After 30 years in technology and transformation leadership—working with organizations like AWS, Airbus, AstraZeneca, and Decathlon—I’ve witnessed a pattern that has become impossible to ignore. The majority of digital transformations fail, and they don’t fail for the reasons most people assume. This persistent reality is why Im building CapabiliSense .
The Uncomfortable Truth About Transformation
Let’s start with a statistic that should concern every business leader: somewhere between 70% and 95% of large-scale digital, AI, or cloud transformations fail to meet their objectives . We’ve all heard these numbers, and many of us have lived through them. The conventional wisdom blames technology choices, budget overruns, or insufficient technical expertise.
But after three decades on the front lines of transformation, I can tell you that’s not the full story.
When projects fail, it’s almost never the software’s fault. It’s not because the tools were inadequate or the core idea was unrealistic. The real culprits are far more human: misaligned priorities, unclear strategies, unrealistic timelines, internal conflicts between departments, and a fundamental lack of clarity about what the organization can actually do .
Think about your own experience. When a major initiative derails, what’s the real cause? Usually, it’s pushback from stakeholders who weren’t properly engaged. It’s poor communication between teams. It’s leaders who aren’t aligned on the fundamental goals. It’s a CISO or Chief Risk Officer who shows up late in the process and blocks everything because something was missed—or simply because they can .
The Crisis of Reality in Modern Organizations
What I’ve observed across dozens of enterprises is what I’ve come to call a “crisis of reality” . Decisions are frequently made based on perceptions rather than facts. Surveys, interviews, and workshops produce subjective insights, but they rarely reveal the complete picture.
Here’s what typically happens: Employees present overly positive views to avoid conflict. Managers interpret data in ways that support their own agendas. Executives rely on high-level summaries that conveniently hide operational issues. Over time, this creates an organizational narrative that feels good but bears little resemblance to what’s actually happening on the ground .
This isn’t malice—it’s human nature. But it’s deadly for transformation.
The challenge I kept seeing was the lack of any way to get a clear, objective view of the entire situation. We had tools for tracking data and processes. But there was no solution that could effectively cut through the noise—the conflicting opinions, the organizational politics, the hidden resistance, the missing information that everyone assumed someone else was handling .
What was missing was a way to get an “apolitical” dashboard. Something that could highlight conflicts and gaps and show what was really going on beneath the surface.
My “Aha” Moment (Or Rather, the Absence of One)
I should be honest: there wasn’t a single “aha” moment when I stopped and said, “I need to build this platform right now.”
It was more like watching the same movie for the thirtieth time and finally realizing you know every line by heart. The same patterns of failure, repeating over and over. The same routine every time: gathering information, assessing business objectives, and then “translating” everything so teams could understand how it affected them personally .
After seeing that pattern enough times, I asked myself a simple question:
“Why can’t we do this through a platform?”
That question took up residence in my mind as a background task. It simmered there through more transformations, more failures, more expensive consulting engagements that produced beautiful presentations but changed nothing .
The Anatomy of a Doomed Transformation
Let me share a specific example that crystallized everything for me.
I was brought in to lead an engineering business unit that provided internal cloud platforms for a large financial institution. On paper, my plan was excellent. It had detailed roadmaps, clear KPIs, team compositions, and defined deliverables.
It was doomed from the start .
The business goal I was handed was a vague hope: “increase digital engagement with our wealth management clients.” I was expected to build the perfect foundation for a skyscraper whose blueprints didn’t even exist.
Before finalizing my platform strategy, I did something simple yet apparently unusual for that organization: I walked the building.
I talked to my “customers”—the leaders of wealth management, retail banking, data, and so on. What I found was systemic chaos.
The business units were all running in different directions. But the deeper problem was with my own internal platform teams. They were exhausted. They had built a moat of process and a backlog filled years in advance—not out of malice, but to protect themselves from the shifting priorities of the business .
And it got worse. They were under constant siege from the security organization. The CISO’s office issued murderous compliance obligations as weapons for power plays. My teams would spend weeks manually patching a “critical” vulnerability (completely derailing all strategic work) not because it was the most effective solution, but because the CISO said so .
My transformation plan couldn’t succeed because the system was designed for failure.
I failed. And that failure taught me more than any success ever could.
What AWS Taught Me About Frameworks
Before that failure—and before CapabiliSense—I spent time at AWS, where I worked on creating and scaling transformation frameworks. I built assessments for things like Cloud Maturity and Migration Readiness, tools that were used by hundreds of clients and thousands of Amazon employees internally .
That experience taught me several crucial lessons.
First, structured roadmaps can indeed help big companies move faster. The discipline of a good framework provides clarity and direction.
