This year, artificial intelligence (AI) has become the center of conversation everywhere: at home, at the park, at the gym, at work, etc. This is largely driven by, but not entirely attributable to or related to, ChatGPT and LLMs (Large Scale Language Models). Conversations about AI, especially in the tech world, have centered on generative AI, creating written content, images, videos, marketing copy, software code, speeches, and countless other things. For a brief introduction to generative AI, check out my article, Generative AI – Chapter 1, Page 1.
While generative AI (specifically ChatGPT) has generated a lot of interest among individuals, it is also having a transformative impact on organizations of all kinds, both in strategic discussions and tactical deployments. Enterprises and other organizations are looking to leverage generative AI to improve productivity (efficiency and effectiveness) in nearly every aspect of their organization.
While many people (and article writers) express concern that this new AI (generative AI) will take away people's jobs, others point out that: AI will not take away your job, but people who know how to use it will.
This last remark is important for the future workforce. Whether or not the future includes ChatGPT or its LLM-like programs, the point is clear: if you don’t learn how to use AI to improve your job productivity and effectiveness, you may find yourself in jeopardy. The same goes for businesses: if you don’t learn how to use AI to improve your organization’s productivity and effectiveness, you risk falling far behind your competitors or failing altogether. I said “may be in jeopardy” and not “will be in jeopardy.” Obviously, different jobs will use AI more or less than others. Similarly, different industries and companies within those industries will use AI more or less than others.
In this context, it is important to stay informed and educate ourselves about AI (especially generative AI) and to be aware of its capabilities, limitations, ethical requirements and limitless possibilities – in other words, to become AI literate.
I wrote an article on 5 traits of AI literacy, which applies to individuals but is primarily focused on universal AI literacy in companies, businesses, industries, and organizations. The 5 traits are AI awareness, AI relevance, AI usefulness, AI application, and AI imperatives. In that article, I emphasized the message that “AI literacy enables more people to beneficially participate in the current implementation and future operation of AI in business, industry, and personal lives.” In retrospect, maybe I should have made it 6 traits of AI literacy. I included AI ethics. My original point was to focus on the technical, business, and workforce imperatives of AI literacy, which implicitly presupposed an ethical framework for their implementation.
With this backdrop of the world seemingly embroiled in discussions about AI this year, I want to shift my focus to the importance of data to enterprise AI, and specifically the importance of storing, managing, and accessing data in driving, driving, informing, and powering AI.
Over the years, I have stated one simple truth about AI many times in presentations and articles: AI devours data. This aligns with my perspective on data-driven AI, where AI automates decisions and actions based on actionable insights and actionable intelligence (AI) derived from multiple, diverse data sources, including customer search history, purchase history, location data, emails, call center interactions, social media, marketing campaign data, location data, time of day, customer sentiment data, sales data, industry reports, machine data, asset performance data, and more.
Enterprises (today, data-soaked and data-driven) must leverage AI for business success, sustainability, and survival. Specifically, at this point, this includes generative AI and ChatGPT type applications.
At the risk of being overly brief, but with the goal of providing enough “food for thought” to drive important conversations around your enterprise’s AI strategy, here are 12 key points for organizations to consider in the context of “AI-readiness is a necessity, not an option.”
- AI has risen to the forefront of nearly everyone's thinking today. Executives, managers, businesses, investors, and consumers everywhere cannot help but hear about it in the news and on social media at any time of the day or night.
- The latest hot trends in enterprise AI and related considerations for those investing in AI show how ChatGPT and Generative AI have made AI more consumable and usable for everyone.
- Data storage is critical for AI and Machine Learning (ML) because AI, ML, and deep learning are entirely dependent on data and how effectively it can be leveraged for insights and value. In other words, business applications of AI rely more on data-specific infrastructure than on general IT infrastructure. Data is the fuel, the engine, and the essential foundation for all AI/ML activities and outcomes.
- The issue of storage is highly relevant to the success of AI/ML activities, including data labeling, classification, indexing, discovery, access, distribution, and writing back results – being able to do all of this in a repeatable, traceable, and verifiable manner can save AI/ML practitioners significant time in data preparation and data usability across the enterprise.
- A powerful, efficient and scalable data storage system that breaks down data silos and delivers results in real time is key. Get maximum performance when production results matter, rather than overspending when you don't.
- Data may be stored on multiple storage devices and servers, but it is needed when it is needed, not later or in pieces. Fast, transparent access to multiple data sources combined with a fast and efficient integration platform is the ideal infrastructure to keep AI/ML practitioners happy and productive.
- ML and data science are about exploration, testing and experimentation, and fast, easy and simple access to the right data is essential, regardless of where that data is stored in your IT infrastructure.
- It has to support multiple parallel AI/ML use cases, workloads, users and applications – making everyone feel special and noticed “on the front line” by the IT side of the business.
- Access to secure, reliable, traceable and simplified data creates trust and confidence in the development and especially deployment of AI/ML products, ensuring data is available when it is needed most.
- Pure Storage on-premise all-flash data storage is a huge benefit to AI/ML practitioners – find out why in our white paper “FlashBlade//S: Storage Built for AI,” which outlines FlashBlade//S™ and AIRI//S™ AI-ready infrastructure built for AI, a pre-validated, proven architecture that simplifies infrastructure for AI.
- AI/ML practitioners shouldn't have to worry about what storage device or file system the data is stored on, if that device has changed, if the system or controller has been upgraded, etc. Data is the encoder of information and knowledge for business insights, so AI/ML needs to be about business, not about IT.
- Pure Storage delivers these capabilities to ensure that AI/ML practitioners' workflows run smoothly, seamlessly, and efficiently today, even as their AI requirements grow.
In conclusion, I hope it's clear that being AI-enabled is not an option, but a necessity for current and future enterprises. Organizations are already leveraging AI for business success, sustainability, and survival. Join this trend and adopt an AI-enabled data infrastructure that delivers the right data at the right time, in the right business context, to the right business applications. This is not an option, it's a necessity.
The original article is herehas been republished on the website with the author's credit and consent. Originally published on Kirk Vaughn's LinkedIn.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/FarbenTech
