Brief news summary

Implementing generative AI (gen AI) technologies can be costly and present obstacles for businesses. Expenses can arise from server provisioning and model deployment, especially for large-scale models. Cloud services offer flexible payment options, while on-premises setups require upfront investments. Regulatory compliance also adds to costs, making it essential for companies to establish strong AI governance systems and consider compliance and security during small-scale tests. AI sprawl is another challenge, where different departments use overlapping AI tools, leading to duplicated costs and inefficiencies. To address this, companies should prioritize areas where gen AI can have the greatest impact and undertake projects gradually and sustainably. To maximize the benefits of AI adoption, companies should focus on use cases with significant business impact and potential cost reduction. AI should automate tasks and free up employees for more complex responsibilities instead of adding manual processes. AI implementation should not be pursued if effective solutions are already in place or solely for the sake of it. When selecting gen AI projects, companies should consider broader scopes, narrower data requirements, revenue generation, cost reduction, and brand improvement. Having clean and well-structured data is crucial for successful AI deployment despite potential costs. In conclusion, businesses should be aware of hidden costs and challenges associated with gen AI to fully benefit from AI adoption.