They seek a common advantage: the structure and ability to move faster from data to insight to taking decisive action.
In a recent interview, Amazon founder Jeff Bezos wasted no time attributing the company’s growth and durability to its ability to make faster decisions. “Decisiveness, moving forward on decisions as responsibly as you can, is how you increase velocity,” he said. “Most of what slows things down is taking too long to make decisions at all skill levels.” He went on to emphasize that this must be embedded not only in culture, but in the very fabric of how the company’s systems work.
While many factors influence enterprise speed, few are as critical as the chain of work that leads to powerful data insight and decisive action. We’re now seeing a tectonic shift as more companies recognize that the quality of their information architecture is a determining factor in how quickly that chain can move. Understanding the root of the problem is where it starts.
| Information Architecture & Taxonomy – Quick Primer |
| A strong information architecture is the operating platform – and organizing principle – for how your data ecosystem is structured, connected, navigated and governed across the enterprise. Taxonomy is the core component and structuring tool – providing the shared language and semantic meaning for the terms, categories, labeling of metatags, and relationships that allow people-and AI-to find and use their data. Factor leverages both to deliver the industry’s strongest data foundations. |
Friction & Drag: The Hidden Cost of Chaotic Information Systems
For years, the industry has referred to it as “the pain with no name.” We see it clearly. Despite massive investments in technology and AI, large organizations grow sluggish and out of sync. The root cause isn’t technology alone, it’s the layer of information chaos lurking beneath the surface.
Data is disjointed, siloed, and scattered across the enterprise: living in different systems, formats, and structures, using conflicting language/labeling, navigation paths, and definitions. What appears invisible on the surface creates friction in the activities vital to their performance and capabilities.
The result isn’t just wasted time or frustration searching, reworking, or questioning data. Teams get stuck in the moments that limit progress,unable to connect the dots or confidently move forward. This prevents the enterprise from treating information as a true asset.
You hear it everywhere:
“My team wastes time hunting down the data points I need.”
“We can’t align metrics across reports – no one trusts the numbers.”
“Net sales means five different things across five divisions.”
“Half the time, no one can connect the dots.”
“We’re confusing customers with inconsistent information across channels.”
“AI keeps getting it wrong.”
Executives at the world’s largest companies are under constant pressure to deliver timely progress and results that move the business forward. Recent surveys of CEOs, CIOs, and COOs reveal a consistent frustration: the widening gap between the promise of technology, especially AI and its operational reality.
Internal misalignment, poor coordination, and information friction continue to slow progress, delaying insight and decision-making at critical moments. Over time these moments compound, dragging down enterprise performance across analytics, customer experience, personalization, knowledge management, sales enablement, and AI readiness.
At a broader level, leaders worry about the long-term impact including: reduced efficiency, higher technology costs, and diminished ability to adapt and scale.
The Friction in AI Results & Application Development
A comprehensive MIT Project Nanda study tracking enterprise AI implementation reveals a stark reality: despite $30-40 billion in GenAI investment, 95% of organizations report zero measurable return.
The research identifies what scholars call the “GenAI Divide” – a sharp split in which just 5% of AI pilots reach production and deliver real value, while the vast majority remain stuck in perpetual experimentation. The cause isn’t model quality or regulation; it’s foundational.
Most AI systems fail due to poor data quality, lack of strategic alignment with business goals, and inadequate infrastructure for deployment and ongoing support, limitations that trace directly back to weak or fragmented information foundations.
A Strong Information Architecture Foundation Accelerates the Chain of Activity that Drives Performance
The largest, fastest enterprises use information architecture and taxonomy to build data foundations that do more than untangle the mess. They remove friction at the source, enabling the organization to move quickly gathering intelligence, creating powerful insights, and taking decisive action.
Strong data foundations realign the information ecosystem with what the business actually needs it to do. They introduce a core framework built on shared context, clear workflows, and governance that people can intuitively understand. Together, these elements create a scalable web of connections across systems, data, and teams. So when people interact with information built on this foundation, they can quickly find what they need, see the bigger picture, and uncover cross-functional patterns that were previously hidden.
This liberates teams to execute the critical chain of work without the constant drag of searching, reconciling, reinterpreting, debating, or reworking data. The path to insight and decision-making becomes faster, easier, and repeatable—without the operational cost and frustration that slow enterprises down.
Think of your data foundation as an operational platform for your data system, with taxonomy as the operating system. It isn’t about building digital libraries or complex schemas for their own sake. It’s about eliminating the structural bottlenecks that prevent organizations from operating at full velocity and value.
| CASE BRIEF – FORTUNE 500 GLOBAL MANUFACTURER Impact on Sales? $2 Billion Marketing Campaign | When a Fortune 100 global semiconductor company sought to understand the true sales impact of its $2 billion marketing investment, its teams lacked the structure to answer the question with confidence and without delay. Using our data foundation, we helped them model the information in key domains-including marketing, sales, and geography-to dramatically improve the speed, accuracy, and depth of their analysis. We did this by unifying the data structures scattered across its systems, semantically linking and tagging data and content by its core meaning—not just by name. This created a clear and scalable web of connections. As a result, teams could immediately pinpoint the data they needed, see its marketing performance holistically, and uncover cross-functional insights previously hidden. (End with something like:) After optimizing their marketing data system, our data foundation became a blueprint for action that fueled other capabilities across the business. |
A True Enterprise-Wide Capability Engine
A strong data foundation built to support one enterprise system can become a blueprint that empowers other capabilities across the business. Rather than solving problems in isolation, information architecture creates a shared model – common language, structure, and relationships – that can be applied and extended across analytics, customer experience, personalization, knowledge management, sales, and AI readiness. This approach compounds value. Each new use case builds on the same foundation, accelerating outcomes while reducing redundancy, rework, and long-term complexity.
