The Top 5 AI & Machine Learning Trends for 2021 And Beyond

Crossing the Chasm to AI Success: Step-by-Step

March 2, 2021

Our CEO Ryohei Fujimaki discusses how building #AI applications because everyone is doing it, is a path to failure and shares his step-by-step advice for AI success with Integration Developer News #iDevNews 

“Building AI applications because everyone is doing it and throwing any problem at AI without concrete objectives is a path to failure,”

AI is taking off like a rocket, but companies need to think carefully about investments in AI. The enterprise’s rapid adoption of AI and machine learning will gain even more steam in 2021. However, the challenges of applying AI to business problems are often not easily solved. AI is a new-age hammer for companies that want to use it to pound at every remotely looking like a nail. Businesses must have clear, quantifiable business goals to leverage AI investments.

AI projects can have murky ROIs due to misunderstood AI capabilities and timelines. AI leaders should create a team of technical and functional experts and have clear objectives to get started. Fujimaki said the first step is to define the business problem and critical business interests. Then clearly define data science objectives and identify ML problem categories. Fujimaki noted that the use of clarity would help AI stakeholders to understand complex data science projects. Read the full article at Integration Developer News

dotData

dotData Inc.

dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at dotdata.com, and join us on Twitter and LinkedIn.