Questions every CIOis asking about AI.

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They are not equivalent alternatives. RAG provides knowledge; fine-tuning changes behavior. Use RAG for policies, documents, regulations, products, procedures and information that changes. Consider fine-tuning when enough examples demonstrate specialized, relatively stable behavior. In critical processes, both still need to coexist with deterministic rules, tools and evaluation.

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When domain requirements, cost, latency, privacy, data residency, scale or behavior economically justify it. Don't build a model merely to call your AI proprietary. The more strategic ownership lies in knowledge, capabilities, rules, data, evaluation and the architecture that allows models to be selected or replaced.

Short answer

Decompose it. Some should become retrievable knowledge; some deterministic rules; some capabilities; some tools; some memory; and only a portion may justify model adaptation. The goal isn't to put the entire enterprise inside an LLM. It's to build a Domain Intelligence layer that combines knowledge, rules, data, tools and models at execution time.

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Use agents when execution requires contextual interpretation, dynamic action selection, tool use, adaptation or coordination across steps. If inputs and rules are predictable and fully deterministic, conventional software or workflows are probably better.

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When there is genuine separation of expertise, context, responsibility or policy. Creating five agents to execute five sequential prompts is not good architecture. Multi-agent systems make sense when specialized capabilities can be independently discovered, composed, evaluated and governed.

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Autonomy should be proportional to risk, reversibility and confidence. Reading data may be automatic. Producing a recommendation may also be automatic. Making a payment, changing a medical record, imposing a sanction or creating contractual liability may require approval. The right design isn't “human or AI”; it's determining where human judgment creates value or controls risk.

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Don't start with document volume or headcount. Analyze effort, repetition, variability, decisions, required knowledge, exceptions, handoffs, current quality, data availability and risk. Then classify each activity as eliminate, automate, augment with AI, keep human or redesign.

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No. Start with processes tied to measurable objectives. And don't document only the formal flow: capture decisions, exceptions, rules, systems, knowledge and informal work. Builder uses that understanding to move from AS-IS to TARGET and turn activities into executable capabilities.

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Not automatically. Many steps exist because people and legacy systems had limitations that AI doesn't have. First ask which steps protect risk, which create value and which are merely historical bureaucracy.

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Not necessarily. AI needs governed access to the right context, not an indiscriminate copy of every dataset. Data can remain in source systems and be accessed through queries, APIs, events, tools or appropriate pipelines.

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Decouple AI from system-specific protocols. Encapsulate systems through APIs, drivers, manifests, events and tools. The AI layer should depend on the required capability, not on internal ERP or mainframe details.

Onni implements this principle through manifest + driver and an integration bus that centralizes queries, commands and events.

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No. MCP standardizes how models and agents discover and use tools and resources. APIs remain fundamental system-to-system contracts. In enterprise architecture, MCP can expose a capability to an agent while an API, event or driver performs the underlying integration.

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Control sensitive data before the model call, not merely through prompting. Detect and pseudonymize data, enforce access policies and rehydrate only at the authorized boundary when necessary. Onni's reference design applies DLP before the LLM call and rehydration at the tool boundary.

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Separate building from publishing. Teams should experiment quickly; production requires identity, versioning, declared capabilities, policies, evaluation, permissions, observability and ownership. Governance stops being a committee reconstructing what happened and becomes part of execution.

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Record not only the final response but the relevant context: identity, capability, version, model, applied policies, sources, tools executed, human decisions, timestamps, costs and evidence. The architecture should answer not only what happened, but why it happened.

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Start before implementation. Establish baselines for time, cost, quality, volume, rework and human decisions. Then compare the new operation. Hours saved are an operational metric; they become economic value only when they increase capacity, reduce cost, avoid losses or improve a business outcome.

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Because proving that a model can answer well is different from operating an enterprise capability. Production requires integration, ownership, security, evaluation, observability, exception handling, fallback, cost control, SLAs, governance and continuous operations.

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The right question is rarely binary. Buy commodity and specialized technology where it makes sense. Build what differentiates the business. Integrate both. And preserve as enterprise assets the capabilities, knowledge, policies, evidence and architecture required to keep evolving.

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Don't try to eliminate vendors; eliminate unnecessary dependencies. Separate model, knowledge, capability, agent, tool, integration and process. The more your processes depend on contracts and capabilities rather than specific endpoints, the easier technologies become to replace.

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AI sovereignty is the ability to control and preserve institutional intelligence independently of changing models and vendors. It includes knowledge, data, rules, capabilities, integrations, policies, identity, decisions, evidence and architecture.