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Across every industry, leaders are pouring time and budget into modernization, investing in new data platforms, warehouse rebuilds, cloud migrations, and analytics tools. Yet when they step back, the story sounds the same:
"We've been modernizing for years, but it still feels like our data can't keep up with the business or with AI."
In our recent webinar, Launch Consulting sat down with Dan McKinney (CTO & CIO at Magna Legal Services), Jonathan Gardner (Technology Lead at Launch), and Q Suliman (Partner Technology Strategist at Microsoft) to unpack why so many organizations are modernizing without moving forward and how to escape that cycle.
The stakes are high. AI adoption has tripled across the enterprise, yet business impact has remained largely flat. Industry research reinforces the gap: 88% of AI pilots fail to reach production and 95% of generative AI initiatives never deliver meaningful returns. Not because leaders lack ambition, but because the foundation underneath AI is fragmented, brittle, and constantly being rebuilt.
We call this pattern the Modernization Treadmill. This article breaks down what it is, why organizations remain stuck, and how the Leapfrog approach turns data chaos into measurable AI value. Rather than treating the platform as the starting point, Leapfrog begins with the highest-value business use case, builds only the data fabric needed to ship the first production slice, and then strengthens the platform with every additional slice. The foundation grows through delivery—not before it.
Here's how to build an enterprise data strategy that works.
If you recognize yourself in this picture, you're not alone:
Jonathan described it as a never-ending cycle:
"It's like you're constantly on a treadmill of upgrades just to keep accessing the data the way the business needs it."
The frustrating part is that as soon as you finish a major modernization phase, the landscape has already shifted. AI advances. Business expectations jump. New data sources appear. Suddenly, you're behind again.
But the treadmill is more than technical work. It is systemic inertia created by several factors.
1. Architectural Fragmentation That Compounds Over Time
Most organizations are juggling decades of architectural decisions layered on top of each other: legacy systems, half-completed migrations, stopgap integrations, departmental tools, and recently adopted cloud services.
Every new business initiative introduces more systems, more data, and more complexity. Instead of simplifying the environment, modernization often adds another layer to manage.
2. Business Pressure That Outpaces Technical Capacity
As Dan noted, data teams are being asked to serve departments with wildly different needs, including operations, finance, legal, sales, product, and compliance. Each requires new datasets, new pipelines, and new dashboards.
Modernization rarely gives teams time to stabilize. They're building the plane, flying it, and redesigning the engines at the same time.
3. Exploding Unstructured Data That Old Architectures Can't Handle
The growth curve isn't just steep. It's overwhelming.
Magna manages transcripts, PDFs, audio, video, and case data that multiply daily. Historically, BI systems were built around structured data. Now AI gives value to everything, which means everything must be processed, classified, governed, and made retrievable.
Jonathan emphasized that this turns linear data growth into logarithmic demand. Traditional rebuild-and-refactor models simply can't keep up.
4. Modernization Efforts That Reinforce Rather Than Solve the Problem
Teams often rebuild systems "the right way" only to discover:
So the cycle starts again.
This is why modernization efforts often generate activity without generating progress. Teams celebrate pipeline rewrites and warehouse consolidation, but the system underneath remains fragile.
Jonathan described it perfectly:
"It's like being in a permanent state of renovation. By the time you finish one room, the rest of the house is already outdated."
Why Traditional Modernization Fails in an AI-First Era
AI raises the bar for what data must do.
Traditional modernization assumes you have time to rebuild everything first. The AI era won't wait.
Traditional vs. Leapfrog: Why Leaders Are Switching Approaches
The shift is straightforward:
Modernization used to be infrastructure first. AI requires intelligence first.
This isn't about upgrading systems for the sake of upgrades. Leapfrogging is about escaping the cycle entirely by moving from rebuilding to rethinking. It allows organizations to bypass years of technical debt, align their data strategy with AI, and shift from reactive maintenance to proactive intelligence.
In short, stop laying more phone lines in a 5G world. Start building for the world you're operating in now and the one AI is rapidly shaping.
The panel started with a simple premise: there is no good AI without good data.
