AI project challenges

The challenge of selecting the best AI project in a company with limited resources and the importance of creating a checklist to compare project ideas.

The key challenge of explainability in AI projects, especially in deep learning, and the actionable steps of preparing explanatory materials and involving future users in the process.

The challenge of ensuring AI solutions are actually used in daily work environments, with a focus on understanding user expectations and making the project feasible for daily application.

The importance of building trust in AI models, including a story about a project where users did not trust the model despite its accuracy, and the actionable step of working on metrics and planning from the beginning.

Addressing the challenges of data scarcity, biased data, and data quality in AI projects, emphasizing the need to check data availability before starting a project.

Strategies for addressing talent shortages in AI projects, such as carefully selecting team members and having backup plans in place, as well as considering buffers in the project schedule.

The challenge of choosing between AI strategies for innovation and incremental improvements, with a focus on balancing the portfolio to include impactful but less glamorous projects.

Highlighting the importance of scalable platforms like Enterprise AI for managing multiple AI projects within a company and recommending solutions from companies like DataRobot and HTO.

The significance of AI literacy among business owners and the impact on communication, with suggestions to share AI knowledge through courses, training, or books to improve understanding and application of AI in business settings.