What to expect from early stage AI Pitches in 2023

The AI field is evolving at a rapid pace. Here are our key considerations to make AI-edge-technology pitches less challenging for early stage VCs and angel investors. These are the points you should expect from the startup.

Clear articulation of the AI tech USP and the tech competitors
The startup in front of you should be able to concisely communicate the technical aspects of their AI model, highlighting what sets it apart from existing solutions. It’s essential for the startup to demonstrate a deep understanding of the AI tech landscape, including their closest tech competition. By thoroughly researching and analyzing the competitive landscape, startups can effectively position their technology and articulate its unique selling points. Understanding the strengths and weaknesses of competing technologies allows startups to highlight the advantages of their approach and explain why their AI product is superior. This knowledge also enables startups to anticipate potential challenges and develop strategies to differentiate themselves in the market. Ultimately, a clear articulation of the AI technology’s uniqueness, coupled with a comprehensive understanding of the AI tech landscape and competition, strengthens the startup’s value proposition and instills confidence in potential investors.

Demonstration of the startup’s access to relevant and quality data sources
Even at the early stage, having a solid data strategy is essential for the success of an AI product. Startups should showcase their ability to access diverse and representative datasets that align with their target use cases and market needs. By demonstrating access to relevant data sources, startups convey their understanding of the specific problem they are addressing and their commitment to building a comprehensive solution. By demonstrating access to relevant data sources, startups can exhibit their capability to train and refine their AI algorithms effectively. Moreover, startups should emphasize their strategies for ensuring data quality, including data collection methods, cleaning processes, and validation techniques.

Awareness of ethical-AI considerations and mitigation strategies
The Startup should proactively emphasize their strategies for ensuring data privacy, consent, and security during data collection and usage. They should have measures in place to handle sensitive or personal information responsibly. Additionally they need to showcase their awareness of potential biases in the data and their commitment to mitigating these biases. This includes techniques such as dataset augmentation, fairness testing, and ongoing monitoring of the AI system for potential biases in its outputs.

Bringing a roadmap for future development and upcoming iterations
The Startup should outline their vision for how they plan to enhance and evolve their AI models to stay ahead of the curve. This includes highlighting their strategy for incorporating emerging AI technologies, enhancing the dataset, exploring new applications, and leveraging advancements in the field. By showcasing an understanding of AI trends and developments, they need to demonstrate their ability to adapt and capitalize on future opportunities.