But AI doesn't just place another demand on your data architecture. It fundamentally changes what that architecture needs to do.
Dan shared Magna's reality as a regulated legal services business managing massive volumes of structured and unstructured data, including PDFs, transcripts, video, audio, and more. Historically, teams built analytics around structured data from source systems. Now AI opens up unstructured content as well.
That means organizations need to:
At the same time, as Jonathan pointed out, data growth has gone from linear to exponential to what feels almost logarithmic. Every new product, every new use case, and every new AI experiment generates even more data.
The old approach of simply building more warehouses and more pipelines no longer scales.
That is where the Leapfrog model comes in.
How Does the Leapfrog Approach Solve Data Chaos Faster?
Instead of asking, "How do we rebuild everything?" the Leapfrog approach asks:
"How do we build an AI-ready foundation as quickly as possible without tearing the house down?"
These three leaps are how organizations build two of Launch's four Frontier Firm dimensions: Platform Readiness and Redesigned Use Cases. Rather than competing frameworks, the Leapfrog model provides the execution path for building those capabilities while delivering measurable business outcomes. Governance and the Director-Verifier-Transformer operating model are layered into every production slice so organizations build trust and scale alongside value—not after it.
The model breaks into three major leaps:
Together, they provide a practical path to shipping a high-value AI slice with defensible ROI in 90 days while the underlying platform matures with each successive slice.
1. How Does a Unified Data Fabric Help You Finally See Your Business Clearly?
The first leap focuses on connection rather than reconstruction.
A unified data fabric, such as Microsoft Fabric built on OneLake, sits across your existing systems and creates:
Q described OneLake as "OneDrive for data." It provides one logical lake that gives organizations one copy of data with many ways to access it.
Instead of copying the same data into multiple silos for every analytics and AI use case, organizations centralize it once and build from there.
For organizations stuck in pipeline purgatory, this represents a major mindset shift.
You don't have to move everything before seeing value. Instead, modernize only what your first business use case requires, prove value in production, and allow each subsequent slice to strengthen the underlying platform.
You can:
2. What Happens When You Replace Manual ETL With AI-Ready Data Products?
The second leap transforms how organizations engineer data.
In traditional environments, ETL is often where projects stall. Pipelines multiply, hand-coded transformations become fragile, and every new use case requires another round of rebuilding.
AI-ready data products solve this challenge by shifting organizations toward:
For Magna, this means moving beyond storing data and turning it into contextual, reusable products.
Dan discussed using data to power:
Those scenarios only work when the underlying data products are consistent, current, and governed.
Once that foundation exists, AI agents and applications can operate with confidence.
3. How Does Self-Service Intelligence Break Your Dependence on IT?
The third leap is where modernization becomes visible to the business.
Self-service intelligence tools, especially Power BI combined with Copilot, allow people to interact with data conversationally.
Users can:
For Magna, this means schedulers, operations leaders, and client-facing teams can get answers independently.
For Microsoft, Fabric is intentionally designed so less technical users can take advantage of AI-powered experiences alongside engineers and architects.
Jonathan summarized it well:
"When insight becomes conversational, intelligence becomes scalable."
Organizations are no longer modernizing simply to produce better dashboards.
They are modernizing to give every decision-maker a smarter AI-powered copilot.
Where Should You Start If You're Already Mid-Modernization?
One of the biggest questions from webinar attendees was:
"What if we're already deep into a modernization program? Do we have to start over?"
The short answer is no.
But organizations often need to rethink the path forward.
Dan recommends starting with low-hanging fruit that builds organizational muscle for AI.
Focus on:
At Launch, Jonathan explained how AI accelerates this discovery process.
Instead of sending teams of analysts to manually reverse engineer pipelines and codebases, Launch uses AI-powered diagnostics and agents to:
What once required several weeks can often be compressed into about a week. In many cases, organizations begin implementing improvements during that same timeframe.
The goal is not to add another pilot.
It is to build organizational capability and create a repeatable process for identifying and delivering high-value AI opportunities.
How Are Leaders Planning for 2026 in an AI-First World?
Looking ahead, Dan shared a challenge many executives are facing.
