According to Gartner, AI is transforming investor positioning for technology service providers, with quantitative analysis set to shift the strategy of investors, while the technology also helps to determine the success of leadership teams.
With this increase in reliability on AI forecasted to come into effect in the next four years, the traditional pitch experience will significantly shift, with AI-enabled models and simulations replacing pitch desks and financial figures.
“Successful investors are purported to have a good ‘gut feel’ — the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner.
“However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making.”
Increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy towards a more modern platform-based quantitative process.
From here, tech service providers seeking investment would need to update and correct quantitative metrics on social media and business sites to ensure company information and financials are correct, according to Gartner’s forecast.
“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time,” said Stakenas.
“Questions such as when to invest, where to invest and how much to invest are becoming almost automated.”
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Determining success of leadership teams
AI is already capable of providing insight on customer demands and behaviours, with natural language processing (NLP) being used within sales and marketing teams to build profiles.
However, by 2025, Gartner predicts that this approach will be utilised by investment organisations to determine which leadership teams are most likely to succeed.
Stakenas continued: “The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured.
“AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”