The Cognitive Stack: Why It’s AI’s Next Frontier in the Enterprise (Part 1)
As 2025 unfolds, we are seeing a sobering reality: despite massive funding, many AI startups are failing to deliver sustainable value, while enterprises struggle with fragmented AI implementations. Recently Gartner that at least 30% of generative AI (GenAI) projects will be abandoned after the proof-of-concept stage by the end of 2025, primarily due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. However other sources suggest that the failure rate for AI projects is a lot higher. For instance, NTT DATA indicates that between 70% and 85% of GenAI deployment efforts fail to meet their desired return on investment (ROI). Additionally report from RAND highlights that over 80% of AI information technology projects fail, which is twice the rate of non-AI IT projects.
The problems we hear are consistent: people don’t quite trust AI, isolated AI capabilities are disconnected from business processes, models operate as black boxes outside business leader’s control and excessive focus on technology over outcomes.
What’s missing isn’t better models but a coherent architecture to deploy AI effectively. At G2C Ventures we’re focused on “the cognitive stack” – the layered architecture that transforms a company’s data and knowledge into automated intelligence.
What is the Cognitive Stack?
The cognitive stack mirrors human cognition in organizational design. Unlike traditional tech stacks serving human decision-makers it creates a brain-like architecture where AI systems handle reasoning, perception, and action while humans guide strategy and creativity.
At its core, the cognitive stack consists of four interconnected layers that put business leaders in control of AI systems:
1. Perception Layer
AI interfaces that understand context and intent, giving business leaders control over:
- Messaging: How the company communicates with customers or users
- Success metrics: What defines successful customer interactions
- Engagement rules: How AI systems should interact with users
Real-world applications include:
- Intelligent Customer Service platforms that understand context beyond keywords
- Context-Aware Sales tools that adapt to customer signals
- Smart Assistants that enhance productivity while respecting company guidelines
2. Reasoning Layer
Real-time decision engines that adapt to business needs, where leaders set:
- Business rules: Operating parameters and constraints
- Risk levels: Acceptable thresholds for automated decisions
- Decision frameworks: The structure for how choices are made
Real-world applications include:
- Dynamic Pricing systems that adapt in real-time while following business rules
- Resource Planning tools that optimize allocation within constraints
- Risk Assessment systems that identify threats within defined parameters
3. Action Layer
Automated Agents that execute within defined enterprise guardrails, where leaders manage:
- Action limits: Boundaries for what actions can be taken autonomously
- Approval flows: When human oversight is required
- Performance targets: Metrics for evaluating effectiveness
Real-world applications include:
- Contract Automation tools that streamline agreements while ensuring compliance
- Deal Intelligence systems that enhance sales workflows
- Marketing Optimization platforms that adjust campaigns within brand guidelines
4. Orchestration Layer
The core engine coordinating all AI systems and data flows, where leaders guide:
- AI Orchestration: How different AI systems interact
- Priorities: What business objectives take precedence
- Learning loops: How systems improve through experience
Real-world applications include:
- Business Operations Hubs that centralize AI-powered processes
- Real-time Optimization systems that continuously improve workflows
- Knowledge Graphs that connect organizational data for improved decision-making
Why the Cognitive Stack Matters Now
According to Gartner over 85% of AI projects fail to deliver business value. Meanwhile, billions in venture funding chase increasingly marginal improvements in foundation models. The cognitive stack addresses these failures through:
The Model Commoditization Inflection: Scott Belsky notes, “AI researchers far smarter than I am have stated that the race to get the best ‘pre-trained’ models is slowing… the game shifts to the inference/reasoning layer.”
Competitive Advantage Shift: Value is moving from model builders to strategic implementers. Anthropic’s research finds “the most successful implementations weren’t using complex frameworks or specialized libraries. Instead, they were building with simple, composable patterns.”
Business Model Transformation: The cognitive stack enables outcome-based pricing over software seats, and intelligence over tools. McKinsey’s research shows AI-native companies achieve 3-5x the efficiency of traditional organizations.
Why G2C Ventures is Focused on the Cognitive Stack
At G2C Ventures,we’re concentrating our investments here because:
Clear thesis: The greatest value in AI will come from architecting complete systems that deploy AI for business outcomes, not from incremental model improvements.
Pattern recognition: Like cloud computing transformed IT, the cognitive stack is revolutionizing organizational design. Early adopters will gain fundamental advantages.
Optimal timing: We’re at the inflection point where foundational technologies are mature enough to build on while implementation patterns are still emerging.
Talent migration: Top technical and business talent is moving from model-building to implementation-focused ventures signaling the next innovation wave.
In Part 2 of this series, we’ll explore each layer of the cognitive stack in depth with specific examples of companies successfully implementing these concepts and delivering tangible business value. We’ll also discuss our investment approach for each layer and how founders can build credibility in this space.
G2C Ventures backs founders building AI-native companies. We support entrepreneurs in creating solutions that put business leaders in control of AI systems that transform competitive advantage.