Why is using an AI ecosystem important?

No company has to cover the use of AI entirely on its own. Large companies often build their own AI teams and solid cooperation structures, while small companies and start-ups usually do better by selectively accessing individual external competencies rather than building them themselves. Sometimes exchanging experiences with another company is sufficient; for a technical feasibility check, a university or research institution is more suitable, and if there is already an appropriate solution on the market, a specialized provider is often the better choice.

Using the AI ecosystem remains a continuous process, closely linked with specific use cases and regularly reviewed. External support also causes effort, costs, and can create dependencies. Therefore, it is not about building as many contacts as possible, but using the ecosystem where it offers a real advantage over a purely internal solution.

Using an AI ecosystem is particularly useful when a company:

• requires technical expertise for a specific AI project that is not available internally,

• wants to first clarify whether an AI idea is technically feasible at all,

• 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 comparable task,

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

• wants to offset skilled labor shortages in its own company through external competency on a situational basis.

The earlier you clarify what kind of support you actually need, the more targeted the AI ecosystem can be utilized for your company.

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Using the AI ecosystem for your company in 5 steps:

Step 1 - Clarify what your company cannot cover itself

Start with a specific gap, not the search for contacts. Is technical expertise missing? Are you seeking a finished solution, or do you want to know how other companies solve a comparable task? Also, note how much time the missing competence currently costs or how often it leads to errors to assess the urgency. Also check what is already available internally – external help is of little use if requirements or processes in the own company are not yet clarified.

Step 2 - Determine the type of support you need

Formulate a specific, measurable goal instead of a general intention – for instance: "In four weeks we will know if our error patterns can be automatically recognized with existing product images." Depending on the need, different contacts may be considered: other businesses or industry formats for experience exchange, a specialized provider or a start-up for a marketable 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 a starting point. "We want to check if quality errors in our product images can be automatically recognized" already clearly limits the necessary experience and technology. Specifically search by industry, field of application, technology, and experience with comparable tasks; research and transfer maps, regional contact points, or suitable events help identify appropriate contacts. Avoid widely spread inquiries without a clear application case, they take up a lot of time and rarely yield appropriate results.

Step 4 – Define responsibilities and check if the collaboration fits

Appoint a fixed contact person who accompanies the cooperation, plans the timeframe, collects feedback, and reports progress to management. They also check if the collaboration is suitable: General AI experience alone is not enough – ask about comparable projects and if the counterpart understands the specific process of your company. Clarify before starting who will take on which tasks, what result is expected, what internal expertise is needed, and what dependencies arise, for example, 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 immediately set up for the long term: A workshop, a feasibility study, or a clearly limited pilot project is often enough to test if it works. Agree on a fixed date at the start for the evaluation after 4 to 8 weeks: Has the goal from step 2 been achieved, did the coordination work, were commitments kept, and has your company gained usable results or relevant knowledge? Document the insights in writing and decide whether the collaboration will end, be expanded, or continue permanently.

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