Narrow focus limits Enterprise return on investment
Why does the Enterprise Market fail to embrace the promise of AI and Machine Learning? Many of the greatest minds in technology have been focused on Artificial Intelligence for years. Virtually every Tech Giant has launched high priority, big ticket initiatives in this area. Pundits from Gartner, Forrester, MIT and Deloitte peddle the allure of AI and Machine Learning.
Yet Gartner's 2018 survey shows that only one in 25 CIOs have implemented AI in their organization. Why?
Many AI enthusiasts are anxious to deploy AI but lack a specific problem or a clear vision to apply it to. Worse, without a high priority objective with a clear return on investment, it is difficult to secure a commitment for such a project.
Today, most AI applications focus on new applications such as evaluating retail customer behavior, detecting fraud in financial transactions, facilitating the recruiting process, assisting with employee productivity and improving shipping logistics. Important, to be sure.
Some Enterprise AI applications focus on mining legacy databases for insights and ways to monetize the data assets. Also important.
Yet all of today’s AI applications seem to assume that legacy enterprise applications and their inherent limitations will live forever. They focus strictly on minimizing legacy limitations (e.g. technical debt and lack of adaptability), monetizing the value of the legacy data or adding value with “bolt on” applications that must be integrated with the legacy applications. In other words, they fail to address the most significant issue.
Utilizing AI for comprehensive Digital Transformation
If AI and Machine Learning are going to meaningfully transform business functions, their power must be applied to the enormous multi-trillion-dollar problem of Enterprise Legacy Digital Transformation. Harnessing the power of this technology will unleash the biggest assets of the world’s largest corporations, governments and organizations – their legacy business rules that run their operations seamlessly every day – from the limitations of proprietary legacy hardware and operating systems.
These business rules locked in monolithic legacy applications are considerably more valuable and less available than the legacy data that many AI applications attempt to classify, interpret and mine for insights. Once the business rules are liberated from their aging platforms, the high cost of maintenance and lack of adaptability to changing business needs will be a thing of the past. The new AI and Machine Learning platforms will enable the enterprise domain and subject matter experts to work with data scientists on detailed, actionable roadmaps with clearly marked milestones for high value digital transformation and to revolutionize how software is developed, modified and maintained. And the world will never to be the same.
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