COMPARISONS

Compare AI agents, chatbots, RPA and copilots before you automate.

These comparisons help choose architecture and delegation level. They start from the real work, separate conversation, assistance, deterministic rules and agentic execution, and explain when combining approaches makes sense.

Other approachesPattern to evaluate
AI EmployeeRole + systems + policies

CHOOSE BY FIT

Five comparisons for making a context-aware decision

Each page explains the differences, when to choose each approach and when combining them makes sense.

ChatbotPattern to evaluate
AI EmployeeRole + systems + policies

AI agent vs chatbot: where an AI Employee fits

Use a chatbot when the main goal is conversation, information or data collection. Use an AI agent—or an AI Employee as a governed role—when work continues after the conversation and must coordinate actions, systems and controls.

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AI agentPattern to evaluate
AI EmployeeRole + systems + policies

AI Employee vs AI agent

AI agent mainly describes a technical capability: interpreting goals, using tools and advancing tasks. AI Employee describes how those capabilities become a governed business role with responsibilities, authority boundaries and oversight the organisation can understand.

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RPAPattern to evaluate
AI EmployeeRole + systems + policies

RPA vs AI: when to use deterministic automation, agents or both

RPA fits repetitive, stable and deterministic steps. AI adds more value when language, context or exceptions matter. Many workflows combine both: AI interprets and coordinates while RPA executes well-defined legacy steps.

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Traditional automationPattern to evaluate
AI EmployeeRole + systems + policies

AI vs traditional automation: use rules where rules are enough

Automate with rules what can be solved with rules. Use AI where variability, language or exceptions make purely deterministic automation insufficient.

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AI copilotPattern to evaluate
AI EmployeeRole + systems + policies

AI agent vs copilot: direct assistance versus governed delegation

A copilot keeps a person as the primary operator at each step. An AI agent can receive delegated work and progress within boundaries. IA Empleado organises that delegation as a role with defined responsibilities, permissions and human control points.

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METHODOLOGY

How we compare

01

We start from the real work to be solved, not the technology label.

02

We separate deterministic tasks, human assistance, contextual interpretation and delegation.

03

We evaluate integration, supervision, traceability, maintenance and operational risk.

04

When two approaches complement each other, we say so explicitly.

COMMERCIAL EVIDENCE

Demos, analysis and estimates with the right evidence label

The site separates what can be visually demonstrated, what is indicative analysis and what is an estimate so the decision does not depend on ambiguous claims.

DEMONSTRABLE

Handoffs, systems and human approval

The simulator shows how a request moves through AI Employees, systems and control points. It is a design and behavior demonstration, not a production-runtime claim.

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INDICATIVE ANALYSIS

Before and after a process

Process Analyzer separates automatable, assisted and human steps for architecture and prioritization discussions. Technical discovery is required before treating the result as a real proposal.

Analyze process
ESTIMATE

Capacity and ROI with visible assumptions

The calculator uses volume, time, cost and expected improvement to build scenarios. It does not present the result as guaranteed savings or a measured metric.

Calculate scenario

FROM APPROACH TO DESIGN

First decide what kind of automation fits. Then design the team.

Use the comparisons to choose the right pattern and the Team Builder to turn it into a concrete composition.