Non-financial reporting with AI response optimization (AIRO)
I would like to briefly touch on my latest learning about AI response optimization (AIRO) and reflect on its impact on non-financial reporting.
SEO vs AIRO, the end of an era in digital marketing?
Search Engine Optimization (SEO) is the way to optimize online content so it ranks higher in search engine results pages (like Google or Bing). But with the increasing importance of AI systems online content is required to be structured so it can be used, cited, or summarized by AI search assistants. That doesn't mean that AIRO entirely replaces SEO, more importantly one should also consider the power of AI system marketing potential when creating online content.
How to address AI response optimization?
In comparison to SEO, AIRO is more subtle to address and more of a continuous process. SEO is quite well established, with Google Search Conosole and Bing Webmaster Tools that enable us to submit sitemaps to index websites and receive feedback on optimization potential (such as missing meta tags for example). AIRO in comparison doesn't have such a structure for submission. It is more about structuring content across platforms (incl. social media) and enable it to be machine readable. There are numerous ways to optimize, here in this blog I would like to briefly touch on QnA as an immediate response to optimize content for AIRO.
What is as a low hanging fruit for AI response optimization
Whilst the design of the website is important to the human perception, AI is more looking for a clear structure with Questions and Answer (QnA). Ideally there is a how question stated with a main header (ideally h3) followed by a short and precise answer. Addressing online content in this way should be considered a low hanging fruit for making online content and corporate reporting readily available and accessible for AI systems.
Why is machine-readable context important?
Nowadays we are left with the impression that anything is digitally readable. AI systems have the capabilities to create pictures as per our command. But we should also keep in mind, that the wealth of online information is only as good as the information that we provide. The more structured we inform, the better the content can be replicated. So maybe it is not entirely the AI systems fault when it produces information wrongly, but it may also be our very own limitation to inform with structured content that is machine-readable in the first place. In any case, it should be in an organizations own interest to ensure that its non-financial reporting is accessible in good quality and reproduced accurately.
Conclusion
Non-financial reporting and its online publication does not stop with producing nice stories and great pictures. Shared information needs to be ready for the digital era. The way AI supports our decision-making and innovation capabilities is constantly evolving. The line between how humans process information via neural networks and how AI processes digital information is becoming increasingly blurred. AI can not only reproduce existing digital information, but also promote innovative capabilities and lead to more informed decisions. How companies use AI is industry-specific and depends on aspects such as corporate culture and the ability to work with AI.