This is the second part of our series on the Cognitive Stack. Read Part 1 here for an introduction to the concept and why it matters for enterprise AI.
In Part 1, we explored the four layers of the cognitive stack (Perception, Reasoning, Action, and Orchestration) and why this framework represents the future of AI-native companies. Now, let’s examine how these layers drive measurable business outcomes and where we see the most promising opportunities for founders.
Each layer of the cognitive stack delivers distinctive value that compounds when implemented together. Let’s look at the real-world impact we’re seeing:
The Perception layer transforms unstructured information into actionable insights, delivering remarkable results:
This layer solves a fundamental enterprise problem: most company data sits idle and underutilized. By implementing perception technologies, businesses can finally harness their dormant information assets, turning unstructured data into structured insights.
The Reasoning layer’s ability to make complex decisions in real-time creates substantial advantages:
What makes the reasoning layer so powerful is its ability to transform organizational knowledge into automated intelligence. Instead of knowledge being trapped in the minds of individual experts, AI systems can apply it consistently across thousands of decisions.
The Action layer creates immediate ROI through automated execution:
This layer delivers the most tangible and immediate value, as it directly affects operational efficiency by automating routine tasks that previously consumed significant human resources.
The Orchestration layer, perhaps the most transformative, drives enterprise-wide performance:
Orchestration represents the true promise of AI in the enterprise: not just automating individual tasks but creating intelligent, adaptive systems that coordinate across departments and functions.
The cognitive stack isn’t just a theoretical framework—it’s a blueprint for building the next generation of enterprise software. The most exciting aspect is how early we are in this journey, creating vast opportunities for ambitious founders.
At G2C Ventures, we’re actively seeking founders building:
1. Multimodal Understanding Platforms
Systems that can simultaneously process text, images, audio, and video to create richer understanding. The enterprise is awash in diverse data types, and founders who can help businesses make sense of all these inputs simultaneously will capture enormous value.
2. Domain-Specific Knowledge Extraction
Technologies that extract industry-specific insights from unstructured data. While general AI systems provide broad capabilities, solutions tuned to specific domains (healthcare, legal, financial services) can deliver dramatically better results by understanding specialized terminology and contexts.
3. Real-Time Signal Processing
Solutions that monitor, filter, and categorize information streams to identify critical signals. The overload of information makes it nearly impossible for humans to spot important developments amidst the noise—creating an opportunity for AI perception systems that highlight what matters.
4. Synthetic Data Generation
Tools that expand limited training sets with realistic synthetic data, particularly for rare events or sensitive information. This is especially valuable in regulated industries where real data is scarce but the need for robust AI systems is high.
The reasoning layer is rich with investment opportunities, including:
1. Explainable Decision Systems
Solutions that not only make recommendations but explain their reasoning in human-understandable terms. As AI plays a larger role in consequential decisions, the ability to understand why a recommendation was made becomes critical for adoption and trust.
2. Rules-Based Configurability
Platforms that allow business leaders to embed their own rules and constraints into AI reasoning. Enterprise leaders need to maintain control while leveraging AI capabilities—founders who enable this balance will find eager customers.
3. Dynamic Resource Optimization
Systems that continuously optimize allocation of resources (budget, personnel, inventory) based on real-time conditions. Static resource allocation doesn’t work in today’s dynamic business environment, creating an opening for adaptive AI systems.
4. Decision Intelligence Platforms
Tools that improve decision quality by combining human expertise with AI capabilities. The most powerful systems will augment human decision-makers rather than replace them, combining the best of both worlds.
The action layer offers clear paths to delivering measurable ROI:
1. Enterprise Process Automation
End-to-end solutions that handle complex multi-step processes autonomously. Moving beyond simple RPA to truly intelligent process automation represents a massive market opportunity.
2. Contextual Communication Systems
AI agents that can engage in natural, context-aware communications with customers and employees. The ability to understand and respond appropriately to nuanced requests is a fundamental requirement for scaling interactions.
