Artificial intelligence has captured the attention and imagination of the business world and public at large. From self-driving cars to curing diseases to having robots do our routine tasks, AI is the foundation of true-to-life innovations that were once strictly the domain of science fiction.
And AI is becoming more real every day. Over 40% of digital transformation initiatives are already supported by AI capabilities, and by next year, 75% of commercial applications will use AI, according to IDC.
The business case for AI is compelling: AI can help lower costs, accelerate time to value, leverage intelligence to enrich the customer experience, strengthen cybersecurity, reduce IT complexity and improve cloud agility.
Yet, take away the hype and hyperbole, and you have the very real, very complex, very challenging task of building real AI workloads and making sure AI is ready for prime time in your organization.
With AI, success comes down to getting the right data; having a modern, end-to-end data pipeline to leverage that data; creating a culture to embrace data and AI; and deploying a technology platform that lets you use AI to enhance and seamlessly integrate with existing workflows and applications. You must also be able to easily scale AI as the needs of your business change and as more useful and sophisticated data is created.
Here are several key factors all business and technology leaders should consider as they embark on the journey to leveraging AI to drive meaningful outcomes.
Point No. 1: AI is not an application by itself
Perhaps the most important point to keep in mind is that AI is just part of the application; it is not the application itself. For companies to be successful in using AI, they need to be able to seamlessly integrate AI into their existing workflows. This means that those deploying AI have to understand what each workflow looks like. A major part of that is ensuring that they are getting the right data in the right place at the right time (see Point No. 2 below).
AI success is not just about technology. It also requires involvement and sign-in from all of the individuals responsible for ensuring the quality and availability of the application—not just the data scientists and data architects who may be directly responsible for building AI models but also the app owners, security engineers, DevSecOps teams and IT operations teams, among others.
Because of this, real-world AI is a collective effort, which means there are cultural challenges to overcome. Part of these cultural challenges are a result of fear—fear of failing by bringing all of these complex pieces together and trying to automate the entire process using DevOps. It is something that must be done, but it also feels like individual departments and teams are losing control.
However, for AI to integrate with applications, whether legacy or cloud-native, everyone needs to be on the same page, with no silos. Think of AI as a giant jigsaw puzzle, with different teams and individuals tasked with solving different pieces of the puzzle. If one part of the puzzle has the wrong pieces, or if that part doesn’t come together properly, the puzzle never gets completed correctly.
Point No. 2: AI is only as effective as the underlying data
The second most important point: AI can be only as reliable and effective as the underlying data upon which it is based. As they say, garbage in, garbage out. In today’s world, IT teams are dealing with massive volumes of data of various types and formats, coming toward them at a rapid, always-on, ever-increasing velocity. While having massive volume is great for AI, it works only if that data is both usable and scalable.
Think in terms of real-world workflows. You have raw data coming in with a variety of characteristics. Some aspects, such as sensitive credit card information, may have to be scrubbed for safety and data protection reasons. Other data may have a variety of different classifications. Say you are trying to identify users in California. Some data may come in with California as the identifier, some might say Los Angeles, and some might say LA.
The point is that you must have technology in place to transform the raw data into something usable for AI. That means having a data pipeline platform that is intelligent and fast, with the ability to handle massive amounts of file- and object-based unstructured data with ultrafast IOPS performance, metadata performance and massive scale.
Point No. 3: In the real world, AI must be able to scale
Many organizations start small with AI, with a few self-contained projects with smaller data sets. This is fine for what it is. But be careful. What may work well in a prototype or sandbox environment, with small data sets and limited scale of the problem, can explode on you when you need to scale and take that piece of the puzzle across the entire application.
That’s when all of sorts complications can arise, particularly if you haven’t properly addressed the technological challenge of having the right data platform in place for AI, if you don’t have a strategy to integrate to the existing SLDC process, and if you haven’t taken care of the cultural challenges.
As you scale, everyone has to work on a common shared data set, and everyone needs to be able to easily share their piece of the AI puzzle with other members of the team.
AI has the potential to be a major source of competitive differentiation across all industries. But AI success is a complicated business, requiring a true analysis and understanding of where AI can help your organization. With that understanding, you can then confidently go about incorporating AI into your corporate culture and investing in a modern data platform to successfully deploy and scale AI.
Are you ready to turn your AI hopes into AI reality? Please take time to review the other articles and resources in this special site and visit Pure Storage and Cisco to learn more about FlashStack for AI.