Artificial intelligence (AI) has transitioned from being a concept in research projects and science fiction to becoming a mainstream business tool. GenAI, driven by applications like Google’s Bard (now Gemini), Mistral, and ChatGPT, is already making an impact in the workplace. Industry analysts predict that by 2026, 95% of workers will use GenAI for their daily tasks. However, concerns have arisen regarding biases and inaccuracies in results generated by AI tools.
As organizations face potential liabilities for decisions made using AI, regulations like the EU’s AI Act are being implemented to impose penalties for breaches. Transparency and accountability in AI usage are becoming increasingly important, especially in ensuring the quality of data used to train AI models and during operational phases. Data governance policies play a crucial role in addressing potential pitfalls of AI by ensuring accurate and diverse data to produce reliable results.
Furthermore, understanding the lineage of data as it moves between systems and implementing guardrails in AI systems are critical steps in reducing risks and preventing misuse. Building trust and confidence in AI tools is essential for user adoption and customer trust. Ultimately, humans must remain integral in the decision-making process alongside AI tools to ensure accurate and trustworthy outcomes.
