Aible in Action

Charlie Merrow
Charlie Merrow, CEO of Merrow Sewing Machine Company
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I don’t just have a prediction problem, I have an optimization and a resourcing problem. I needed to figure out how to maximize my profits, and this involved figuring out which opportunities to pursue and how many sales people I should have. Aible gave me the optimized solution with just a few clicks and in a few minutes.”
Michael Kisch
Michael Kisch, Founder and CEO, Beddr

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I can count on Aible to find the AI that improves my business. Aible provides the AI for me — the business guy — not the data scientist.”
Gregory La Blanc
Gregory La Blanc, Distinguished Teaching Fellow at the Haas School at UC Berkeley
When we think about AI, we can’t just focus on simple metrics of AI quality. We need to start with business outcomes if the AI is going to have a business impact. I will use Aible’s Real World AI in my ‘Data Science and Data Strategy’ MBA class this semester to help students understand that the business impact of AI matters more than measures like accuracy.”
Tim Darling
Tim Darling, Chief Analytics Officer of Laudio
The translation between the business owner and the data analyst takes a lot of time in my experience, and Aible shortcuts most of that communication chasm. For example, out-of-the-box machine learning models optimize on accuracy, which gives equal weight to the cost of failing to predict something vs. incorrectly predicting something will happen. There are ways to adjust for this – you can manually adjust thresholds after a model has been run or you can run a model with a penalty matrix. But both of those require a lot of mental gymnastics and accurately communicating business needs to the analysts running the model. It takes time and clear communication. There are many other steps in the process of setting up and iterating models that take weeks. Aible talks to business owners in the ROI and constraints language they use and understand, reducing the back-and-forth discussions. In our case, we predict which nurse might quit their job so a hospital can intervene early and try to retain that nurse. The cost of failing to predict a nurse quitting is very different than the cost of unnecessarily intervening with a nurse who would have never quit their job. Hospitals have limited resources to follow up with nurses to retain them. Building cost-benefit tradeoffs and constraints into the models that reflect the business realities of the organization is hard to do; Aible does this automatically and gives me optimal recommendations we can act upon.”