How do you budget for a future where AI is both a cost center and a force multiplier?
Instead of focusing solely on model costs, Magna evaluates:
Internally, Magna views AI as a force multiplier across IT, security, investigators, and trial consultants.
The question is no longer whether AI replaces people.
The question is how AI helps people support more clients, more work, and greater complexity.
Keep humans in the loop to operate as Directors, Verifiers, and Transformers while allowing AI to extend their reach.
Why Does the Leapfrog Mindset Matter for AI's Future?
The webinar arrives at a simple conclusion.
Organizations do not have to modernize the old way.
Those that adopt the Leapfrog mindset can:
Instead of trying to perfect an architecture that will be outdated before implementation is complete, organizations should:
This is not about starting over.
It is about starting smarter.
Why do so many AI pilots fail?
Most fail because the underlying data foundation is fragmented, poorly governed, or difficult to access. AI cannot succeed on top of siloed, brittle data structures.
Does Leapfrog modernization replace traditional cloud migration?
No. It accelerates and focuses modernization by allowing organizations to unify and activate data earlier while broader migration efforts continue.
How quickly can organizations begin realizing AI value?
With the right operating model, organizations can ship their first production AI slice with measurable ROI in about 90 days while continuing to mature the underlying platform and data foundation as each new use case is delivered.
Is Leapfrog only for greenfield environments?
Not at all. It is designed for complex environments that combine legacy systems, cloud platforms, and SaaS applications. The goal is to connect rather than restart.
What use cases should organizations start with?
Look for data-heavy, repetitive, business-critical processes such as scheduling, document review, or customer support. These often produce quick wins and measurable ROI.
Modernize Once. Leap Forever.
Modernization does not have to become a prolonged, expensive, and frustrating cycle.
With the Leapfrog approach, organizations can step off the treadmill, build an AI-ready foundation, and begin turning data chaos into competitive advantage.
Instead of rebuilding everything for the third or fourth time, organizations can:
You do not need to wait for the perfect architecture before creating business value.
You can begin your leap today and modernize in a way that keeps pace with AI instead of constantly chasing it.
Across every industry, leaders are pouring time and budget into modernization, investing in new data platforms, warehouse rebuilds, cloud migrations, and analytics tools. Yet when they step back, the story sounds the same:
"We've been modernizing for years, but it still feels like our data can't keep up with the business or with AI."
In our recent webinar, Launch Consulting sat down with Dan McKinney (CTO & CIO at Magna Legal Services), Jonathan Gardner (Technology Lead at Launch), and Q Suliman (Partner Technology Strategist at Microsoft) to unpack why so many organizations are modernizing without moving forward and how to escape that cycle.
The stakes are high. AI adoption has tripled across the enterprise, yet business impact has remained largely flat. Industry research reinforces the gap: 88% of AI pilots fail to reach production and 95% of generative AI initiatives never deliver meaningful returns. Not because leaders lack ambition, but because the foundation underneath AI is fragmented, brittle, and constantly being rebuilt.
We call this pattern the Modernization Treadmill. This article breaks down what it is, why organizations remain stuck, and how the Leapfrog approach turns data chaos into measurable AI value. Rather than treating the platform as the starting point, Leapfrog begins with the highest-value business use case, builds only the data fabric needed to ship the first production slice, and then strengthens the platform with every additional slice. The foundation grows through delivery—not before it.
Here's how to build an enterprise data strategy that works.
If you recognize yourself in this picture, you're not alone:
Jonathan described it as a never-ending cycle:
"It's like you're constantly on a treadmill of upgrades just to keep accessing the data the way the business needs it."
The frustrating part is that as soon as you finish a major modernization phase, the landscape has already shifted. AI advances. Business expectations jump. New data sources appear. Suddenly, you're behind again.
But the treadmill is more than technical work. It is systemic inertia created by several factors.
1. Architectural Fragmentation That Compounds Over Time
Most organizations are juggling decades of architectural decisions layered on top of each other: legacy systems, half-completed migrations, stopgap integrations, departmental tools, and recently adopted cloud services.
Every new business initiative introduces more systems, more data, and more complexity. Instead of simplifying the environment, modernization often adds another layer to manage.
