IA Empleado
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PROCESS ANALYZER · IA EMPLEADO

See your process before and after coordinating it with AI Employees.

Choose a reference workflow, mark where time or context is lost and compare current work with an indicative proposal that separates automation, assistance and human decisions.

Analyze a processDesign an AI Team

BEFORE → AFTER

Find where to automate, assist or keep human control

The proposal does not try to automate everything. Every step keeps a visible classification and explains which role, system and control point is involved.

1. Process you want to review

Start with the pattern closest to your current operation.

2. General bottlenecks

Select the problems that appear most often today. You can also mark specific steps in the current flow.

TriggerA customer reports a problem by email or chat.
OutcomeFinal response with the case updated and consequential decisions traceable.
01

How it often works today

Mark any step that creates waiting, errors, rework or context loss in your organization.

  1. 01Support team

    Receive and classify

    A person opens the message, interprets the reason and creates or finds the case.

  2. 02Support

    Find customer and order

    The agent jumps between CRM, ecommerce and ERP and copies data into the ticket.

  3. 03Support + Back office

    Ask another department to review

    Support forwards information to billing or operations and waits, often without a shared structure.

  4. 04Human owner

    Decide compensation or cancellation

    A person reviews terms, amount and exception before authorizing a consequential action.

  5. 05Support

    Reply and close

    Support reconstructs the story, writes the reply and updates several records.

02

How it could be reorganized

Indicative proposal based on bounded responsibilities, explicit handoffs and human approval when impact requires it.

  1. 01Rule-based automation candidate

    Classify and open context

    Customer Support AI identifies intent and links the request to the correct case using approved sources.

    Customer Support AI
    Control: Escalate when customer or request identity cannot be established reliably.
  2. 02Rule-based automation candidate

    Query approved sources

    Required context is retrieved from permitted systems and attached to the case without manual copy-paste between screens.

    Customer Support AIOrder Management AI
    Control: Do not assert a status that no authoritative source confirms.
  3. 03AI prepares / person or policy validates

    Structured handoff to the specialist

    The case moves to the specialist role with the minimum required context and an explicit expected result.

    Customer Support AIAccounting & Billing AIOrder Management AI
    Control: Policy defines what data may travel between roles and which exception requires human review.
  4. 04Human responsibility

    Keep human authority

    AI prepares evidence and a proposal, while compensation, out-of-policy cancellation or sensitive amounts remain under human approval.

    Accounting & Billing AI
    Control: A person retains the decision and the system records approval before execution.
  5. 05Rule-based automation candidate

    Communicate outcome and record

    Customer Support AI retrieves the approved outcome, prepares the response and updates permitted history to close the case.

    Customer Support AI
    Control: Sensitive or ambiguous messages can remain subject to review before sending.

Quick redesign summary

Customer issue

You have not marked specific steps; the proposal uses the full reference pattern.

3Rule-based automation candidate
1AI prepares / person or policy validates
1Human responsibility

AI Employees involved

Customer Support AIOrder Management AIAccounting & Billing AI

Systems to evaluate

EmailTicketingCRMEcommerceERPBillingOrder management
Discuss this process

The analysis runs locally in your browser. The website does not store your process marks. The CTA opens your email client with a summary if you choose to share it.

METHOD

Not every repetitive step should become an autonomous action

The analyzer separates repetitive execution, assisted preparation and human responsibility so that improving speed does not remove necessary controls.

01

Rule-based automation

Repetitive, verifiable work that can execute inside explicit permissions, sources and conditions.

02

Assisted

AI gathers context, validates data or prepares an action while deterministic policy or a person confirms the consequential step.

03

Human

Negotiation, sensitive judgment, high-impact exceptions or reserved authority remain under human responsibility.

04

Traceable handoff

When ownership changes, the allowed context, expected result and authoritative system should be explicit.

EXPLAINED PATTERNS

Four reference processes, also rendered as crawlable HTML

Interaction helps comparison. The content below explains the same processes semantically for accessibility, SEO and generative engines.

Customer issue

A support request arrives and ends up involving an order, invoice or exception that may need approval.

OutcomeFinal response with the case updated and consequential decisions traceable.
  1. 01
    BeforeReceive and classify

    A person opens the message, interprets the reason and creates or finds the case.

    Rule-based automation candidateClassify and open context

    Customer Support AI identifies intent and links the request to the correct case using approved sources.

  2. 02
    BeforeFind customer and order

    The agent jumps between CRM, ecommerce and ERP and copies data into the ticket.

    Rule-based automation candidateQuery approved sources

    Required context is retrieved from permitted systems and attached to the case without manual copy-paste between screens.

  3. 03
    BeforeAsk another department to review

    Support forwards information to billing or operations and waits, often without a shared structure.

    AI prepares / person or policy validatesStructured handoff to the specialist

    The case moves to the specialist role with the minimum required context and an explicit expected result.

  4. 04
    BeforeDecide compensation or cancellation

    A person reviews terms, amount and exception before authorizing a consequential action.

    Human responsibilityKeep human authority

    AI prepares evidence and a proposal, while compensation, out-of-policy cancellation or sensitive amounts remain under human approval.

  5. 05
    BeforeReply and close

    Support reconstructs the story, writes the reply and updates several records.

