Is Your Organization Ready to Implement AI? A Practical Guide

In our first post, we dove into the world of agentic AI, exploring how it differs from traditional chatbots and why it’s such a game-changer for organizations. Now, we’re shifting gears and asking a crucial question: Is your organization ready to implement AI, and how can you set yourself up for success?

AI is more than just a buzzword—it’s a transformative force that will touch every part of your business. AI-based platforms are springing up every day, and these tools will eventually be integrated into every part of the business. That could be a great thing–if your organization is ready.

When we talk about AI, there are three things that guide our understanding of the risks, benefits, and requirements for using AI. 

1. Good decisions rely on good data - whether human or AI. 
2. Good data is usable data - that means clean and deduped, but also accessible in places where employees work, and it’s transformed to fit their needs.
3. Using data fractures data systems - when an employee moves data into a spreadsheet to add a column, they’re making a transformation that isn’t visible to your source of truth. The same can be said for an AI agent working in a marketing platform that isn’t connected to your warehouse. If every team has their own AI tools generating and transforming data in separate platforms, you run the risk of exacerbating silos.

Given these risks, any company wishing to implement AI effectively should consider their governance capabilities, tech stack, and data quality to tailor their plan to their readiness level. 

The Three Pillars of AI Readiness

1. Process: Building a Solid Governance Framework

AI isn’t something you just plug in and forget about. It requires strong processes in place to manage how AI is used, how it’s governed, and how you ensure it’s acting in ways you can trust. Here’s what to consider:

• Governance Framework: Do you have processes to ensure AI agents are working effectively and ethically? Do you trust your models to prevent hallucinations?
• Compliance: Are the tools you’re using compliant with data regulations and industry standards? 
• Cross-Team Collaboration: AI won’t live in a silo. Do you have a system in place to ensure all teams are aligned and collaborating?

Without these processes, AI can quickly become a black box, where no one really knows what’s going on—or worse, it might cause confusion and mistrust across teams.

2. Technology: Building a Connected Tech Stack

Think of your technology stack as the infrastructure that supports your AI agents. If your tools don’t talk to each other, your AI agents will make decisions based on incomplete or fragmented data, which leads to poor results. Here’s what you need to consider:

• Interoperability: Can your tools seamlessly share data and insights?
• Adaptability: As AI evolves, can your technology scale and adapt with it?
• Scalability: Can your infrastructure handle the increasing demands of AI, especially as you scale?

When your tech stack is siloed or incompatible, it’s like trying to build a house with mismatched materials. It doesn’t matter how good the AI is—it won’t function properly without the right foundation.

3. Data: The Heart of AI

AI is only as good as the data it’s fed. If your data is dirty, disorganized, or locked away in silos, it’s going to negatively impact the outcomes of your AI initiatives. Here’s what to assess:

• Data Quality: Is your data clean, well-organized, and free of duplicates?
• Data Readiness: Is your data in a format an AI can understand and effectively utilize?
• Accessibility: Can everyone who needs it access trustworthy, up-to-date data?
• Consistency: Are data definitions and metrics standardized across teams to avoid confusion?

If your data is bad, your AI will be bad. Poor data leads to poor decision-making—and in AI’s case, that means poor automation and ultimately worse business outcomes. Poor data can also lead to negative customer experiences - “personalized” emails with the wrong assumptions, ineffective ticket routing. All of these can damage your brand and lead to churn.

The Three Realms of AI Implementation

Once your processes, tech stack, and data are in place, it’s time to think about where to implement AI. There are three main “realms” in which AI can be deployed within an organization’s tech stack. Each one plays a different role and requires different considerations.

1. Data Surfaces: Laying the Groundwork

Data surfaces are the foundational layer for AI. They focus on cleaning, unifying, and ensuring the accuracy of your data. These are typically handled by your data teams.

• Purpose: To ensure that AI is working with high-quality, unified data.
• Ownership: These tools are managed by data specialists who have deep technical expertise.
• Examples: Snowflake Cortex, Databricks AI, Census.

Tip: Getting your data surfaces right is crucial, as they lay the groundwork for the rest of your AI initiatives. Without clean, reliable data, any AI applications downstream will be compromised.

2. Usability Surfaces: Making Data Accessible

Once your data is cleaned and organized, usability surfaces take over. These tools make the data accessible and useful to people throughout your organization. They help internal teams make smarter decisions based on the insights AI provides.

• Purpose: To give teams actionable insights and tools to optimize their workflows.
• Ownership: These are typically managed by operations or knowledge management teams.
• Examples: Glean, Census, Clay.

Risk to Watch: Usability tools can sometimes create silos or transform data in ways that disconnect it from the original source of truth. Make sure they’re used thoughtfully to avoid distorting the data.

3. Execution Surfaces: Actioning Data at Scale

Execution surfaces are where the rubber meets the road. These tools take the democratized data and use it to directly impact customer-facing activities, like running marketing campaigns, optimizing customer journeys, or generating content.

• Purpose: To scale customer engagement and business operations quickly and efficiently.
• Ownership: Typically managed by marketing, sales, or customer success teams.
• Examples: Campaign automation platforms, customer journey optimization tools.

Risk to Watch: Execution surfaces can amplify any issues in data quality. If the data is incorrect or incomplete, executing actions at scale can rapidly multiply mistakes—potentially causing significant customer dissatisfaction or even reputation damage.

How to Choose the Right AI Surfaces for Your Organization

So, which AI surfaces are right for your organization? The answer depends on where you are in your AI journey:

• Just Getting Started? Focus on Data Surfaces to create a strong foundation. Clean, reliable data is the bedrock for AI success.
• Intermediate Stage? Look to Usability Surfaces to democratize insights across your organization and empower teams to make data-driven decisions.
• Ready for Scale? Dive into Execution Surfaces to take full advantage of AI’s ability to automate and scale customer-facing activities.

Conclusion: Build Your AI Foundation First

AI has the potential to transform your business, but only if you set it up for success from the start. By focusing on the key pillars of readiness—process, tech stack, and data—you can lay a strong foundation for AI that will pay off in the long run.

Remember, AI is a multiplier: it amplifies both the strengths and weaknesses of an organization. The more prepared you are, the more you’ll benefit.