From Chain-of-Thought to Reasoning Models: How AI Learned to Think Strategically for Marketing and Comms
Generative AI can draft a social post in seconds, but what about when you need Gen AI to think strategically? This article breaks down when to use reasoning models over basic AI models, helping marketing and comms teams get outputs that are clear, structured, and aligned with their goals.
November 19, 2025
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AI Generated Summary:
AI Reasoning vs. Base Models: Base models generate fast outputs without true reasoning, while reasoning models break down problems, analyze data, and self-check for aligned results.
Optimal Use Cases for Reasoning Models: Best for strategy planning, data analysis, structured reporting, and decision-making in marketing and comms.
When to Use Base Models Instead: Quick tasks like social posts, summaries, and brainstorming are better handled by base models for speed and efficiency.
Choosing the Right Model: Read below to find out which reasoning models (OpenAI, Google, Anthropic, DeepSeek, xAI) are best for advanced analysis and structured outputs.
We’ve all been there: you ask ChatGPT, “What’s the best way to improve my campaign?” and within seconds, it delivers a confident list of suggestions. The answer sounds right, but you’re not sure how it got there. You roll with it because it’s fast and convenient.
That’s because not all AI models “think” the same way. Basic Gen AI models (or base models), like GPT-5.2 Instant or Gemini 2.5, work like a GPS. They give you a destination without showing you how the route was chosen. They generate outputs based on patterns in their training data, delivering drafts and quick summaries, but they don’t deeply evaluate whether your goals or tasks have been addressed comprehensively.
Reasoning models, on the other hand, think more like strategists. They break problems into logical steps, explore different paths, use tools and data to improve accuracy, all while self-checking their reasoning before delivering an output. This means you get clearer, more structured, and insight-driven outputs that align with your input.
While it might seem like all AI models reason, most base models simply generate outputs by predicting patterns in data, without evaluating whether each step makes sense for your goals. Reasoning models, in contrast, are purpose-built to think deeper and deliver more insightful outputs when your team needs it most.
That way, you’re not stuck drafting yet another vague, AI-generated strategy proposal or campaign plan that misses the depth your team needs.
From Prompting “Think Step by Step” to Reasoning
If you’ve heard of Chain-of-Thought (CoT), you know it started as a prompting technique where users instruct AI to “think step by step” to improve outputs on complex tasks. This approach emerged as a way to help earlier AI models generate more structured, logical outputs on multi-step problems by making them break down the task and work their way through it.
For example, if you asked a standard ChatGPT model (like the GPT-5.2 in Instant mode)), “What’s the right positioning for our Q4 sustainability campaign?”, and added “Think step by step”, it might output a breakdown like:
Reviewing previous campaign performance
Analyzing audience sentiment around sustainability
Checking brand messaging alignment
Drafting positioning angles
While this improves the output you will get from a base model, it is still generating these steps by predicting what comes next based on patterns in the training data (i.e. all the text the model has seen across the internet and other sources) without truly understanding the task or verifying whether each step is meaningful for your audience or goals. This can result in messaging that feels generic, misses key context, or fails to align with your priorities. CoT gives the appearance of structured thinking, but without true reasoning, self-verification, or adaptability.
Luckily, today’s reasoning models have evolved beyond CoT. Models like OpenAI’s GPT-5.2, Claude 4.5 Sonnet, Gemini 2.5 Pro, DeepSeek R1, and Qwen 2.5 use CoT internally as a tool within a broader toolkit (e.g. tree-of-thought, tool use, self-verification), automatically deciding when to apply these through step-by-step reasoning. We will explore the whole toolkit in a future article!
When Should You Use Reasoning Models?
You don’t need a reasoning model for every task. Using one for simple outputs can be overkill. Some tasks, such as planning how to write a simple social media caption, don’t require that deep level of analysis.
Reasoning models are best when your tasks require structured thinking, multi-step logic, and clear justification, particularly when you need to make decisions, analyze complex data, or communicate layered insights to stakeholders. Recognizing when to use them helps you get outputs clear, data-driven, and strategically aligned with your marketing and comms goals.
Use Reasoning Models For:
Strategic & Scenario Planning
When evaluating campaign options, channel strategies, or crisis response scenarios, reasoning models can map out risks, simulate outcomes, and help you choose the best path before launch.
Example: Predicting how a brand statement, influencer collaboration, or how a social listening approach might impact audience sentiment during a product recall using Claude 4.5 Sonnet or GPT-5.2 Pro Mode.
Analytics, Data Interpretation & Performance Insights
All models can analyze data and find patterns, but reasoning models do it better and more accurately. They retain more context, handle larger volumes of information, and can analyze different data points together to deliver deeper insights.
Example: Use Gemini 2.5 Pro to analyze a drop in engagement by connecting it to audience shifts, channel changes, or message timing.
