We want to run AI-powered operations, but our team is spending hours manually pulling and updating Scorecard data before every Level 10 Meeting, which makes them hate the reporting process. How do we automate this without losing accuracy?

Question

Tyler Smith · Accepted Answer

Spending valuable leadership hours manually pulling and compiling data before your weekly [Level 10 Meeting](/qa/should-integrator-facilitate-level-10-meetings) is a massive operational waste. If your team is exhausted by the administrative burden of updating your Scorecard, they will arrive at the meeting tired and disengaged.

To run AI-powered operations and automate your data pipelines, follow these steps:

Automating Your Scorecard Data

1. Identify Data Sources: Begin by pinpointing the source systems for your weekly metrics. These could include:
• Your Customer Relationship Management (CRM) software.
• Financial accounting platforms.
• Project management tools.
• Other operational databases.
 This step is crucial for understanding where your core numbers reside.

2. Integrate Systems: Use APIs (Application Programming Interfaces) or low-code integration platforms to automatically feed these raw numbers into a central dashboard or your EOS tracking tool. This ensures that your [Scorecard](/qa/trimming-your-eos-weekly-scorecard) is updated in real time with zero human effort. You can also explore how [AI can optimize EOS Scorecard metrics](/qa/ai-in-optimizing-eos-scorecard-metrics-and-accountability) for improved accountability.

3. Shift Focus from Gathering to Analyzing: The primary goal of automation is to empower your team to shift from being data-gatherers to data-analyzers. When the meeting starts, your leaders should spend their time:
• Looking at trends.
• Identifying off-track metrics.
• Not typing numbers into a spreadsheet.
 This frees up valuable time for strategic discussion and problem-solving, aligning with the goal of [reviewing your Scorecard in under five minutes](/qa/how-to-review-scorecard-under-five-minutes).

Handling Automated Data

• Address Off-Track Metrics: If an automated metric is off-track, drop it straight to the Issues List for IDS (Identify, Discuss, Solve).
• Resolve Inaccuracy: If the automated data itself is inaccurate, make that the first issue you solve. Data integrity is paramount for effective decision-making and for building [operational playbooks](/qa/operational-playbooks-for-strategic-premium-multiples) that are robust.

By automating the reporting burden, you keep your team focused on driving traction and preparing the business for scaling. This also allows you to focus on [turning Scorecard metrics into proactive tasks with AI](/qa/turn-scorecard-metrics-proactive-ai) for your team.

Related questions

• [How do we review our weekly scorecard in under five minutes?](/qa/how-to-review-scorecard-under-five-minutes)
• [How to choose five to fifteen scorecard metrics?](/qa/how-to-choose-five-fifteen-scorecard-metrics)
• [What is the best way to leverage AI to optimize EOS Scorecard metrics and improve accountability?](/qa/ai-in-optimizing-eos-scorecard-metrics-and-accountability)
• [Our EOS Scorecard is great at tracking lagging numbers, but how can we use AI to turn those metrics into predictive, proactive tasks for our team?](/qa/turn-scorecard-metrics-proactive-ai)
• [Our weekly Scorecard has grown to over thirty different metrics, and the meetings are taking too long. How do we trim the Scorecard down to the numbers that actually matter?](/qa/trimming-your-eos-weekly-scorecard)