85% of new Ai companies are failing and here is why.....

Why AI Projects Fail and How AI TRiSM Can Help Companies Succeed

According to a report from Gartner, a surprising 85% of AI projects end up failing. Several key reasons contribute to this high failure rate, such as poor data quality, lack of the right data, and not fully understanding how AI works. These challenges highlight how important it is for companies to have strong data management and clear planning strategies, especially when using advanced AI models like cloud-based solutions or large language models (LLMs).

The Data Problem in AI Projects

Data plays a huge role in making AI and machine learning (ML) projects successful. AI models need good-quality data to provide accurate and useful results. If the data isn’t up to standard, the AI model won’t perform well. A 2024 survey by NewVantage shows that nearly 93% of executives see data as the biggest challenge when it comes to using AI. Another survey by Vanson Bourne revealed that 99% of AI and ML projects run into problems because of poor data quality. These numbers prove that managing and monitoring data correctly is essential for making AI work.

How AI TRiSM Can Help Companies Succeed

AI TRiSM (Trust, Risk, and Security Management) is a framework that helps companies build reliable and secure AI systems. Here’s how AI TRiSM can help overcome the common reasons AI projects fail:

1. Trust: AI TRiSM focuses on making AI systems trustworthy by ensuring transparency and fairness. It helps businesses understand how AI makes decisions, which builds confidence in using AI systems.

2. Risk Management: AI projects come with risks, like bad data or unintended consequences. AI TRiSM helps companies identify and manage these risks early, so they can avoid failures before they happen.

3. Security: AI systems can be targets for cyberattacks. AI TRiSM helps companies protect their AI models and data from security threats, ensuring that the system stays safe and reliable.

4. Data Governance: AI TRiSM promotes strong data management practices, which is key for overcoming data quality issues. By constantly monitoring and improving data, companies can ensure their AI systems provide accurate and trustworthy results.

By using AI TRiSM, companies can avoid the common pitfalls that lead to AI project failures. Focusing on trust, risk management, and security can help businesses successfully implement AI, improve decision-making, and achieve long-term success.