Knowledge is hard to find
People search across approved documents, policies, notes or technical records to answer recurring questions.
AI & INTELLIGENT AUTOMATION
Use AI where it can improve a real process and keep people in control of the result.
MiCiS helps you find practical uses for AI and prepare the data and access it needs. We implement assistants, document processing and intelligent workflows that fit how your teams work. The focus is not AI for its own sake; it is a defined outcome, a reviewed operating model and a supportable path after launch.
Designed around approved data, access boundaries, human review, testing and documented ownership.
AI operating model
Human-led
From business context to reviewed action
Approved inputs
People + requests
Questions, email, tickets, forms
Approved knowledge
Documents, policies, records, data
Business tools
Microsoft 365, platforms, workflows
MiCiS AI layer
Retrieve · classify · draft · recommend
Context, instructions, permissions, model choice, tool boundaries and confidence
SearchExtractDraftAct
Review gateCheck evidence, confidence, policy and the action permitted
Accountable
Controlled outcomes
Answer with contextProvide a response with supporting sources where available
Prepare the next actionDraft, classify, summarise or recommend for review
Update after approvalTrigger a permitted workflow or system action
Outcome
Use case before model
Define the task, user, baseline and result that matters.
Context
Approved data + access
Use only the information, identities and tools agreed in scope.
Oversight
Human review where needed
Keep decisions, exceptions and actions visibly accountable.
Lifecycle
Measured + maintained
Evaluate quality, cost, exceptions, adoption and later change.
WHERE AI ADDS VALUE
The best AI opportunities begin with repeated work, not a request to “add AI”.
The useful question is not whether AI can be added. It is whether a repeated task involves enough searching, reading, classifying, drafting or follow-up to justify a different operating path. MiCiS maps the task, the people involved and the acceptable result before selecting a platform or model.
Start with the task and a baseline
Capture frequency, handling time, quality expectations, risk, existing systems and the point at which a person must review or decide.
Common AI opportunities
Signals that a task may benefit from intelligent assistance
Documents need repeated reading
Forms, invoices, reports, correspondence or attachments are manually reviewed and re-keyed.
Drafting follows a pattern
Responses, summaries, updates or internal notes begin from the same context and structure.
High-volume items need triage
Tickets, messages or records must be classified, prioritised or routed before work can begin.
Context is spread across systems
A person assembles information from Microsoft 365, business platforms or operational records before deciding.
Follow-up is easy to miss
A useful result still depends on a task, approval, notification, exception or documented next owner.
FROM REQUEST TO REVIEWED OUTCOME
AI should assist the process without hiding how the result was produced.
Generating text is only one part of the operating path. The design also needs to control what context is available, which tools may be used, how unsupported answers are handled and who remains responsible for the final decision or action.
- Ground the context. Use approved sources, identities and permissions rather than an unrestricted information pool.
- Constrain the action. Define what the assistant may draft, recommend, retrieve or trigger—and what it may not.
- Keep review and evidence visible. Provide a human checkpoint, exception path and enough context to verify the result.
Illustrative AI control path
Example only
A question, document or event turned into a reviewed next step
Inputs
01User requestQuestion, email, ticket, form or instruction
02Approved contextDocuments, records, policies or system data
03Operating rulesIdentity, permissions, limits and review requirements
Retrieve relevant contextFind permitted information and retain the source relationshipGround
Generate, classify or extractApply the defined instruction and format for the taskAssist
Check policy and confidenceApply thresholds, validation and an exception or refusal pathCheck
Human review or approvalConfirm the answer, decision or permitted system actionOwn
Outcomes
01Answer with supporting contextMake the basis of the response easier to review
02Create a draft or recommendationPrepare work while a person remains accountable
03Trigger an approved workflowCreate a task, update or notification within scope
Actual models, connectors, data sources, thresholds and review requirements depend on the platform, information sensitivity and agreed scope.
WHAT MICIS CAN ADVISE, DESIGN AND IMPLEMENT
The right solution may be Copilot, an assistant, document intelligence or an AI-enabled workflow.
The right output depends on the task, the available data and the operating controls required. MiCiS can combine Microsoft platforms, supported AI services, workflow automation and custom integration where that creates a clearer and more supportable result.
AI readiness and use-case discovery
Map candidate tasks, users, data, permissions, risk, baseline effort and the point where human judgement remains essential.
Typical outputPrioritised opportunity map
Microsoft 365 Copilot adoption
Review licensing context, information access, governance, user scenarios and the practical changes needed around Microsoft 365.
Typical outputGoverned adoption plan
Knowledge assistants and enterprise search
Use approved content and permission-aware retrieval where supported so staff can find context and review the source behind an answer.
Typical outputFaster access to trusted context
Document intelligence and data extraction
Read supported forms, invoices, reports, correspondence or attachments and present structured information for validation and routing.
Typical outputReviewable structured information
Intelligent workflow automation
Combine classification, summarisation, drafting or extraction with approvals, tasks, notifications and exception paths.
Typical outputA clear next action
Custom AI assistants and integrations
Connect supported model capabilities to business platforms, APIs, portals or custom modules with documented tool and data boundaries.
Typical outputFit-for-purpose AI capability
HOW AI WORK IS DELIVERED
A controlled path from opportunity discovery to measured operation.
AI work is safest when the user, task, information sources, evaluation cases, human review and support ownership are explicit before the first live release.
