Partnering with HumanFirst, Infobip generated over 220 knowledge articles, unlocked 30% of their agents' time, and improved containment by a projected 15%.
Reviewing the state of employee experimentation and organizational adoption, and exploring the shifts in thinking, tooling, and training required for workforce-wide AI.
With a one-click integration to Conversations, Infobip’s contact center solution, HumanFirst helps enterprise teams leverage LLMs to analyze 100% of their customer data.
Partnering with HumanFirst, Infobip generated over 220 knowledge articles, unlocked 30% of their agents' time, and improved containment by a projected 15%.
Reviewing the state of employee experimentation and organizational adoption, and exploring the shifts in thinking, tooling, and training required for workforce-wide AI.
This research produced a list of occupations and their level of exposure with the advent of LLMs and Generative AI.
LLMs on their own is impactful, but there has been an explosion of LLM-powered software, also referred to as Generative Apps. This explosion is being fuelled by the advent of LLM specific IDEs like Stack, Flowise, LangFlow and more.
Considering this proliferation of real-world implementations, OpenAI found that:
80% of the U.S. workforce can have > 10%of their work tasks affected by LLMs.
While 19% of workers will have > 50% of their tasks impacted.
And 15% of all worker tasks in the US could be expedited considerably.
LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.
OpenAI estimate that GPTs and GPT-powered software are able to save workers a significant amount of time completing a large share of their tasks, but it does not necessarily suggest that their tasks can be fully automated by these technologies.
In closing, it has been estimated by human assessments that only 3% of U.S. workers would have half or more of their tasks exposed to LLMs without installing any more software or modalities.
However, taking into account other generative models and complementary technologies, it is estimated that up to 49% of workers may have half or more of their tasks exposed to LLMs.
I’m currently the Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces and more.
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