Data-Driven Reports & Technical Content
When drafting post-campaign reports, RFPs, or executive messaging decks, reasoning models help structure complex content into clear, actionable insights.
Example: Building a multi-phase go-to-market strategy with justifications for each recommendation using GPT-5.2 Pro.
Problem-Solving & Market Analysis
Reasoning models can turn research, audience insights, and competitor analysis into prioritized action plans that guide marketing decisions.
Example: Synthesizing competitive positioning insights to refine your brand’s messaging and campaign approach using DeepSeek R1 or Qwen 2.5..
When to Skip Reasoning Models
For straightforward, quick-turn tasks, using a reasoning model can be like overthinking a problem. For fast, low-complexity content or task, you can use a base model or opt for lightweight models that prioritize speed over structured reasoning, such as:
OpenAI‘s GPT-5.2 or GPT-5.1 Instant Mode
Google Gemini 2.5 Flash
Anthropic’s Claude 4.5 Haiku
In terms of knowing when to use base models, here are some marketing and comms use cases:
Quick social posts or headlines where creativity and tone matter more than deep analysis.
Summaries and meeting recaps where speed and clarity are the priority.
Brainstorming taglines or high-volume ideation where you need many options quickly.
Routine content or draft generation where step-by-step analysis would slow delivery without adding value.
In any other scenario where you need clear structure and deeper insights, we recommend using a reasoning model (full list below).
Choosing the Right Reasoning Model
Today’s reasoning models are available across platforms like OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini), DeepSeek, xAI (Grok), and open-source ecosystems like Qwen.
The table below provides an overview of major CoT models available today, highlighting their release context, key features, and ideal use cases. Understanding these differences can help you select the right model for the right task.
Provider
Model
Release Context
Key Features
Example of When to Use
OpenAI
GPT-5.2
December 2025
Improved adaptive reasoning that adjusts thinking depth based on task complexity; better balance between speed and structured analysis.
Compact multimodal reasoning model for fast creative tasks.
Social asset review, creative concepting, presentation generation.
How to Prompt Reasoning Models
Prompting reasoning models effectively ensures you get structured, actionable outputs aligned with your marketing and comms goals. While these models reason internally, the quality of their outputs depends on how you frame your request.
Provide clear goals and detailed context: Explain what you need, why you need it, and who the output is for. This helps the model align its reasoning with your campaign objectives, audience, and brand tone.
Specify the desired output format: Whether you need a step-by-step plan, a summary with recommendations, or a bullet-pointed framework, let the model know so it structures outputs in a way that is immediately usable.
Avoid vague “think step by step” instructions: Reasoning models already plan their outputs, so instead of simply saying “think step by step,” it’s more effective to outline the specific steps or structure you want the model to follow.
Refine with layered prompts if needed: You can build depth by following up with additional questions or requests to refine insights, clarify reasoning, or adapt the output for different channels.
For example, instead of asking a reasoning model from ChatGPT (like GPT-5.2 Pro), “Explain step by step how to improve my campaign”, try using RTC (Role, Task, Context) prompting like:
"As a marketing strategist (Role), review our Q3 campaign results and develop an actionable plan to improve performance next quarter (Task), structured for stakeholder presentation (Format)."
OR
“As a brand strategist (role), using the brand guidelines and past campaign data provided (context), create a campaign positioning framework with three positioning options and pros and cons for each, formatted for easy stakeholder presentation (task/format).”
Prompting reasoning models in this way results in outputs that feel considered, original, and genuinely useful, avoiding the generic, repetitive AI responses you often see on all over the marketing and comms space. For more prompt engineering tips and tricks, check out our free downloadable guide!
How to Find Reasoning Models Across the Different Gen AI Platforms
Choose the variant that best fits your workflow or platform integration (most are freely available for research and enterprise use):
Qwen 3-Max (flagship reasoning and language model with long-context and tool-use capabilities) – FREE (open-source)
Qwen 3-VL-235B-A22B (vision-language reasoning model for multimodal tasks and asset analysis) – FREE (open-source)
Qwen 3-Coder (analytical and logic-driven model optimized for data reasoning and code-based workflows) – FREE (open-source)
Qwen 3-VL-32B (compact multimodal reasoning model for fast, creative planning and visual-text alignment) – FREE (open-source)
Choose the variant that best fits your workflow or platform integration (most are freely available for research and enterprise use):
Conclusion
Reasoning models continue to change, but what is behind their reasoning? (pun intended). Knowing when and how to use them can elevate your planning, analysis, and decision-making, allowing you to work more efficiently and drive better outcomes.
Are you still not sure which AI model fits your marketing and comms needs? Contact us today so we can help you select and implement the right models for your teams to excel in the fast-changing world of AI!
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