By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics, according to Gartner, Inc.
The number of (innovative) startups keeps growing. Consequently, the number of applications to VCs and related startup support bodies keeps growing also, thus frequently overflowing their onboarding workflows.
New non-expert agents (Education institutions, Public Administrations, and Corporations) want to enter the startup coaching market, but they lack the required expertise to do that.
This growing demand is hardly addressed by existing offerings providing limited and/or specific startup management/analytics capabilities, and frequently missing impact (ESG)/inclusive ventures.
Statistics show that 90% of the startups end up failing. Even those which are backed by VC firms/others show a failure rate of 75%. This is a giant waste of resources: sometimes supporting doomed-to-failure ventures, sometimes mismanaging potentially successful startups.
Causes of death are varied. According to Failory and other sources, the most frequent reasons for failure are: poor understanding of market demand, running out of funding, weak founding team and extremely hard competition.
Startups (and other kinds of highly innovative projects too) still offer limited ROI to their investors (private, but also public ones), mostly given they are run on an intuitive (or inexperienced) basis, thus frequently leading to suboptimal management.
So, there is a need for autonomous decision technology, complemented by deep VB knowledge, to allow entrepreneurs to increase their chances to transform ideas into successful commercial ventures at reasonable costs.
Acceleralia’s Venture Building as-a-Service (VBaaS) is a software platform that maximises the chance of success of startups, by assisting entrepreneurs in making the best decisions along their venture building itinerary. To do so, VBaaS is developing an AI-based technology able to automatically and continuously do the following:
- Estimate the potential performance of startups (value proposal, financial performance, investment capability, etc.)
- Identify gaps and points-of-improvement
- Recommend context-aware management decisions (including recommendation of specific resources –advisors, technology providers, methodologies, financials, etc to solve the previously identified gaps/points-of-improvement).
VBaaS’ AI is being fed by real-time and up-to-date both quantitative (e.g., statistics, financial data, surveys, etc.) and, mainly, qualitative sources (e.g., interviews, opinion blogs, press releases, etc.) by developing in-house methodologies and algorithms.
- Average failure rate around 60% for the time being (as compared to general 90% / 75% of mentored startups).
- Time-to-series-A reduced by 25% on average.
- More gender-rich portfolio: women-led startup share grew from 5% to 15% of the portfolio during the period of use of VBaaS methodology and MVP. A promising social innovation venture got invested which would otherwise have been missed by their legacy assessment methodology.
- Startups managed showing greater engagement and satisfaction because of the networking and well-supported decision-making capabilities provided by VBaaS.