3. Compliant Workflow Automation
Tools that automate regulated processes while ensuring compliance with industry requirements. Regulated industries face unique challenges in adopting AI, creating opportunities for specialized automation solutions.
4. Physical-Digital Integration
Platforms connecting AI systems to physical operations through IoT, robotics, and sensor networks. As AI extends beyond the digital realm, founders who can bridge this gap will enable new categories of automation.
Perhaps the richest territory for new venture creation is the orchestration layer:
1. Multi-Agent Coordination Platforms
Systems that manage cooperation between specialized AI agents to accomplish complex goals. As enterprises deploy more AI capabilities, the need to coordinate them becomes critical.
2. Human-AI Collaboration Frameworks
Tools that seamlessly integrate human expertise with AI capabilities within workflows. The future isn’t AI replacing humans but a new paradigm where each amplifies the other’s strengths.
3. Enterprise Knowledge Orchestration
Solutions that connect and activate knowledge across organizational silos. Most enterprises struggle with fragmented information systems—founders who can unify these into coherent knowledge networks will create tremendous value.
4. Cross-Enterprise AI Coordination
Platforms that enable AI agents from different organizations to work together securely. Supply chains, healthcare networks, and financial systems all require coordination across organizational boundaries.
If you’re a founder looking to build in this space, here are five principles that will increase your chances of success:
1. Start With a Clear Business Problem
The most successful cognitive stack ventures aren’t built around technology for technology’s sake—they solve specific, painful business problems. Identify an existing function where:
Remember: enterprises don’t buy AI; they buy solutions to business problems. Frame your vision in terms of outcomes, not capabilities.
2. Focus on a Single Layer Initially
While the full cognitive stack creates compound advantages, trying to build across all layers simultaneously is a recipe for failure. Instead:
This focused approach allows you to create a wedge into the enterprise that can grow over time.
3. Design for Business Leader Control
Enterprise leaders won’t adopt solutions they can’t understand or control. Build your product with clear mechanisms for business leaders to:
The most successful cognitive stack startups put business leaders in control rather than creating black-box systems.
4. Build Data Moats Through Learning Loops
The defensibility of cognitive stack companies comes from accumulating proprietary data and insights over time. Design your product with:
These learning loops create increasing returns to scale that make your solution more valuable with each customer and interaction.
5. Create Clear Paths to Enterprise Integration
Enterprise systems aren’t built on greenfield sites—they must integrate with existing infrastructure. Prioritize:
Founders who understand the realities of enterprise integration can move from pilots to production much faster than those who don’t.
At G2C Ventures, we’re actively seeking founders building across all four layers of the cognitive stack. Our investment thesis centers on backing teams that:
We believe the greatest value in AI will accrue not to model creators but to those who apply AI capabilities to transform how enterprises operate. The cognitive stack provides a framework for identifying these opportunities.
Our approach goes beyond capital. We work closely with our portfolio companies to:
The cognitive stack represents the future of how enterprises will operate. We’re still in the early innings of this transformation, creating a once-in-a-generation opportunity for founders who can execute effectively.
If you’re building solutions that enable any layer of the cognitive stack, we want to hear from you. The companies that will define the next decade of enterprise software are being formed right now, and we’re committed to backing the ambitious founders creating this future.
Unlike previous waves of enterprise technology, the cognitive stack isn’t just about efficiency—it’s about fundamentally transforming how companies operate, compete, and create value. By enabling businesses to perceive more accurately, reason more intelligently, act more consistently, and orchestrate more effectively, cognitive stack companies can drive unprecedented performance improvements.
The question isn’t whether enterprises will adopt the cognitive stack, but which founders will build the platforms that enable this transformation. We believe the opportunity is now, and we’re excited to partner with the entrepreneurs who will make it happen.
G2C Ventures backs founders building AI-native companies. We support entrepreneurs creating solutions that put business leaders in control of AI systems that transform competitive advantage.
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