The Enterprise AI Paradox
Here's a puzzle that should concern anyone pursuing enterprise AI:
Nearly 80% of companies report using generative AI. An equal number report no significant bottom-line impact. Another study shows that despite $30-40 billion in enterprise investment, 95% of organizations are getting zero return.
This isn't a capability problem. Foundation models can reason, write, and analyze at near-human levels. And generative AI is delivering real value at the individual level — copilots accelerate email drafting, chatbots handle routine inquiries, code assistants reduce development cycles. These gains are meaningful.
But task-level productivity improvements won't compound into enterprise-scale transformation. That requires moving from individual tools to autonomous systems — agentic AI that can automate entire workflows, orchestrate across systems, and deliver the exponential gains that justify the investment.
That's where things break down.
The Architecture Problem
The answer comes down to how current agentic systems are built.
Current approaches (whether pre-built agents or DIY frameworks) share a fundamental flaw: they require organizations to predict what employees need before observing how they actually work. Design predates discovery, when it should be the other way around.
- Pre-built agents assume generic workflows that don't reflect how individuals actually operate.
- DIY frameworks ask time-starved employees and already-stretched IT teams to self-assemble their agentic future, requiring deep AI expertise and bandwidth that most organizations simply don't have.
Both approaches can't bridge the gap between individual work patterns and enterprise-wide deployment. They operate outside workflows rather than embedded within them.
McKinsey estimates agentic AI could unlock $2.6-$4.4 trillion in annual productivity gains. Yet 60% of CEOs remain stuck in the piloting phase, well behind where they expected to be. The gap between projected value and actual returns won't close without reimagining how we build agentic systems.
What We're Building: Behavioral Agent Automation Platforms
The solution requires a fundamentally different approach — one that observes before it automates, learns before it deploys, and adapts continuously to how work actually happens. That's the foundation of Behavioral Agent Automation Platforms (BAAPs), the next architectural evolution of enterprise agentic AI we're building.
A BAAP is a horizontal, adaptive automation layer that inverts the traditional sequence entirely. Instead of requiring organizations to predict workflows upfront, the Liminal Platform:
- Observes how people actually work
- Identifies automation opportunities from real behavioral data
- Assembles agentic capabilities autonomously
- Deploys solutions that solve verified problems without manual configuration
- Improves continuously by learning from outcomes and adapting to changing work patterns
This shift — from prediction to proof — changes everything. Rather than requiring IT teams to guess which workflows to automate or employees to assemble multi-step automation sequences, the system learns from actual behavior and adapts continuously.
This creates transformation from the ground up. As individuals work, the platform automatically identifies friction and opportunity, deploys solutions, and learns from outcomes — all without manual intervention. Those individual improvements aggregate into organizational intelligence, turning everyday work patterns into enterprise-wide automation without requiring top-down workflow design or specialized technical expertise and bandwidth. This is how organizations move from limited returns to realizing AI's transformational value at scale.
Building From Our Proven Foundation
We've spent the past several years building the foundation for Behavioral Agent Automation Platforms: sensitive data protection, comprehensive governance capabilities, complete observability, secure workflow tooling, enterprise search, a behavioral insights engine. These aren't theoretical components: they're production systems helping customers in some of the most regulated industries in the world safely leverage AI.
What we're building now is the intelligence layer that unifies these components into an automation platform that observes, learns, and autonomously deploys agentic capabilities, enabling truly adaptive automation at enterprise scale.
Organizations using Liminal today are building the foundation for their own adaptive automation. Every secured workflow, every observed interaction, every identified pattern creates the behavioral data and organizational memory that seeds automation opportunities. By capturing this organizational intelligence now, they're proactively enabling the platform to identify the highest-impact opportunities to address as the system fully evolves its adaptive automation capabilities.
Explore Behavioral Agent Automation
We know AI delivers when it meets people where they are, and that's the principle behind Behavioral Agent Automation. BAAPs adapt to how employees actually work, surface automation from observed behavior rather than relying on predicted workflows, and enable enterprise transformation that builds from the ground up—without requiring organizations to redesign how they operate.
To learn more about Behavioral Agent Automation Platforms, explore our BAAP resource page or check out our comprehensive foundational paper.