By Dr. Ryan Ries, Chief AI and Data Scientist at Mission, a CDW Company 

AI adoption is accelerating fast. While nearly 80% of organizations report using AI in at least one function, fewer than 20% say it's driving real business results. 

That gap says a lot. It's not that the technology isn't powerful - AI has already proven it can transform operations, boost productivity, and uncover insights that were previously out of reach. But ambition is outpacing readiness. Organizations are diving into AI without the strategy, infrastructure, or skills in place to support it. 

After working with hundreds of organizations across industries like healthcare, software, and retail, three consistent success factors stand out: aligning AI to business strategy, building a scalable data foundation, and investing in people. 

Here's what it takes to get it right. 

1. Align AI to Business Strategy (Not the Other Way Around)

One of the most common missteps we see is treating AI as a separate initiative. Teams experiment in silos, explore tools without clear use cases, or adopt AI simply because they feel like they have to. 

This results in a patchwork of pilots with no path to production and no connection to business value. 

AI delivers the most impact when it's mapped directly to outcomes. That could mean reducing customer churn, speeding up fraud detection, or personalizing the digital experience. But if the problem isn't clearly defined-or if the solution isn't tied to broader goals-momentum quickly stalls. 

To drive ROI, organizations need to take a top-down view. And that starts with creating a clear, organization-wide AI strategy that defines: 

• Clear business objectives
• Priority use cases aligned to those objectives
• Metrics for measuring success 

It sounds obvious, but only about 25% of enterprises have a defined AI roadmap today. Without one, AI can become a science experiment instead of a value generator. 

2. Build a Scalable Data and Infrastructure Foundation

Once the strategy is in place, the next question is: Can your tech stack support it? 

AI thrives on data, but not just any data. It requires clean, well-organized, high-quality data that can be accessed securely and efficiently. It also depends on infrastructure that can ingest, process, and serve that data to models in real time. 

Many organizations hit roadblocks here. Data often lives in silos, infrastructure may not scale, and pipelines aren't optimized for performance. 

Unstructured data accounts for an estimated 80 to 90% of enterprise data, much of it untapped, limiting AI's potential without foundational improvements in architecture and governance. A full audit of your current data architecture can help answer key questions: 

• Where does your critical data live today?
• How is it governed and secured?
• Are systems cloud-optimized and elastic enough to scale AI workloads?

Cloud-native platforms can be essential in supporting modular, cost-efficient environments that handle everything from foundational data prep to advanced model training and deployment. 

3. Upskill (Don't Just Hire)

The third ingredient is often the hardest: talent. 

AI isn't a plug-and-play solution. It's a mindset shift. Beyond data scientists, organizations need business analysts who understand how to work with models, engineers who can build responsible pipelines, and leaders who know how to ask the right questions. 

But there's a challenge: a growing AI skills gap. More than half of IT leaders report that AI talent shortages are slowing their adoption efforts. While hiring plays a role, forward-looking organizations are increasingly focused on upskilling. 

And it's a worthwhile investment. Employees who receive the right tools and training are more than twice as likely to say AI improves their performance. The impact isn't just technical - it's cultural. When marketing teams know how to evaluate LLM outputs or finance teams understand model limitations, the entire organization becomes more AI-literate and effective. 

The Bottom Line: Readiness Is the Real Differentiator

Everyone's excited about AI. But for all the buzz, the real differentiator isn't who adopts AI first - it's who adopts it best. 

That's what readiness is all about. Do you have the right strategy? The right data foundation? The right mix of skills? If not, you risk spending a lot of time and money without seeing meaningful results. 

Because in a world racing toward AI, the most prepared organizations will be the ones who win. 

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ABOUT THE AUTHOR

Dr. Ryan Ries is a renowned AI and data scientist with more than 15 years of leadership experience in data and engineering at fast-scaling technology companies. Dr. Ries holds over 20 years of experience working with AI and 7+ years helping customers build their AWS data infrastructure and AI models. After earning his Ph.D. in Biophysical Chemistry at UCLA and Caltech, Dr. Ries has helped develop cutting-edge data solutions for the U.S. Department of Defense and a myriad of Fortune 500 companies. As Chief AI and Data Scientist for Mission, a CDW company, Ryan has built out a successful team of Data Engineers, Data Architects, ML Engineers and Data Scientists to solve some of the hardest problems in the world utilizing AWS infrastructure.