Though business leaders have been intrigued by the public reaction and engagement to this emerging technology, it still requires the right talent, governance, maturity, and budget to succeed. The top four gaps and barriers to entry that businesses encounter when implementing generative AI models are IT governance, data gathering and quality, maturity of tech stack, and cost. This article further explains that governance models should include guiding principles, focused on privacy, data security, algorithmic transparency within AI models, and cybersecurity vulnerabilities. Data quality is the second most significant capability gap, while generative AI adoption requires a level of modernization that most non-traditional tech companies are still embracing. Cost, although not considered a capability gap, is a barrier to entry for most businesses as large models require a lot of computing power. Vendors have already started customizing generative AI models to specific business needs, making it more accessible and affordable for enterprises.

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