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Businesses are turning to private AI models to gain a competitive edge and ensure data security, moving away from reliance on public AI platforms. These custom-built AI systems offer the promise of hyper-personalized forecasting and operational insights derived from proprietary enterprise data. However, experts caution against blindly trusting these systems. A significant risk lies in the potential for private AI to reinforce existing biases and past patterns embedded within an organization’s historical data, effectively automating institutional history. The technical complexities of customizing and maintaining these models also demand a high level of AI literacy and data science expertise. Thought leaders advocate for treating AI as a ‘co-pilot’ rather than an autonomous decision-maker, emphasizing the need for continuous scrutiny and validation of its outputs. Decision-makers should also be mindful of the inherent biases of AI solution providers like Deloitte and Accenture. While private AI excels at rapidly identifying trends and analyzing vast datasets, overreliance and the problem of data obsolescence remain critical concerns. The industry consensus is that human oversight is crucial, mirroring the control mechanisms inherent in traditional business intelligence platforms like SAP and Microsoft Power BI. Ultimately, private AI should be viewed as a valuable addition to a strategist’s toolkit, but not a panacea. Early adopters are urged to balance their enthusiasm with a healthy dose of skepticism, recognizing that AI in business intelligence is still in its nascent stages.