How I Use AI to Automate Marketing Tasks - Examples

Updated: by Heysho

What is AI Marketing?

Today, I'd like to revisit the topic of digital marketing after quite some time. (And no, this article wasn't written by AI.)

  • What exactly is AI marketing and what can it accomplish?
  • What benefits come from implementing it?
  • Which tools should you choose?

I'll be discussing "AI marketing," a topic that's been generating significant buzz lately, for those with these questions.

Since December 2022, I've been integrating AI into various aspects of my work, and I've noticed a dramatic improvement in my capabilities as a professional.

To illustrate, I used to tackle challenging tasks through solo trial and error, but now it feels like I'm collaborating with exceptional, lightning-fast consultants, programmers, and data scientists.

In this article, I'll share practical examples, recommended applications, and useful tools based on my personal experiences.

Table of Contents

1. What is AI Marketing?

A workflow that connects AI-assisted market research, content creation, and user insight analysis to marketing initiatives

AI marketing is a relatively new concept that I define as "leveraging generative AI to streamline and enhance marketing activities."

Specifically, it can be applied to areas such as content creation, business automation, and data analysis.

Key Applications

  • Market Research: Harness AI's vast information processing capabilities to conduct competitor analysis and consumer trend research in a fraction of the time.
  • Content Creation: AI assists with drafting articles, social media posts, and ad copy, while also providing quality-improving proofreading.
  • User Insight Extraction: AI analyzes thousands of customer reviews and inquiries to identify valuable patterns and emotional tendencies.

2. Why is AI Marketing Gaining Traction?

According to Grand View Research, the global AI marketing market will continue to grow.

Grand View Research also publishes data for Japan's AI marketing market.

As the market expands, companies will face a serious shortage of AI talent.

The Importance of Early Adoption

Currently, AI adoption in the marketing industry remains in its early stages.

Investing in AI marketing now can provide a significant competitive advantage over your rivals.

I believe that establishing your company's AI marketing infrastructure before the market matures and competition for talent intensifies will be crucial for future success.

3. Implementation Examples (From My Experience)

I have used these AI marketing applications in my work.

Customer Survey Analysis

A workflow that converts free-form customer survey responses into categorized, aggregated, and prioritized insights with AI
  • Challenge: Processing massive amounts of unstructured data.
    • I analyzed approximately 7,000 customer survey responses.
    • All responses used free-form text, so traditional methods would have required an estimated 200+ hours to classify and aggregate the data.
  • Solution: I used text analysis to categorize and aggregate the responses, then set priorities for each issue.
    • Processing time fell from about 200 hours to about 10 hours. Labor costs fell from several hundred thousand yen to about ¥10,000 in API fees.
    • Technology used: Python + ChatGPT API (GPT-3.5-turbo)
    • The API cost about ¥10,000 in 2023. The same analysis now costs less than ¥100 as AI models have improved.
  • Remarks: This was my first AI automation project, and I still remember the impact it had.

SEO Keyword Classification

A workflow that uses AI to classify mixed SEO keyword data and organize it by category
  • Challenge: In SEO strategy, it's standard practice to analyze "branded keywords" (containing the brand name) separately from "generic keywords" (without the brand name). Traditionally, this classification required manual work, creating a significant burden and inefficiency.
  • Solution: I used an automatic classification script built with Python and GPT-4.1-mini. It classifies about 10,000 keywords for less than ¥10 and takes about 20 minutes, a major improvement over manual work.
  • Remarks
    • Generative AI improves efficiency and accuracy when it analyzes text data.
    • This basic SEO example can extend to product, topic, and other category-based classification.

Enhanced KPI Reports with AI-Powered Market Research

A workflow that uses AI to investigate changing search trends, organize findings, and connect them to KPI reports and SEO actions
  • Challenge
    • Branded search volume can strongly affect SEO performance on a brand website.
    • Report presentations bring questions such as, "Why did branded keyword search volume increase or decrease?" Multiple factors make the cause difficult to identify.
  • Solution: I use ChatGPT's deep research capability with prompts like "Investigate why search volume for the [brand name] rose sharply in [month]."
  • Result
    • ChatGPT's output lists the factors behind the fluctuation, including brand initiatives, influencer activity, and competitor movements.
    • Including this analysis in KPI reports helps the team understand specifically "which actions drive branded keyword search volume," improving overall SEO performance.