Second, every roadmap has to be tailored. There’s no one-size-fits-all solution, and anyone who claims otherwise is selling something.
Third—and most importantly—I learned just how difficult it is to take a good theory and make it actually work at scale. A static PDF or a 200-slide presentation (a very expensive presentation) is one thing. A living, adaptive approach that responds to real organizational reality is something else entirely .
The Insight That Changed Everything
Recently, I had a fascinating exchange with Aleix Morgadas, a respected engineering strategist. We were debating whether strategy should be a collaborative effort where Business, Data, and Tech are different “languages” for the same co-created plan, or whether there’s a necessary hierarchy.
I argued for hierarchy based on painful experience. Business must come first, then Data, then Technology.
In the end, Aleix articulated a point that crystallized the entire challenge of transformation:
“If you only know tech, the Crux will look techy.”
This is profoundly true. Every group sees the organization’s biggest challenges through their own lens. Tech teams see technical problems. Data teams see data problems. Business teams see business problems. And everyone works on what they know, often at cross-purposes.
Let me show you what I mean by revisiting that failed financial transformation with the discipline we now have:
Step 1: The REAL Business Crux
The true friction point wasn’t “digital engagement.” It was: “Our most valuable high-net-worth clients are leaving because their digital banking experience is completely disconnected from the long-term relationship they have with their personal advisor.”
Step 2: The Implied Data Crux
This immediately defines the data challenge: “Our client data is trapped in disconnected silos across wealth management, retail banking, and mortgage platforms, making a unified, real-time view of a single client impossible.”
Step 3: The Implied Technology Crux
Only now can we define the technology challenge: “The internal platforms we provide lack the modern, event-driven architecture and API gateways needed for customer teams to integrate their siloed systems in real-time.”
Once you have this clear, ordered chain of logic, the operating model writes itself. The job is no longer to build a generic “Self-Service Stack” that no one trusts. The job is to deliver a “Real-Time Unified Client View Service” as a primary product. That’s a mission an engineering team can believe in .
What Is CapabiliSense?
CapabiliSense is my response to these decades of observation, failure, and hard-won insight.
At its core, CapabiliSense is an AI-powered platform that analyzes an organization’s existing documents to deliver a clear, evidence-based assessment of its readiness for change . The name combines “capability” and “sense-making”—two essential elements of effective transformation.
Instead of relying on interviews, surveys, and workshops (which are inherently subjective), CapabiliSense examines what actually exists across an organization’s internal content: strategy documents, operating models, governance artifacts, meeting notes, compliance summaries, performance reports, and even email chains .
These documents contain hidden signals about priorities, conflicts, and capability gaps. For example, a strategy may promise rapid innovation while operational plans focus on stability and cost reduction. Such contradictions often go unnoticed until they derail a major initiative .
The platform uses AI to interpret these materials and identify demonstrated capabilities, their maturity, and their limitations. It builds a real-world baseline that reflects how work is truly performed—not how it’s described in presentations or what people say in interviews .
How It Works: From Abstract Ideas to Measurable Indicators
Let me walk you through how the platform actually functions.
When an organization uploads its documents, the system searches for both direct and indirect evidence of strategic capabilities. This might be an explicit AI policy or an architecture decision log that hints at how AI risks are being considered. It could be a slide titled “Outcome Metrics” or a hidden OKR in a team wiki. The goal is to observe capabilities in practice, not just in theory .
Once the analysis is complete, the prototype presents what we call a “table of ideals and metrics” .
On the left, you see a list of capabilities—things like “Responsible AI Governance” or “Customer-Centric Planning”—each with an assessed maturity level. On the right, for each capability, we show the achieved and bottleneck levels of its underlying indicators. These indicators are the specific, verifiable signals extracted from the evidence: whether accountability is documented, whether metrics are regularly reviewed, whether conflicting goals across silos are being reconciled .
Think of it as a structured view of three things:
- What we say we’re doing
- What the evidence says we’re actually doing
- What’s missing in between
This helps decision-makers move beyond slogans to genuine, trackable organizational maturity .
Why This Matters: The Human Side of Transformation
What excites me most about CapabiliSense isn’t the technology—it’s what the technology enables.
When you have a clear, evidence-based view of organizational reality, several things become possible.
Leaders gain clarity. Instead of navigating by intuition and political consensus, they can see actual capability gaps and prioritize what matters most .
Teams gain purpose. When engineers understand that their mission is to deliver a “Real-Time Unified Client View Service” rather than a generic platform that no one trusts, they can commit to something meaningful .
Conflict becomes productive. When the CISO’s manual patching mandate is shown to directly undermine client retention, you can have an honest conversation about trade-offs rather than a power struggle .