2. Business Pressure That Outpaces Technical Capacity
As Dan noted, data teams are being asked to serve departments with wildly different needs, including operations, finance, legal, sales, product, and compliance. Each requires new datasets, new pipelines, and new dashboards.
Modernization rarely gives teams time to stabilize. They're building the plane, flying it, and redesigning the engines at the same time.
3. Exploding Unstructured Data That Old Architectures Can't Handle
The growth curve isn't just steep. It's overwhelming.
Magna manages transcripts, PDFs, audio, video, and case data that multiply daily. Historically, BI systems were built around structured data. Now AI gives value to everything, which means everything must be processed, classified, governed, and made retrievable.
Jonathan emphasized that this turns linear data growth into logarithmic demand. Traditional rebuild-and-refactor models simply can't keep up.
4. Modernization Efforts That Reinforce Rather Than Solve the Problem
Teams often rebuild systems "the right way" only to discover:
So the cycle starts again.
This is why modernization efforts often generate activity without generating progress. Teams celebrate pipeline rewrites and warehouse consolidation, but the system underneath remains fragile.
Jonathan described it perfectly:
"It's like being in a permanent state of renovation. By the time you finish one room, the rest of the house is already outdated."
Why Traditional Modernization Fails in an AI-First Era
AI raises the bar for what data must do.
Traditional modernization assumes you have time to rebuild everything first. The AI era won't wait.
Traditional vs. Leapfrog: Why Leaders Are Switching Approaches
The shift is straightforward:
Modernization used to be infrastructure first. AI requires intelligence first.
This isn't about upgrading systems for the sake of upgrades. Leapfrogging is about escaping the cycle entirely by moving from rebuilding to rethinking. It allows organizations to bypass years of technical debt, align their data strategy with AI, and shift from reactive maintenance to proactive intelligence.
In short, stop laying more phone lines in a 5G world. Start building for the world you're operating in now and the one AI is rapidly shaping.
The panel started with a simple premise: there is no good AI without good data.
But AI doesn't just place another demand on your data architecture. It fundamentally changes what that architecture needs to do.
Dan shared Magna's reality as a regulated legal services business managing massive volumes of structured and unstructured data, including PDFs, transcripts, video, audio, and more. Historically, teams built analytics around structured data from source systems. Now AI opens up unstructured content as well.
That means organizations need to:
At the same time, as Jonathan pointed out, data growth has gone from linear to exponential to what feels almost logarithmic. Every new product, every new use case, and every new AI experiment generates even more data.
The old approach of simply building more warehouses and more pipelines no longer scales.
That is where the Leapfrog model comes in.
How Does the Leapfrog Approach Solve Data Chaos Faster?
Instead of asking, "How do we rebuild everything?" the Leapfrog approach asks:
"How do we build an AI-ready foundation as quickly as possible without tearing the house down?"
These three leaps are how organizations build two of Launch's four Frontier Firm dimensions: Platform Readiness and Redesigned Use Cases. Rather than competing frameworks, the Leapfrog model provides the execution path for building those capabilities while delivering measurable business outcomes. Governance and the Director-Verifier-Transformer operating model are layered into every production slice so organizations build trust and scale alongside value—not after it.
The model breaks into three major leaps:
Together, they provide a practical path to shipping a high-value AI slice with defensible ROI in 90 days while the underlying platform matures with each successive slice.
1. How Does a Unified Data Fabric Help You Finally See Your Business Clearly?
The first leap focuses on connection rather than reconstruction.
A unified data fabric, such as Microsoft Fabric built on OneLake, sits across your existing systems and creates:
Q described OneLake as "OneDrive for data." It provides one logical lake that gives organizations one copy of data with many ways to access it.
Instead of copying the same data into multiple silos for every analytics and AI use case, organizations centralize it once and build from there.
For organizations stuck in pipeline purgatory, this represents a major mindset shift.
You don't have to move everything before seeing value. Instead, modernize only what your first business use case requires, prove value in production, and allow each subsequent slice to strengthen the underlying platform.
