Organizations rushing to embrace generative AI face significant challenges, including soaring costs and unclear ROI stemming from inadequate planning and financial oversight. Many enterprises, like Thermo Fisher Scientific, experienced cost overruns and unmet expectations due to erratic pricing models and insufficient alignment with goals. Gartner anticipates $644 billion in spending on generative AI by 2025, with effective cost management becoming vital. Additional challenges encompass data risks, model inaccuracies, and talent shortages. Without strong governance, organizations may struggle with vendor lock-in and ultimately risk costly deployments failing to deliver value.
Kwiecien's experience is common. While more than 80% of enterprises will leverage or deploy genAI by 2026, most if not all will run into staggering cost overruns and failed expectations.
The consumption and pricing models for the technology were "all over the place," and promises of "robust" AI solutions fell short, indicating significant challenges in planning and implementation.
Unclear ROI, weak governance, and vendor lock-in further complicate the deployment of generative AI, highlighting the need for meticulous financial and strategy planning by organizations.
Fear of missing out leads to poor planning, which can result in costs for genAI deployments leaping into millions of dollars, catching organizations completely off guard.
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