Welcome and thank you for accessing our AI Readiness Checklist!

To get the most out of this checklist, we recommend making a copy of this page in your own Notion workspace. This way, you can customize it to fit your needs, share it with your team, and track your progress. Simply click the "Duplicate" button in the top right corner to create a copy.

We will walk you through a step-by-step evaluation of your organization's AI readiness, covering key areas like data, technology, and team preparedness.

By the end of it, you'll have a clear picture of your strengths and weaknesses, and a roadmap to help you successfully integrate AI into your business strategy. So, if you're just starting out with AI or maybe even looking to take your initiatives to the next level, this checklist is here to help.

Quick prep work

Preparation step 1️⃣. Choose a specific pain point where AI adds unique value. Is it automating repetitive tasks, enhancing customer experience, or making predictions?

Preparation step 2️⃣. Identify a clear, measurable problem AI can solve. For example, if you have a SaaS or commerce platform, you can use AI to predict customer preferences or prevent fraud.

Level 1. Data readiness

Are your data assets prepared for AI applications?

• Data inventory and quality
  • [ ] We have identified all relevant data assets, assessing their quality, quantity, and accessibility.
  • [ ] Data cleansing processes are in place to maintain high data quality.
• Addressing data gaps
  • [ ] We have identified data gaps and developed strategies to acquire or generate the necessary data.
  • [ ] Partnerships or external data sources are considered to supplement our data needs.
• Data governance and compliance
  • [ ] A robust data governance framework is established, ensuring data quality, privacy, and security.
  • [ ] We comply with all relevant data protection regulations (e.g., GDPR, CCPA).
• Efficient data management
  • [ ] Processes for data acquisition, storage, and management are secure and efficient.
  • [ ] Real-time data processing capabilities are implemented where necessary.
• Data accessibility
  • [ ] Authorized personnel have easy access to data through centralized systems.
  • [ ] Role-based access controls are in place to protect sensitive information.

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Insight 1. Instead of just relying on your internal data, consider external data sources (APIs, web scraping, partnerships) that can enhance your AI models. For example, sentiment analysis on X(Twitter) for customer insights.

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Insight 2. If you don’t have enough real data, consider creating synthetic datasets to train your models. It works nicely if you’re in a niche industry with limited real-world data.

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Level 2. Technology infrastructure