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Generative AI (or GenAI) creates new content based on user prompts, meaning that generative AI is reactive, responding to inputs from users. You could consider a sales person wanting to gain insights on clients and products with simple prompts that bring back instant results. Previously they would have had to wait for a data engineer to produce a report.

Agentic AI autonomously makes decisions and performs tasks with minimal human oversight. Thus, agentic AI is proactive, managing processes to achieve goals. This could be writing source code to speed up engineering productivity, albeit experience is needed to validate what has been written to ensure it has met the product brief and behaves with integrity. Experienced engineers are still needed to fix defects, manage non-functional performance, and business continuity, for example.

AI is not famed for having a good relationship with the truth, and you need to approach it with skepticism. Similarly, you need to be mindful of data leakage, and audibility of all AI actions. This requires systems thinking about data governance, access controls and cyber security. With AI, we need to treat it the same we would for any new technology and ensure we don't let it run without oversight, logging and monitoring.

We can provide AI insights for leadership to help you make informed decisions about how you use and deploy AI in ways that don’t increase risks for your business.