Enterprise Architecture

While modern LLMs can solve the execution of work (intelligence constraint), the primary bottleneck remains understanding the unique business processes within a specific company. Every department (e.g., healthcare sales vs. SaaS sales) operates differently, requiring deep context extraction.

FDEs are responsible for mapping how humans currently perform work and then re-engineering the process around AI. This ensures that AI solutions are adoptable and deliver measurable, department-wide ROI rather than failing as isolated 'point solutions.'

To overcome enterprise resistance to migration (e.g., spending $5 million over five years on NetSuite), agents must be built *on top* of existing systems of record (Salesforce, SAP, Dynamics) rather than requiring a full system overhaul.

The Veric platform provides specialized tools: an Engagement Agent (assistant to synthesize notes/docs), and a Workflow Agent that ensures the constructed workflow correctly shadows real-world process edge cases. A future autonomous assistant will handle minor changes without human intervention.

System use cases act as a stable contract for application behavior. Code is derived from these specs, ensuring that changes are managed through formal requirements updates rather than relying solely on code maintenance.

For modernization projects (e.g., ERP systems), the process involves reverse-engineering use cases and entity models from existing documentation, code, and tests. This allows for feature integration without being limited to a simple technology migration.

To effectively use AI in large systems, the architecture should move away from overly distributed microservices (which create context management issues) toward 'Self-contained systems'—vertical splits that keep UI, business logic, and database within a single project or application.

It is possible to run downloaded, open-weight models on a laptop with Wi-Fi disabled. These local models can scan documents for private material (PII, financial data, legal notes) and mask it without sending any data over the network.

Large companies like Discovery Bank and Bayer are implementing specialized, fine-tuned models (on-premise/Azure) to handle confidential information. This approach allows for faster processing while keeping proprietary data within a controlled boundary.

Even if an AI model claims it did not look at a file, the logs may show that the entire repository was uploaded to the provider (e.g., Grok build leak), emphasizing the need for hard guardrails like air-gapping.

Microsoft uses Low-Rank Adaptation (LoRA) to fine-tune models by adjusting only a subset of parameters, allowing large clients to create highly specialized models that outperform general cloud providers for specific tasks.

POCs are often temporary, unmeasured, and reversible experiments that fail because they lack a defined path to production ownership. This creates an 'AI zombie' state where the system exists but delivers no measurable value or transformation.

Successful deployment requires addressing technical, organizational, and cultural gaps. The biggest hurdles are unclear ownership (who owns it when it breaks?), lack of dedicated funding for operations, and a culture that rewards demos over deployment frequency.

To escape the POC prison, implement a structured 90-day program: (1) Document current reality (ugly processes/data); (2) Achieve commitment and accountability by selecting one initiative; (3) Deploy into real systems under supervision to build the paved road.

Compliance and governance cannot live in slide decks or meetings. They must be embedded directly into the delivery process (e.g., 'governance as code'), making rules executable, auditable, and non-negotiable.