Google’s AI Adoption Framework.
                     Google’s AI 
Adoption 
Framework
Introduction
Google Cloud, recently released its AI Adaptation 
Framework whitepaper, written by Donna Schut, Khalid 
Salama, Finn Toner, Barbara Fusinska, Valentine 
Fontama and Lak Lakshmanan, to provide a guidance 
system for companies to effectively exploit AI control.
4 Pillars
People Data
To learn, access Secure access of 
and lead data
Process Technology
Secure and Automate by learning 
Automated new technology
Learning
The learning process will help companies determine 
which analytics and machine learning capabilities 
are required for the company and, in the middle of 
this challenge, they will strategize their recruiting 
strategy. 
It involves the process of updating existing staff, 
hiring new candidates, and growing "experience 
associates" in analytics and engineering 
professionals.
Leading
Leading concerns whether or not organizational leaders 
provide the data scientists and engineers with sufficient 
support and guidance to deploy machine learning and 
artificial intelligence in their business projects.
Data Access
First is the 'entry' to data where organizations understand 
data collection techniques and leaders in analytics are 
able to capture, exchange, find, analyze data and other 
ML objects.
Scaling
In the scaling process, where businesses can define their 
ability to use cloud-native ML services, and scale large 
amounts of data with reduced business costs. 
This method will also assist you in understanding the 
cloud-based applications and how they are assigned to 
workloads.
Securing
The fourth is the cycle of 'securing' and is highly important for 
organizations. Companies will consider their compliance 
policies in this phase in order to secure confidential business 
data and critical information. 
In addition, this step will also help companies to ensure that 
responsible and explainable AI practices are deployed which 
will drive their business value internally.
Automation
Sixth is the 'automation' phase, where organizations will 
understand their ability to implement, conduct, and 
manage the system, develop and streamline data 
processing and ML outputs. This approach will also help 
businesses identify and trace the history of data, and 
manage the operation. 
For companies to adopt effective AI practices for their 
organizations, these processes are critical.
Conclusion
Strategic-phase companies concentrate on providing 
positive market performance, with many ML solutions 
being implemented and retained in manufacturing that 
utilize both ready-to-use and custom models. 
ML is no longer seen as the realm of a small few, but 
now is in the process of becoming a key market 
accelerator.
Source: google cloud
Thank You