Becoming an AI-first organization

The term “AI-first” has received its share of attention lately, especially in the boardroom where strategies to gain a competitive advantage are always welcome. But before a company embarks on an AI-first strategy, it pays to understand what it is and how it will transform the organization.

If you’re AI-first, that means you have figured out how to leverage artificial intelligence to boost organizational agility so you can continuously adapt operational processes to deliver the right business outcomes. 

It means your company has automated the processes of collecting, understanding and acting on data across the board, from production to purchasing to product development to understanding customer priorities and preferences.

An AI-first company is a business that doesn’t need to be told twice about the best course of action. Thanks to continuous data collection and interpretation, the business intuitively knows how to adapt to changing market dynamics to meet evolving customer needs and overcome hiccups such as the supply chain issues that have persisted since mid 2020.

Artificial intelligence involves getting computers to perform tasks that historically only humans can accomplish – voice recognition, language interpretation, visual perception and fact-based decision making – as well as skill- and labor-intensive activities.

But AI also  accomplishes tasks people could never do. That’s where another type of intelligence comes in – the one that involves gathering and interpreting information to drive decisions. Computers are very good at this type of intelligence. So good, in fact, that machine learning (ML) algorithms can be trained to pick up patterns and anomalies that elude the human senses.  

Where a human might make an educated guess about something or apply “business intuition” to it, a machine learning (ML) algorithm provides an actual likelihood and a percentage, making a data-driven prediction that a person could not provide. 

For a human to read 45 million documents and extract the key terms, it would take several lifetimes. For AI or ML, it takes a few minutes. This is why ML and AI have been woven into the fabric of cybersecurity intelligence-gathering and defense. 

Getting AI engines to deliver real-world benefits from gathering and interpreting intelligence requires lots and lots of data. Organizations need to implement advanced systems such as the Cloudera Data Platform, often in conjunction with other tools, to manage and automate the entire data lifecycle, from data creation and ingestion to organization to decision-making.

Systems used in operations and production can be monitored on a continuous basis to improve performance and prevent downtime. Monitoring platforms capture data that delivers valuable knowledge about systems health and performance, enabling decisions that benefit the business and its customers. In time, self-healing capabilities will trigger automated corrective action, minimizing human intervention.

Automated data collection can inform development processes by taking into account customer feedback, market dynamics, supply chain information and organizational capabilities. As data accumulates over time, AI and ML engines get better and better at identifying the most relevant data points so that the right products can be developed and introduced to the market.

Data collection and interpretation when purchasing products and services can make a big difference. By extracting cues from data about product and service performance, supply chain issues, pricing and potential risks, organizations can make the right investments to optimize business operations, empower employees and enhance customer experience. 

If a corporation could be given super powers, it would most likely choose the ability to predict what customers will want in the future. An AI-first company leverages purchasing and browsing histories to understand customers’ wants and needs before customers even can. Amazon has been working on an anticipatory shipping strategy to do just that. Getting it right, of course, requires tons of data and knowing how and when to act on it.