Why Spreadsheets Beat Requirements Management Tools After 40 Years | Business Analyst Software Crisis
88-94% of spreadsheets contain errors, yet business analysts still choose Excel for requirements gathering over specialized requirements management software. Why? Psychology and habit beat logic. But in 2025, as software complexity explodes and context switches multiply, the linear spreadsheet approach hits its breaking point. Product managers need AI-powered requirements traceability, not more sophisticated Excel templates.
The Crime Scene: Four Decades of Excel Dominance
““We’ve grown so fast. We’ve brought in a lot of newer people... It’s bringing them up to speed a little bit faster. It’s a little rough because their learning process can be painful,” ”
Every product manager knows this routine. The stakeholder meeting starts, someone mentions requirements traceability, and suddenly everyone's scrambling to find "the latest version" of that Excel file. Version_Final_FINAL_v3.xlsx, anyone?
Business requirements management tools lose to Microsoft Excel and Google Spreadsheets despite being purpose-built for the job.
88-94% of spreadsheets contain critical errors
Teams burn 27% more time on requirements documentation with Excel versus modern requirements management software
One 50-person team wastes 4,800 hours annually fixing requirement-related defects that proper software traceability could prevent
Keywords floating around this mess: "requirements gathering templates," "business analyst tools," "product management software," "requirements traceability matrix." Yet Excel keeps winning.
It is not about logic and convenience; it is just about years of getting things done without noticing how the world has changed.
The Apple Legacy: When Innovation Becomes Obsolete
1985: Excel debuts on Macintosh as revolutionary spreadsheet software.
1993: Visual Basic for Applications turns Excel into a programmable platform.
2025: Same tool, exponentially more complex requirements.
The original Apple innovation that made Excel powerful—visual interface, mouse manipulation, programmable cells—was groundbreaking. But here's the smoking gun: Excel was designed for financial modeling, not product complexity.
Today's products juggle AI, cloud architecture, regulatory compliance, multi-platform deployment, and real-time data processing. Excel's linear, row-column structure is like using a horse and buggy on the Interstate.
As one ServiceNow consultant shared: "You can utilize Excel for this purpose and use Excel Tabs to describe separate sections of the catalog item such as Catalog Item Description... Workflow Tab... Variables Tab..." It’s like building requirements management systems inside Excel—proof the tool has been stretched beyond recognition.
The Familiarity Trap: Excel as Security Blanket
Psychologists call it cognitive comfort - the state of mental ease and satisfaction experienced when information, interfaces, or environments align with our natural cognitive patterns and preferences, reducing mental load and improving well-being. Spreadsheets feel like "warm blankets and teddy bears" in uncertain environments of new projects. They offer the illusion of control through familiar interfaces.
The research shows that over 90% of employees use spreadsheets daily, creating a shared organisational language and knowledge. When hiring, product managers consistently look for "strong Excel skills" as a fundamental competency.
But comfort becomes a trap. One aerospace company's requirements system was described as "a spreadsheet nightmare... with about five tabs at the bottom, tons of different color codes, twenty-five columns, and hundreds of rows." They persisted until the pain became unbearable.
The familiarity effect creates addiction, not efficiency.
Most Requirements Management Tools Are Just "Spreadsheets on Steroids"
Here's the uncomfortable truth about enterprise requirements management software: most tools simply digitize the same linear thinking. They slap collaboration features and version control onto the fundamental spreadsheet paradigm.
Even modern AI-powered requirements management platforms follow this pattern because they were built in the pre-AI era. They add natural language processing to analyze requirements quality against INCOSE standards, but the underlying data model remains tabular: requirements in rows, attributes in columns, and hierarchical breakdowns that mirror Excel's structure. The thinking hasn't evolved since 1985.
““Excel is incredibly good for lists of things, and at first glance, requirements seem a lot like a set of attributed lists. And they aren’t anymore.””
But software requirements aren't lists. They're interconnected webs of context, dependencies, stakeholder needs, and evolving understanding. Linear tools can't capture this complexity, which explains why requirements traceability remains such a persistent problem across the industry.
2025: When Software Development Complexity Breaks Linear Thinking
Today's product development lifecycle demands far more than Excel can handle. Modern applications require:
Cross-platform compatibility across web, mobile, desktop, and IoT devices
Real-time AI integration with context awareness and machine learning models
Regulatory compliance spanning GDPR, HIPAA, SOX, and industry-specific standards
Security frameworks for data protection, authentication, and threat mitigation
Performance optimization for global scale and edge computing
Linear requirements management tools collapse under this complexity. The insurance company in our study experienced this firsthand. Their distributed teams, changing personnel, and complex system integrations consistently broke traditional business requirements documents and Excel-based processes.
This context juggling exceeds human linear processing capacity. Enterprise software development now requires understanding relationships between dozens of microservices, API dependencies, data flows, user personas, compliance requirements, and technical constraints simultaneously.
The old requirements gathering process of documenting functional requirements in rows and columns can't map these interconnected realities. Teams need requirements analysis tools that understand context, not just content.
The Neural Solution: Knowledge Graphs vs. Spreadsheets
Your brain doesn't think in rows and columns. It thinks in neural networks—interconnected nodes of context, relationships, and patterns. This is how requirements actually work.
Knowledge graphs mirror brain architecture:
Nodes represent requirements, features, stakeholders, constraints
Relationships show dependencies, conflicts, impacts
Context emerges from connection patterns
Intelligence derives from relationship analysis
Unlike spreadsheets' rigid structure, knowledge graphs adapt and evolve. They capture the messy reality of product development where requirements shift, stakeholders change minds, and context constantly evolves.
As demonstrated in our research, AI-powered knowledge graphs can detect missing requirements that cause system crashes—something impossible with linear spreadsheet thinking.
Why Neural Wins Over Linear
Spreadsheets force artificial constraints:
Requirements must fit predefined columns
Relationships require manual maintenance
Context gets lost in cell references
Scale breaks at complex interdependencies
Knowledge graphs embrace natural complexity:
Requirements exist as connected entities
Relationships self-maintain through algorithms
Context emerges from connection patterns
Scale improves with more data points
The insurance company in our study discovered this firsthand. Their distributed teams, changing personnel, and complex integrations consistently broke spreadsheet approaches. They needed "traceability matrix that will be helpful for us to map the requirements"—exactly what knowledge graphs provide natively.
The Verdict
After 40 years, spreadsheets still dominate requirements because they satisfy psychological needs—familiarity, control, and immediate gratification. But 2025's software complexity has exposed their fundamental flaw: linear tools can't handle non-linear realities.
The future belongs to neural approaches. Knowledge graphs that think like brains, not calculators. AI agents that understand context, not just content. Interconnected intelligence that evolves with understanding.
The spreadsheet addiction is finally meeting its match: the limits of linear thinking itself.
Time to graduate from rows and columns to nodes and networks. Your brain—and your requirements—will thank you.
