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How to Choose Your First AI Pilot

AI Pilot is the first process where you test AI automation. Selecting the right pilot determines the success of the entire implementation. We’ll show you how to find it, assess risks, and start with minimal losses.

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What is an AI pilot, and why is it needed?

An AI pilot is a small, isolated process where you test an AI solution in real-world conditions before large-scale deployment. It shouldn’t be critical or complex—the goal is to gather feedback, refine integration, and understand how AI interacts with your data and business logic.

Without a pilot, the “deploy and forget” risk is high: AI may fail, cause errors, disrupt processes, or simply not deliver the expected savings. A pilot is an experiment with a fixed budget and timeline.

AI Pilot Lifecycle

flowchart TD  
    A[Select Process] --> B[Define Metrics]  
    B --> C[Create MVP Automation]  
    C --> D[Test on 10–50 Cases]  
    D --> E{Results Match Expectations?}  
    E -->|Yes| F[Scale Up]  
    E -->|No| G[Analyze: Data? Instructions? Integration?]  
    G --> H[Iterate or Cancel]

Example: AI pilot in the sales department

Process: Automatic generation of proposals based on the client’s brief form.

Why the pilot: Simple input (form → text), clear result (PDF), easy to measure (time at CP reduced from 15 to 3 minutes).

How to launch:

  1. Select 10 typical brief forms from the past week.
  2. Configure an AI agent (e.g., via LangChain + prompt engineering) to generate proposals using a template.
  3. Compare the result with the manual version: quality, time, edits.
  4. Collect feedback from sellers.

If the result is stable and acceptable, it can be connected to CRM and scaled to 100+ cases.

Typical mistakes when selecting a pilot

  • We select the “most important” process. — Dangerous: error in a critical process = loss. The pilot must be “safe.”
  • Ignoring data quality. — AI works with what you provide. If data is fragmented or unstructured, the pilot will fail.
  • We expect immediate ROI. — The first pilot is evaluated based on learning, not savings. The goal is to determine whether to continue.
  • We don’t track metrics. — Without measurements (time, error, satisfaction), a decision cannot be made.

Checklist: Is the process suitable for the first pilot?

CriterionCheck
RepeatabilityThe process is performed frequently (≥5 times per week).
StructureInput data: text, table, form (not voice/video without transcription)
InsulationDoes not depend on critical systems (ERP, 1C, payment gateways)
Visible resultYou can estimate “by eye”: text, PDF, notification, report
Ease of replacementIf AI fails, you can switch to manual mode in 5 minutes.
Interest for participantsParticipants are ready to test and provide feedback.

Pseudocode: How to check the process before the pilot

Before launching, run the “manual” AI simulator: verify that the process can be described as a pipeline of 3–5 steps.

// Example of checking the proposal generation process  
function can_automate_proposal():  
    inputs = ["client_name", "product_list", "budget", "deadline"]  
    if not all(input in form_schema for input in inputs):  
        return False  // Insufficient structure  

    template = load_template("proposal_v2")  
    if not is_template_compatible(template, inputs):  
        return False  // Template does not support inputs  

    output = generate_proposal(inputs, template)  
    if not is_output_valid(output):  // Check for errors, empty fields  
        return False  

    return True  // Ready to launch pilot

Next step

After selecting the pilot: 1) Record baseline and post-metrics, 2) List participants, 3) Define success criteria (e.g., “90% KPs without revisions”), 4) Set pilot duration (7–14 days). Then launch the MVP: even a simple prompt in Notion + Airtable can be the first step.

Next article: First Questions Before AI Implementation.

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