Organizations recognize that they need a systematic approach to “operationalizing” AI in order to drive AI success.
The pace of digital transformation has massively accelerated with artificial intelligence (AI) technologies and is already transforming every aspect of business. With massive improvements in storage systems, processing speeds, and analytic techniques, we’ve reached an inflection point where AI and cloud technologies are enabling tremendous sophistication in analysis and decision making.
But the reality is, despite the hype around AI, the majority of companies are still either kicking their heels or simply failing to get AI strategies off the ground.
But why do so many AI projects fail?
The road from data to successful data-driven, AI- and ML-powered projects is no straight line. There are many variables that go into building effective artificial intelligence, which makes it difficult to prescribe set steps that will work well every time for every company. It is known that launching pilots is deceptively easy but deploying them into production is notoriously challenging. This is the reason why despite the early adoption of AI by many organizations, few have managed to reap consistent business value on their AI investments.
Reasons for AI failure – The Known and Unknowns
AI is still seen as risky business — an expensive tool that’s difficult to measure and hard to maintain. Organizations often start AI projects based on competitive pressures, fearing they will be left behind if they are not investing in AI. Not understanding what AI can be best used for is a top reason for failure. Other challenges can be attributed to reasons such as lack of a cohesive artificial intelligence strategy, no collaboration between business and IT stakeholders, poor organizational alignments, slow and complex implementations, and lack of continued C-suite commitments.
Continue reading: https://www.rtinsights.com/addressing-artificial-intelligence-unknowns-to-achieve-success/
The pace of digital transformation has massively accelerated with artificial intelligence (AI) technologies and is already transforming every aspect of business. With massive improvements in storage systems, processing speeds, and analytic techniques, we’ve reached an inflection point where AI and cloud technologies are enabling tremendous sophistication in analysis and decision making.
But the reality is, despite the hype around AI, the majority of companies are still either kicking their heels or simply failing to get AI strategies off the ground.
But why do so many AI projects fail?
The road from data to successful data-driven, AI- and ML-powered projects is no straight line. There are many variables that go into building effective artificial intelligence, which makes it difficult to prescribe set steps that will work well every time for every company. It is known that launching pilots is deceptively easy but deploying them into production is notoriously challenging. This is the reason why despite the early adoption of AI by many organizations, few have managed to reap consistent business value on their AI investments.
Reasons for AI failure – The Known and Unknowns
AI is still seen as risky business — an expensive tool that’s difficult to measure and hard to maintain. Organizations often start AI projects based on competitive pressures, fearing they will be left behind if they are not investing in AI. Not understanding what AI can be best used for is a top reason for failure. Other challenges can be attributed to reasons such as lack of a cohesive artificial intelligence strategy, no collaboration between business and IT stakeholders, poor organizational alignments, slow and complex implementations, and lack of continued C-suite commitments.
Continue reading: https://www.rtinsights.com/addressing-artificial-intelligence-unknowns-to-achieve-success/
