The AI landscape is constantly changing and evolving, making it challenging for CIOs, data, and AI executives to prioritize their focus. To shed light on this issue, tech entrepreneur Bruno Aziza reviews Scale AI’s AI Readiness Report in this week’s CarCast.
According to the report, the adoption of gen AI has seen significant growth. Last year, 19% of companies had no plans for gen AI, but that number has dropped to just 4% this year. Furthermore, the report reveals that 38% of companies are now in production with gen AI, compared to 21% the previous year.
Despite the rapid growth in gen AI adoption, there are still challenges that organizations face when deploying it. The top barrier encountered is security and governance. Ensuring the security and ethical compliance of AI models is crucial to prevent bias and maintain trust in the technology.
To navigate the complexities of gen AI, Aziza highlights key questions that executives should ask. These include identifying the right gen AI use cases to pursue, budgeting for gen AI, and knowing when not to use it. These questions help in making informed decisions and aligning gen AI initiatives with organizational goals.
When considering gen AI use cases, Aziza suggests looking at three ways to approach it: internal customers, external customers, and embedding gen AI capabilities into existing applications. Internal customers present low risk and high reward opportunities, such as improving data quality or enhancing performance across various disciplines. Companies like Twilio have successfully utilized gen AI to help their customer support reps find answers faster or summarize calls.
External customers can benefit from gen AI through applications like chatbots for customer support or context-based solutions like Wayfair Decorify. By uploading a photo of their living room, users can receive relevant product recommendations from Wayfair’s inventory.
Embedding gen AI into existing applications is particularly powerful when targeting specific use cases within applications like ERP, HCM, or CRM. The value and sensitivity of data in these applications make the selection criteria different from wider and larger datasets.
In conclusion, the AI Readiness Report highlights the increasing adoption of gen AI and the challenges organizations face. By asking the right questions and considering different use cases, executives can effectively leverage gen AI to drive innovation and improve their business processes.