DIY how AI will revive e-commerce – Practical e-commerce

Many e-commerce businesses have adopted a culture of AI and automation that encourages experimentation and problem-solving.

The effect is a resurgence of do-it-yourself projects reminiscent of early innovations in the e-commerce industry. I’m willing to try it out.

AI trends

Countless studies and reports point to the beginning of a shift towards DIY.

A 2025 LinkedIn study found that 80% of executives believe that AI adoption is important and will foster a more innovative workplace culture. Gartner reported in December 2025 that 65% of employees said they were excited about using AI in the workplace.

This trend suggests that three priorities are converging.

• management They fear that if they don’t adopt AI and automation, their companies will fall behind.

• employee Because AI will make your job easier and the knowledge you gain will become an important career skill.

• cost of Off-the-shelf software and its development make AI an attractive alternative.

what’s old is new again

Here is an example. I worked with a company in the northwest US that uses OpenAI, Gemini, and the workflow automation platform n8n to provide access to premium accounts to nearly all of its employees.

Management encouraged employees to tackle AI issues. When I reviewed the example, I discovered that the staff had built a simple n8n-based tool to monitor competitor prices.

It was relatively basic content. We collected prices, compared them to our own data using an AI agent, and added them to Google Sheets weekly. Its weekly updates provided a pivot table that marketers used to identify changes.

This was similar to my 2015 article “Monitoring Competitor Prices with Python and Scrapy.” In this article, we discussed a simplified price checker offered by local retailers that is cheaper and less capable, but still provides problem-solving capabilities.

DIY

A simplified version of the n8n price checker also does not require AI and could have only four steps.

This is how it works.

• Use the n8n cron node (scheduler) to run the automation once a week.

• The HTTP Get request node retrieves the competitor’s products and prices. In some cases, collecting data can be as simple as adding it. /product.json Go to the shop URL.

• The code step uses JavaScript to find the lowest price for a set of product variations.

• Data is captured using a Google Sheets integration.

Sellers may not even need to manually assemble workflows. The generation AI tool can generate n8n importable JSON files from a simple prompt.

culture

The importance of the price monitoring example lies not in the workflow, but in the attitude developed in the workflow. A marketing team member with little programming experience built a problem-solving automation.

The end result is that developers may improve their workflow or clean up their code. Nevertheless, the move towards building something reduces the friction between operational problems and solutions.

This new set of tools has reinvented the DIY attitude that drove e-commerce entrepreneurs years ago.

With a culture that prioritizes AI and automation, teams can build custom workflows that:

• Inventory monitoring. AI agents monitor inventory levels and sales velocity. This tool alerts you when stock is low and suggests promotions when stock is high.

• Check emotion extraction. AI analyzes each new product review and infers its sentiments and themes. Insights feeds support content prioritization and marketing content without manual sorting.

• FAQ chatbot. Using the FAQ database n8n and ChatGPT, merchants build custom chatbots that answer shoppers’ questions.

• Customer service email filter. Connected to Gmail, Slack, and customer service ticketing systems, AI agents monitor customer service inboxes, categorize messages into tickets, and send Slack messages in emergencies.

• 3D video generation. This workflow uses Google Drive, Remove.bg, and Fal.ai to convert product videos for your Shopify store.

opportunity

The DIY trend is an opportunity for AI and workflow needs to converge. Management seeks competitive protection, employees seek efficiency and skill, and budget constraints limit software and development.