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Top 10 AI Best Practices

by | Aug 11, 2022

Users and use cases of automation are growing by the minute. As one of the originators of no-code AI and provider of the unique solution Intelligence Automation, InRule presents a helpful guide, excerpted from our new eBook Getting Started with Automation. To those considering dipping their toes into the expanding sea of AI, enjoy our expertly curated top ten success strategies, based on experience gained over two decades and 500 customers:

  1. State your purpose and principles.
    • The best way to ensure your AI practices align with your mission and tenets is to put them in writing. Clarify the whys, hows and ideal outcomes of your automation implementation. Address concerns head-on. State clearly why you are engaging intelligence automation and paint your ideal vision of how it will impact your organization.
  2. Know your info architecture, all of it.
    • Data is the lifeblood of AI. The more quality data available to feed your automation system, the better your results. Unfortunately, the technical infrastructures of many organizations are a phalanx of disconnected systems added piecemeal over time as the company grows and new processing needs arise. Conduct a thorough audit of your IT to ensure no siloed data troves remain untapped.
  3. Scrub and verify your data.
    • Ensure your automation ROI by investing the resources necessary to ensure your raw data is as free as possible from typos, inaccuracies and other corruptions that will negatively impact outcomes. Engage experts to fully understand the quality, scope and limitations of source info.
  4. Identify primary pain points.
    • Organizational pain points are obvious for most users. Such would be the case of a lender plagued by a disjointed loan application system. Whether addressing a single glaring issue or choosing between several, get the biggest bang for your initial AI endeavor by deploying your first automation process where it will have the greatest impact.
  5. Brainstorm more use cases.
    • Initial intelligence automation successes will surely leave you hungry for more. Potential applications are infinite and often surprising. Unconventional machine learning use cases include perfecting beer flavors, designing student lesson plans, composing fortune cookies and predicting the size of forest fires. Query team members across departments and functions for processes and interactions that automation can improve.
  6. Test, test and test again.
    • Before launching automated logic and processes, make sure they function as intended. Conduct thorough testing of each component in isolation and as a whole. Incorporate diverse, representative user groups and needs. Then repeat as dictated by complexity and uncertain variability.
  7. Beware of harmful bias.
    • Harmful, misaligned biases can creep into decision logic and machine learning algorithms, often through inadvertent human influences or biased training data. Stay vigilant that your AI stays protected from these negative factors that can adversely affect determinations.
  8. Keep humans in the loop.
    • Human referral and intervention are key components of any successful automation deployment and are vital to limiting negative biases and outcomes. Human oversight is essential to maintaining compliance in regulated industries such as insurance and financial services. Build human QA and exception handling into customer and applicant interactions to ensure outcomes are free from harmful bias and undesired, disproportionate results.
  9. Track results.
    • Make sure your decision logic, process automations and machine learning predictions yield projected outcomes. Track results at every stage of engagement. Consider both quantitative and qualitative metrics such as customer feedback.
  10.  Ever improve.
    • Continue to monitor results and conduct periodic automation audits. Devote the resources, training and collective brainpower and imagination to facilitate better and better performance and ensure your outcomes ongoingly align with your stated objectives. In AI, as in business operations generally, always mind the adage “optimization is a journey, not a destination.”

If you enjoyed these top 10 AI best practices and would like to learn more about the InRule® Intelligence Automation Platform including unique use cases, frequently asked questions, and how to select an automation partner, consider downloading our latest eBook: Getting Started with Automation.

Alternatively, if you are ready to get started in earnest. Request a 30-day free test drive here.




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