IBM Global AI Adoption Index 2022 reported that global AI adoption has risen to 35%, a 4% increase from the previous year, with 44% of respondents actively integrating AI into their businesses. This reflects a strong commitment by companies to leverage AI’s potential in a data-driven world.
However, it’s crucial to understand the limitations of artificial general intelligence (AGI) in meeting enterprise needs. AGI is a broad form of human-like intelligence, whereas enterprise general intelligence (EGI) is tailored to specific organizational requirements.
EGI is like a chef specialized in a particular cuisine, while AGI is a versatile Swiss Army knife. EGI’s advantages include domain-specific biases aligned with enterprise goals and clear output evaluation metrics, which AGI lacks.
For enterprises, issues like bias, data privacy, and regulatory compliance are paramount. Four key considerations for enterprise-ready AI are:
- Explainability: Understanding how AI reaches decisions is essential for trust, compliance, and bias correction.
- Auditability: Thoroughly examining AI’s inner workings helps ensure accuracy and compliance with ethical and legal standards.
- Controllability: AI should allow humans to influence and intervene in its actions, with mechanisms for feedback and adjustments.
- Reliability: AI systems should consistently produce accurate results, even in challenging conditions.
New Corporate Management believes that in a world where data privacy and security are paramount, enterprises should prioritize EGI over generic AI, focusing on the four key considerations mentioned earlier. To succeed, businesses must define clear objectives, adhere to ethical guidelines, and keep their AI systems updated and secure. Ask is for a demo today.