More than 80% of AI projects fail, which is twice the already-high failure rate in corporate IT projects that do not involve AI. Key reasons for failure include:
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Problem misunderstanding. Lack of clarity about the problem AI is intended to solve and AI’s capability to address it leads to misaligned objectives.
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Insufficient data. Inadequate or poor-quality data hampers the development of effective AI models and project outcomes.
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Overemphasis on technology. Focusing on the latest AI trends and tools rather than addressing real-world issues reduces project relevance.
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Lack of infrastructure. Weak or inadequate infrastructure for managing data and deploying models undermines project execution.
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Overreach. Applying AI to problems beyond its current capabilities leads to poor outcomes.
Strategies for Success
To overcome these challenges, industry leaders and developers should:
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Bridge the gap between AI’s potential and its successful implementation, ensuring more impactful and sustainable outcomes.
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Clearly define project goals and focus on solving meaningful, real-world problems.
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Invest in robust infrastructure for data management and AI model deployment.
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Recognize AI’s limitations and conduct feasibility assessments with input from technical experts to ensure realistic expectations.
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Collaborate with government and private agencies to address data collection challenges.
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Support employees’ continuing education and training to build expertise in AI implementation.
In the evolving AI landscape, professionals must expand their expertise beyond technical skills to remain competent and relevant. This includes staying updated on AI advancements, exploring the potential of AI in their work, addressing ethical and regulatory challenges, mitigating risks, and cultivating multidisciplinary knowledge. Having the knowledge, skills, and abilities to manage AI systems effectively is critical for the quality and success of AI applications.
By establishing clear success metrics, business leaders can identify underperforming AI experiments early and terminate them before costs escalate. However, in some cases, pausing a project rather than abandoning it may be more effective, as emerging AI capabilities could address the underlying issues.
[For more from the author on this topic, see: “Shining a Light on AI’s Dark Side.”]
