From identifying the right use case to investing in modern data platforms, there are several things companies need to consider while implementing AI.
A 2019 Gartner survey found that nearly 40% of organizations have implemented AI in some form. However, keeping up with the growing number of Artificial Intelligence (AI) applications in businesses is only a part of a much larger whole. The crucial task is to apply these AI solutions smartly, ethically, and economically since each of them comes with unique challenges.
Here are five key things organizations should consider while implementing AI.
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Narrow down the cause for AI implementation
Although this seems obvious, this is a key requirement. AI implementation without a cause is a bad idea that results in losing a significant amount of an organization’s time and resources. Business leaders can become overwhelmed by the vast possibilities AI can bring to their company; hence, it is even more crucial to focus on the actual requirement and see if it is feasible. At the same time, it is also essential for leaders to try different formats and not worry only about failures.
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Stay connected with businesses
It is essential to stay connected with the business throughout the lifecycle of an AI initiative. Also, IT leaders should understand what the business needs to achieve and learn about their issues as there are several decisions that need to be taken during the journey of the AI application.
This understanding will help companies not only secure the necessary investment but also garner additional funds if the AI application proves to be more promising than anticipated. Business and IT leaders need to understand that AI can never guarantee 100% automation or accuracy.
Hence, understanding how the business plans to resolve errors or exceptions is vital. This helps in analyzing how and where AI can help the business, and where it may not be adequate enough.
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Focus more on outcomes
IT and AI projects are completely different, as targets can be set for the former for the desired output, while the latter is used mostly to explore new possibilities. Therefore, in the case of AI projects, it is not possible to know what the output will be ahead of time.
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AI projects need to be monitored, fine-tuned, and modified continuously over time. In order to be successful with AI, it might take multiple unplanned iterations, and it still might not deliver the same level of accuracy or automation as initially imagined. Hence, it is essential to measure the success by the degree of impact it creates and the amount of value it will yield.
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Select the right for AI implementation
Many businesses make the common mistake of choosing a business challenge for AI that will create the biggest ROI. It is critical for leaders to understand that doing this may not yield the biggest result for the business as a whole.
Although ROI plays a key role in decision making, there are several simpler problems that can generate more value, more rapidly. Lastly, having a good estimation of the expected ROI of any AI solution is significant.
Final Thoughts
In order to incorporate AI into a business strategy, the company along, with its employees and partners, should believe in it. It is necessary to build both capability and appetite for AI for a successful result.
