This article on AI readiness initially appeared on Kirk Borne’s LinkedIn. The blog has been republished with the author’s credit and consent. 

This year, artificial intelligence (AI) has become a major conversation centerpiece at home, in the park, at the gym, at work, everywhere. This is not entirely due to or related to ChatGPT and LLMs (large language models), though those have been the main drivers. The AI conversations, especially in technical circles, have focused intensively on generative AI, the creation of written content, images, videos, marketing copy, software code, speeches, and countless other things. For a short introduction to generative AI, see my article “Generative AI – Chapter 1, Page 1”.

While there has been huge public interest in generative AI (specifically, ChatGPT) by individuals, there has been a transformative impact on organizations everywhere, both in strategy conversations and tactical deployments. Businesses and others are seeking to leverage generative AI to increase productivity (efficiencies and effectiveness) in nearly all aspects of their organizations.

While many people (and article writers) express concerns about this new AI (generative AI) taking people’s jobs, other people are quick to point out the following: AI will not take your job, but someone who knows how to use AI will.

That last statement is key for the future workforce. Whether or not the future includes ChatGPT or its LLM kin, the point is clear—learn how to use AI to improve your own productivity and effectiveness at work, or your position may be at risk. The same can be said for businesses—learn how to use AI to improve your organization’s productivity and effectiveness, or your business may be at risk, either of falling significantly behind your competitors or of complete failure. I said, “may be” at risk, not “will be.” Obviously, different jobs will use AI more or less than others. Similarly, different industries and businesses within those industries will use AI more or less than others.

What’s important in this situation is to be informed, to learn about AI (especially generative AI), and become aware of its capabilities, limitations, ethical requirements, and limitless opportunities—that is, you need to become AI literate.

I wrote an article on the five characteristics of AI literacy. This is applicable to individuals, but it is primarily focused on AI literacy within enterprises, businesses, industries, and organizations universally. The five characteristics are AI awareness, AI relevance, AI utility, AI application, and AI imperative. In that article, I emphasized this message: “AI literacy empowers many more people to participate usefully in the current implementations and future operations of AI in businesses, industries, and personal lives.” In hindsight, perhaps I should have made it six characteristics of AI literacy—to include AI ethics. My original point was to focus on the technical, business, and workforce imperatives of AI literacy, which implicitly assumed an ethical framework for those implementations.

With this background on why the whole world seems to be drawn into AI discussions this year, I now want to shift focus to the importance of data to enterprise AI, specifically the importance of data storage, management, and access in fueling, powering, informing, and driving the AI.

Over the years, I have stated many times in my presentations and articles one simple truth about AI: AI devours data! That is consistent with my view of data-driven AI, in which AI automates decisions and actions based on the actionable insights (AI) and actionable intelligence (AI) derived from multiple diverse data sources—customer search histories, purchase histories, 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, etc.

Businesses (which are data-drenched and data-driven these days) need to leverage AI for business success, sustainability, and survivability—specifically right now, that would include generative AI and ChatGPT-type applications.

At the risk of too much brevity, but with the intent of providing just enough “food for thought” to fuel essential enterprise AI strategy conversations, here are 12 key points for organizations to consider within the context of “AI readiness is not an option, but an imperative”:

  1.   AI has come to the forefront of thinking for just about everybody these days. Executives, managers, businesses, investors, and consumers everywhere can’t help but hear about AI in the news or on social media day and night.
  2.   The latest hot trends in AI for businesses and the associated considerations for those investing in AI are demonstrating how ChatGPT and generative AI have made AI more consumable and usable for everybody.
  3.   Data storage matters to AI and to machine learning (ML) because AI, ML, and deep learning are totally dependent on the data and how it can be effectively utilized for insight and value. In other words, business applications of AI are not dependent so much on the general IT infrastructure as much as on the data-specific infrastructure: Data is the fuel, the power source, and the essential basis of all AI/ML activities and results.
  4.   Storage matters are significantly relevant to the success of AI/ML activities—that includes data labeling, classification, indexing, discovery, access, distribution, write-back of results, and being able to do all of that in a repeatable, traceable, verifiable way that saves massive time in data preparation and data useability across the whole enterprise for AI/ML practitioners.
  5.   A powerful, efficient, and scalable data storage system that breaks down data silos and gives you results in real time is critical. Don’t overspend when you don’t need to—but get maximum performance when production results are critical.
  6.   You need data when you need it, not later or in pieces, though it might be stored on multiple storage devices or servers. Fast, transparent access to multiple data sources in conjunction with a fast, efficient consolidated platform is the ideal infrastructure to keep AI/ML practitioners happy and productive.
  7.   ML and data science are about exploring, testing, and experimentation—fast, easy, simple access to the right data is essential, no matter which part of the IT infrastructure holds the data.
  8.   Multiple parallel AI/ML use cases, workloads, users, and applications must be supported—everyone feels special and sees that they are receiving “front of the line” attention from the IT side of the business.
  9.   Secure, dependable, traceable, and simplified access to data creates trust and reliability in AI/ML product development and especially in deployment—the data will be there when it is needed the most.
  10. Pure Storage on-prem all-flash data storage is a big win for AI/ML practitioners! Why? You can learn why in the white paper “FlashBlade//S: Storage Built for AI,” which provides overviews of FlashBlade//S™ built for AI and of AIRI//S™ AI-ready infrastructure, which is a pre-validated, proven architecture that simplifies infrastructure for AI.
  11. The AI/ML practitioner doesn’t need to worry about which storage device or file system the data lives on, or if that device changes, or if there is an upgrade to the system or the controllers. AI/ML should not be about IT—it should be about the business, as data is the information and knowledge encoder of the business insights.
  12. Pure Storage provides these capabilities to keep AI/ML practitioners’ workflows running smoothly, seamlessly, and efficiently now and as your enterprise AI requirements grow.

In conclusion, we hope that it is clear that AI readiness is not an option but an imperative for enterprises now and into the future. Organizations are already leveraging AI for business success, sustainability, and survivability. Get on board with that trend and with an AI-ready data infrastructure that delivers the right data at the right time in the right business context to the right business application. That is not an option. That is an imperative!

Built for AI: Learn how the right storage platform can help power your organization’s AI initiatives. Download the white paper.

Continue reading in our next article posted here: Top 9 Considerations for Enterprise AI

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