AI Model Selection Cheat Sheet - Practical AI Tool Choice & Agentic Workflow Guide

Oct 13, 2026

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A practical cheat sheet for selecting AI tools and building repeatable, decision-ready workflows in business settings. It provides tool categories at a glance, a decision framework with do/don'ts and pitfalls, and checklists for drafting, research, strategy, document analysis, coding, data work, and automation. It also introduces agentic workflows, including chaining tools, human-in-the-loop reviews, logging/monitoring, and iterative experiments, plus guidance for implementation via a 2-week pilot and clear next steps.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages10

Reading Time4 min

AccessFree to download

Tool Categories at a Glance

Chat models, code assistants, retrieval & analytics, and image tools

Decision Framework

Practical checklist for drafting, research, strategy, analysis, and coding

Agentic Workflows

From tool to action with human-in-the-loop and monitoring

Practical Scenarios I

Writing/editing, research, strategy, document analysis, coding, data analysis, and support

Practical Scenarios II

Internal knowledge search, automation, knowledge sharing, governance, and security

Implementation

2-week pilot and setup

Next Steps

Closing: practical path forward

CATEGORIES Tool Categories at a Glance Founders, executives, managers, business leaders A practical taxonomy of AI tools used in business, focused on decision clarity. Key move Map your data flow before tool choice. PRACTICAL CHECKLIST 01 Chat models (ChatGPT, Claude, Gemini): natural language drafting, Q&A, and creative writing. 02 Code assistants (Copilot, code LLMs): autocomplete, templates, and debugging. 03 Retrieval and analytics (Perplexity, similar tools): facts, briefs, and knowledge queries. 04 Image and multimodal tools: generate visuals; create prompts for marketing and design. | 05 | Automation and workflow tools: connect apps and automate repetitive tasks. | | --- | --- | | 06 | Local models and private hosting: on-premises AI for sensitive data. | | --- | --- | | 07 | Data tools and dashboards: AI-assisted insights from tables and BI. | | --- | --- | Suhas Bhairav AI Resource Decide the right AI tool for each task, confidently and quickly. DECISION FRAMEWORK Founders, executives, managers, business leaders Decision Table: When to Use What A simple guide to choosing tools, with do/don'ts and pitfalls. Key move Fill this table for your mission-critical tasks. PRACTICAL CHECKLIST 01 Drafting/editing: Use chat models for drafts; verify facts; don't rely on style alone. 02 Research: Use browsing-enabled tools; fact-check with sources; avoid unsourced claims. | 03 | Strategy: Use scenario analysis; compare options; avoid opaque assumptions. | | --- | --- | 04 Document analysis: Extract key points; summarize; verify extraction accuracy. 05 Coding: Use copilots for scaffolding and templates; review results. | 06 | Data analysis: Connect data sources; run basic models; beware biased outputs. | | --- | --- | | 07 | Automation: Use for repetitive tasks; validate end-to-end; monitor failures. | | --- | --- | Suhas Bhairav AI Resource Decide the right AI tool for each task, confidently and quickly. AGENTIC WORKFLOWS Founders, executives, managers, business leaders From Tool to Action: Building Agentic Workflows Learn how to chain tools into repeatable workflows that deliver outcomes. Key move Prototype with a single end-to-end task. PRACTICAL CHECKLIST | 01 | Define the business outcome and success metric for the workflow. | | --- | --- | | 02 | Map data flow: inputs, transformations, and outputs across tools. | | --- | --- | 03 Choose a primary AI agent as the decision-maker for the task. 04 Add supporting tools for search, analysis, and automation as needed. | 05 | Incorporate human-in-the-loop at critical decision points and reviews. | | --- | --- | 06 Implement logging and monitoring to track performance and reliability. 07 Iterate with small experiments to learn quickly and improve. Suhas Bhairav AI Resource Decide the right AI tool for each task, confidently and quickly. PRACTICAL SCENARIOS I Founders, executives, managers, business leaders 9 Real-World Scenarios and Steps Guidance for executing tasks with AI across common business domains. Key move Test each scenario on real data before committing. PRACTICAL CHECKLIST 01 Writing/editing: draft memos, reports, and emails; refine tone and clarity. | 02 | Research: collect market data, summarize sources, and create briefs. | | --- | --- | | 03 | Strategy: run scenario analyses, stress tests, and option comparisons. | | --- | --- | | 04 | Document analysis: extract actions, risks, and decisions from reports. | | --- | --- | 05 Coding: generate templates, scaffolds, and refactor guidance. | 06 | Data analysis: pull KPIs, run quick analyses, and visualize results. | | --- | --- | | 07 | Customer support: craft responses; route complex queries to humans. | | --- | --- | Suhas Bhairav AI Resource Decide the right AI tool for each task, confidently and quickly. PRACTICAL SCENARIOS II Founders, executives, managers, business leaders Internal Knowledge Search and Au

FAQs

How should teams choose between chat models, code assistants, and retrieval/analytics tools?

Use chat models for drafting, Q&A, and creative writing, while using code assistants for autocomplete, templates, and debugging. For facts and knowledge queries, prefer retrieval and analytics tools and verify claims with sources.

What are the key steps to build an agentic workflow from tools to outcomes?

Define the business outcome and success metric, map the data flow across tools, select a primary AI agent, add supporting tools for search and analysis, and insert human-in-the-loop at critical decision points. Finish with logging/monitoring and iteration via small experiments.

What does a 2-week AI pilot and setup typically include?

Week 1 covers selecting a single use case, picking the primary tool, defining success metrics, gathering data access, establishing governance, and configuring security controls. Week 2 runs an end-to-end test, collects results and feedback, iterates prompts, tunes workflows, and addresses bottlenecks before deciding whether to scale.

Oct 13, 2026

Upload by Terk