12 AI Automations for SMBs: Where to Start Concretely

The average French SMB in 2026 has a ChatGPT Team subscription, two accountants drowning in manual invoice entry, and a CEO who knows he "should get into it". Between those two, nothing. Almost nothing, anyway. I see this in nearly every engagement I run, and the root cause is the same every time: people confuse "doing AI" with "shipping an AI tool to production". The first needs a vision. The second needs 25 minutes and a credit card.

Here are 12 projects your team can start this week. No AI CTO, no five-figure budget, no IT rewrite. For each one: the lived problem, the tool, a 5-minute setup, the expected outcome. Everything comes from real engagements.

1. Auto-replies to incoming emails

Email is still the number-one friction point for a SMB owner. Of the 80 to 120 emails received daily, around 60% are acknowledgements, polite follow-ups, or repeat questions ("did you get it?", "you have the PDF?", "still on for Tuesday?"). The hidden cost: 1h30 per day, which works out to 7 full days a month for a team of five.

Concrete tool: Gmail (or Outlook) + Make.com (free tier covers up to 1000 ops/month) + OpenAI API. Realistic total cost: 5 to 12 € per month for 1000 emails handled.

5-minute setup:

1. Create a Make.com account, connect Gmail via OAuth.
2. Build a "New email matching filter" scenario with a simple filter (a manual "Reply needed" label, for instance, that you tag by hand at first).
3. Add an OpenAI Chat Completion step with a short prompt ("You are Anis, founder of SolidScale. Reply in 3 lines max, direct tone, sign 'Anis'."), then a "Create draft Gmail" step.

Expected outcome: in the first week, you validate drafts in 10 seconds instead of typing for 2 minutes. After a month, you can switch to direct send on the most stable patterns (meeting confirmation, quote acknowledgement). Count 40 to 60% inbox time saved once calibrated.

2. Automatic categorization of support tickets

Customer support at a growing SMB always makes the same move: manual triage. Refund request, pre-sale question, technical bug, complaint, feature request. Every ticket gets a label, sometimes two, and the agent picking it up spends 30 seconds to 2 minutes just figuring out where it goes. Across 300 tickets a day, that's half a full-time person.

Concrete tool: Zendesk, Intercom, or Freshdesk (depending on your existing stack) + Make + GPT-4o-mini as a classifier. Count 0.001 € per classification, so under 1 € per day for 300 tickets.

5-minute setup:

1. Pull your real tag list (the actual one, not the wiki version, do a CSV export).
2. Build a Make "New ticket" scenario sending the body to OpenAI with a prompt that forces a closed-list pick ("Reply only with a tag from this list: billing, bug, question, complaint, other").
3. Pipe the result back to your support tool's API to apply the tag.

Expected outcome: 85 to 92% accuracy on top-level tags from day one, no training needed. The remaining 8-15% gets corrected by hand, and the prompt improves as you review errors. Team gain: 1 FTE of triage time on 300 tickets/day.

3. Meeting recap generation

Nobody wants to write the recap. Nobody reads it either if you take 4 days to produce it. And this is probably where the gap between current practice (nothing) and the AI outcome (a structured recap in Notion 5 minutes after the call ends) is the biggest of the whole list.

Concrete tool: Otter.ai ($29/month) or Fireflies ($18/month) for transcription, + Make + GPT for structuring, + Notion (or Teams, or Slack) for storage. If you already use Microsoft Teams or Google Meet Enterprise, transcription is included, leaving you only the structuring step to automate.

5-minute setup:

1. Turn on automatic transcription in your video tool (Teams, Meet, or install Fireflies as a Zoom bot guest).
2. Create a Make webhook that fires when the transcript is ready.
3. Typical GPT prompt: "Summarize this meeting in 3 sections: Decisions, Actions (with owner and deadline), Open questions. Markdown format.", then push to Notion.

Expected outcome: -90% recap writing time, +100% of recaps actually produced (because nobody was writing them before). And your actions have owners and dates, so from one meeting to the next, you can actually follow up.

4. Personalized quotes from a client brief

A well-written quote takes 2 to 4 hours in B2B services. Within that span, 80% is rote work: rephrasing the brief, walking through your standard methodology, plugging in pricing, formatting. AI was made for this ratio.

Concrete tool: Make + GPT + a PDF generation library (PDFMonkey, DocRaptor, or a Google Docs template with the Docs API). Count 20 to 40 € per month for a few dozen quotes. The intake form can be a plain Typeform or Tally.

