Integrate AI into your processes – all the way to the autonomous organization
There is a big gap between a few ChatGPT use cases and a company where AI productively runs processes. I guide technical SMEs through this transformation—systematically integrating AI into their existing processes and handing over tasks to AI agents step by step.
Where can AI actually take over work in your company?
- Identify AI potential
- Redesign processes with AI
- Put AI agents to productive use
Dr. Hai Van Le Jorks
Founder · Electrical Engineer · Consultant for AI Transformation & Autonomous Organizations
- PhD in Electrical Engineering
- Around 10 years as a development engineer in the automotive industry – power electronics and high-voltage battery systems
- Founder of Osos AI GmbH (OSOS/Omega) – run as an AI-native, autonomous organization
“I don’t just advise on autonomous organizations – I build and run one myself.”
My approach: I run an AI-native company myself
My approach: I run an AI-native company myself
My own company is set up as an autonomous operating organization – a large share of operational tasks is already supported or handled by AI agents. So I know not only the theoretical potential of agentic AI, but also the practical questions:
- Which tasks can really be automated?
- Where do AI agents need clear processes and control mechanisms?
- How do you organize collaboration between people and AI?
- How do you prevent many AI tools from turning into an unmanageable system?
- How does a company change when AI is no longer just an assistant but an operational actor?
Engineering expertise meets AI expertise
Engineering expertise meets AI expertise
As a development engineer in the automotive industry, I experienced complex development organizations, technical interfaces and processes first-hand: V-model and systems engineering, requirements engineering, development and release processes, technical documentation, testing and validation, and the collaboration between different departments.
That is why I don’t look at AI only from an IT or management perspective – I understand the reality of technical development processes.
You can start small.
One specific process, one team or a single AI agent.
Typical questions companies bring to me
Typical questions companies bring to me
- “Where does AI actually pay off in our company?”
- “Which of our processes can we already automate today?”
- “How can AI agents take load off our engineers and project managers?”
- “How do we integrate AI into existing systems and processes?”
- “Which tasks can AI agents handle on their own?”
- “How can we start with a small pilot project instead of a large AI program?”
- “What could our company look like if AI takes over a large part of the operational work in the future?”
Where AI creates real value
Technical companies in particular have a lot of knowledge, many documents and recurring alignment work – but limited resources for automation. That is where the greatest potential lies.
Engineering: requirements & technical documentation
Engineers get back to developing instead of maintaining documents.
Engineering: requirements & technical documentation
Engineers get back to developing instead of maintaining documents.
- Today
- Requirements, specifications and release documents are written, cross-checked and updated by hand – often spread across Word, Excel and ALM tools.
- With AI
- AI agents draft requirements and documents, check them for gaps and contradictions, and keep the links between requirement, design and test up to date.
- Value
- Less routine work for engineers, more consistent documentation and faster development and release cycles.
Testing & validation
Test cases and evaluations come straight from the requirements.
Testing & validation
Test cases and evaluations come straight from the requirements.
- Today
- Test specifications are derived from requirements by hand, and test results are painstakingly collected and assessed.
- With AI
- AI derives test cases from requirements, summarizes test results and flags deviations that a person should review.
- Value
- Higher test coverage for the same effort and faster feedback to development.
Project management & coordination
Project managers steer instead of chasing people.
Project management & coordination
Project managers steer instead of chasing people.
- Today
- Minutes, status reports and task lists cost project managers many hours every week – and follow-up actions still get lost.
- With AI
- AI agents write minutes and status reports, derive tasks, remind owners and trigger follow-up actions on their own.
- Value
- More time for decisions and customers, less friction between departments.
Sales & quotation
Faster, better-founded quotes for technical inquiries.
Sales & quotation
Faster, better-founded quotes for technical inquiries.
- Today
- Technical customer inquiries have to be read, matched against past projects and turned into a quote – often by scarce engineering resources.
- With AI
- AI analyzes inquiries and specifications, finds comparable projects and prepares draft quotes including open questions.
- Value
- Shorter response times, less load on engineers and a higher quote rate.
Purchasing & data transfer between systems
No more copy & paste between datasheets, ERP and Excel.
Purchasing & data transfer between systems
No more copy & paste between datasheets, ERP and Excel.
- Today
- Datasheets and supplier quotes are compared manually, and data is transferred between systems by hand.
- With AI
- AI extracts technical data from datasheets and quotes, prepares comparisons and transfers data between systems in a structured way.
- Value
- Fewer transfer errors, faster purchasing decisions and relieved departments.
Knowledge management & internal administration
Technical knowledge becomes usable for everyone – not just experts.
Knowledge management & internal administration
Technical knowledge becomes usable for everyone – not just experts.
- Today
- Knowledge sits in people’s heads, emails and thousands of documents. New employees take a long time to find their way around.
- With AI
- An AI assistant answers questions based on your own documents – with sources – and takes over recurring administrative tasks.
- Value
- Faster onboarding, fewer questions to key people, and knowledge that stays in the company.
5 stages to the autonomous organization
I support you from wherever you are today.
5 stages to the autonomous organization
I support you from wherever you are today.
- 1
Identify AI potential
We analyze your existing processes and identify tasks where AI delivers concrete business value – e.g. in engineering, project management, documentation, sales, purchasing or administration.
- 2
Redesign processes with AI
Existing workflows are restructured so that AI can be integrated meaningfully – not as an add-on tool, but rethought from the ground up.
- 3
Use AI agents for specific tasks
AI agents handle defined tasks largely on their own: analyzing information, creating documents, transferring data between systems, preparing decisions, triggering follow-up actions.
- 4
Build agentic workflows
Several specialized AI agents work together and take over complete process chains. People step in where decisions, responsibility or expertise are needed.
- 5
Develop the autonomous organization
Entire areas of work are run by AI agents – people increasingly take on a steering and supervising role.
Who is this for?
Technical SMEs with complex products and processes.
Who is this for?
Technical SMEs with complex products and processes.
- Mechanical engineering and technical product development
- Automotive and supplier industry
- Electrical engineering and electronics development
- Energy technology
- Electrical installation and technical planning
- Photovoltaics and solar technology
- Battery and energy storage systems
- Engineering and development services
My goal
My goal
Not to introduce as many AI tools as possible.
But to create an organization where people and AI work together meaningfully – and AI takes over operational work wherever it creates real value.