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AI implementation levels

I'm not limited to one industry — the offer is structured by the complexity and risk of the implementation, not by sector. Below are six levels, from diagnosis to full process automation, with examples from different industries.

AI Readiness Audit

A diagnosis of processes, data, systems, and team skills — no implementation yet. An entry point that fits any industry.

  • Process review focused on automation potential
  • Data quality and availability assessment
  • Systems (APIs, integrations) and team-skills readiness assessment
  • A report with recommendations and priorities

Before implementing anything, we check whether — and where — AI actually makes sense for your company. The audit ends with a concrete report: what’s worth doing first, what not to do at all, and which risks (data, integrations, security) need to be accounted for.

It’s the cheapest and fastest way to verify whether a further conversation is worthwhile — without the risk of a costly implementation that misses the mark.

Point quick-wins

2-4 weeks, low risk. A concrete, narrowly-scoped implementation with a measurable effect.

  • FAQ/support chatbot (e-commerce, SaaS, internal HR)
  • Categorizing and prioritizing emails and tickets (customer service, IT helpdesk, freight forwarding)
  • Summarizing meetings and documents, generating drafts (legal, sales, content production)
  • Extracting data from documents (invoices, waybills, contracts) into structured form

The first implementation doesn’t have to be big. At this level we look for one concrete process where AI delivers a quick, measurable effect — without rebuilding systems and without much risk.

It’s a good starting point for companies that want to see real value before investing in something bigger.

Communication vs. system-state analysis

Automatically comparing communication content (emails, orders) against ERP/CRM/WMS system state and flagging discrepancies.

  • Comparing a customer's order against its status in the TMS (freight forwarding)
  • Comparing an order against warehouse stock levels (manufacturing)
  • Detecting anomalies in customer communication (sentiment, churn risk, escalations) correlated with transactional data

Here AI starts connecting two worlds that live separately in many companies: unstructured communication and transactional data in systems. The result is earlier detection of discrepancies and risks, before they become a problem visible to the customer.

This already requires integration with at least one internal system — a natural next step after a successful Level 1 quick-win.

Integration with internal systems

RAG and LLM integrations connected directly to CRM/ERP/ticketing and team collaboration tools.

  • RAG over internal documentation / knowledge base connected to Slack or Teams
  • LLM integration with CRM/ERP/ticketing via API — auto-filling records
  • Generating responses with context pulled directly from the system

At this level the implementation stops being an add-on — it becomes part of the team’s daily work, connected to the systems the company actually runs on. This requires exactly the kind of foundation I’ve built over 18 years: integration, monitoring, security, and maintenance under high SLA.

This is where SLA, security, and monitoring expertise become critical — the client is buying stability, not just functionality.

Agentic implementations / end-to-end automation

An agent handling the entire process — from ticket triage to escalating to a human when needed.

  • Ticket triage → proposed resolution → system update → escalation to a human
  • Logistics: exception tracking and route re-planning
  • Manufacturing: downtime prediction and recommendations
  • Social media: moderation, responses, and reporting

This is the level where AI stops supporting a single step in a process and starts driving the whole process on its own — with a clearly defined moment where it hands the decision back to a human. It requires mature Level 3 integrations and a solid operational foundation (monitoring, alerting, a fallback plan).

An end-to-end agent needs clearly defined boundaries of responsibility and solid monitoring — otherwise the risk of error grows along with its autonomy.

AI strategy and building dedicated systems

A full audit, transformation roadmap, building a dedicated platform or agents, and retainer maintenance.

  • 12-24 month AI transformation roadmap
  • Building a dedicated platform or agents tailored to the company
  • Ongoing retainer: development, maintenance, security monitoring

A natural next step after a successful Level 0-1 engagement: a full analysis of where AI can change how the company operates over the long term, plus building and maintaining the systems that make it possible. A long-term engagement, usually on a monthly retainer.

Let's talk about your process

30 minutes, no strings attached. Describe the problem and get a candid assessment of whether — and where — AI will actually help, and what it takes for the implementation to survive contact with production.

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