Earlier this year, KnowledgeLake deployed AI-powered document intelligence at one of our largest Document Service Provider (DSP) customers that focuses on digitizing critical government records.
Within months, the system fundamentally changed how records were processed: What previously required 2,000 people is now handled by 200, with accuracy exceeding 99%.
The downstream impact was significant:
Their exact words were, “In no world is there a way we could internalize this function. We need KnowledgeLake as our partner.” This isn’t just a vendor relationship. It’s mission-critical infrastructure.
AI is more than a productivity tool. It’s the operational backbone that enables compliance, scale, growth, and efficiency simultaneously.
Why Most AI Conversations Miss the Point
For the past two years, most AI discussion has focused on interfaces—Chatbots and Copilots—wrapping large models around existing workflows. They demo well and feel magical.
But that's not where durable AI companies are built.
The next generation of category-defining AI businesses will emerge in less glamorous places:
In other words: AI where it actually matters.
From Novelty to Embeddedness
Most AI products today sit on top of workflows. The strongest ones are embedded inside them.
When AI is embedded in a core system—compliance monitoring, infrastructure operations, regulatory workflows—it stops being a feature and starts becoming part of how the institution functions.
Switching it out is no longer a product decision. It’s an operational risk decision.
That’s where defensibility forms.
We've been investing in digitizing critical infrastructure for years, with investments such as KnowledgeLake, Recycle Track Systems, RapidDeploy, Overhaul, 120Water, Seismos, and others, and have realized a common theme: The most valuable AI deployments are the ones that become foundational to how critical operations actually run.
In practice, that looks like:
Across each of these, AI isn’t a feature layered on top of a workflow. It’s embedded in the system itself, where reliability, trust, and integration matter more than novelty.
Why the Opportunity is Underappreciated
AI penetration in critical infrastructure and regulated operations is still in the single digits.
Compare that to:
This isn’t because regulated industries are slow. It’s because, until recently, the technology wasn’t reliable enough in environments where failure has real consequences.
You can tolerate 85% accuracy in an email subject line. You cannot tolerate it in healthcare records, building safety inspections, or financial compliance monitoring.
That’s changed.
Purpose-built AI systems with human oversight are now achieving 99%+ accuracy, not because the models are magic, but because the systems are designed with domain expertise, regulatory requirements, and operational reality from day one.
At the same time, global infrastructure replacement cycles—from the $1.2T U.S. Infrastructure Investment and Jobs Act to smart-city initiatives across Europe and Asia—are forcing a decision: Build dumb systems that will be obsolete in five years or embed intelligence that improves over time.
Soft Assets vs Hard Assets
I think about this opportunity in two buckets:
Soft Assets: Compliance, risk, monitoring, and workflow systems across government, financial services, legal, environmental, and healthcare contexts. AI here enables humans to operate at a scale that was previously impossible.
Hard Assets: Buildings, utilities, transportation, communications, and physical infrastructure. AI here must operate in noisy, imperfect environments: aging assets, partial connectivity, and operators who value reliability over novelty.
In both cases, the real challenge isn’t intelligence. It’s integration.
The Three Tests for Mission-Critical AI
To separate signal from noise, I use three questions:
True mission-critical AI passes all three.
What I Look for in Founders
The signals that matter here are different from traditional enterprise SaaS:
The strongest pitches don’t lead with TAM slides. They show operational data, scale advantages, incidents prevented, and audits passed.
Why I Spend My Time Here
Over the past 15 years, I’ve worked on $200B+ across investments, M&A, and IPOs. Different sectors. Different cycles. Different technologies.
But one pattern keeps repeating.
In regulated systems, real-time data environments, and mission-critical operations, the most enduring technology companies start by asking, “Where is failure unacceptable—and how can AI make this system safer, more resilient, and more trustworthy?”
That framing changes everything. It forces rigor. It demands integration. It attracts customers who care about outcomes, not demos.
These companies are harder to build. They are slower to scale. And they quietly outlast every hype cycle—because once embedded, they don’t get ripped out.
That’s why I’m here.
Looking Ahead
In this series, I’ll cover:
This is where AI stops being impressive and starts being indispensable.
I’ve spent years investing in this category, but the best insights always come from founders in the trenches, seeing customer behavior, regulatory dynamics, and deployment realities every day.