Decoding the Failure of AI Projects: From Risks to Solutions
However, the complexity of AI projects and a general lack of understanding about AI often results in project failure. According to Gartner, a lack of clear objectives and ill-defined R&D project management processes cause 85% of AI projects to fail. Furthermore, 87% of such projects never reach the production phase, with 70% of clients reporting minimal or no impact from their AI initiatives.
One case in point is IBM’s collaboration with The University of Texas M.D. Anderson Cancer Center, where a whopping $62 million was lost due to improper planning and unrepresentative data training for AI solutions. Despite the many challenges, AI project development is still a high priority for many companies striving to provide unique user experiences and create disruptive digital products.
This leads us to the question:
What are the common pain points with AI project development, and how can they be addressed?
Unrealistic Expectations: AI technology often over-promises and under-delivers. Companies, whether startups or large enterprises, may have high expectations for quick, outstanding results from AI technology, only to be met with delays, budget overruns, and uncertainty.
Insufficient Data: Choosing the correct data to train the system is crucial, as it should accurately reflect real-world patterns and scenarios without bias.
Lack of Expertise: The shortage of competent AI/ML engineers and data scientists poses a significant challenge. Moreover, complex areas such as healthcare, law, and construction require specific understanding and data for proper analysis.
High Failure Rate: Even venture-capital-backed startups leading the technological frontier experience a high failure rate of about 75%. The key takeaway is that failure is an inherent part of the process, and the ability to learn from these failures and pivot accordingly is what sets successful companies apart.
Conclusion
In summary, traditional staffing methods may not suffice for successful digital transformation within R&D projects. For successful implementation of AI/ML projects, companies need to incorporate a strategic viewpoint, an agile co-creation flow, change automation, innovation enablement, and efficient result delivery.
So, how do AI projects differ from traditional ones, and what misconceptions do businesses need to shed?
High Uncertainty: AI projects carry a higher level of uncertainty and risk compared to traditional software development projects. This often leads stakeholders to perceive that AI technology overpromises and underdelivers.
Complexity: AI projects are more complex, given the technology’s reliance on data, algorithms, and pattern recognition. Therefore, these projects require multi-disciplinary teams with diverse skill sets.
Staff Augmentation Pitfalls: Staff augmentation in AI projects often leads to failure due to micro-management, communication gaps, and the lack of a well-established AI product delivery process.
In-house Team Challenges: Building an in-house team for AI projects may result in long, burdensome hiring and onboarding processes. Furthermore, maintaining such a team could increase costs significantly due to taxes, leaves, office space, and equipment expenses. To address these challenges, companies should consider partnering with managed service providers who can help save up to 50-70% of IT expenses.
This approach not only allows for flexibility but also ensures the presence of a high-performing engineering culture with a proven AI project management methodology.
In the end, the success of an AI project depends on understanding its complexity, setting realistic expectations, having sufficient data, and adopting an effective project management process. Remember, failure is part of the journey, and the ability to learn from it and pivot accordingly is key to success in the world of AI.
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