You can:
2. What Happens When You Replace Manual ETL With AI-Ready Data Products?
The second leap transforms how organizations engineer data.
In traditional environments, ETL is often where projects stall. Pipelines multiply, hand-coded transformations become fragile, and every new use case requires another round of rebuilding.
AI-ready data products solve this challenge by shifting organizations toward:
For Magna, this means moving beyond storing data and turning it into contextual, reusable products.
Dan discussed using data to power:
Those scenarios only work when the underlying data products are consistent, current, and governed.
Once that foundation exists, AI agents and applications can operate with confidence.
3. How Does Self-Service Intelligence Break Your Dependence on IT?
The third leap is where modernization becomes visible to the business.
Self-service intelligence tools, especially Power BI combined with Copilot, allow people to interact with data conversationally.
Users can:
For Magna, this means schedulers, operations leaders, and client-facing teams can get answers independently.
For Microsoft, Fabric is intentionally designed so less technical users can take advantage of AI-powered experiences alongside engineers and architects.
Jonathan summarized it well:
"When insight becomes conversational, intelligence becomes scalable."
Organizations are no longer modernizing simply to produce better dashboards.
They are modernizing to give every decision-maker a smarter AI-powered copilot.
Where Should You Start If You're Already Mid-Modernization?
One of the biggest questions from webinar attendees was:
"What if we're already deep into a modernization program? Do we have to start over?"
The short answer is no.
But organizations often need to rethink the path forward.
Dan recommends starting with low-hanging fruit that builds organizational muscle for AI.
Focus on:
At Launch, Jonathan explained how AI accelerates this discovery process.
Instead of sending teams of analysts to manually reverse engineer pipelines and codebases, Launch uses AI-powered diagnostics and agents to:
What once required several weeks can often be compressed into about a week. In many cases, organizations begin implementing improvements during that same timeframe.
The goal is not to add another pilot.
It is to build organizational capability and create a repeatable process for identifying and delivering high-value AI opportunities.
How Are Leaders Planning for 2026 in an AI-First World?
Looking ahead, Dan shared a challenge many executives are facing.
How do you budget for a future where AI is both a cost center and a force multiplier?
Instead of focusing solely on model costs, Magna evaluates:
Internally, Magna views AI as a force multiplier across IT, security, investigators, and trial consultants.
The question is no longer whether AI replaces people.
The question is how AI helps people support more clients, more work, and greater complexity.
Keep humans in the loop to operate as Directors, Verifiers, and Transformers while allowing AI to extend their reach.
Why Does the Leapfrog Mindset Matter for AI's Future?
The webinar arrives at a simple conclusion.
Organizations do not have to modernize the old way.
Those that adopt the Leapfrog mindset can:
Instead of trying to perfect an architecture that will be outdated before implementation is complete, organizations should:
This is not about starting over.
It is about starting smarter.
Why do so many AI pilots fail?
Most fail because the underlying data foundation is fragmented, poorly governed, or difficult to access. AI cannot succeed on top of siloed, brittle data structures.
Does Leapfrog modernization replace traditional cloud migration?
No. It accelerates and focuses modernization by allowing organizations to unify and activate data earlier while broader migration efforts continue.
How quickly can organizations begin realizing AI value?
With the right operating model, organizations can ship their first production AI slice with measurable ROI in about 90 days while continuing to mature the underlying platform and data foundation as each new use case is delivered.
Is Leapfrog only for greenfield environments?
Not at all. It is designed for complex environments that combine legacy systems, cloud platforms, and SaaS applications. The goal is to connect rather than restart.
What use cases should organizations start with?
Look for data-heavy, repetitive, business-critical processes such as scheduling, document review, or customer support. These often produce quick wins and measurable ROI.
Modernize Once. Leap Forever.
Modernization does not have to become a prolonged, expensive, and frustrating cycle.
With the Leapfrog approach, organizations can step off the treadmill, build an AI-ready foundation, and begin turning data chaos into competitive advantage.
Instead of rebuilding everything for the third or fourth time, organizations can:
You do not need to wait for the perfect architecture before creating business value.
You can begin your leap today and modernize in a way that keeps pace with AI instead of constantly chasing it.
