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How to implement AI projects in your organisation

10 1
27.01.2024

According to McKinsey, artificial intelligence (AI) could add around $13 trillion to the global economy by 2030. While the excitement around AI is palpable, it’s essential to discern where its implementation can be genuinely beneficial. Drawing lessons from blockchain implementations, it becomes clear that the solution should align with the problem. AI is a powerful tool, but it’s essential not to overestimate its capabilities. A clear understanding of AI’s potential sets the foundation for a realistic and effective implementation strategy. We can’t force-fit AI solutions where they have no utility.

To successfully implement AI projects, it is crucial to follow a strategic approach. Firstly, rigorous technical and business due diligence is necessary. The proposed AI project must be technically feasible, supported by the availability of pertinent data and skilled Software/Machine Learning engineers. Furthermore, the AI system should genuinely contribute value to the business. There should be clarity on what AI can or cannot do. According to Andrew Ng, a pioneer in AI and a professor at Stanford University, a general guideline is that if a task typically takes a second or so for humans to complete, it possesses substantial potential for AI application. For instance, AI can effectively classify restaurant reviews as positive, negative or neutral or identify objects like cars and pedestrians.

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Secondly, it is important to understand the difference between Machine Learning and Data Science. Machine learning and data science are often used interchangeably, but they serve distinct purposes. Machine learning primarily is used for automation, while data science delves into deciphering patterns and insights within vast datasets. Acknowledging this difference is crucial for a nuanced approach to AI implementation.

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Thirdly, it is imperative to grasp that AI doesn’t automate jobs but tasks within jobs. A job is made up of several tasks. Factors such as complexity, repetition and the amount of data involved should be considered in recognizing AI potential of a task. For example the job of a customer service representative is composed of tasks such as answering inbound phone calls from customers, answering customer chat queries, checking status of customer orders and keeping records of customer interactions. While the second and fourth tasks have high automation potential, the other two may not be the ideal candidates. Therefore, it is advised to break down a job into tasks and assign them a rating (high, medium and low) based on their AI potential. This granular approach helps in identifying specific areas where AI can optimize processes and enhance overall productivity.

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© The Financial Express


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