In recent years, large organizations have committed billions to AI/Machine Learning (AI/ML) investment. According to CIO Magazine, the retail and banking sectors estimated that their 2019 spend on AI/ML would be, cumulatively, in excess of $11.6 Billion. The Healthcare sector was estimating an investment of approximately $36 Billion by 2025. (final totals are still pending)
Even with these huge financial commitments, some analysts predict that 87% of AI/ML Projects will fail to deliver as promised or never make it into production.
Of particular note is that the vast majority of AI/ML projects today are targeted for internal datacenter deployment. This legacy, “inside the corporate fabric” metaphor, imposes a number of constraints and these constraints are a primary reason for failed projects:
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Organizations can’t keep up with AI/ML infrastructure needs. AI/ML demand for computing and storage is huge and accelerating. But unfortunately, the handcuffs imposed by corporate datacenters, in particular, the lack of elastic scalability and timely access to suitable infrastructure and perpetual need to evergreen infrastructure and upskill staff, makes the infrastructure demands of AI/ML untenable.
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AI/ML demand for data, and in particular sensitive data, is stretching the capability of corporate security. The inability to address, in a practical manner, the security constraints required to access sensitive data makes data availability cumbersome at best and, more often, prohibitive.
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