AI tools can significantly aid in interpreting the results of data analysis, and their role becomes particularly important in data-driven decision making. Here's how:

1. Automated Insights: AI tools can automatically identify and communicate key findings from data analysis. They can highlight significant trends, anomalies, correlations, or patterns that may not be readily apparent.
2. Predictive Analytics: AI can leverage machine learning models to predict future trends based on historical data. These predictions can be interpreted to make informed decisions about future strategies.
3. Natural Language Processing (NLP): AI tools with NLP capabilities can translate complex data analytics results into easy-to-understand, human-like text. This helps non-technical team members understand the results and contributes to more inclusive decision-making.
4. Sentiment Analysis: AI can interpret sentiments and emotions from qualitative data like customer reviews, social media posts, etc. This interpretation can guide decisions related to customer satisfaction, brand reputation, and more.
5. Explainable AI: With the advancement of AI, there's a growing focus on 'explainable AI' or 'XAI', which aims to make the decision-making process of AI models more understandable and interpretable for humans.

The importance of interpretation in data-driven decision-making is multi-fold:

1. Informing Decisions: Interpreting the results of data analysis provides insights that form the basis of data-driven decision making. Decision-makers need to understand what the data is saying to act upon it.
2. Understanding Trends: Interpretation helps in understanding underlying trends and patterns, which can inform both immediate decisions and long-term strategies.
3. Assessing Impact: Interpretation of results can help assess the impact of past decisions and guide future actions. This feedback loop is essential for continuous improvement.
4. Risk Assessment: Accurate interpretation can identify potential risks and challenges, helping organizations mitigate them before they escalate.
5. Democratizing Data: When data analysis is appropriately interpreted, it can be understood by people across an organization, not just data scientists. This democratizes data and promotes a more inclusive, informed decision-making process.

In essence, the accurate interpretation of data is as important as the data itself. Without proper interpretation, even the most comprehensive data can fail to provide value. AI tools play an instrumental role in facilitating this interpretation, bridging the gap between raw data and actionable insights.