5-minute setup:

1. Build a Typeform with 8 to 10 key questions (sector, size, need, estimated budget, urgency, constraints).
2. Make webhook that pipes responses into a structured GPT prompt ("Generate a SolidScale quote with Context, Approach, Deliverables, Timeline, Pricing HT. Pricing: Scan 0€, Solve from 4500€, Scale upon quote.").
3. Push the output into a PDFMonkey template or a Google Doc, then send to the client.

Expected outcome: 3 hours saved per quote. For an SMB shipping 8 quotes a month, that's 24 hours, the equivalent of 3 days of sales capacity unlocked for real selling.

5. Weekly Slack and Discord recaps

You come back from vacation. 4000 unread Slack messages. You know 3 or 4 important decisions are buried in there, drowned in #random memes and #dev debug threads. You'll read 200, give up, and ask your co-founder. Multiply by 10 people, and you've got the picture.

Concrete tool: Slack or Discord API + Make (weekly cron) + GPT-4o-mini. Marginal cost: 1 to 3 € per month.

5-minute setup:

1. Connect your Slack workspace to Make (via the official app).
2. Build a cron scenario "Every Friday 17h" that fetches the last 7 days of #general, #product, #sales.
3. Prompt: "Summarize this week in 5 bullets max per channel: decisions made, blockers identified, hot topics to track."

Expected outcome: a "SolidScale Weekly Recap" email in your inbox every Friday evening. 90% of useful content captured in 2 minutes of reading. Bonus you didn't plan for: the team starts structuring messages a bit better, because they know everything is going to be summarized.

6. Smart CV screening for hiring

This is the section where I walk on eggshells, because the topic is legally and ethically sensitive. AI can help you screen 200 CVs in 10 minutes instead of 4 hours. It can also reproduce, at scale, the exact biases you're trying to avoid. Both things are true at once, which is what makes the topic uncomfortable.

Concrete tool: a Google Drive folder as input + Make + GPT for structured extraction (not final scoring, more on that below).

5-minute setup:

1. Create a "Incoming CVs" Drive folder that Make can watch.
2. On every new PDF, trigger a GPT extraction: "Extract years of experience, mastered technologies, current role level, announced availability. Strict JSON."
3. Push into Airtable or Notion with a "Match score" column at 3 levels (worth a look / maybe / not for this role) that you fill in by hand afterwards.

Expected outcome: you go from 2 minutes per CV (read + sort) to 20 seconds (validate the extraction). Final scoring stays human. Critical point: never ask GPT to score on attributes correlated with discriminatory criteria (name, photo, school, age). French law is clear, and a GDPR fail on this topic costs you reputationally before it costs you in fines.

7. E-commerce product description generation

If you've got 500 product references in Shopify or WooCommerce, you've got 500 descriptions that all read the same ("quality product, fast delivery..."). Google has been penalizing this in SEO since 2024, and your conversions are flat because nobody is really reading.

Concrete tool: Shopify or WooCommerce + Make + GPT + a prompt that enforces length and tone. Count 0.003 € per description, so 1.50 € for 500 listings.

5-minute setup:

1. CSV export of your products with title, category, key attributes (material, size, use).
2. Make batch scenario: for each row, GPT prompt "Generate an 80-word description, 2 paragraphs, [direct / warm / technical depending on your brand] tone, include 1 customer benefit and 1 verifiable technical detail."
3. Push the result back to Shopify via the API (description_html field).

Expected outcome: unique descriptions across 100% of the catalog in an afternoon. Measurable SEO effect between 3 and 6 weeks. Conversion gain: +5 to +15% observed on product pages moving from generic to specific copy.

8. Automatic replies to qualified contact forms

Someone fills out your contact form at 11pm on a Saturday. You answer Monday morning. The lead has already requested 2 other quotes in the meantime. That's standard SMB commercial life, and it's 100% avoidable.

Concrete tool: contact form (Typeform, Tally, or built into the site) + Make + GPT + your CRM.

5-minute setup:

1. Wire the form webhook into Make.
2. GPT prompt: "Write a 5-line max reply, acknowledge receipt, offer a Calendly slot within 48h, sign 'Anis'. Adjust tone if the brief mentions urgency."
3. Send via Gmail or your ESP, and create the prospect record in Pipedrive / HubSpot in parallel.

Expected outcome: -22h in average first-reply time (for evening and weekend leads). Conversion effect: +15 to +30% of leads engaging with the proposed slot. When you answer in 5 minutes instead of 48 hours, it sends a completely different signal to the prospect, and they pick up on it.

9. Automated competitive intelligence

Manual competitive intel, nobody does it. We all have that "to read" tab piling up. Meanwhile your competitor shipped a feature, dropped a price, hired your former sales lead.

