{"id":18374,"date":"2023-11-16T10:52:01","date_gmt":"2023-11-16T10:52:01","guid":{"rendered":"https:\/\/uxmag.com\/?p=18374"},"modified":"2023-11-16T10:52:01","modified_gmt":"2023-11-16T10:52:01","slug":"open-ai-dev-day-great-technology-light-on-ux-and-business-value","status":"publish","type":"post","link":"https:\/\/uxmag.com\/articles\/open-ai-dev-day-great-technology-light-on-ux-and-business-value","title":{"rendered":"Open AI Dev Day: Great Technology, Light on UX and Business Value"},"content":{"rendered":"\n

As someone who\u2019s been working with the various versions of GPT and other large language models for a number of years, I was excited when ChatGPT supercharged the adoption of (and interest in) conversational AI. By putting a chat interface on their pre-trained language model, OpenAI played a major role in ushering in an accelerated and exciting era in technology. In a rapidly growing field of LLMs, their GPT models remain near the top. Attending the recent OpenAI Dev Day, I noticed that their team\u2014including CEO Sam Altman\u2014did a great job presenting their platform, but their mantra was “we can’t wait to see what you guys create.”\u00a0<\/p>\n\n\n\n

This felt like code for, \u201cwe know this is important, but we\u2019re not sure how, or for what.\u201d Sadly, this theme endured through most of what was presented, I didn\u2019t see a whole lot of holistic design in how their virtual agents (they\u2019re calling them GPTs) will integrate into company architectures and our daily lives. The focus on UX was lacking, and perhaps by extension, so was a sense of how businesses will generate value with generative AI. It left me feeling that the industry as a whole does not yet know how to drive real business value out of the technology that OpenAI and other generative AI platforms provide. Here are some things that stood out to me about the conference and how these technologies were presented. <\/p>\n\n\n\n

What About Brand Alignment?<\/h2>\n\n\n\n

There are massive opportunities for businesses that can align AI with their brand identity, but I didn\u2019t hear this addressed. Speakers partially hit on UX, showing how to customize responses (e.g. friendly, serious, detailed responses, short replies) but seemed to completely ignore the value of matching an AI to the deeper elements of a company’s brand. For example, a company like Peloton might want experiences with generative AI to model the ways their instructors communicate to riders. This could include combination tones, ranging from chipper and supportive (i.e. \u201cYou\u2019ve got this. I know you can do it!) to challenging and blunt (e.g. \u201cIf you can\u2019t show up for yourself, how can you show up for anyone else?\u201d). This kind of alignment also demands lexicography or brand-approved terms and usages.<\/p>\n\n\n\n

The conference lacked discussions on how to control AI responses effectively. I was expecting demonstrated mechanisms for refining AI interactions to ensure they align with a brand’s messaging and guidelines, but I didn\u2019t hear anything about how to control what the AI can respond to or to what degree. They also missed an opportunity to provide examples of customized avatars, names, and conversational tones that could resonate with a company’s image, creating a more cohesive and engaging user experience.<\/p>\n\n\n\n

How Will it Work on Different Platforms?<\/h2>\n\n\n\n

In terms of harnessing the power to create next-level process automation, OpenAI could have explored various platforms where their GPTs can be deployed, such as embedded in websites, in mobile apps, and used for interactive voice response (IVR) systems. Given the number of businesses across industries that will be looking to integrate generative AI, flexibility and adaptability across channels seems paramount. Conversational AI can enhance UX across diverse touchpoints, but only after these considerations have been addressed.<\/p>\n\n\n\n

 What About Handling Unanswered Questions?<\/h2>\n\n\n\n

There was no acknowledgment of what happens when AI cannot provide satisfactory responses. Businesses need strategies for gracefully handling these situations, including options like politely declining to answer, transitioning the conversation to a different topic, or (in a business setting) connecting the user with a human in the loop. Properly designed experiences can\u2019t leave users frustrated when faced with AI limitations. A user is less likely to re-use an agent that can not get a question answered or a task completed. Great exception handling is the bedrock of great UX.<\/p>\n\n\n\n

Will There Be Human-AI Interaction? <\/h2>\n\n\n\n

There are so many use cases where interactions with AI can contribute to rewarding UX. These require a strategy for integrating AI seamlessly into customer service processes. This includes scenarios where AI is collaborating with human agents (the human-in-the-loop I referenced above). There\u2019s a ton of value in creating a harmonious balance between AI and human interactions and I didn\u2019t hear anything about it at Dev Day. This was a bit surprising given that Gartner recently predicted that by 2025, generative AI will be a workforce partner for 90% of companies worldwide [1]. Interactions between humans and machines shouldn\u2019t be left to design themselves. These relationships will require careful calibration and ongoing collaboration between humans and machines.<\/p>\n\n\n

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\"\"<\/figure><\/div>\n\n\n

Interesting and somewhat useful on their own, many of these apps could become massively rewarding if they were part of business-wide use cases.<\/em><\/p>\n\n\n\n

Where Are the Real Business Use Cases?<\/p>\n\n\n\n

The conference featured many intriguing examples of conversational AI in action\u2014talking like a pirate, controlling lights, explaining board games\u2014but they were ultimately rather frivolous. These are cool applications that seemed nearly impossible only a few years ago, but companies are looking for practical business use cases. OpenAI could have highlighted some of them. Tasks like appointment scheduling, feedback collection, real-time invoice generation, and customer support are ripe for automation using generative AI. According to a recent CNBC report<\/a>, almost half of top technology officers across industries are putting AI as their top budget item over the next year, while nearly two-thirds indicated that their AI investments are accelerating. This was a staggering missed opportunity.<\/p>\n\n\n\n

My Takeaways<\/h2>\n\n\n\n

Despite these shortcomings, the conference was thrilling. For technologists, it\u2019s hard to imagine a more exciting time to be alive. For those of us who have been working in conversational AI for years, we finally have a way to make real human-machine interactions meaningful. Tools like OpenAI\u2019s platform make it possible to avoid piecing applications together with various NLU tools and strict dialog-driven approaches.<\/p>\n\n\n\n

However, this industry is still in its inception. For as fast-evolving as generative AI is, we can\u2019t forget that it is just a tool, like a hammer, and not everything is a nail. It\u2019s mission-critical to focus on real business outcomes and the UX that drives them.<\/p>\n\n\n\n

We\u2019ve all seen generative AI write poetry. It\u2019s time to see it start generating real business value.<\/p>\n\n\n\n

    \n
  1. Gartner, Gartner IT Symposium 2023 Presentation, \u201cWe Shape AI – AI Shapes Us\u201d, Mary Mesaglio and Don Scheibenreif, Oct. 16-19, 2023<\/em><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    As someone who\u2019s been working with the various versions of GPT and other large language models for a number of years, I was excited when ChatGPT supercharged the adoption of (and interest in) conversational AI. 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