# Startup Sustainability Forecasting with Artificial Intelligence
## 5 Conclusions  
Startup evaluation is becoming increasingly difficult due to the growing number of companies and the volume of digital data they generate. This difficulty is attributed to multiple factors, including the increasing complexity of new business plans, which require significant expertise for their evaluation. This presents a new challenge for professional investors and analysts, as they have to estimate the feasibility and sustainability of thousands of startup ventures. AI presents an excellent opportunity to address this issue. Through intelligent decision support tools, expert analysts can improve their efficiency and make fewer errors in their investment decisions. However, until recently, there has been very little research regarding the use of AI for startup sustainability forecasting. Similarly, there are no studies investigating the feasibility and defining the value proposition of AI for startup evaluation. This can be explained by researchers' limited access to startup data, as these are mostly private and collected by accelerators, incubators, and VCs. In this paper, we conducted a survey that provides insights regarding the desirability, feasibility, value proposition, and specification of AI-driven startup evaluation. The survey was conducted in collaboration with four major startup organizations in Greece. The findings reveal that startup organizations (VCs, accelerators, and incubators) are well aware of the benefits of AI and are currently taking action to integrate this new technology into their decision-making processes. According to the conducted survey, the most desirable feature for an AI-driven system was forecasting startup sustainability. Sustainability is a complicated attribute that depends on multiple factors, including team cohesion, financial projections, burn rate, market conditions, and the developed product/service. Based on the survey findings, this paper also presents a comprehensive AI-driven architecture for startup sustainability forecasting. Therefore, this architecture is in alignment with the actual feasibility and desirability constraints dictated by the survey. It considers multiple sources of data, including business plans, web activity data, psychometric assessments, company financials, and macroeconomic indicators. Due to the heterogeneous nature of the data, the system integrates multiple machine learning methods, such as NLP for document understanding and timeseries models to process financial projections. Several key challenges have also been addressed, including feature selection, overfitting, and the incorporation of human factors. This paper also presents the experimental results of seven machine learning algorithms on 44 Greek startups. The purpose of these experiments is to predict startup sustainability scores annotated by expert analysts. These results highlight the high accuracy of Random Forest in predicting sustainability scores, effectively distinguishing good from bad cases. In addition, the decision tree structure is analyzed to provide insights into the discovered rules, which are highly explainable. In future work, we plan to train and evaluate our startup sustainability forecasting system with multiple data and in collaboration with the aforementioned organizations. Another direction for future work is to investigate how the requirements and constraints of an AI-driven sustainability forecasting system would differ in the US startup ecosystem. Conceptualization, N.T. and E.K. ; methodology, E.K. and G.L. ; software, G.L. ; validation, N.T., K.M., I.V. and D.K. ; investigation, E.K. and K.M. ; resources, N.T. ; data curation, G.L. ; writing--original draft preparation, E.K., G.L. and K.M. ; writing--review and editing, N.T., K.M., I.V., D.K., E.K. and G.L. ; visualization, E.K. and G.L. ; supervision, I.V. and D.K. ; project administration, E.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.