Artificial intelligence and old infrastructure? No problem!

You don’t want to miss progress and need an application of artificial intelligence to strengthen your production, marketing or services?
But you are not an IT-driven company, and your infrastructure has not been renewed for decades?
Also, the organisation and regulations make the introduction of artificial intelligence difficult?
At first glance, this does not appear to be the optimal conditions for implementing such projects.

However, it should not be an abort criterion for providing your company with machine learning.

First, your IT: Since these are modular systems, retrofitting is possible at any time without having a significant impact on business operations or even bringing them to a standstill. In this case, the existing systems are extended by additional servers or a cloud instance and are therefore explicitly used for the new applications. This is quickly sufficient for smaller calculations and is already widely used. Another, somewhat more radical option is the transition architectures, which, as the name suggests, are used for the transition from an old to a new system. The extent of the transformation, i.e. partial overhaul or complete new procurement, is up to them. Thus, there are many possibilities in IT, and usually, only a few prerequisites are necessary.

Business mechanisms: Your company structure is the other vital infrastructure that must be appropriate for the introduction of AI. Here, the dimensions vision, organisation and governance should be considered and critically questioned. The vision is the most substantial criterion here, which often directly outweighs many other problems. „I would like to become the market leader in my sector“ or „I would like to benefit from today’s technical possibilities“ are good visions when it comes to AI. Such goals serve as enablers to advance AI and to recognise the various advantages of AI and thus to create a breeding ground for such ideas.
As long as the vision is right, many other problems are more comfortable to overcome, although the organisation and governance structures of the company must nevertheless be compliant. If the competences in an organisation are managed centrally, it is more difficult to use a decentralised AI for decision making. Hybrid forms can lead to the fact that the decisions of AI are not perceived or are perceived only indirectly. The same is true for governance rules. If only a few people are authorised to use the AI for data protection or similar purposes, the AI offers value to only a few people. If the data for the AI is only scarce and are fed in from a few departments, the evaluation will also only provide poor results or results tailored to these few departments. Again, this would not add any overall value to the company.

To approach the topics in a meaningful order and to define the focal points, it is essential to identify the individual use cases and their requirements precisely. Based on this, the necessary Infrastructures and organisations can be defined and implemented.
It is, therefore, vital to consider each case to create environments tailored to it and thus achieve the most significant synergy effects. Individual advice from qualified companies or institutions is very often a good starting point for this, as other companies have already requested many application cases and the planning phase can, therefore, be accelerated considerably if existing knowledge is used.

Also, for such tasks, MEJOIN offers support to assist in the planning of companies.

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