Why is using an AI Ecosystem important?

No company has to cover AI deployment entirely on its own. Large companies often build their own AI teams and fixed cooperation structures, while smaller companies and start-ups benefit more from selectively accessing individual external competencies rather than building them themselves. Sometimes the exchange of experiences with another company suffices, while a university or research institution might be more suitable for a technical feasibility study. If a suitable solution already exists in the market, a specialized provider is often the better choice.

The use of the AI ecosystem remains an ongoing process, closely linked to specific application cases and to be regularly reviewed. External support also incurs effort and costs and may create dependencies. The goal is not to establish as many contacts as possible, but to use the ecosystem where it offers a real advantage over a purely internal solution.

Using an AI ecosystem is particularly useful if a company:

• needs technical expertise for a specific AI project not available internally,

• wants to first determine if an AI idea is technically feasible,

• is looking for an already available AI solution on the market instead of developing one itself,

• wants to benefit from the experiences of other companies with a similar task,

• does not yet know which contact person is suitable for an AI project, or

• wants to temporarily compensate for a skills shortage in its own company with external expertise.

The earlier you determine what type of support you actually need, the more targeted you can use the AI ecosystem for your company.

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How to use the AI Ecosystem for your company in 5 steps:

• Step 1 – Clarify what your company cannot cover itself
  Start with a specific gap, not by searching for contacts. Is technical expertise missing? Is a ready-made solution sought, or do you want to learn how other companies solve a similar task? Additionally, note how much time the missing competence currently costs or how often it leads to errors to assess urgency. Also, check what is already available internally – external help is of little use if requirements or processes in your own company have not yet been clarified.

• Step 2 – Determine what kind of support you need
  Formulate a specific, measurable goal instead of a general intention – for example: "In four weeks, we will know if our error images can be automatically recognized with existing product images." Depending on the need, different contacts may be appropriate: other companies or industry formats for experience exchange, a specialized provider or a startup for a market-available application, research institutes for an open technical question or feasibility study. If it is still unclear who can help, transfer offices, chambers, or other public contact points narrow down the search.
  

• Step 3 – Search based on the specific task
  Choose a narrowly defined, specifically formulated task as an entry point. "We want to check if quality defects can be automatically recognized in our product images" already significantly narrows down the required experience and technology. Search specifically for industry, application field, technology, and experience with similar tasks; research and transfer maps, regional contact points, or suitable events help identify appropriate contacts. Avoid broadly spread requests without a clear application case; they take a lot of time and rarely deliver suitable results.

• Step 4 – Establish responsibilities and check if the collaboration fits
  Appoint a fixed contact person to accompany the cooperation, plan the timeline, gather feedback, and report progress to management. They also check if the collaboration fits contextually: General AI experience alone is not enough – ask about comparable projects and if your counterpart understands the specific process of your company. Clarify before starting who takes on which tasks, what the expected outcome is, what internal expertise is needed, and what dependencies arise, such as if only the external provider can adjust or explain the solution.
  

• Step 5 – Start with a manageable project and evaluate after 4 to 8 weeks
  A new collaboration does not have to be planned as long-term immediately: A workshop, a feasibility study, or a clearly limited pilot project is often enough to check if it works. Agree on a fixed date at the beginning for the summary after 4 to 8 weeks: Was the goal from step 2 achieved, did coordination work, were promises kept, and did your company gain usable results or relevant knowledge? Record the insights in writing and decide whether the collaboration will be ended, expanded, or continued permanently.

You can find this and many more pieces of information on the AI Compass topic "Ecosystem" in our download portal: