One of the fastest-growing areas in digitization is Artificial Intelligence (AI). According to Gartner, by the year 2025, the use of AI will be widespread and will lead to technology investment by companies worldwide.
This technology has become a fantastic tool with an exemplary algorithmic technique, but on many occasions, it has yet to be known how to use it to generate business value.
Perhaps, for this reason, Artificial Intelligence projects still need to reach production phases as much as they should. As psychologist and Harvard University professor Howard Gardner points out, 85% fail, leaving teams frustrated.
In this context, Kepler Data Tech points out eight steps that any company must undertake to scale the use of AI throughout their organization:
Data infrastructure: Every company should consider the public cloud as an environment to build their infrastructures. The primary public clouds invest more than 90 billion dollars in research and development, guaranteeing an infrastructure with capacities to assume future situations and needs.
Dedicated resources: It is necessary to dedicate specialized people to this type of data platform, either hiring or relocating those key employees who, due to their technical and organizational knowledge, can lead the initiatives.
Organizational silos: Generate a culture around data that avoids organizational silos since this prevents the reuse of project investments between one business unit and another.
Data Silos: Avoid data silos so that a functional disconnection does not occur since data loses value over time. We believe that the most appropriate approach is to have them as close as possible to where they are produced and make a more federated model for the data access ( data mesh ), which allows the use of data in the context where it is generated and also can be reused in other areas for specific use cases. The data mesh is a natural evolution for organizations that are scaling AI use cases.
Value proposal: It is tough to analyze what a use case will bring to a company, but one must consider that working with data is iterative and experimental. The companies that succeed are the ones that fail quickly and quickly move on to another goal. At this point, we always recommend carrying out incremental, iterative approaches in which the data is explored, a pool of limited information is identified, a proof of concept is carried out, knowledge is extracted, and, above all, decisions are made based on the results of this project.
AI governance model: The company’s data model will depend on their organisational model. There are highly regulated companies in which decision-making is highly centralized, and others that can delegate that decision-making to other business areas, which leads them to be highly independent. It is the great dilemma between speed and control over the data. Despite this debate, there are a series of minimums at the level of security in design, privacy in design, and management of services. All this must be automated and centralized but capable of supplying the different areas.
Access to the talent that allows data to be used appropriately is currently very complex and scarce. 46% of companies need help finding the digital profiles they need and are looking for. To counteract this, companies are beginning to make analytical career plans related to new technologies, but it is still a topic that many companies have to define clearly.
Effective implantation: It is necessary to align AI and process automation efforts that help from the business point of view. Small experimental projects that have been proven to work remain in the drawer and are only helpful if they are adequately implemented or put into production, which is the added value of this type of project.
The public cloud multiplies the capabilities of artificial intelligence. Still, a series of essential components are necessary to know and know how to use to prevent these silos from being generated, or these experiments with AI remain in the drawer.
It can do many things, but it must take some components around the fundamental technology and must take some parts around the basic technology into account to make a scaled and actual use in AI.
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