Concrete tool: Google Alerts (free) + Make + GPT digest + a Slack channel or recap email. Count 0 to 5 € per month depending on volume.

5-minute setup:

1. Create 5 to 8 Google Alerts on your 3 main competitors + 2 or 3 sector keywords.
2. Pull alerts into Make (via RSS, Google Alerts exposes them as a feed).
3. Daily or weekly cron: "Summarize these 10 articles in 3 sections: Competitor moves, Sector trends, This week to investigate."

Expected outcome: 15 minutes per week of passive intel vs 0 minutes today. The value shows up over 6 months: you catch weak signals (key hire at a competitor, funding round, new partnership) with a 3 to 8 weeks lead.

10. Automated client onboarding

You close a new client. Great. Now: create the Slack channel, invite them to the Notion workspace, schedule the kickoff, send the brief template, create the shared Drive folder, push them to the CRM as "active"... You do this in 45 minutes the first time, 30 once you know the routine, and you systematically forget two steps.

Concrete tool: your CRM as trigger (the "Signed" status, for instance) + Make + Slack, Notion, Google Drive, Calendly APIs.

5-minute setup:

1. Define a clear CRM status ("Contract signed") that fires the scenario.
2. Make cascade: create a Slack channel named after the client, add internal members + external guest, duplicate the Notion project template, create the shared Drive folder with the right permissions.
3. Send a "Welcome to SolidScale" email with the links and a pre-filled Calendly for the kickoff.

Expected outcome: -40 minutes per onboarded client, and above all, 0 forgotten steps. On the client side, the first email lands 2 minutes after signature instead of 2 days. That says a lot about your operational level, and clients notice.

11. Dynamic pricing for recurring services

If you sell subscriptions or monthly retainers, your price is probably the same as 18 months ago. Inflation has eaten 6 to 10% of your margin, your production costs are up, and you haven't raised because it's uncomfortable. AI won't negotiate for you. It can, however, tell you what doing nothing costs and who to talk to first.

Concrete tool: your billing tool (Pennylane, Stripe Billing, or an Excel export) + Make + GPT + Notion or Airtable for tracking.

5-minute setup:

1. Monthly or quarterly export of recurring clients: current price, last adjustment date, ARPU, estimated margin.
2. GPT prompt: "For each client, compute the margin erosion across 18 months of cumulative inflation. List the top 3 clients to prioritize for price revision (absolute amount at stake)."
3. Generate a brief per client: justification for the new price, anchoring points for the conversation, alternatives if refused.

Expected outcome: 5 to 12% extra revenue on the installed base within 6 months, with no new clients. No other lever in SMB services is this underused. And it's also the least glamorous, which is why nobody does it.

12. NPS and text feedback analysis

You send out an NPS. You get 200 responses back. The score is 42. Now what? The score alone tells you nothing actionable. What matters is what's hiding in the verbatims. Except reading 200 verbatims and coding them by hand is 4 to 6 hours of work that nobody actually does.

Concrete tool: your NPS tool (Typeform, Delighted, or Google Form) + Make + GPT classifier + a Notion or Airtable summary table.

5-minute setup:

1. Make webhook on every new NPS response.
2. GPT prompt: "Classify this feedback in 1 sentiment (positive / neutral / negative) and 2 themes max (product, support, price, onboarding, other). Strict JSON."
3. Aggregate into a table with a "top negative themes last 30 days" view.

Expected outcome: you know in real time why your detractors are detractors. Across 6 engagements where I've shipped this kind of analysis, the top negative theme was different every time from what the team thought before the analysis. That's what you're after when you run this script: breaking an assumption nobody on the team was challenging.

What now?

Each of these 12 automations is worth between 30 minutes and a day of clean setup. Stacked, they free up the equivalent of 1.5 productivity days per week for an SMB owner, and significantly more for the team. That's the first wave. Generative AI will keep sinking into every SaaS tool you already use, and the automations that need Make today will be native in 18 months.

The useful question today isn't "should we automate". Everyone knows the answer is yes. The real question is: where do you start so you don't blow it on the first project. Because blowing the first project rarely costs money. It costs momentum. And momentum is what decides whether you ship a second one in the same quarter, or shelve the topic for 6 months.

That's exactly what we do in the Scan inside the S3 framework: 30 minutes to identify the 3 AI projects most relevant to your specific context, no pitch, no commitment. We look together at what you're already doing, what's costing you, and where AI can come in without breaking what exists. If we identify a follow-up engagement, great. If you leave with a plan of 3 projects to tackle in-house, that's also a fine outcome.