Launching your first AI project with a grain of RICE: Weighing reach, impact, confidence and effort to create your roadmap
2 min read
Summary
A framework has been proposed to help businesses looking to implement AI decide where to start based on factors including business value, time to market, risk and scalability.
Inspired by the RICE (robustness, importance, certainty, effort) prioritisation model commonly used in project management, the framework scores the above factors on a scales of one to five, with one being the lowest score.
An alpha value can be applied to the risk factor, allowing companies to tailor the model based on their risk tolerance and strategic goals.
By adding up all the scores for each factor, companies can prioritise AI projects that score highly on the above criteria, balancing a desire to experiment with more high-risk projects with the need to ensure that the projects that are undertaken have a reasonable chance of success.
The author also highlights the need for companies to start small and learn, building trust and expertise gradually before scaling AI implementation. On a separate note, a new model crafted by Google has been found to be more accurate at spotting pneumonia in lung X-rays than radiologists, offering hope that it could be a valuable tool in the diagnosis of lung diseases.