What AI readiness really means?

Why preparing for the future requires putting AI to work today

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The current AI era has introduced new tools, new possibilities and new pressures, but I still approach most projects the same way: start with the business problem you’re trying to solve, then work backward to the information and capabilities needed to solve it.

Consider a retailer trying to understand why it’s losing sales despite having plenty of stock. Rather than jumping straight into better forecasting or new promotions, I’d start with a simpler question: Are the right products available where and when customers want them? An inventory check alone won’t answer that. You need to connect stock levels with store-level demand, sales trends and incoming shipments. That broader context may reveal why plenty of stock isn’t translating into sales.

We are at a point where the connection of data is becoming just as important as overall quality—and it’s the combination of the two that helps solve more complex challenges. Transportation understands delivery costs, supply chain tracks delivery performance and customer service monitors complaints about late shipments. Bring those datasets together and the business can answer a much more valuable question: Is this delivery route actually profitable?

The challenge is that making those connections often exposes problems that are harder to see when data is considered in isolation. Definitions may not align across systems, records may be duplicated, and important information may still sit in disconnected applications or business functions. Organizations need to address enough of that complexity, including data quality, governance and security, to trust what AI produces, without spending years trying to perfect their entire data environment in a tech landscape that continues to change.

This creates a difficult balancing act. Move too quickly without addressing the data and context required for a particular use case, and AI can amplify bad information or assumptions. Move too slowly, and the technology may outpace the roadmap you spent years building.

This is why I’ve become skeptical of the idea of becoming “AI ready.” How can a business be ready when the technology itself is constantly changing?  

While overall readiness can and should be assessed by measuring factors like data quality, operating model flexibility, governance and workforce skills, the reality is that AI readiness isn’t something you achieve before you begin. It’s something that you need to assess and build continuously.  

The future is here… but it is also out of reach

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I’ve been in too many meetings where an AI transformation discussion starts with what the technology can do rather than what the business is able to achieve.  

An AI-powered demand forecasting system might predict a surge with remarkable accuracy, but is the business ready to connect those insights to inventory, replenishment and the wider supply chain and take action based on them? If it’s not, then the value will be lost no matter how good the technology is.  

Closing the gap between what’s possible with AI and what’s possible within the business requires bringing strategy and execution closer together. The people defining the future need to understand what it will take to build it, while the people building it need to understand the business problem well enough to recognize when the strategy needs to change. That connection also needs to extend to how AI is governed and measured, so teams can determine whether what they build is delivering the intended value and operating within the right guardrails.

For example, many organizations initially adopted Snowflake to migrate and modernize their data. Today, the platform offers a much broader range of analytics capabilities, as well as the ability to build AI-powered applications. Teams working directly with the platform may see new possibilities before strategists do. On the other hand, strategists may see the potential of a new feature without fully understanding the configurations or constraints that need to be addressed to implement it.

Connecting the two creates value on both sides: strategic recommendations become grounded in what can actually be implemented, while implementation reveals new possibilities, constraints and opportunities that can reshape the strategy itself.

In other words, execution should be an input to strategy, not simply the step that happens afterwards.

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The approach: Solve a problem + Strengthen the foundation

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Becoming AI ready comes down to asking:

- What do you want to achieve?
- What data needs to be connected to do so?
- Which systems and workflows need to work together?
- What needs to change across the business, from operating models and executive ownership to ways of working and governance, to make the solution viable?

- How will you track and measure value?

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The answers can inform the strategy, but so should its execution. This push and pull between planning and operations is how organizations can draw value out of AI today, while unlocking more advanced capabilities for the future. It also helps keep expectations and investment grounded in what the business can realistically deliver.

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Here we offer four recommendations to put this approach into action.  

1. Rationalize what you already have. Many enterprises have accumulated hundreds of reports, databases and BI tools that are costly to maintain, duplicative or are no longer supported. Before adding more technology, understand what is already there. A platform like Snowflake can help organizations examine how data assets are used and connected, identify overlapping or redundant reports, and determine what should be consolidated, retired or migrated. Remember: the goal is not to clean up everything before moving forward. It is to reduce complexity where it stands in the way of value.

2. Deliver value while building the foundation. Pursue one or more high-impact use cases that solve real business problems. Use each implementation to identify and address the data, technology and operational gaps that stand in the way. Embrace the idea that every deployment should deliver value today… but it should also propel the business towards what it wants to do next.

3. Continuously reassess what technology makes possible. What a platform can do today may be materially different from what it could do just a few months ago. We’ve seen the pace of that change firsthand at BlueCloud. In January 2026, we were only beginning to use CoCo, Snowflake’s AI-powered coding agent. By June, we had been named its 2026 CoCo Catalyst Snowflake Partner of the Year for Services AMS Adoption. Staying current on platform evolutions like CoCo takes a lot of time and effort—which is why it can be really helpful to have a partner take on some of the heavy lifting.  

4. Build for adaptation, not a fixed end state. No one can predict what the business or tech landscape will look like in just a few years time—not even the AI leaders themselves. This is why it is so important to create a data environment and a way of working that can evolve as new capabilities emerge, new use cases become viable and business priorities change. Strategy should guide what gets built today, while what gets built today informs the strategy for tomorrow.

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Intelligence built through action

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The myth around AI readiness is the idea that there is some point at which the data, technology and organization will all be sufficiently prepared and the real work can begin. That is a practical impossibility because technology, the market, the business, the customer… none of it stands still. In practice, readiness will always be a moving target and it can only be achieved through the work itself.  

Even as the world around us changes at an extraordinary pace, the approach remains the same: connect the data and context that matter to a business problem, learn from what you build and use those lessons to shape what comes next.

That, to me, is how the business can become AI ready no matter where the future takes us.  

Want to continue the conversation? Connect with Jim to discuss how bringing Data + AI strategy and execution closer together can help organizations create value while building for what comes next.

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