Content Creation at Scale

A workflow that updates large numbers of e-commerce category pages in stages with AI to improve SEO traffic
  • Challenge
    • I led a project to optimize on-page SEO for 2,000 category pages on an e-commerce site.
    • Improving meta descriptions and adding lead text took too much time. An SEO vendor quoted ¥50,000 per page, so applying the work to 1,000 pages would cost ¥50 million. The project was difficult to execute at that price.
  • Solution
    • I built an automation system combining Python, LangChain, and generative AI APIs, reducing the production cost to less than ¥1 per page.
    • As a result, search traffic from generic keywords increased by 80% year-over-year.
  • Remarks
    • To reduce risk, I used several safeguards:
      • Using multiple AI models with sophisticated prompts
      • Deploying updates gradually in batches of 50 pages
    • Updating many pages at once with a basic model and simple prompts carries significant risk.

Data Analysis and Data Science Applications

A workflow in which marketers use AI-assisted data analysis for forecasting, advertising impact analysis, and KPI reports
  • Challenge
    • I began learning Python and data science in 2023, but initially lacked confidence in my coding skills, making practical implementation time-consuming.
    • Previously, data science projects often struggled to demonstrate clear business value, making it difficult to secure resources or budget approval.
  • Solution
    • With ChatGPT's support, I can perform advanced data analysis on my own and finish it faster.
    • Key projects I've completed include:
      • SEO traffic forecasting using machine learning
      • Analyzing advertising impact on organic search traffic
      • Automated KPI report generation
      • Various other Python-based data analyses
  • Insights
    • My results with limited data science experience suggest that more marketers will use data science themselves.
    • When marketers use AI for data analysis, they can help build a "data-driven culture" across the organization.

Creating AI Tools for Operational Support and Customer Acquisition

A workflow that uses a self-developed AI tool to automate email replies and rap generation, then attract search traffic after public release

As a personal project rather than a company initiative, I develop and operate AI tools that improve business efficiency.

For example, my "Email Reply Tool" generates polite, professional responses when I paste the original message and enter "polite response" as a keyword. ChatGPT offers a similar function, but my tool runs faster and produces text with a more precise tone.

Making this tool public also attracts potential customers through Google search.

When I released another tool, a "Rap Generator," it ranked highly for keywords like "rap generation" and attracted approximately 2,000 sessions per month.

4. Recommended AI Tools

New AI tools appear all the time, but I recommend starting with ChatGPT Plus ($20 per month) and learning to use it well.

If you are willing to explore more technical work, learning these three areas will expand what you can do:

  • Codex
  • Python
  • APIs for AI models

Many AI tools are convenient, but services can change or disappear within a short period. You will get more long-term value from combining a coding agent such as Codex with Python and APIs so you can build your own tools and automation.

You do not need to become a Python expert. A basic understanding of how it works is enough to use it with a coding agent.

These are the tools I use most often.

ChatGPT Plus

The first tool I recommend is ChatGPT Plus, which costs $20 per month.

ChatGPT includes writing, web search, research, image generation, and coding, so I can cover most of my daily AI-assisted work with one service.

These are the features I use most:

Codex

Codex is the tool I use most often.

I spend a lot of time programming and automating work, so I delegate much of my coding to Codex. It handles more than basic code generation. I also use it to modify existing code, debug projects, and build new tools.

I used to write the code myself. Now I usually define the requirements and design, then let the coding agent handle the implementation.

Image Generation

I also use image generation often.

I can create images for blogs, websites, and documents in a short time, and the quality has improved a great deal.

For a small header image or illustration, ChatGPT often gives me what I need without another image service.

ChatGPT's Standard Interface

I use the standard ChatGPT interface in my daily work as well.

It works well as a sounding board for business questions, idea organization, writing, and data analysis.

I use a Thinking model for complex questions and web search when I need current information.

I also use Deep Research for topics that require a closer look, such as competitor and market research.

Claude / Claude Code

Claude and Claude Code are also worth learning.

I now use Codex as my primary coding agent, but Claude Code's coding ability impressed me when it first appeared.

Many developers still use Claude Code as their main coding tool.

Its strength extends beyond code generation. It provides a rich environment for running coding agents.

For example, you can combine:

  • Slash Commands
  • Subagents
  • Skills

These features can automate repetitive development work at a high level.

Depending on your plan, you may hit hourly or weekly usage limits. A long coding session can stop when you reach them.

I now center my workflow on Codex for this reason, but Claude Code remains a strong option for anyone who wants to automate serious coding work with AI.

Claude also handles writing well.

I sometimes use Claude for web content that needs natural Japanese. Its writing style differs from ChatGPT, which makes it useful when I adjust the final wording.

Gemini / Google AI Pro

I also subscribe to Google AI Pro.

I do not use Gemini chat often, but Google's AI tools include several useful options.

I use Google Antigravity as a coding IDE.

