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Posted by Richy George on 4 August, 2021This post was originally published on this site
Two decades after businesses first started deploying AI solutions, one can argue that they’ve made little progress in achieving significant gains in efficiency and profitability relative to the hype that drove initial expectations.
On the surface, recent data supports AI skeptics. Almost 90% of data science projects never make it to production; only 20% of analytics insights through 2022 will achieve business outcomes; and even companies that have developed an enterprise-wide AI strategy are seeing failure rates of up to 50%.
But the past 25 years have only been the first phase in the evolution of enterprise AI — or what we might call Enterprise AI 1.0. That’s where many businesses remain today. However, companies on the leading edge of AI innovation have advanced to the next generation, which will define the coming decade of big data, analytics, and automation — Enterprise AI 2.0.
The difference between these two generations of enterprise AI is not academic. For executives across the business spectrum — from health care and retail to media and finance — the evolution from 1.0 to 2.0 is a chance to learn and adapt from past failures, create concrete expectations for future uses, and justify the rising investment in AI that we see across industries.
Two decades from now, when business leaders look back to the 2020s, the companies who achieved Enterprise AI 2.0 first will have come to be big winners in the economy, having differentiated their services, scooped up market share, and positioned themselves for ongoing innovation.
Framing the digital transformations of the future as an evolution from Enterprise AI 1.0 to 2.0 provides a conceptual model for business leaders developing strategies to compete in the age of automation and advanced analytics.
Starting in the mid-1990s, AI was a sector marked by speculative testing, experimental interest and exploration. These activities occurred almost exclusively in the domain of data scientists. As Gartner wrote in a recent report, these efforts were “alchemy…run by wizards whose talents will not scale in the organization.”
Two decades from now, when business leaders look back to the 2020s, the companies who achieved Enterprise AI 2.0 first will have come to be big winners in the economy.
But the data science bottleneck — the need for everything to funnel through a small team of experts — was not the only hurdle to scaling. AI is only as powerful as the data systems it’s plugged into. Many companies experimenting with AI at the time had data spread across silos with inadequate data infrastructure and processes to optimize the technology.
Moreover, early iterations of B2B AI involved complex horizontal “machine learning” platforms focused on model development. Operationalizing these hand-curated models required crossing a deep chasm related to customization and integration with enterprise applications and workflows. These Enterprise 1.0 solutions were cumbersome and clunky to operate yet still required large investments to deploy.
Most initiatives started from the bottom up. Data scientists developed them as exploratory projects focused on speculative use cases largely decoupled from business objectives. Many turned out to be science projects and the failure rates were extraordinarily high.
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