Generative AI a key tech for analytics, but within limits

By neub9
3 Min Read

The increasing popularity of Generative AI has led to a surge in new capabilities being developed by vendors. Since the release of OpenAI’s ChatGPT, there has been a significant improvement in the capabilities of large language models (LLMs), leading to the unveiling of new generative AI capabilities by analytics and data management vendors. While many of these tools are still in preview stages, some are already generally available for use.

Organizations have started to integrate generative AI models into their decision-making processes by combining their proprietary data with LLM technology. This integration is seen as a way to make data experts more efficient by increasing process automation capabilities and eliminating the need to write code.

However, Howard Dresner, founder and chief research officer at Dresner Advisory Services, warns that generative AI has its drawbacks and needs to be deployed with extreme care. Despite its transformative potential, generative AI can lead to incorrect outputs, known as hallucinations, that may result in bad decisions. This highlights the need for careful governance of generative AI, similar to data governance practices within organizations.

Dresner emphasizes the need for organizations to carefully govern the use of generative AI, as it has the potential to be a transformative technology but should not be viewed as a panacea. He advises that policies and processes be put in place to regulate the use of generative AI, and that someone should be in charge of overseeing its deployment to avoid chaos.

As for the future of generative AI, Dresner predicts that it will become more widely used beyond just analytics, especially in consumer-facing applications. The next wave of generative AI capabilities is expected to include broader usage in requests for information and documentation, as well as integration with platforms for analytics and performance management.

In conclusion, while generative AI holds promise for organizations seeking greater automation and natural language interfaces, careful governance is crucial. As the industry continues to evolve, there will be a focus on making the core technologies more reliable and accurate, ultimately leading to more widespread adoption of generative AI.

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