How generative AI for business enhances collaboration
Generative AI has become a leading technology topic. Digital assistants and idea generation are two ways the technology could increase employee productivity.
ChatGPT has been a hot tech trend, and few innovations in any field have attracted as much attention in so little time. For those who view this as the "Year of AI," ChatGPT has already provided all the validation needed, and we've barely scratched the surface.
That said, as with other new forms of technology, adoption starts in the consumer world. Many have experimented with ChatGPT, whereas commercial deployments in business environments have only started coming into play. Generative AI for business is starting to take shape for collaboration and contact center use cases but cautiously, given such a short track record.
Microsoft Teams has been an early adopter of ChatGPT, so it's important to watch the company's moves as a major investor in OpenAI. Unified communications-as-a-service players are following Microsoft's lead. However, it's important to note that ChatGPT is just one form of generative AI, and innovation can come from any vendor or platform provider. The possibilities are endless, and to provide a sense of what to expect, here are two emerging areas for generative AI in the collaboration space.
1. Digital assistants
This use case takes a few different forms, such as an intelligent personal assistant or virtual meeting assistant. In time, other variations will be added, but the underlying idea is for how generative AI can enhance applications built to manage and automate personal workloads. To a large extent, this is already being done using speech recognition, where workers interact with bots through voice to automate routine tasks, like calendaring, managing meetings, transcribing meeting notes and dictating emails.
The AI component means that these assistants learn more and more about the worker over time with machine learning, enabling AI to take on a greater range of tasks with more complexity. As trust builds, each worker can essentially have a personal virtual secretary, leaving more time to engage in work that calls for deep thinking, intense collaboration and creativity.
With an AI foundation in place to enable these digital assistants, generative AI can add new value for collaboration that goes beyond speech recognition. AI's ability to accurately handle voice communication is essential for building trust with end users, and once that develops, end users will be more comfortable relying on applications like ChatGPT.
These applications will be different, as they use AI and search not just to gather information from a wide range of sources, but also to generate new forms of content. The outputs need to be specified by the end user, and as would be done with a human secretary, the bot would be directed to generate whatever is needed, such as email replies, meeting summaries and reports.
What takes this beyond what you can do with conversational AI today is the ability to tailor each output for a specific audience or even individual. Not only can generative AI create these forms of content, but with enough guidance, the technology recognizes the characteristics of the reader well enough to create a more personal and relatable environment. However, ChatGPT can be wrong as much as it can be right, leaving room for refinement, but for now, the possibilities are clear.
2. Idea generation
This use case highlights how generative AI is different from the earlier forms of AI. Most AI use cases in the workplace, as well as in contact centers, are applied to either speech recognition or search, largely to automate workflows or manage large volumes of data.
These applications clearly have business value, but their domain is drawn entirely from existing sources of information, either in speech or text forms. Being digital in nature, AI can only work with digitized inputs, which is why digital transformation is so crucial to AI's success and why most AI applications are still in an emerging state. A great deal of historical information will become part of the ocean of data that is the lifeblood of AI.
Generative AI represents an entirely different use case for AI -- namely, taking those existing digital inputs and generating new forms of content. Some might say that it's creating new forms of content, but that raises questions about AI that are not covered here.
While it's fair to say people shouldn't look to generative AI to do the thinking for the user, idea generation is indeed a valid use case. At some point, most collaboration scenarios require creative thinking and fresh ideas, such as developing new products, refreshing a brand or responding to a new competitor. The same can be applied to more mundane tasks that may not even be team-based, such as writing a business plan or a report.
The common thread to all of this is finding the initial inspiration or big idea to kick-start the process. Most people struggle to tap into creative instincts and can't get past staring at a blank page awaiting any input, especially when on a tight deadline. Enter generative AI, where users feed the bot with context and round up some ideas to help get started.
With some trial and error, whether you need a basic structure for a report, a visual for what a new product might look like or examples of great branding, this type of bot scans a far wider pool than any human possibly can and in much less time. These are the inherent strengths of AI, and with generative AI, users can improve work or projects when creative inspiration just isn't there.
