AI is shaking up the world of analytics, but it’s not all smooth sailing. Businesses are rushing to adopt AI-powered tools, hoping for game-changing insights, but they’re also hitting some serious roadblocks.
Kurt Schlegel, a Gartner analyst, breaks it down: while AI has the power to transform analytics, organizations need to navigate some tricky challenges before they see real value.
From sky-high costs to trust issues and governance headaches, AI in analytics is more complex than it seems. Schlegel takes a balanced approach, he’s excited about AI’s potential but also skeptical about the hurdles.
He lays out five key challenges companies must address to make AI work in the real world.
Why It Matters: AI in analytics is here to stay. But without the right approach, businesses risk spending a fortune, creating a mess of ungoverned data, or making decisions based on unreliable AI outputs. Understanding these five challenges can help leaders move past the hype and build an AI strategy that actually delivers results.
- Building Trust in AI: AI is powerful, but it’s not perfect. Organizations need transparency, explainability, and a solid development process (Dev-Test-Prod) to ensure AI-driven insights are reliable.
- Keeping Costs in Check: AI can get expensive fast, especially with token-based pricing models. Instead of rolling out AI to everyone at once, start small with specialists who know how to use it efficiently.
- Bringing Order to the Chaos: AI tools can either streamline analytics or create a total mess. The key is balancing control and flexibility. Think of it like a ski resort, where beginners start on green slopes and only experts tackle the black diamonds.
- Focusing on Solutions, Not Just Tech: It’s easy to get caught up in the latest AI breakthroughs, but what really matters is how AI helps businesses make smarter decisions, allocate resources better, and improve key metrics.
- Shifting from Ownership to Influence: AI isn’t something one team can control alone. The approach Schlegel recommends is a “franchise model” (or hub-and-spoke), where different teams have autonomy but follow a shared governance structure.