Risk is reduced. By providing a fact-based view of organizational reality, we help leaders make decisions with a clearer understanding of constraints and opportunities. Projects are more likely to succeed when they’re built on accurate baselines .
The Philosophy: Building with Partners, Not Just for Funding
I want to be transparent about how we’re building CapabiliSense. We’re not chasing rapid funding or growth metrics. We’re developing the platform with real design partners—organizations that provide genuine feedback and use cases .
This approach ensures that we’re solving actual problems rather than hypothetical ones. It also allows the product to evolve based on real organizational needs rather than investor expectations.
Yes, we’ve paused active work on the startup while I focus on re-establishing my foundation for the long term . But the mission is far from dead. This recalibration is a strategic move—a deliberate maneuver to go back into the field, armed with new experience, and regain a clear, unfiltered signal from the market’s most painful problems .
Looking Ahead: The Future of Objective Strategy Execution
As AI becomes more integrated into business processes, the ability to analyze complex organizational data will become a competitive advantage. Companies that truly understand their capabilities will make better decisions, allocate resources more effectively, and avoid costly transformation failures .
Objective clarity will replace optimistic assumptions. Evidence will guide strategy.
That’s the future I’m building toward with CapabiliSense. Not more dashboards, reports, or theoretical frameworks. Not another “leadership model” or training program. Just a clear, honest view of organizational reality—so leaders can stop guessing and start building transformations that actually work .
A Personal Note
I’m writing this not as a corporate announcement or a polished marketing piece, but as a genuine attempt to share what I’m doing—the wins, the failures, and everything in between .
This blog is for anyone curious about a startup journey. It’s for my “ambassadors”—the people who know me and support what I’m doing, even if they aren’t totally sure what I’m building yet. It’s for “investors” of all types: not just those with money, but those who invest their valuable time and expertise. And it’s for the “locomotives”—the thought leaders and founders who’ve walked this path before me, whether they succeeded or failed .
If you see something I’m missing, or if you’d like to point out where I’m on the right track, I’d love to hear from you.
Let’s see how this goes.
Conclusion
The idea behind CapabiliSense is rooted in a simple but powerful realization: most digital transformations fail not because of technology, but because organizations misunderstand their own capabilities. Decisions are made based on assumptions, opinions, and incomplete information, which leads to misaligned strategies and unrealistic expectations .
CapabiliSense is intended to change that dynamic by introducing objective, evidence-based clarity into the transformation process. By analyzing the documents organizations already produce, it helps leaders see the true state of their strategy, capabilities, and execution risks.
Ultimately, this is about restoring reality to enterprise decision-making. With clearer insights and a more accurate baseline, organizations can plan transformations that are not only ambitious but also achievable, sustainable, and aligned with their true capabilities .
Frequently Asked Questions
What is CapabiliSense?
CapabiliSense is an AI-powered platform that analyzes organizational documents to provide objective, evidence-based insights into capabilities, strategy alignment, and transformation risks .
What problem does CapabiliSense solve?
It addresses the lack of objective, evidence-based assessments in digital transformations. Most initiatives fail because leaders operate without a clear understanding of their organization’s actual capabilities, leading to misaligned strategies and unrealistic expectations .
Who is building CapabiliSense?
The platform is being developed by Andrei Savine, a transformation leader with over 20 years of experience at organizations including AWS, Airbus, Decathlon, and AstraZeneca .
How is this different from traditional capability assessments?
Traditional assessments rely on interviews, surveys, and workshops—methods that are inherently subjective and influenced by bias, politics, and inconsistent interpretation. CapabiliSense analyzes actual organizational documents to determine what capabilities are demonstrably in place, reducing subjectivity and providing an auditable, defensible baseline .
What types of organizations benefit most from CapabiliSense?
Large enterprises, regulated industries, and organizations undergoing complex transformations benefit the most due to their need for transparency, consistency, and evidence-based decision-making .
Can the assessment be repeated over time?
Yes. The methodology supports repeat assessments, allowing organizations to track progress and validate improvements using the same evidence-based standards .
Does CapabiliSense replace human expertise?
No, it augments expert judgment by automating analysis and providing consistent insights, allowing experts to focus on interpretation and strategic decision-making rather than manual data gathering .
What kinds of documents does the platform analyze?
It analyzes strategy documents, operating models, governance artifacts, meeting notes, compliance summaries, performance reports, policies, procedures, and other internal documentation .
How does the platform handle conflicting or outdated documents?
The system is designed to detect inconsistencies and contradictions. Documents often disagree with each other, or something marked as “completed” in one report may still be in the future column of a project plan. The platform accounts for these complexities .
Is the platform available now?
The system is functional and ready for practical deployment. The team continues to add features like user logins and data management to make it publicly accessible. Interested organizations can reach out to learn more .