Enterprise Analytics
Analytics and BI depend on data that is clean, consistent, and semantically aligned. Information architecture provides the layer of meaning that makes data understandable and trustworthy, for both humans and machines. The result is more reliable dashboards, clearer insights, and stronger downstream AI performance, delivering higher ROI from analytics and data investments.
Customer Experience & Personalization
Information architecture shapes how people experience content, tools, and systems. When language, categories, and pathways reflect how customers and employees think, experiences feel intuitive rather than frustrating. This leads to higher engagement, faster onboarding, and more effective personalization, while reducing support, training, and operational friction.
Sales Enablement
Sales teams move faster when information is easy to find, understand, and apply. Strong IA organizes content around customer needs, industries, use cases, and buying stages, eliminating time wasted searching across tools spotting patterns and opportunities. Reps spend more time selling, content adoption increases, and deal cycles shorten through faster, more confident execution.
Knowledge Management
Effective knowledge management accelerates how quickly teams learn, solve problems, and act. Strong information architecture removes friction from fragmented repositories by organizing knowledge around how people think and work, enabling faster discovery, clearer understanding, and more confident decision-making.
AI Readiness & Performance at Scale
When a single data foundation supports multiple enterprise capabilities, it also becomes the prerequisite for AI readiness. Providing the structure, context, and trust AI systems require to perform at scale.
Enterprise speed increasingly depends on how quickly AI can turn data into reliable insight. Information architecture and taxonomy provide the foundation that makes this possible. Giving AI systems the structure and meaning they need to perform accurately and consistently.
By aligning data around common language, clear classifications, and governed relationships, data foundations reduce the friction that impedes AI success. AI models are trained on coherent, context-rich information instead of fragmented or contradictory data, accelerating deployment and improving output quality from day one.
Semantic richness through metadata, taxonomies, and knowledge graphs allows AI to understand context, disambiguate meaning, and return more relevant results. This reduces rework, limits hallucinations, and enables AI systems to scale without constant intervention.
As AI moves beyond simple tasks into complex, high-value workflows, static structures break down. Dynamic, contextual information architecture adapts to user intent and business needs, reducing cognitive load and speeding decision-making across the enterprise.
The fastest enterprises are not the ones adopting AI first; they’re the ones with the foundations that let AI move fast. Strong information architecture transforms AI from an experiment into a velocity engine, accelerating the path from data to insight to decisive action.
Governance Sustains Speed and Trust
Information architecture foundations must evolve as your business does. Governance ensures your foundation doesn’t erode, allowing you to meet the challenge of ever-changing conditions.
The demand AI applications now place on your data infrastructure will expose its weak points, such as broken usage patterns, failed searches, ambiguous labels, and low-value categories. All of these jeopardize your investment in your data and create organizational drag.
The dependency of AI applications on your IA foundation demands a disciplined approach to governance in order to maintain speed, accuracy, and trust. Clear ownership, shared standards, and regular feedback cycles ensure your IA foundation evolves deliberately, not haphazardly.
Acceleration Starts with Alignment
The transformation to a faster enterprise must follow a phased approach: one designed not just to connect systems, but to build momentum that compounds over time. When done right, this process creates a flywheel effect: alignment fuels activation, activation enables adaptation, and each cycle accelerates productivity and enterprise performance.
Align & Build
Acceleration begins by surfacing misalignments in your information ecosystem and realigning it with your business needs. Building this core framework establishes a common language, workflows, and governance that everyone can intuitively understand.
Activate & Connect
With alignment in place, organizations activate these foundations to drive real value – connecting systems, improving data visibility, and enabling AI to operate on clean, structured information. Clarity becomes speed: teams access insights faster, leaders decide with confidence, and AI delivers more reliable results.
Adapt & Scale
Organizations sustain velocity by continuously adapting through governance and feedback loops. Information architecture evolves alongside the business, supporting change, scaling AI effectively, and maintaining resilience as needs and technologies shift.
Companies that embrace this model not only improve efficiency, they build the foundation for a faster enterprise, gaining the ability to move, compete, and grow with sustained speed and confidence.
From Friction to Velocity to Enterprise Value
| LEADING ENTERPRISE INDICATORS – of – STRONG DATA FOUNDATIONS | ||
| Performance | Capabilities | Value |
| Improving the velocity of critical data to insight & decision cycles | Extending foundation models & performance gains across functions | Compounding models & performance gains over time |
| Lower avg search & reporting times Higher first search success Reduced data workarounds, duplication & reconciliation Fewer data conflicts & disputes Quicker technology adoption Faster, wider knowledge base usage | Analytics Customer Experience Personalization Knowledge Management Sales Enablement AI Data Readiness Cross-functional system alignment & efficiency Cross-team data collaboration & speed Faster system integration | Data clarity & vision Improved agility, change management & scalability Faster workflows & higher throughput per employee Cost efficiency incl lower long term technology debt Reduced compliance risk Improved employee satisfaction/retention |
The strongest data foundations are a force multiplier, turning information from a liability into an asset. It removes friction, aligns people and systems, and creates the structural clarity required for efficiency, innovation, and sustained growth.
More than efficiency, information architecture provides a durable foundation for scale. It enables organizations to integrate new systems faster, adopt AI more effectively, and evolve their business without constant rework – reducing long-term technology debt while increasing organizational agility.
The path from information chaos to enterprise speed isn’t complex but it does require treating information architecture as a core strategy. The fastest enterprises aren’t defined by technology or AI alone but by the structures that underpin and fuel them.
Information architecture is the foundation for a faster enterprise – and those who invest in it first will outpace those who don’t.