AI for E-Commerce: 5 Quick Wins Under £5k

I work with a lot of e-commerce businesses, and they almost always start the conversation the same way: "We know AI could help, but we don't know where to start."

Here are five projects I have built (or would build) for online retailers, each under £5,000, each delivering measurable ROI within the first quarter. These are not experiments. They are production systems that pay for themselves.

1. Product description generation

The problem: Writing product descriptions is slow, repetitive, and never-ending. A mid-size retailer with 2,000 SKUs needs descriptions for every product, and they need variations for different channels (website, Amazon, Instagram).

What I build: A pipeline that takes your product data (name, category, specs, images) and generates SEO-optimised descriptions in your brand voice. Not generic AI copy. Descriptions that sound like your existing best-performing listings because the model is fine-tuned on your top 100 descriptions.

How it works:

• Feed in your product CSV or connect directly to Shopify/WooCommerce via API
• The model generates 3 description variants per product (short, medium, long)
• Each version includes your target keywords, matches your tone, and follows your formatting rules
• A human reviews and approves before publishing (usually takes 30 seconds per product)

Cost: £2,000-£3,500 to build. Running costs of £30-£80/month depending on volume.

ROI: A retailer I worked with was spending 20 hours/week writing descriptions for new products and seasonal updates. The tool cut that to 3 hours of review time. At £15/hour for the copywriter, that is £255/week saved, or roughly £13,000/year. The build paid for itself in under a month.

2. Automated review analysis

The problem: You have hundreds or thousands of product reviews across multiple platforms. Reading them all is impossible. But buried in those reviews are product defects, shipping complaints, recurring praise, and competitor mentions that should be driving decisions.

What I build: A nightly pipeline that pulls new reviews from your platforms (Trustpilot, Google, Amazon, on-site), runs sentiment analysis, extracts key themes, and delivers a daily summary to Slack or email.

How it works:

• API integrations pull new reviews from each platform overnight
• Each review gets a sentiment score (positive, negative, neutral) and confidence rating
• Natural language processing extracts themes: product quality, shipping speed, packaging, customer service, pricing
• Negative reviews with high confidence get flagged immediately
• Weekly trend reports show sentiment shifts by product, category, and time period

Cost: £1,500-£3,000 depending on how many review sources you need.

ROI: One client discovered that 18% of negative reviews mentioned the same packaging issue. Fixing it cost £2/unit but reduced return rates by 12%, saving roughly £8,000/quarter. The review analysis system spotted the pattern within the first week.

3. Smart inventory alerts

The problem: Basic inventory management tells you when stock hits a threshold. That is reactive. By the time you hit your reorder point for a seasonal item, it is often too late to restock in time.

What I build: A predictive system that analyses your sales velocity, factors in seasonality and marketing campaigns, and alerts you 2-3 weeks before a stockout would happen.

How it works:

• Connects to your inventory system (Shopify, WooCommerce, or direct database)
• Calculates rolling sales velocity per SKU with seasonal adjustment
• Factors in known events: marketing campaigns, bank holidays, historical sales spikes
• Generates a daily reorder report: "You will run out of SKU X in 14 days at current velocity. Recommended reorder: Y units."
• Alerts escalate as the predicted stockout gets closer

Cost: £2,500-£4,500. Running costs under £50/month.

ROI: Stockouts cost e-commerce businesses 4-8% of annual revenue on average (that is an industry-wide figure from IHL Group). For a retailer doing £500k/year, even preventing half of those stockouts saves £10,000-£20,000 annually. The system also reduces overstock by highlighting slow-moving SKUs, freeing up cash tied in dead inventory.

4. Returns classification

The problem: Returns eat margin. But not all returns are equal. Some are legitimate product issues that need fixing. Some are "wardrobing" (wear and return). Some are serial returners gaming your policy. Treating them all the same costs you money.

What I build: A classification system that scores each return request based on the customer's history, the return reason, the product category, and known fraud patterns.

How it works:

• When a return is requested, the system pulls the customer's order and return history
• It scores the return across several dimensions: customer lifetime returns rate, return reason consistency, product category return norms, time-to-return
• Low-risk returns get auto-approved with prepaid labels (faster experience for good customers)
• Medium-risk returns get standard processing
• High-risk returns get flagged for manual review before a refund is issued
• Monthly reports show patterns: which products have abnormal return rates, which customers are serial returners

Cost: £2,000-£4,000. Integrates with Shopify, WooCommerce, or custom systems.

ROI: A fashion retailer I worked with had a 22% return rate. The classification system identified that 8% of customers were responsible for 35% of returns, and many of those followed a wardrobing pattern. By flagging high-risk returns for review, they reduced fraudulent returns by 40%, saving approximately £3,500/month on a £200k/month revenue base.

5. Customer support triage

The problem: Your support inbox is a mess. Shipping questions sit next to product defects sit next to billing issues. Everything gets the same priority. Agents spend time reading and categorising before they can even start helping.

What I build: An automated triage system that reads incoming tickets, classifies them by type and urgency, tags them with relevant metadata, and routes them to the right team or agent.

How it works:

• Connects to your helpdesk (Zendesk, Freshdesk, Intercom, or email)
• Each incoming ticket gets classified: shipping, product defect, billing, account access, return request, general enquiry
• Urgency is scored based on keywords, customer value (repeat customers get higher priority), and time sensitivity ("my order arrives tomorrow")
• Tickets are auto-tagged with the relevant order number, product, and category
• High-urgency tickets jump the queue. Low-priority FAQs get an auto-reply with relevant help centre links
• Agents see a pre-filled context panel: order details, customer history, suggested resolution

Cost: £1,500-£3,000 for the initial build. Running costs of £50-£100/month.

ROI: First-response time typically drops 40-60% because agents do not waste time reading, categorising, and looking up order details. For a team handling 200+ tickets/day, that is 2-3 hours of agent time saved daily, roughly £1,500-£2,000/month at UK support staffing costs.

Which one should you start with?

Pick the one closest to your biggest pain point. But if you are not sure, my recommendation is customer support triage (project 5). It touches every ticket, delivers visible improvements in the first week, and the data it generates (ticket categories, common issues, response times) informs what to build next.

If your catalogue is large and growing, product description generation (project 1) is the other strong starting point. It directly reduces labour costs and scales with your SKU count.

The key with all five: start with one. Get it working. Measure the results. Then decide whether to build a second. Trying to do all five at once is how AI projects stall.

If you run an e-commerce business and want to figure out which of these would deliver the most value, I can audit your current setup and recommend a starting point. Get in touch and I will give you a straight answer.