Rolling Up Our Sleeves: Implementation Reflections from the AIIM Global Summit

The AI+IM Global Summit, held March 31 to April 2 in Atlanta, focused on how artificial intelligence is being integrated with information management, governance, and process automation. AIIM brings together professionals responsible for building, implementing, and governing systems that manage enterprise content, drive decisions, and automate complex workflows.

The experimentation phase isn’t fully behind us — but we’re well into deployment. Some demos at the event still reflected early-stage design, but many represented real software doing real work. Classification, retrieval, redaction, summarization, routing — not as prototypes, but as operational components.

This is a familiar shift — the kind seen during the rise of RPA, IDP, and case management — though this cycle is moving much faster and demanding earlier clarity around system structure. The initial excitement gives way to what comes next: execution frameworks — the retrieval, memory, escalation, and audit layers that turn models into operational systems. It’s time to roll up our sleeves and do the detailed, often unglamorous work that makes these systems not only auditable and trustworthy, but actually efficient, scalable, and fit for real production use.

The sections that follow define the components of an execution framework: how to log decisions, govern retrieval, modularize workflows, trace execution, and manage inference as part of live system logic. This isn’t speculative. It’s implementation.

1. AI Deployments Are Real. Governance Controls Are Now the Priority.

AI is being embedded in production workflows. In production systems, model outputs are being used to drive decisions, trigger workflows, and initiate escalations — all under traceable, policy-defined conditions. As systems begin to make real decisions, traceability becomes essential.

Missing capabilities commonly include:

• No persistent lineage to show which document version was used, in what state, and under what context
• No structured logging of the full prompt — including what was retrieved and how the model responded
• No versioning of the retrieval index used at query time, allowing silent drift
• No durable connection between model output and downstream actions

These aren’t exceptions. Logging, retrieval versioning, and output linkage are required for any production system expected to scale.

What to enforce in production:

• Track retrieved documents with full version, state, and context
• Log complete prompt construction and model response
• Version retrieval indices at query time and store snapshot references in the inference log
• Record how model output triggered workflow actions, approvals, escalations, or other system events

2. Inference and Execution Are Now Intertwined. Logging Must Reflect That.

Inference is now part of the execution path. Model outputs affect how tasks are routed, labeled, escalated, or closed. Execution and inference no longer operate in separate spaces — and the logging must reflect that reality.

Systems that treat inference as an isolated layer miss critical connections. Failures in prompt logic or retrieval quality often appear downstream as workflow bugs. Without end-to-end instrumentation, there is no reliable way to debug or audit outcomes.

What to enforce in production

• Log inference inputs and outputs and bind them to resulting system behavior
• Instrument transitions between deterministic logic and inference-driven branches
• Test bidirectional failure paths: where inference corrupts execution, and where execution logic disrupts inference

3. Retrieval Is an Execution Surface. It Must Be Governed.

Retrieval-augmented generation is now common in enterprise AI systems. It grounds model output in internal content. But in most implementations, the retrieval layer is not treated as part of the execution stack — even though it shapes model behavior directly.

Common gaps include:

• Retrieval logic that ignores user context or task boundaries
• Index drift caused by content change or embedding degradation
• Lack of logging for retrieved inputs, making model decisions non-reproducible
• Poisoned or malformed content introduced into the index without validation

What to enforce in production:

• Apply retrieval filters based on user role and task scope
• Snapshot and version retrieval indices at time of query
• Store retrieved chunks with each inference log
• Validate and clean source content before indexing; block corrupted documents

4. Composable Architectures Are Holding Up. Monoliths Hide Failure.

Modular systems are performing better under pressure. Components with scoped responsibilities, defined interfaces, and clear escalation logic are easier to observe and recover. Monoliths fail without trace and propagate error silently.

Composability supports:

• Scoped prompts with well-bounded logic
• Retrieval tuned to business process and policy
• Confidence thresholds with fallback or escalation routing
• Explicit contracts between modules

These patterns are core components of an execution framework: modular prompts, scoped retrieval, testable logic, and escalation boundaries.

What to enforce in production:

• Break workflows into modules that can be tested and observed independently
• Route low-confidence outputs to deterministic fallbacks or human review
• Require validation and type-checking between components
• Log input/output at every module boundary

5. Multi-Agent Systems Are Emerging. Orchestration Requires Structure.

Multi-agent architectures are being explored across document understanding, reasoning, and task completion. But many implementations lack the structure required to make them stable and auditable.

Typical issues include:

• No formal memory boundaries between agents
• Untracked handoff of state or partial results
• No degraded-mode or escalation logic
• No intermediate logging between agent steps

What to enforce in production:

• Define shared memory schemas and agent roles with boundary enforcement
• Enforce statelessness or scoped memory where required
• Simulate degraded operation and test failure recovery
• Persist all intermediate agent actions and transitions

6. Traceability Is the Baseline. Not an Add-On.

Traceability is no longer optional. If a system cannot show what it did, why it did it, and how it produced an outcome, it cannot be governed.

Lack of trace leads to:

• Broken audit chains
• Unverifiable decisions
• Incomplete root cause analysis
• Regulatory exposure

What to enforce in production:

• Log the full execution path: user input, retrieval context, prompt, model response, system action
• Store logs in structured, queryable formats accessible to engineering and governance
• Make trace coverage a condition of deployment — not a postmortem patch

Conclusion: Execution Frameworks Define the Next Phase

These systems are already in use. They perform classification, retrieval, redaction, and escalation inside live workflows, under policy, with audit constraints. They are no longer pilots. They are infrastructure.

Like with RPA, IDP, and case management, once systems move into execution, they expose what’s missing. Static logs and informal routing aren’t enough. Execution requires structure: scoped retrieval, prompt construction, versioned indices, testable workflows, and traceable outputs. Every component must operate under control.

This is the execution framework — the operational layer that defines what the system sees, how it acts, and how each outcome can be explained.

What to enforce in production:

• Track document versions, retrieval scope, and access context
• Log prompt inputs, retrieved chunks, model outputs, and triggered actions
• Connect inference to routing, escalation, or approval behavior
• Modularize workflows with interface validation and fault containment
• Simulate degraded and edge-path behavior in agent orchestration
• Block deployments without complete, queryable trace logs

The model doesn’t define the system. The execution framework does.

This blog post was originally published on LinkedIn and republished with permission.