.svg)
Artificial intelligence has transformed process automation. By 2026, automating with AI will no longer be limited to connecting two applications: modern tools can analyze documents, classify emails, generate content, and make decisions autonomously.
But where to start? How can you identify the right processes to automate? Which tools should you use? This practical guide answers these questions and provides you with a five-step methodology for a successful AI automation project.
Which tool should you choose?
For a comparison of the best AI automation tools, check out our dedicated guide:
→ What software to use to create AI automations?
What is AI automation?
Automating with AI means using artificial intelligence to perform tasks without human intervention. Unlike traditional automation (if/then rules), AI can process unstructured data, learn from its mistakes, and adapt to new situations.
The 3 levels of AI automation
→Level 1 — Simple automation: Connecting applications (when X happens, do Y). Example: send an email when a form is filled out.
→Level 2 — Intelligent automation: AI analyzes and decides. Example: automatically classify incoming emails by urgency and route them to the right department.
→Level 3 — Autonomous AI agents: AI reasons, plans, and executes complex tasks. Example: an agent that searches for information, writes a report, and sends it to stakeholders.
AI agents represent the most advanced level of automation. To find out how they can be applied to your business, visit our page dedicated to AI agents for businesses.
What processes can be automated using AI?
Not all processes are good candidates for automation. Here are the criteria for identifying those that offer the best ROI:
The ideal processes for automation with AI
✓ High volume: Tasks performed dozens or hundreds of times per day
✓ Repetitive: Same steps, same rules each time
✓ Rule-based: Clear logic (even if complex)
✓ Multi-application: Data to be transferred between multiple tools
✓ Time-consuming: Significant human time consumed
Concrete examples of AI automation by function
Marketing & Sales:
→ Automatic enrichment of CRM leads with LinkedIn data
→ AI scoring of prospects based on their behavior
→ Generation of personalized content (emails, posts)
Finance & Accounting:
→ Automatic extraction of invoice data (OCR AI)
→ Smart bank reconciliation
→ Detection of anomalies in expense reports
Human Resources & Administration:
→ Sorting and pre-qualifying incoming resumes
→ Automated onboarding of new employees
→ Automatic responses to frequently asked HR questions
IT & Support:
→ Classification and routing of support tickets
→ Automatic resolution of Level 1 incidents
→ Monitoring and smart alerts
💡 Concrete example: automating invoice processing
A medium-sized company receives 500 invoices per month by email. Before: 1 FTE to open, read, and enter them into the ERP system. After AI automation: the tool automatically extracts the data (99.5% OCR), validates it, creates the accounting entry, and archives the document. Savings: 80% of time, 0 data entry errors.
How to automate with AI? A 5-step methodology
Step 1: Map your current processes
Beforeautomating with AI, you need to understand your existing processes. For each candidate process, document:
• The exact steps (who does what, in what order)
• The applications used at each stage
• Volume (number of executions per day/week/month)
• Time spent per execution
• Common errors and their causes
Step 2: Prioritize by ROI
Rank your processes according to the formula: ROI = (Time saved × Hourly cost × Volume) / Cost of automation
Start with the "quick wins": high-volume, low-complexity processes with a fast ROI. Save complex projects for later, once the team is up to speed.
Step 3: Choose the right AI automation tool
The choice of tool depends on the complexity of your needs:
→Simple workflows between SaaS apps → Zapier, Make, Save Time Factory
→Automation with advanced AI (OCR, NLP) → Make, n8n, FlexFlow
→Integration of critical systems (ERP, HRIS) → FlexFlow, MuleSoft
→Autonomous AI agents → n8n (LangChain), custom development
Step 4: Build and test the automation
Once you have chosen the tool, build your automation:
1. Connect applications via API or native connectors
2. Define the trigger: what event initiates the automation?
3. Configure actions: what should the AI do at each stage?
4. Add conditional logic: handle special cases
5. Test on real data: check each branch of the workflow
Step 5: Deploy, measure, and optimize
Automation is never "finished." After deployment:
• Monitor executions: success rate, errors, execution time
• Measure the actual ROI: time saved, errors avoided, user satisfaction
• Continuously optimize: adjust rules, add cases, improve AI accuracy
What mistakes should be avoided when automating with AI?
Mistake 1: Automating a poorly defined process
If the manual process is unclear, automation will amplify the chaos. Take the time to document and optimize the process BEFORE automating.
Mistake 2: Wanting to automate everything at once
Start small. A successful first project builds confidence and expertise. Overly ambitious projects often fail due to a lack of resources or buy-in.
For complex projects or if you lack internal resources, calling on a service provider specializing in AI automation can speed up implementation and secure results within the first few months.
Mistake 3: Neglecting error handling
What happens when the API does not respond? When the data is incorrectly formatted? Plan for alerts, fallbacks, and recovery procedures.
Mistake 4: Forgetting the human factor in the loop
For critical decisions, keep human validation. AI can make suggestions, but humans have the final say. This is also a compliance requirement in certain sectors.
Automating with AI: where to start?
Want to automate with AI but don't know where to start? Here are three quick-win projects to get you started:
→Automate lead management: When a form is filled out → enrich the contact → score using AI → create in the CRM → notify the sales rep. Tools: Make + CRM + enrichment (Clearbit, Dropcontact).
→Automate email processing: Classify incoming emails using AI → route them to the right department → automatically respond to frequently asked questions. Tools: Make or n8n + OpenAI.
→Automate competitive intelligence: Monitor competitor websites → extract new content → summarize using AI → send a weekly report. Tools: n8n + scraping + LLM.
Conclusion: Take action to automate with AI
Automation using AI is no longer reserved for large companies or technical experts. With the right tools and a structured methodology, any organization can increase productivity, reduce errors, and free up time for value-added tasks.
The key is to start small: identify a simple, repetitive process, automate it, measure the gains, then gradually expand. Each successful automation creates expertise and confidence to go further.
French companies now have access to high-performance sovereign solutions: Save Time Factory for turnkey automation, FlexFlow for IT integration for mid-sized and large companies. There's no excuse not to get started.
Need assistance? → Talk to an IT Systems expert
.svg)