Algorithms & AI - Any Public Company’s Most Influential Audience
For some years now, the team at Sinter as well as our network partners have spent a lot of time thinking about AI and its underlying basis, algorithms. Well before ChatGPT and other “consumer” AI platforms became a thing, we and companies like Literate AI were wrestling with how to make algorithms, and the market behavior they drive, more understandable and manageable for the average business.
And so, long before AI went mainstream, we developed and learned how to apply proprietary AI-enabled software that gave our consulting teams and clients a better, more practical ability to “speak,” and listen to, machines.
In other words, Sinter and its partners don’t see algorithms just as math-based rules. We look at algorithms as an audience, and often the most influential stakeholder for every business - especially public companies.
Why this is useful - actually, why it’s a very big deal - is so obvious that it can escape the notice of most Boards and management teams, let alone the average person.
Daily Life Is Algorithmic
To start: for two decades-plus, algorithms have shaped an increasingly large part of everyday human experience. This is no exaggeration.
Algorithms mediate the news we read -on social media feeds, and even directly under major media breaking-news brands like AP, Dow Jones and Reuters - as well as in mainstream individual outlets.
They curate what we buy on Amazon and Wal-Mart.com.
They navigate for us. They shape our social and dating activity, and even our mental health, for good and bad.
Most importantly for Sinter and our clients, algorithms dominate daily trading across the US and international equity capital markets, by most measures accounting for over 70% of trading activity.
You needn’t just take our word for it, or even the expertise of the people and platforms referenced in all the links just above. Watch this TED talk (by a former colleague of one of Sinter’s co-founders). Delivered in 2011, it’s even more on point today.
The depth of algorithmic influence on money and culture might be uncomfortable. It’s also the truth.
Many Common Business Challenges: Also Algorithmic
For Sinter’s teams, that truth has meant that many client business and marketing questions, as it turns out, are also algorithmic ones, e.g.:
Why is a stock price compressed, or volatile, despite decent results, well-performed earnings calls, regular press releases and conference attendance?
Why do competitors get more press attention? Why is a business story underappreciated?
How do we get investors and other stakeholders to better acknowledge our competitive advantages, alongside results?
How do we get more analyst coverage/visibility for our stock?
How do we position a successful IPO, merger, or acquisition?
The best answers to these questions, and related activities/outcomes, require considering and managing how algorithms interpret information, and how people make decisions based on that machine curation.
This brings us to analytics, measurement and AI.
The primary reason we began exploring and developing AI products and applications was because we consistently ran into market patterns - such as equity trading that seemed consistently disconnected from operating strategy - and unstructured data sets - like media coverage or customer commentary - that weren’t easy to understand or explain with conventional analysis or software tools.
To Catch A Thief…
We realized that we needed to use algorithms to understand algorithms.
And from there, even a few years ago, it wasn’t a huge leap to realize that AI offered a more powerful way to grasp the way information is used by both humans and software to make decisions, like buying or selling stocks.
In particular, AI offers a way to better understand how algorithms read, score and rank qualitative as well as quantitative data. Put another way, Sinter’s teams can unpack how a platform like FactSet interprets and grades an earnings call transcript, vs. the way a human does.
This kind of understanding, in turn, helps us mediate much more precisely and impactfully how people and algorithms determine value.
In practical terms, that means we can better assess business models; better define markets; and better execute marketing, content, PR and IR programs that articulate and amplify information that will support, vs. subtract from, value.
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