In 2026, enterprise AI and BI are evolving fast. Recent trend reports show that core practices such as data quality, security, governance, and data-driven culture remain at the top of priorities, even as AI/ML, generative AI, and advanced analytics gain traction.

At the same time, businesses are investing heavily in AI-powered enterprise systems, real-time analytics, and domain-specific models, shifting from experimentation toward measurable business impact.

This raises a practical question for teams building intelligence capabilities:

• When should organizations focus on strengthening foundational BI elements like data quality, trust, and governance?
• And when should they prioritize newer AI-driven analytics and automation capabilities?

Looking for practical perspectives, real-world trade-offs, or frameworks others have used to strike that balance as BI and AI converge.