The Key to a Successful AI Project? It’s Not What You Think

Last updated on March 12th, 2023 at 04:39 pm

Did you know that just 15% of artificial intelligence initiatives succeed in getting to the production or operation stage? Why is this the case, exactly?

It is widespread for organizations to think of innovative ways to employ AI to enhance their customers’ experience or streamline their operations.

The time and money required to get these initiatives from concept to reality are often the factors that stand in their realization as successful endeavors. 

But, as we’ve seen with OpenAI’s brand-new ChatGPT, artificial intelligence (AI) has the potential to be both fun and harmful.

There have been so many failed initiatives, or even worse, erroneous results, that there is a good likelihood that many of these businesses are repeating the same errors. 

The following are some suggestions that, if followed, will increase the possibility of your achievement.

Important to Start Out Right

Developing AI is plagued by issues relating to inadequate planning, project management, and technical flaws. Most of today’s business executives learn about AI from the media. 

It is problematic since the media often portrays the usefulness of AI as something like magic or something that can be put into production with just a few dashes.

By applying AI, it will be possible to reduce expenses, enhance margins, and increase revenue. 

The fact that competitors are already working on AI creates a “fear of missing out” (AI FOMO), and executives feel pressured to take action as soon as possible, even though they do not have a clear understanding of the overall impact, plan, cost, and resources involved in developing a successful and accurate AI project.

Since there is a need for more awareness of the technical environment, the first natural move should be to hire data scientists to map out and outline how the team would tackle any potential issues. 

Yet, these data scientists often need more expertise in their subject.

Starting your AI project without giving any thought to the models is the most reliable approach to guarantee that you are progressing down the right road for the creation of AI.

When data scientists join a new company to automate and better business operations, they often attempt to manually gather sufficient data to demonstrate the benefit of developing AI.

When a suitable proof of concept has been created, the team will usually encounter difficulties regarding data management. 

8 key roles of successful AI projects | CIO
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Read: Role of AI in Future Industries

It’s possible that the company doesn’t acquire, store, or handle the data in a manner that’s “friendly” to artificial intelligence.

For instance, a manufacturing facility that wants to use intelligent defect inspection on a production assembly line can quickly demonstrate the AI project by using a single camera on a machine for a few minutes. 

It will only take a few minutes. Nevertheless, for the project to enter production and be utilized daily, the demo with a single camera will need to be expanded to include 500 cameras that are operational around the clock. 

Completing this task and demonstrating AI’s value in a demonstration will take several months or even years.

Executives should, of course, have a clear notion of the issue they intend to address and a business case in mind before making any decisions. 

Nonetheless, the AI core team should consist of at least three personas: a data scientist, a data engineer, and a domain expert. Each persona will play a crucial role in ensuring the project’s success.

Read: From Keyword to Conversational: How AI is Revolutionizing Search

A Beginning That Is Centered On Data

The first AI initiative that is effective inside an organization should not entail algorithms or sophisticated AI models, even if this seems unusual. 

In its most basic form, artificial intelligence (AI) is an attempt to automate knowledge. 

As with any work involving automation, it is a good idea to demonstrate the usefulness of a few instances in a way that is manual, sluggish, and non-scalable.

The data engineer will use data to construct a few case studies during the kickoff, and the domain expert will turn these case studies into examples. This stage reveals the essential workings of the AI process. 

The company will have access to the raw data and a specific objective in mind; next, they will wish to get a sample of the output data in its most optimal form.

This procedure is referred to as data development, and as no modeling is involved, the technique is data-centric by definition. 

In comparison to the model-first approach, this method offers various benefits, including the following:

  • It takes less investment;
  • It makes use of the capabilities that the firm already has (process, data, and subject knowledge);
  • That is much quicker; and
  • It reduces the danger.

As soon as a few samples have been finished manually, the company can determine the route the AI will take to go into production.

Related: 8 AI Applications That are Part of Our Daily Life



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