Identify your ideal use cases for generative AI interactions. Whether generative AI will be used largely in customer support, commerce, website, workplace search or in some other way, is a key determining factor for whether it makes sense for a brand to jump in early or wait.
If the use case can tolerate the potential for hallucinations and fabrications, perhaps with a layer of human-guided reinforcement learning applied post-op to teach the model and/or coerce it into compliance with your business rules and objectives, then this use case is worth considering.
Assess the risk of your IP or other sensitive information leaking out into the public domain. There is always a possibility that sensitive information (i.e.: IP, personally identifiable customer data) could emerge online via security breaches, but now there’s another avenue for that with the use of company content to “train” chatbots. LLMs are certainly susceptible to data extraction attacks but also there’s a degree of uncertainty with regard to responses that may deviate from the company’s official position on various matters, despite their access to extensive repositories through training data.
Brand decision-makers may not feel comfortable experimenting with AI until the unknowns have been addressed. And this technology is evolving daily, most recently with OpenAI adding new privacy options to avoid information being used to train their public models.
It’s no wonder that there has been so much buzz around generative AI; it has created a true paradigm shift, providing the business world with incredible potential. As more brands and technology companies dabble in this realm, it’s entirely possible that we will find new ways to address the brand experience challenges that are currently inherent in generative AI. This will open up the arena to more brands in industries where the challenges are presently prohibitive.
Ultimately, a focus on relevance, accuracy and security will empower brands to take the leap.
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