The added value of AI: solve all of the above — and more
Today, computers can take over most of our back-breaking work. Not just to save us from thankless tasks, but to bring us real benefits that could never have been conceived without high computing power and advances in data science. Let’s have a quick look at the added value a computer brings to the innovation and startup scouting table.
It can read, fast and a lot
Analysts spend their time reading company sheets, websites and other information about startups they will never engage with. The 99% ratio is a well-known metric to VCs: 1% of startups they meet will be invested eventually. No reason it would be different for corporate venturing. Reading useless content is sad indeed.
The computer is able to read millions of text pages almost instantly, to understand topics in the text, and to process multiple semantic analyses. Natural Language Processing (NLP) is one of your most precious companion in your startup scouting activities. Using NLP, you can delegate the reading to the computer — it will never blame you for killing it on the job.
It can screen out the unfits
Gating startups takes time. Analysts must absorb a minimum amount of information about the startup before reaching a disqualification point. The decision to rule out a candidate is based on the distance between the venturing thesis and the startup’s profile (a profile covers more than mere firmographics and financials, it also includes activity, technology, models, people and all contextual data).
Artificial intelligence, when properly designed and trained, is able to instantly calculate the semantic distance between the two matching ends: the company’s innovation strategy on the one side, and the startup context on the other side. This includes the capacity for the computer to process and understand complex, elaborated inputs that go far beyond keywords. As said earlier, innovation is a multidimensional matter.
It can make links and create context
The more data you have, the better your decisions will be. Common sense. Adding data means reducing uncertainty proportionally. This is why analysts are keen on contextual information flows like social media, news feed and other weak signals. Problem is that all those sources are not automatically embraced in a single interpretation process. Adding context is often manual, and the value drawn from it is left to the human brain.
Data is the raw material of AI. The computer makes links automatically between data sources. When we feed it with contextual data about startups, it will add it to the perimeter of its semantic understanding. The semantic distance between a startup context and a given briefing is calculated using all data we have, and including social media, news, patents, articles, people, etc., will further improve the matching accuracy.
It can rank out the best ones
As mentioned above, a keyword is, by nature, a reduction of reality, a gross simplification, a wide angle, a photo overview with macro lenses. Using keywords for a thorough, accurate search will always result in shambolic results — even on Google.
Since AI goes beyond keywords and calculates a matching score, result lists suddenly become ranked lists, so that analysts will first review the startups that best fit the unique strategy given as the campaign input. The ranking is not based on a number of occurrences or a popularity score whatsoever: it’s the real result of a semantic distance that goes down to the thinnest granular details. We call this ranked list a Golden Basket.