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The Hidden Perils in the AI Model Assembly Line - Price: Rs. 0
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To discover more about the unspoken risks associated with the AI model assembly line, read this exclusive article from AITech.
Artificial intelligence (AI) is transforming the way financial services institutions operate and engage with customers. From automating routine processes to offering personalised financial advice, AI’s potential to drive efficiency, innovation, and customer satisfaction is substantial. However, realising this potential is another matter. Gartner research shows that while 80% of executives believe that automation can be applied to any business decision, just 54% of AI models make it from pilot to production. This statistic underscores the gap between the promise of AI and its practical implementation, raising the question of what are the pitfalls in the AI model assembly line that stand in the way of success?
Also Read: The Hidden Perils in the AI Model Assembly Line
Pitfall One: protracted and disjointed processes dampen momentum
While financial institutions ambitions for AI projects are rapidly growing, most do not yet have a mature AI development infrastructure that is ready to meet this demand. Research from Deloitte shows that the number of AI-related activities undertaken by companies (across several sectors) is increasing, but that productionalising projects is a key issue, with organisations relying on manual, ad hoc processes to bring projects to life.
In financial institutions the AI model development process lasts 5.5 months on average, involves a significant number of specialised employees, and comprises multiple stages. Each of these stages has its own pain points, which act as a brake on momentum and significantly add to the cost of model development. If, as Gartner’s data shows, 46% of models don’t make it to production, this represents a huge sunk cost to financial institutions and a major dampener on staff morale.
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Artificial intelligence (AI) is transforming the way financial services institutions operate and engage with customers. From automating routine processes to offering personalised financial advice, AI’s potential to drive efficiency, innovation, and customer satisfaction is substantial. However, realising this potential is another matter. Gartner research shows that while 80% of executives believe that automation can be applied to any business decision, just 54% of AI models make it from pilot to production. This statistic underscores the gap between the promise of AI and its practical implementation, raising the question of what are the pitfalls in the AI model assembly line that stand in the way of success?
Also Read: The Hidden Perils in the AI Model Assembly Line
Pitfall One: protracted and disjointed processes dampen momentum
While financial institutions ambitions for AI projects are rapidly growing, most do not yet have a mature AI development infrastructure that is ready to meet this demand. Research from Deloitte shows that the number of AI-related activities undertaken by companies (across several sectors) is increasing, but that productionalising projects is a key issue, with organisations relying on manual, ad hoc processes to bring projects to life.
In financial institutions the AI model development process lasts 5.5 months on average, involves a significant number of specialised employees, and comprises multiple stages. Each of these stages has its own pain points, which act as a brake on momentum and significantly add to the cost of model development. If, as Gartner’s data shows, 46% of models don’t make it to production, this represents a huge sunk cost to financial institutions and a major dampener on staff morale.
Visit AITechParkNews for cutting-edge Tech Trends around AI, ML, Cybersecurity.
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