    Rule-based automation candidateCommunicate outcome and record

    Customer Support AI retrieves the approved outcome, prepares the response and updates permitted history to close the case.

Invoice intake and validation

An invoice arrives by email or document, data is extracted, validated and prepared for posting or exception handling.

OutcomeInvoice prepared, validated or escalated with evidence and traceability.
  1. 01
    BeforeCollect invoice

    A person downloads attachments, renames files and decides where to store them.

    Rule-based automation candidateIngest and classify

    Administrative AI identifies the document, preserves its source and hands it to the accounting flow with basic metadata.

  2. 02
    BeforeType data

    Supplier, date, net amount, tax and total are manually copied into the system.

    Rule-based automation candidateExtract and normalize

    Fields are extracted and prepared in structured form for validation before any final posting.

  3. 03
    BeforeCheck invoice

    Duplicates, totals, supplier, due date and purchase or service reference are reviewed.

    AI prepares / person or policy validatesApply deterministic controls

    Accounting & Billing AI checks rules, duplicates and references and separates clean cases from exceptions.

  4. 04
    BeforeResolve discrepancy

    A person contacts the supplier or internal owner and decides how the exception should be handled.

    Human responsibilityKeep professional decision-making

    AI presents the discrepancy, evidence and context; a person resolves exceptions involving accounting, tax or authorization judgment.

  5. 05
    BeforeRecord and report

    After review, ERP is updated and information is then replicated into controls or reporting.

    AI prepares / person or policy validatesPrepare posting and traceability

    The system receives validated status and Reporting AI can reuse confirmed information without creating a second manual source.

Sales lead and follow-up

A commercial signal arrives, is researched, recorded and followed up until a person takes control when negotiation starts.

OutcomeContextualized opportunity, updated CRM and a clear next action.
  1. 01
    BeforeCapture signal

    The lead arrives from a source and someone manually creates or completes the CRM record.

    Rule-based automation candidateRecord source and context

    The signal is normalized and its source preserved before entering the sales process.

  2. 02
    BeforeResearch account

    The seller searches scattered information and decides which details to copy into CRM.

    AI prepares / person or policy validatesPrepare traceable research

    Sales SDR AI gathers permitted context, preserves source and prepares a summary for the next-step decision.

  3. 03
    BeforePrepare follow-up

    An email is written, context checked and follow-up scheduled across separate tools.

    AI prepares / person or policy validatesPrepare contact and next action

    The SDR prepares messaging and CRM; Email Manager links the reply to the correct opportunity and maintains continuity.

  4. 04
    BeforeCoordinate meeting

    Availability is negotiated by email and calendar and CRM are updated afterwards.

    Rule-based automation candidateCoordinate calendar and context

    When intent is clear, permitted times are coordinated and opportunity context follows into the next stage.

  5. 05
    BeforeNegotiate terms

    A person adapts proposal, terms, price and commercial commitment.

    Human responsibilityKeep negotiation human

    AI prepares history and drafts, while negotiation, discounts, commitments and consequential terms remain under human authority.

Order exception

An order has a delay, stock issue, fulfillment error or delivery problem and crosses support, operations and logistics.

OutcomeCoordinated exception with consistent status, informed customer and approval when required.
  1. 01
    BeforeDetect exception

    The issue appears in one tool and may take time to reach the team that needs to act.

    Rule-based automation candidateDetect and classify event

    Order Management AI identifies the exception type and starts the workflow with the correct order and state.

  2. 02
    BeforeCheck stock and transport

    A person checks several systems and reconstructs what happened.

    Rule-based automation candidateCross-check approved states

    Order, warehouse and transport are queried as separate sources and the discrepancy is summarized without hiding which system states what.

  3. 03
    BeforeCoordinate solution

    Operations, logistics and support exchange messages until they agree on reshipment, a new date or another next step.

    AI prepares / person or policy validatesHandoffs with explicit outcome

    Each role receives a concrete task and returns structured status so the next owner can continue without rebuilding the case.

  4. 04
    BeforeAuthorize compensation

    If there is a refund, discount or compensation, an owner reviews impact and terms.

    Human responsibilityVisible exception approval

    The team prepares context and options, while consequential financial or commercial authority remains reserved to a person.

  5. 05
    BeforeInform customer

    Support gathers internal responses, explains the outcome and updates the case.

    Rule-based automation candidateClose the loop with the customer

    Customer Support AI receives the approved final state, communicates the outcome and keeps history linked to the order and case.

FAQ

Process Analyzer frequently asked questions

Does the analyzer automatically study my real process?

No. This first version uses predefined business patterns. It helps explore a possible architecture before technical discovery with real data, exceptions and systems.

Why do some steps remain human?

Technical capability and business authority are different. Sensitive decisions, negotiation, exceptions or actions above defined thresholds can remain under human control.

Does a listed system mean a connector already exists?

No. Systems represent process dependencies. Real integration must be validated against provider, API, permissions, data and architecture.

Are the bottlenecks I mark stored?

Not in this version. State remains local during the session. A summary is only shared if you choose to open and send the prepared email.

FROM PROCESS TO TEAM

Once you know which process to improve, define the team that should coordinate it.

Combine this analysis with the Team Builder and simulator to move from an operational problem to an explainable composition and visible workflow.

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