Did you miss a session at the Data Summit? While interest in AI remains high among enterprise organizations, particularly for its potential to improve decision-making and automate repetitive tasks, many of these businesses are struggling to deploy AI into production. In a February survey from IDC, only a third of companies claimed that their entire organization was benefitting from an enterprise-wide AI strategy. The same poll found that 69% of companies hadn't yet reached production with AI, and instead remained in the experimentation, evaluation, or prototyping phases. The challenges vary from organization to organization, but some common themes include infrastructure and data.

Did you miss a session from the Future of Work Summit? Austria-based Mostly AI, a startup that simulates synthetic data for AI model training and testing, today announced it has raised $25 million in a series B round from Molten Ventures. The company plans to use the investment to accelerate its work in setting the groundwork for responsible and unbiased AI, hiring fresh talent, and strengthening its presence across Europe and North America. For any modern-day enterprise, the biggest challenge associated with leveraging data for AI/ML is ensuring the privacy of its consumers -- the original source of the data -- and eliminating the possibility of any sort of bias due to historical or social inequities in that data. Organizations often find a hard time dealing with the two problems and either end up facing fines for privacy violations (under regulations such as GDPR) or train a model which is unfair on one or more parameters.

With Abacus.AI, an organization’s team of developers--with access to the organization’s data--can use its platform to create deep-learning systems that can make accurate predictions without having to invest the time and resources to do so from scratch.