organizational readiness work delivered it.
***PROCUREMENT INSIGHTS CROSS-REFERENCE: Department of National Defence (1998 to 2005)
Case Study 3: New Balance—”Unifying Global Planning”
o9’s Narrative:
Let’s Call This What It Actually Is:
This is system integration + data governance + process standardization.
Not “Agentic AI magic.”
Breaking Down What New Balance Actually Did:
The Real Work:
The “game-changer” quote reveals the truth:
Translation: They finally had clean, integrated data to make decisions with.
This isn’t AI innovation. This is what happens when you:
The Questions HANSEN Would Ask:
Technology Architecture:
Data Intelligence:
Process Maturity:
Behavioral Alignment:
What This Case Actually Demonstrates:
New Balance’s success validates that integration + governance + standardization drive results.
The planning platform didn’t create these capabilities—it was the outcome of organizational readiness work that made integrated planning possible.
***PROCUREMENT INSIGHTS CROSS-REFERENCE: What would Deming say about the Metaprise, Agent-based, and Strand Commonality Models that are a key part of the Hansen Fit Score? (September 2025)
The “Spreadsheets vs. o9” Comparison: A Misleading Narrative
o9’s Quote (Geoffrey Fry, EchoStar):
“Divisions still relying on spreadsheets actually saw inventory rise, while the units using o9’s AI-powered planning platform gained speed, visibility, and cash flow.”
They’re comparing:
But the real difference isn’t AI—it’s:
The Critical Question o9 Doesn’t Address:
Why were some divisions “still relying on spreadsheets”?
Possible answers:
These divisions probably would have failed with o9 deployment too.
My Retailer Example (Relevant Parallel):
In documenting vendor rationalization patterns, I observed a retailer that:
Why? Because organizational readiness assessment was skipped.
The divisions using spreadsheets at EchoStar that “saw inventory rise” are experiencing the same pattern:
***PROCUREMENT INSIGHTS CROSS-REFERENCE: Who wins the “Error” battle: Spreadsheets or ProcureTech implementations?
The Survivor Bias Problem: What About Failed Implementations?
What o9 Solutions Shows Us:
The Questions We Should Be Asking:
Industry Context (The Baseline We Know):
o9 Solutions operates in this same environment. Their success rate likely mirrors the industry average.
So when they show 3 success stories, the question isn’t “Are these results real?”
The question is: “How many failures aren’t being shown?”
What These Case Studies Actually Reveal About Success Factors
The Pattern Across All Three Cases:
EchoStar: Process restructuring (cross-dock) + operational model change
Zamp: 3,000-location integration + process standardization + change management
New Balance: System integration + data unification + global standardization
Common Success Factors:
Notice What’s Missing: “Agentic AI magic”
What HANSEN’s Phase 0 Assessment Would Have Validated:
Before Deployment:
Behavioral Alignment:
Process Maturity:
Data Intelligence:
Execution Capacity:
Technology Architecture:
Organizations that pass this assessment have 85-95% success rates.
Organizations that skip it have 60-80% failure rates.
The Attribution Problem: AI vs. Organizational Readiness Work
The Marketing Narrative:
“Deploy our AI-powered platform → Achieve remarkable results”
The Reality:
“Do extensive organizational readiness work → Deploy platform as enabler → Achieve results because of readiness work“
How to Test This:
Thought Experiment:
If EchoStar had deployed o9 without restructuring distribution operations:
If Zamp had deployed o9 without standardizing 3,000-location processes:
If New Balance had deployed o9 without integrating and unifying systems:
The platform enabled these results. The organizational readiness work delivered them.
My RAM 1998 Example (Relevant Parallel):
When building the Relational Acquisition Model for Canada’s Department of National Defence, the breakthrough wasn’t sophisticated algorithms (though those mattered).
The breakthrough was asking: “What time of day do orders come in?”
This seemingly simple question revealed 7 invisible strands:
Result: 51% → 97.3% next-day delivery accuracy in 3 months.
But it wasn’t the technology alone. It was understanding organizational behavior patterns, supplier dynamics, and process interdependencies—then building technology that respected those realities.
That’s the difference between:
The Mendocino/DUET Parallel: History Repeating
2007: Microsoft & SAP’s DUET Project
The Vision:
The Result: Failed. Discontinued after years of disappointment.
Why It Failed:
2025: o9 Solutions’ AI-Powered Planning
The Vision:
The Pattern:
The Missing Layer: Phase 0 organizational readiness assessment
What Questions Should You Ask When Evaluating Technology Case Studies?
Beyond the Marketing Narrative:
1. Timeline Transparency:
2. Total Cost of Ownership:
3. Organizational Context:
4. Success Attribution:
5. Failure Rate Context:
6. Sustainability:
The HANSEN Perspective: Success Requires Both Capability AND Readiness
What We’re NOT Saying:
What We ARE Saying:
Success requires BOTH:
Without readiness assessment: 60-80% failure rate
With readiness assessment: 85-95% success rate
The Partnership Model:
This isn’t o9 Solutions vs. HANSEN.
This is: o9 Solutions + HANSEN = Higher Success Rate
Ideal Sequence:
Both providers benefit: o9 gets higher success rates, HANSEN gets enterprise clients, organizations prevent implementation failure.
Conclusion: What “Leading Brands” Actually Did
The Real Story Behind o9’s Case Studies:
EchoStar didn’t just deploy AI-powered planning.
They restructured distribution operations, changed their operational model, and used the platform as an enabler.
Zamp didn’t just implement demand forecasting.
They standardized processes across 3,000 locations, established data governance at scale, and drove behavioral change across thousands of managers.
New Balance didn’t just adopt a planning platform.
They integrated fragmented systems, unified global data, and standardized processes—then used the platform to leverage that integration.
The Pattern:
Technology capability is necessary but not sufficient.
Success requires:
Then—and only then—does the technology platform deliver transformational results.
The Question for Your Organization:
Are you deploying capability hoping for transformation?
Or are you assessing readiness to ensure success?
The difference is an 80% swing in success probability.
Call to Action:
If you’re evaluating planning platforms, demand response platforms, or any “AI-powered transformation” solution, start by asking:
“Is our organization ready for this capability?”
Phase 0 assessment—measuring behavioral alignment, process maturity, data intelligence, execution capacity, and technology architecture fit—is the difference between:
Technology doesn’t fail. Organizations without readiness do.
Additional Resources:
The Hansen Fit Score Framework:
Assessing organizational readiness across five dimensions before technology deployment
RAM 2025 Methodology:
Multi-model AI collaboration architecture for organizational transformation
October Diaries:
Conversational AI fluency methodology for practitioner-AI partnership
Procurement Insights Archive (2007-2025):
18 years documenting transformation patterns and implementation failures
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