Gartner:  Digital Workplace Summit 2022, Americas (June 21-22)
IT - AI

5 AI Adoption Mistakes To Avoid
The Enterprisers Project, May 5th, 2022
Artificial intelligence (AI) and related technologies are becoming increasingly mainstream business tools. If you're planning an AI implementation, beware of these potential pitfalls

Artificial intelligence (AI) and machine learning can be invaluable assets to business success. By implementing AI, businesses can automate hours' worth of manual labor sifting through data to enable smarter and faster business decisions. However, automation and AI do not remove the need for human responsibility.

It's important to follow best practices to ensure AI helps versus hurts your business. Here are five mistakes to avoid in leveraging AI to meet company goals.


Zscaler discusses how AI is evolving its DLP strategy, which is particularly challenging with today's distributed data

Data management and security practices are changing rapidly as data becomes fully distributed and fully situated in the cloud today.

Every organization is using hundreds of software-as-a-service (SaaS) apps-many that aren't company approved. SaaS apps allow users to access them from anywhere at any time, boosting productivity and collaboration, which is why 'shadow IT' has become one of the biggest headaches for corporate IT.


Recording the model development process on the blockchain can make that process more structured, transparent, and repeatable, resulting in less bias and more accountability.

The past few years have brought much hand wringing and arm waving about artificial intelligence (AI), as business people and technologists alike worry about the outsize decisioning power they believe these systems to have.

As a data scientist, I am accustomed to being the voice of reason about the possibilities and limitations of AI. In this article I'll explain how companies can use blockchain technology for model development governance, a breakthrough to better understand AI, make the model development process auditable, and identify and assign accountability for AI decisioning.

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