Delivery path
Five stages with an agreed decision at each point
- 01
Define the task and success measure
Confirm the user, current handling, acceptable output, risk, frequency and measurable baseline.
OutputUse-case brief + baseline - 02
Prepare data, access and controls
Confirm sources, permissions, retention, model options, tool access, review and fallback requirements.
OutputContext + control plan - 03
Prototype and evaluate
Build the smallest useful version and test representative, difficult, incomplete and out-of-scope cases.
OutputEvaluated prototype - 04
Pilot with the people doing the work
Use a controlled group, collect evidence, refine instructions and confirm the review and escalation path.
OutputApproved operating model - 05
Operate, measure and improve
Document ownership, support, cost, logging, review cadence and a controlled backlog for later change.
OutputHandover + improvement path
RESPONSIBLE OPERATION
Useful AI needs more than a model and a prompt.
The long-term risk in an AI solution is often not the first demonstration. It is a permission change, stale source content, a model or vendor update, an unreviewed action, rising cost or a result that people trust more than the evidence supports. MiCiS designs the operating context as well as the technical capability.
- Known data boundariesApproved sources, identities, permissions, retention and sensitivity.
- Explicit human responsibilityWho reviews, approves, acts, escalates and accepts the outcome.
- Observable performanceEvaluation cases, exceptions, usage, cost, feedback and later change.
Responsible operation checklist
AccountableThe operating controls documented with the AI solution
01Approved data sources and content ownersWhat information may be used and who maintains itDefined
02Identity, permissions and information boundariesWho can ask, retrieve, see or initiate each functionControlled
03Instructions, prompts and tool limitsWhat the assistant is expected and permitted to doMapped
04Evaluation cases and acceptance criteriaHow useful, incomplete, difficult and unsafe outcomes are checkedTested
05Human review, escalation and fallbackWhat happens when confidence, evidence or scope is insufficientOwned
06Logging, cost, support and change pathHow usage, exceptions, vendor change and later releases are managedObserved
The depth of controls is proportionate to the information, decision impact, system access and operating model in scope.
COMMON STARTING POINTS
AI is most valuable when it is connected to a clear operating task.
These are common patterns, not fixed bundles. The task, platform, information sources, review point and responsibilities are confirmed for each engagement.
Help people find answers across approved business knowledge.
Use policies, procedures, technical documents or operating records where supported so staff can retrieve context and review the source.
Useful outcomeLess time searching for trusted context.
Summarise, classify and prepare the next response while a person remains accountable.
Assist with tickets, calls, messages, notes or customer updates using the context and escalation path agreed for the workflow.
Useful outcomeA clearer first response and next owner.
Turn repeated reading and re-keying into a reviewable data path.
Extract and classify supported invoices, forms, reports, memberships, quotes or correspondence before validation and routing.
Useful outcomeLess repeated handling between documents and systems.
Turn records and events into summaries, exceptions and owned next actions.
Combine structured data and narrative context to prepare recurring updates, identify exceptions or create review tasks.
Useful outcomeMore visible exceptions and follow-up.
FREQUENTLY ASKED QUESTIONS
Questions to settle before AI or intelligent automation begins.
A useful AI solution should be clear about the task, information sources, platform, evaluation, human review and ownership after release.
Data and permissions firstInformation sources, identity, licensing, vendor terms, security controls and tool access are confirmed before the technical approach is committed.
AI & automation FAQ
Answers before the first pilot begins
What types of AI work does MiCiS help with?
MiCiS can help assess use cases and implement supported assistants, Microsoft 365 Copilot adoption, knowledge retrieval, document extraction, classification, summarisation, drafting and AI-enabled workflows or integrations. The appropriate approach depends on the task, platform, data, licensing and controls required.
Is Microsoft 365 Copilot the right starting point?
Sometimes. It may be a practical fit where the task and information already sit within Microsoft 365 and the organisation has suitable licensing, permissions and information hygiene. Other use cases may need a workflow, a specialised service, a custom assistant or no AI at all.
Can an AI assistant use our internal documents?
Potentially, where the selected platform supports the required sources and permission model. MiCiS first confirms content ownership, access, sensitivity, freshness, retrieval method and whether the response can retain a useful relationship to the supporting source.
How is sensitive or confidential information handled?
Data handling depends on the chosen platform and scope. Identity, permissions, storage, retention, vendor terms, logging and system access are reviewed before implementation. Information that cannot be handled appropriately should remain outside the solution.
How do you reduce inaccurate or unsupported answers?
The design can use approved sources, constrained instructions, representative evaluation cases, confidence or validation checks, visible supporting context and a refusal, fallback or human-review path. No generative system should be assumed to be correct in every case.
Can AI take actions in other systems?
It may be possible through supported APIs, workflow tools or approved connectors. The permitted action, identity, fields, validation, approval, logging, exception handling and rollback or containment path must be defined for the specific system and process.
Who owns and supports the solution after launch?
Ownership is agreed for each engagement. The operating model can identify the business owner, content and data owners, platform administrators, human reviewers, support contacts, vendor dependencies and how later model, prompt, workflow or source changes are released.
Have a repeated task that takes too much searching, reading, drafting or follow-up?
Describe the task, the people doing it, the information involved and what a useful result would look like. MiCiS will help identify whether AI, automation, integration or a simpler process change is the right first step.