TL;DR
Every enterprise is moving toward AI solutions
But there are two fundamentally different ways to deploy them
One relies on external, service-heavy engineering teams
The other uses AI systems to deploy AI itself
That difference defines speed, cost, and scalability
Over the past year, enterprise AI spending has exploded.
Boards are asking for AI strategies, budgets are being approved, teams are actively trying to move from pilots to production.
The question is no longer “Should we build AI solutions?” That’s already decided. The real question is: How are those solutions deployed?
This discussion is specifically about enterprises that choose not to build everything in-house. While some organizations do develop AI internally, many of them rely on external solution providers to move faster and reduce execution risk.
Model 1: Forward-Deployed Engineers
Today, most enterprise AI solutions are delivered through external solution providers.
Firms like Palantir follow this model.
Once a use case is defined, they deploy forward-deployed engineering teams into the enterprise to build and integrate the solution.
These teams typically:
clean and structure enterprise data
integrate across multiple systems and APIs
map workflows and business logic
build applications and interfaces
This approach works.
But it is fundamentally service-heavy.
It depends on large teams, it takes months to deploy, and every new use case requires repeating the same process.
Model 2: Forward-Deployed Agents (Meta Agents)
A different approach is now emerging.
Instead of relying on external engineering teams, the deployment itself is handled by AI systems.
At Zenera, this is how we approach it.
We use what we call forward-deployed agents (internally powered by our platform) to automate the work traditionally done by these engineering teams.
The system ingests enterprise documentation, APIs and data sources, and workflow requirements
And then automatically:
builds integrations across systems
constructs business logic
generates application layers and interfaces
deploys the complete solution
In other words:
Rather than just running AI, it builds the system that runs AI.
From Service Model → Agent Factory
This changes the deployment model entirely.
Instead of sending teams to build solutions manually, we create what we call an Agent Factory.
A system that can:
deploy solutions with minimal human effort
reduce implementation time from months to weeks
extend or modify workflows without restarting from scratch
It trumps over the Palantir model of forward deployed engineers in every way:
Time to value: Solutions move from idea to production much faster.
Cost structure: Fewer engineers → significantly lower deployment cost
Scalability: Each new solution becomes easier, not harder, to build.
The companies that win will be the ones choosing the right deployment model.
That’s the shift I am betting on.
If you’re thinking about how to move your AI initiatives from pilot to production, happy to compare notes.
Signing off,
Ramu Sunkara
Co-founder,
CEO at Zenera AI