List the key strategic decisions that affect the business and the related indicators that need improvement (e.g., better customer satisfaction, increase conversion, reducing claims and so on).
Identity AI areas
Solving a particular business challenge very likely will involve more than one AI area. Ensure that you map all appropriate AI areas (e.g., NLP, machine learning, image analytics) to the problem you want to address.
Think big, start small
AI’s potential to influence decision making is huge, but you will need to build the right data, techniques, skills, and executive decision-making to exploit it. Have an evolutionary path towards more advanced capabilities. AI’s full power will become available when the AI platform continuously learns from both the environment and people.
Create your own proprietary datasets for training and measuring the accuracy of your algorithms. For example, create your own proprietary database of “crash images” and benchmark the accuracy of your existing algorithms against them. You should consistently aim to improve the accuracy of the algorithms against comparable human decisions.
Build a pilot of your AI solution using existing vendor solutions or open source tools. Conduct parallel runs of the AI solution with human decision makers. Compare and iteratively improve the performance/accuracy of AI solution.
Once the AI solution has proven itself, scale it with the appropriate software/hardware architecture, and institute a broad change management program to change the internal decision-making mindset.