With Gemini's fast Flash models, it works well for light coding tasks where I make a small change, check the result, and repeat.

Gemini API

I use the Gemini API more often than the chat interface.

Flash models cost little to run, so I use them for large-scale data processing.

Examples include:

  • Translating 10,000 pieces of text
  • Classifying large volumes of product reviews
  • Sorting keywords into categories
  • Extracting required information from large volumes of text

For repetitive work like this, writing a Python program and processing the data through an API can cost less than sending each item to Codex one at a time.

I also choose the low-cost Gemini API when I need an LLM API for an external tool.

Complex work and development → Codex

Large-scale repetitive processing → Python × API

Python / LangChain / LangGraph

Python is also a strong choice for serious AI-assisted automation.

Coding agents have become capable enough that I write code from scratch far less often than I used to.

Even so, the three to four years I have spent using Python continue to help me with AI-assisted coding.

You do not need to memorize every piece of Python or write every program from scratch.

Focus on the basics:

  • What Python can do
  • A program's structure
  • What an API is
  • How to load, process, and output data

Even a basic grasp of these ideas makes it easier to give coding agents such as Codex and Claude Code clear instructions.

I also use LangChain and LangGraph when I build workflows with LLMs.

They help when I need to build a multi-step process such as:

  1. Load data
  2. Ask an AI model to analyze it
  3. Branch the process based on the analysis
  4. Ask another AI agent to review the result
  5. Compile and output the final result

When I ask AI to write the code, specifying "Use LangChain" or "Build the workflow with LangGraph" can also make the process easier to organize.

What to Learn First

If you want to use AI at work, you do not need to subscribe to a large number of AI tools at the start.

Start with ChatGPT Plus and explore these in order:

Codex → Python → API

Once you can combine these three, you can build AI tools tailored to your own work instead of relying only on existing services.

I believe this skill will help more with workplace efficiency and automation in the age of AI than memorizing how to use one particular AI service.

5. Information Gathering

These are the main sources I use to stay informed about AI. Rather than simply keeping up with the latest AI tools, I have recently placed more importance on sources that help me understand how the technology works and consider where AI is heading.

Andrej Karpathy (X / YouTube / GitHub)

Andrej Karpathy is an AI researcher and one of OpenAI's founding members. He led Tesla's AI team and now runs Eureka Labs, an AI education company. In addition to content explaining how LLMs work, he frequently shares new ideas and practical approaches to using AI, including projects such as LLM Wiki and AutoResearch. I pay particular attention to him because he often introduces ideas that create new currents in the AI industry.

Nyanta (YouTube)

This YouTube channel explains AI and data science concepts and implementation methods in a way that is accessible even to beginners. Beyond introducing new AI tools, its videos often cover why something works and how to implement it in practice. The technical content is particularly easy to follow, so I check it regularly for practical insights.

AI Sennin (YouTube)

I often watch this channel when I want to take in AI information in a more casual way. It is more entertainment-oriented than a formal technical lecture, making it easy to learn about AI while enjoying the hosts' banter. At the same time, their conversations reflect deep AI knowledge, and they explain the latest technologies clearly. It is a great channel for learning about AI while listening casually.

Satoshi Nakajima (Newsletter / YouTube)

A former Microsoft engineer, Satoshi Nakajima offers in-depth explanations of AI and technology from a programmer's perspective. In his weekly newsletter, Life is beautiful, he goes beyond generative AI to explore U.S. tech companies and broader technology-industry trends in considerable depth.

Some articles are paid, but they often go beyond simply reporting the news to explain why developments are happening. I find them valuable for expanding my perspective on the technology industry.

Iketomo (YouTube / Podcast)

Iketomo makes the latest AI news accessible to marketers and business professionals. I do not follow AI news as closely as I used to, so I watch less frequently, but it is useful when I want a concise overview of the most important updates.

The podcast Iketomo Obara: Deep AI News, co-hosted with Kazuhiro Obara, also offers more in-depth discussions of technology and business than a typical news roundup, making it another useful reference.

Interviews with AI Company Leaders such as Sam Altman and Dario Amodei

When considering where AI may be heading, I refer to interviews with leaders and researchers such as OpenAI's Sam Altman and Anthropic's Dario Amodei, who are directly involved in developing state-of-the-art AI models.

Many influencers make predictions about AI's future, but those developing the models themselves may have visibility into technology roadmaps further ahead. For that reason, I place particular value on these primary sources when thinking about AI's medium- to long-term direction.

6. Summary

Thank you for reading this article.

Though I'm still learning, I'd be delighted to connect with anyone interested in transforming their business using AI.

Contact me if you are working on an AI-related project or want to collaborate on an interesting challenge.

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