How to Get Your SaaS Business Data Ready for AI Operations

You have some killer AI applications ready to implement in your business operations. You’re excited to get moving.

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But is your business data ready for AI?

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AI models are data-hungry machines. The more data you can give them, the better results you will get. However, if you haven’t prepared the data, you won’t get good results either—even if you feed the AI model a lot of data. 

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The evergreen GIGO principle applies to AI systems too: Garbage In, Garbage Out. Without clean, structured, comprehensive data, you’ll get no value from your AI solutions.

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Let’s walk through four steps you can take to get your business data ready for AI operationalization. These actions will help you make sure you get the most out of operational AI. 

Key Takeaways: The AI Preparation Process

1. Align databases and systems with business processes
2. Audit data and correct inaccuracies
3. Ensure comprehensive data collection
4. Implement and enforce effective data policies

1. Align Databases and Systems with Business Processes

To give AI enough context for analysis, make sure your systems accurately reflect business strategies and operations. This comes back to the outcomes and processes we defined in the first step of this guide. If your systems don’t accurately line up with the way the business runs, no AI model can do any useful analysis.

System Alignment Example

You might have a clear pricing strategy and review process, but your CRM isn’t set up to track target deal values and actual deal values for comparison. Your AI solution won’t have the context it needs to perform analysis on those metrics if they aren’t tracked in the first place. Modeling the business with your systems will help AI analyze your operations and deliver valuable insights.

2. Audit Data and Correct Inaccuracies

Any process involving data requires accurate inputs. Make sure your data is clean. This may involve manually reviewing your data and fixing the errors you find. While this will take some time and effort, data quality is essential for a successful AI implementation.

Data Correction Example

you might find close dates preceding entry dates in your CRM system. Implementing a new CRM may have reset close dates for all active deals at the time. To get an accurate AI analysis of your sales cycle, you would need to identify all of the inaccurate dates and correct them. Resolving this issue will make sure the analysis your AI does is accurate and useful.

3. Ensure Comprehensive Data Collection

Remember, AI needs as much data as you can possibly give it. Make sure you a) have all of the data you need analyzed and b) have a defined pathway for feeding the data to the AI model. Providing all relevant data will improve the quality of any AI analysis. 

Comprehensive Data Collection Example

We’ve seen companies track some of the stages of their sales process in their CRM, and the rest in a spreadsheet or other system. They may have good reasons to set their system up this way. However, if they can’t feed all of that data to the AI, it will miss critical context. They need to make all of that data accessible for analysis.

4. Implement and Enforce Effective Data Policies

If you do all of these steps without reviewing data policies, you will still run into problems. You need to make sure that data gets recorded appropriately going forward, or you will have to do all of this work again. To prevent that outcome, make sure to implement and enforce effective data policies. Without rules about how to structure, record, and verify data, analysis quality will decline over time. 

Data Policy Example

For example, you might hold regular data training sessions and reviews with sales reps. You can teach them proper methods for entering lead information into the CRM after specific interactions. Discussing best practices and holding team members accountable to following them will make sure quality data enters your systems in the future. Without some form of data accountability, you won’t get consistent, reliable results from AI.

The Bottom Line: Better Data, Better Results

Without clean, structured, comprehensive data—and policies to keep it that way—you won’t get much value from your AI applications. It takes work, but preparing your business data for AI will pay dividends in operational efficiency and revenue performance. Follow these steps to maximize the ROI of AI in your SaaS business operations.

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If you need support preparing your SaaS business data for AI, we can help. We can even analyze your business data and give you a data health report. This report will help you identify areas for improvement and understand the actions you can take to ensure data quality across the business.

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To learn more, schedule a consultation today.