CIOs have a tough balance to strike: On one hand, they’re tasked with maintaining a large number of applications – research from Salesforce shows that , an approach of “innovating around the edges” which often requires a mindset shift away from monolithic systems and instead toward assembling a mix of people, vendors, solutions, and technologies to drive business outcomes. And nothing necessitates this shift more than AI.
AI is a generation-defining paradigm shift in the way the world works and lives. The technology has made tidal waves in society, as for everything from writing term papers to debugging code. And, as explained in . His article notes that over the past decade, AI’s performance has exceeded that of humans when it comes to speech recognition, image recognition, reading comprehension, language understanding, and common-sense completion.
With such rapid development underway, your enterprise must have the flexibility to choose the right AI vendor to deliver the right AI solution at the right time in order to drive the best business outcomes. And while SAP and Oracle could emerge as major AI players, there’s a lot of greenfield out there. Your organization must direct a business-driven IT roadmap to stay ahead of the curve.
Challenge 2: Leaving on-premises data behind
For AI algorithms to be successful, they need a massive amount of historical data to draw from. As Gene Marks, a contributor to Forbes wrote, “: More than half of the AI models Henshall analyzed since 2020 have training sets of 100 million or more data points. “In general, a larger number of data points means that AI systems have more information with which to build an accurate model of the relationship between the variables in the data, which improves performance,” , many companies that have moved to cloud have incurred complex software licensing issues and costs that can reach as much as 24 percent of total information enterprise technology spend. Even after initial TCO analysis, “many organizations still encounter a cost explosion when the actual migration begins, in part because they were unaware of the licensing requirements for cloud, which can include licensing transfer, purchasing, and visibility issues,” , where cloud vendors are charging the same price for reduced functionality.
Examples of SaaS shrinkflation include non-cumulative pricing, reduced discounting, and feature bundling/unbundling. Vertice advises that to be in a strong negotiating position, you should start due diligence 6-8 months before renewal. But ultimately, to secure the best possible price you need leverage. And without the leverage of software license ownership, considerable cost and shrinkflation risks persist.
Ready or not, the AI revolution is here
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