In my article series, Transforming Your Organization’s Personality With AI, I postulated that if you want to influence your personality image, you can figure out a strategy to impact the input sources. For example, you believe that news data inputs are not harping on the innovation aspect enough. And you think that you are already generating enough innovation. Then, you can formulate a communications strategy to ensure the message gets delivered and published across all media channels. I will elaborate a bit more on that postulation in this article series.

In the first part of this article series, we will overview the critical elements of a communication strategy from a technology and innovation perspective. The second and third parts will explore how a deep-learning algorithm can help. The second part of this article will be published on 01/09.

Corporate communication strategy is both external and internal. In this video, we will focus only on the aspects of external communication. Within external communication, we will focus only on aspects that AI, specifically deep learning, can impact. A strategically formulated corporate communication strategy takes into account the following key elements:

• Target audience(s)
• Context
• Intended outcomes
• Key messages
• Appropriate medium
• Preferred messenger(s)

Using a similar design to the deep learning algorithm suggested in the above-mentioned article series, you can design an algorithm that can help you gain insights into each of the elements above and provide clues into what needs to change for changing an element, like the intended outcome. Let us explore how the algorithm can help across these critical elements. Let us overview how these elements interact in a communication strategy.

Every communication strategy centers around a set of target audiences. A comprehensive corporate communication strategy will generally have a set of target audiences, meaning more than one segment of the target audience. The latest Gen AI race is an excellent example of this. Those leading this race are leveraging their communication strategy not only to impact the perception of the masses and external stakeholders (including shareholders) but also a large potential partner ecosystem.

The context in this specific example is that no leading technology player wants to be seen as lagging behind in the AI race. While naysayers abound, the destiny of the corporate world will be dominated heavily by AI in approximately a decade. And since that IS going to the future, technology companies need to be seen as cutting-edge in this race. Note that most of the leading player had their major success pertaining to innovation more than a decade ago and have been improving upon that innovative product or service.

Therefore, the communication strategy’s intended outcome here is pretty straightforward. The pressure now mounts to showcase new innovation in an age where AI-enabled tools showcase new capabilities almost daily. The intended outcome is to be seen as a leading innovation in AI. However, every organization in the race actually needs to have a different key message. Confusing? Note that while the intended outcome is the same, the key message needs to be tailored based on current capabilities, progress, and the next development steps. That is why the key message needs to be tailored differently.

Medium and messengers are obviously also very critical. And these were the elements that our “personality evaluation” algorithm used as a data source. These will be critical data sources in this algorithm as well. But what are the messengers and mediums? We covered some examples of the “medium” in the personality algorithm. Analyst coverage is an example of a medium technology companies use to propagate their message. An example of a messenger is a paid influencer who leverages their audience to help bear the message. These are obviously just examples and there are several mediums and messengers that large companies use.

In the second and third parts of this three-part article, we will explore the logic of an algorithm that can help organizations formulate their communication strategy by generating more scientific input points.