# Make AI work with the systems your business already runs. > The hard part of enterprise AI is rarely finding a capable model. It is connecting that model to the right information, the right actions and the right safeguards, inside an organization built from existing software and established responsibilities. Canonical URL: https://marmation.com/solutions/enterprise-ai-systems/ ## The decision How will AI help employees finish work across the systems you already run? Connect approved company information to a specific employee task. Keep source permissions, business rules and required approvals in place so a useful answer cannot become an unauthorized change. ## An organization is not a single prompt. Information lives across documents, tickets, enterprise applications, spreadsheets, messages and the judgment of people doing the work. Some of those sources should be broadly accessible. Others are confidential to a team, a customer or a role. A workflow may cross multiple systems of record before a decision is ready. An internal AI pilot often works beautifully with five clean documents and one user. The enterprise version must cope with outdated material, conflicting records, revocations, unusual inputs, incomplete integrations and employees with different permissions. We design for that reality. The question is how to make one important job work end to end, without creating a new system that is less governable than the ones it was meant to improve. ## What that can involve ### Knowledge applications with real access boundaries Search, retrieve and explain approved company material. A useful knowledge assistant respects source permissions, retains relevant citation links, distinguishes current from superseded documents and can say when evidence is insufficient. We evaluate commercial enterprise search before proposing custom retrieval-augmented generation (RAG), which supplies a model with relevant source material. ### Intelligent workflow applications Some processes require extraction, classification or reasoning before deterministic actions can occur. The complete system includes inputs, checks, approvals, integrations, exception queues, retries and audit history. AI assists a bounded part of the work rather than operating without oversight by default. ### Private and controlled AI environments A company's definition of 'private' may concern training terms, data retention, region, network path or full inference control. Those requirements point to different architectures. We assess approved enterprise APIs, private-cloud endpoints and, where justified, licensed open-weight model hosting. ### Custom operational systems and integration Keep established ERP and CRM platforms where they remain useful. Build the missing workflow around them. An integration layer or focused operations application may solve the problem with less cost and risk than replacement. ## Consider the buy option first Microsoft Copilot Studio, Glean, managed knowledge bases and enterprise workflow tools can solve substantial use cases. A custom application should earn its cost. The justification may be unique permissions, deep workflow integration, nonstandard user experience, deployment restrictions, differentiated business logic or model/provider flexibility. Technology independence means being comfortable with a result that is a configuration or integration, not a new codebase. ## Example system: company knowledge with actions An employee asks how to handle an unusual supplier exception. The application retrieves policy and account context the employee is authorized to access, cites those sources and proposes an action. The employee reviews the proposal; a business rule verifies the selected step; an approved API updates the operations system; a log records the decision and source versions. The model's job is constrained. Identity controls determine what data it sees. Deterministic rules and human approval govern what the business actually does. The system has a defined response when the knowledge is incomplete or the action fails. ## Architecture and quality decisions Early work should establish where information comes from, how users sign in and how source access changes reach the application. Agree what the model may read, where data is processed and stored, how pending and completed work is tracked and who may approve changes. Plan how outputs are evaluated, what gets logged and what the system will cost to run. If these are unknown, a broad build estimate is not credible. Acceptance tests should include a request the system can answer, a request it should refuse, a user without sufficient permission, an outdated document, a failed integration and a task requiring explicit human approval. ## The outcome we are pursuing A system people can use within an approved environment, with an understandable scope, a documented architecture and a maintenance path. Not a promise that AI is always correct or that every workflow can be made autonomous. ## Related reading - [Retrieval cannot be allowed to widen a user's permissions.](https://marmation.com/expertise/permission-aware-rag/) - [Private AI doesn't mean training your own model.](https://marmation.com/review/private-ai-does-not-mean-owning-a-model/) - [Human approval is a system design decision, not a concession.](https://marmation.com/review/human-approval-in-ai-workflows/)