AI as a communications co-pilot? (#4)

Look at the intelligence side when collaborating with AI

At the beginning of 2023, the resounding echo of Generative Artificial Intelligence discourse ignited a spark of curiosity in our collective consciousness. When looking back at the huge volume of digital conversations surrounding the topic, it is clear the dialogue was split into two contrasting practitioner perspectives. The first perspective was bursting with anticipation and optimism, heralding the arrival of a "generative" era, signaling that their moment in the spotlight is at hand. The second perspective, however, struck a chord of caution, emanating concern about the "artificial", with sentiment ranging from dismissing it as a mere plaything, rather than a practical tool, to expressing apprehension about the potential onslaught of "fake news" and misleading narratives.

Personally, I have always approached emerging technologies with delighted anticipation. Amidst the debate around “generative” and “artificial”, it was always the “intelligence” aspect that captivates me, which continues to compel me as I venture into identifying use cases of AI within the realm of communications. These use cases range from the categorization and analytical reporting of vast quantities of structured and unstructured data - encompassing media, social platforms, primary research - to the intriguing confluence of these diverse elements.

Leveraging the “intelligence” to DETECT provides many promising scenarios because it can uncover the most predictive elements or attributes in a bunch of data, identify the most revealing characteristics, and guide us in determining which deserves attention and which should be disregarded.

Recently, I encountered a case that centred around the methodology of foresight: AI-driven systemic modelling as a tool for navigating the turbulent waters of unstable, recurring crises marked by uncertainty. This avant-garde approach utilizes an intricate blend of data, advanced analytics, and the invaluable acumen of industry and human expertise. This dynamic trio is harnessed to chart a comprehensive map of interconnected variables triggering societal, industrial, and consumer changes. The vision is not merely to envisage the future, but to quantitatively identify and measure the signals of change.

Use cases for such methodologies can vary from continuous surveillance and tracking of alterations to ascertain growth trajectories and accordingly adjust business priorities. Alternatively, brands could opt for a deep-sea exploration of specific themes, with the knowledge that these topics hold substantial significance for their businesses and the communities they serve. When looking at current platforms, I’ll happily admit that I like Meta’s initiative of using the “intelligence” for interpreting content on its platform, especially in identifying hate speech. This is indeed a commendable venture, given the historical challenges machines have faced in decoding multimodal communications on such platforms, where textual and visual components are often intertwined.

In addition, we can continue to explore how to utilize “intelligence” to DELIBERATE. Even in the “generative” side, we look forward to seeing more transformative roles of “intelligence” for businesses seeking to enhance operational efficiency. When generative AI is seamlessly integrates with brand-specific knowledge and an AI solution trained on recorded customer interactions, the result is a laser-focused identification of personalized actions and next steps for business elevation and result-driven strategies. A prime example here is CarMax, which utilized GPT to refine the car-buying experience by generating concise summaries of extensive customer reviews - a task that would have otherwise taken its editorial team more than a decade.

We also find tools that enable advertisers to dissect and evaluate the impact of creative elements in their competitor's work. By analyzing facets such as typeface, colors, sounds and text, these tools provide creative teams with data-driven prompts that can fuel either a generative AI production platform or their human counterparts. Either way, we should all be keeping our eyes open to this landscape and the opportunities it holds as it continues to unfold.

The opinions expressed in this article are my own