Two decades later Companies first started implementing AI solutions, it can be argued that they have made little progress in achieving significant gains in efficiency and profitability relative to the hype that drove initial expectations.
On the surface, recent data backs up AI skeptics. Almost 90% of data science projects never go to production; only twenty% of analytical insights until 2022 will achieve business results; and even companies that have developed an enterprise-wide AI strategy are experiencing failure rates of up to fifty%.
But the last 25 years have only been the first phase in the evolution of enterprise AI, or what we might call Enterprise AI 1.0. That is where many companies remain today. Yet companies at the forefront of AI innovation have advanced to the next generation, which will define the next decade of big data, analytics, and automation: Enterprise AI 2.0.
The difference between these two generations of business AI is not academic. For executives across the business spectrum, from healthcare and retail to media and finance, the evolution from 1.0 to 2.0 is an opportunity to learn and adapt to past failures, create concrete expectations for future uses, and justify the increasing investment in AI to see in all industries.
Two decades from now, when business leaders look back to the 2020s, the companies that achieved Enterprise AI 2.0 first will have become big winners in the economy, differentiating their services, gaining market share, and positioning themselves for innovation. keep going.
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 to develop strategies to compete in the age of automation and advanced analytics.
Enterprise AI v1.0 (the status quo)
Beginning in the mid-1990s, AI was an industry 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 … led by magicians whose talents will not scale in the organization.”
Two decades from now, when business leaders look back to the 2020s, the companies that achieved Enterprise AI 2.0 first will have become big winners in the economy.
But the data science bottleneck – the need for everything to be funneled through a small team of experts – wasn’t the only obstacle to scale. AI is only as powerful as the data systems to which it is connected. Many companies experimenting with AI at the time had siled data with inadequate data infrastructure and processes to optimize the technology.
Additionally, the first iterations of B2B AI involved complex horizontal “machine learning” platforms focused on model development. Putting these carefully selected models to work required crossing a deep chasm related to customization and integration with business applications and workflows. These Enterprise 1.0 solutions were cumbersome and clumsy to operate, but still required large investments to implement.
Most of the initiatives started from the bottom up. Data scientists developed them as exploratory projects focused on speculative use cases, largely decoupled from business goals. Many turned out to be scientific projects, and failure rates were extraordinarily high.