Despite unprecedented investment in AI, most organizations remain stuck in experimentation, unable to convert AI activity into sustained, scalable business value. The unfortunate reality is that 95% of AI pilots fail to deliver significant impact on P&L (MIT, State of AI Business, 2025). In this whitepaper, we explain why failure is common and provide actionable guidance on how to succeed.

Preventing Eight Common Mistakes

AI failure almost always stems from poor inputs, weak design, and flawed execution. Low quality data, unclear objectives, and weak integration produce poor outcomes, just as entering the wrong numbers in your calculator produces the wrong results. The issue isn’t the calculator but how you use it. The same is true for AI. We address how to prevent these eight common mistakes when it comes to approaching organizational AI initiatives:

1. Poor data quality and lack of AI-ready data
2. Hype-driven initiatives instead of clear strategy
3. A lack of clear business goals and expected ROI
4. Ownership gaps and organizational misalignment
5. Weak change management and AI readiness
6. Poor integration into workflows and systems
7. Governance, risk, and control failures
8. Talent and capability gaps

Leaders vs Laggards

In addition to avoiding these common pitfalls, there are a few key differentiates that separate those who succeed and fail in their organizational AI journeys. As AI adoption accelerates, a clear gap has emerged between organizations that generate sustained ROI and those that fall behind. This divide is real and measurable, and it already affects how organizations compete across industries.

While a small group (the 5% seeing ROI on GenAI initiatives) pulls ahead, the rest fall into a trap of never-ending, never-scaling pilots. We address how leaders move out of experimentation and into operationalization, as well as where AI most clearly delivers measurable ROI. The key to bridging the gap between leaders and laggards lies in four pillars:

1. Align AI with core business outcomes
2. Build a data-ready AI foundation
3. Operationalize AI across the enterprise
4. Build organizational readiness and AI literacy

Bridging the Divide: A Competitive Necessity

The question is no longer whether AI can create advantages but whether your organization is structured to leverage them. At some point, top performers will lead and those who fail will be disrupted.

But past AI missteps don’t have to define your future. With clear, strategic goals and consistent reinforcement of the choices that support those goals, you can replace experimental pilots with business-focused initiatives that produce value and major wins in areas like:

• Risk reduction
• Decision quality
• Revenue acceleration
• Regulatory and compliance efficiency
• Margin expansion
• Customer retention
• Speed to market
• Productivity expansion

AI Success is Not Accidental

It is designed. And failure is rarely caused by weak technology.

If you're look to join the top 5% of organizations whose modern AI initiatives not only succeed but also exceed expectations, check out the full whitepaper to learn how to prevent missteps that cost millions and how to, instead, put your organization on the winning side of the AI divide.