8 Success Keys Experimentation of Artificial Intelligence

8 Success Keys Experimentation of Artificial Intelligence

One of the fastest-growing areas in digitization is Artificial Intelligence (AI). According to Gartner, by 2025, AI will be widely used and will lead technology investment by companies worldwide. This technology has become a fantastic tool with an exemplary algorithmic technique, but on many occasions, it is not known how to use it to generate business value.

Perhaps, for this reason, Artificial Intelligence projects are still not reaching production phases as much as they should. As Harvard University psychologist and professor Howard Gardner point out, 85% of them fail, leaving teams frustrated. 

In this context, Kepler Data Tech points out eight steps that any company must take to scale the use of AI throughout its 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 capabilities to assume future situations and needs.

Dedicated Resources

Dedicating specialized people to this data platform is necessary by hiring or relocating those key employees. Due to their technical and organizational knowledge, they 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 to make a more federated model of data access ( data mesh ), which allows the use of data in the context where it is generated and also be reused in other areas for specific use cases. Data mesh is a natural evolution for organizations that are scaling AI use cases.

Value Proposal

It isn’t straightforward to analyze what a use case will contribute to a company, but we 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 incremental interactive approaches, where you explore the data, identify a limited data pool, perform a proof of concept, extract insights, and make decisions based on the results of your research—this project.

AI Governance Model

The company’s data model will depend on its organizational model. There are highly regulated companies in which decision-making is highly centralized, and others can delegate decision-making to other business areas, which leads them to be very independent. It is an excellent dilemma between speed and control over 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 of 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 Spanish companies experience difficulties finding the digital profiles they need and are looking for. To counteract this, companies are starting to make analytical career plans related to new technologies, but it is still an issue that has to be clearly defined in many companies.

Effective Implementation

It is necessary to align efforts in AI and automation of processes that help from the business point of view. Small experimental projects that have been proven to work remain in the drawer and are useless if an adequate implementation or start-up is not achieved, 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 those experiments with AI from being left in the drawer. It can do many things but must take some components around the basic techniques and must take some parts around the basic techniques into account to make a scaled and actual use in AI.

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