Implementing AI: The 6 critical components

In the ongoing fourth industrial revolution, organisations are trying to implement artificial intelligence for getting a competitive edge in the market. Nonetheless, the lack of information and AI experience leads to common mistakes. In this post, we talk about how to avoid the most common pitfalls by means of the 6 critical components of AI.

Today, artificial intelligence is widely recognized as one of the technologies leading the fourth industrial revolution. Organizations are aware about this and their management are already putting in place different strategies and initiatives to implement the technology.

The problem they face though, is the yet unknown nature of artificial intelligence. Even though literature about the topic is exponentially increasing, the business implementations are still just a few and therefore the experience on how to deal with it scrase. In order to avoid the recurring mistakes that AI is causing, organizations must be aware of the 6 AI critical components for a proper infallible AI implementation. In this post, I am going to talk about first, the most common AI mistakes and how the 6 critical components can stop them from happening.

Organizations have understood the relevance and benefits of AI for the next decades. They have therefore developed a certain urge to get AI in place. The problem is that most companies decide to simply “do AI” without a meaningful strategy to roll it out. Of course this leads to a waste of resources, energy, and worst of all, lust for the technology.

Imagine for instance an AI enthusiast convincing his boss to invest 30% of his capacity to develop a predictive maintenance model for their lab equipment and, after 6 months looking for the necessary data, not finding it. Now imagine that the data stewarts were blocking the release of this data because they had some sort of fear towards the use case. Now let’s suppose the data is found and the people in the lab are engaged. Few months after gathering all the data, the AI engineer tells the boss that the cloud computing power necessary to carry out the project costs 70% of the entire budget from that department. The project is declined.

These are situations that repeat constantly in organizations which, as aforementioned, lead to a demotivation towards AI. Here are some of the most common mistake in AI:

  1. Development of randomly chosen AI use cases.
  2. Failing to bring pilots into production.
  3. Lack of required skills and resources to implement AI.
  4. Wrong investment in AI resources due to lack of understanding.
  5. AI after-development not considered (e.g. maintenance)

In order to avoid these mistakes and therefore roll out AI in a proper infallible way, I am going to briefly recap the post “3 steps to successfully implement AI in your organization” that I wrote a few weeks ago. These three steps are:

1.) Defining an AI vision: This is an imperative first step which will serve as a standard for any decision taken towards  AI. 

2.) Identifying AI use cases: The identified use cases will be in line with the vision and company goals. This will in turn serve as a motivation for all organization members to pursue them with the right amount of resources and engagement.

3.) Implementing the 6 AI critical components: Having an AI vision in place and identifying appropriate use cases is a really good start, but just the half of the job is done. As we have seen during the introduction, there are several things that could go wrong when implementing AI “for the sake of implementing AI”. The solution to it are the AI critical components.

There are a total of 6 AI critical components for an infallible AI rollout: organization, expertise, culture, data, infrastructure and ethics and consciousness. The degree to which to consider them will differ from organization to organization depending on their size, shape, context and overall vision. Following a brief description of each critical component:

  • Organization: In order to successfully implement AI, organizations need to put in place the appropriate AI environment and governance. It is especially important to help employees think about AI and how to work with it.
  • Expertise: Organizations need to have the right talent in place to support their overall AI strategy. A ”core AI” team should slowly be conceived and grown.
  • Culture: Besides organization and expertise, culture is another important critical component based on fostering collaboration. Organizations should carry out activities to ensure such collaboration.
  • Data: Without data there is no AI. Data capturing, storing, pre-processing, analyzing and visualizing is part of any ML process. Organizations must have the appropriate systems to ensure available ready-to-use data.
  • Infrastructure: AI is a science that requires certain infrastructure characteristics that differ from those of typical IT. Organizations need the optimal infrastructure and technology to scale AI.
  • Ethics and consciousness: AI could be both healing and harmful for society depending on its use. Organizations must carry out a set of activities to ensure that AI has a positive impact on society. In AI Shepherds we are proud to promote this component as one of the critical ones. You can read more about the ethics here “Three main AI ethical values”.

It is important to highlight that there is not a specific order to implement each critical component. Totally the contrary. Our suggestion is to slowly develop all of them in parallel. With this approach, a faster implementation of AI in your products, processes and services is ensured, rather than waiting for each critical component to be developed before starting with the next one, which will lead to loss in market competitiveness. In the following image I am connecting how the different AI critical components are mitigating the aforementioned mistakes.

AI is what we do for living, so we know what we talk about. We firmly believe that the right steps to follow when venturing into AI technology are: defining an AI vision, identifying AI use cases, and developing the 6 AI critical components accordingly. Especially with the latter, knowing the right timing and degree of implementation can be tricky and really costly. In AI Shepherds we have defined a set of services to guide you efficiently through each step of the way. Avoiding mistakes and ensuring success. It’s our mission. It’s our fate.

Albert Pujol
Mechanical Engineer with an MBA and a Master in Big Data and Data Science. Co-founder and CEO of AI Shepherds. Building stuff is what I love most and do best. My vision is a conscious world.

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