Key Economic Drivers for Intelligent Systems

Science-fiction novelist Neal Stephenson said, “See, the world is full of things more powerful than us. But if you know how to catch a ride, you can go places.” 

Growing up reading the works of Stephenson, I never could’ve imagined the level of innovation we’re witnessing today. I’m talking, of course, about artificial intelligence. It’s certainly taking us places, and it will soon be a part of nearly every aspect of our daily lives. 

We’re living in the age of the intelligent systems economy, in which AI and data-powered intelligent devices and systems -- from robots to business automation processes -- have become key economic drivers. Every industry is being revolutionized by AI and data, and organizations need to learn how to “catch a ride.” 

The companies that build and leverage this technology also have an inherent responsibility to ensure that it is used for social and economic good. The potential is massive: PwC global projects AI could contribute a staggering $15.7 trillion to the global economy by 2030, more than the current output of China and India combined.  

It’ll also be critical to ensure that AI-powered technology is accessible so that it can benefit as many individuals as possible. AI is already helping people by accelerating the development of life-saving treatments in healthcare and acting as a great equalizer in education, empowering students with more information than ever before. The possibilities of what else it could do to improve lives in just a few short years are limitless.  

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Organizations must move quickly if they want to succeed and make meaningful contributions in the intelligent systems economy. There are two critical aspects to making that happen: data and people.  

Data: There’s No AI Without It 

AI would not exist if not for big data. To fuel AI initiatives, companies need accurate and reliable data to avoid pitfalls like AI hallucinations, and they also must have the ability to understand their data so that it can be leveraged to its full potential. But companies oftentimes don’t know where to start on their intelligent systems journey. Factors like cost, added complexity, fragmented workloads, and privacy (especially when it comes to generative AI) further add to this uncertainty. In order to understand their data and address the aforementioned concerns, organizations need to adopt supportive technology.  

First and foremost, modern organizations need solutions that do more than just store their data; they need the ability to cleanse and curate their data – fast -- so that it’s readily consumable for intelligent applications. Essentially, they need tools that let them inject intelligence directly into their data stack. Observability is also crucial: AI has an insatiable appetite for data from a plethora of sources, and businesses need visibility into this process across their various data estates. 

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Additionally, data gravity has created the need for compute to occur as close to the originating data source as possible. Take autonomous vehicles (AVs), for example. An AV is constantly generating data from its cameras, speedometer, GPS, and various sensors. For the AV to react to its environment in real time, the compute must take place within the vehicle itself -- there’s no time for data to roundtrip to the cloud when it comes to split-second decisions, like slowing down to avoid an impending collision. 

Companies need solutions that are flexible enough to run both in the cloud or on-premises depending on their specific needs and workloads. AI is still a new frontier, so companies should opt for technologies with an extensible underlying data model that also provide ongoing support as they navigate building these novel intelligent applications.  

People: The Most Important Differentiator 

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An organization can have all the right data, robust technology to support it, and a strategy for the intelligent systems economy. But without the right people, achieving its vision won’t be possible. 

AI is impacting data management roles across organizations -- all the way from the chief data officer (CDO) to data analysts. While AI won’t replace many data-related roles, it will empower data professionals to do more, especially those who are eager to acquire new AI-related skill sets. This is one reason why it’s important to hire people with a growth mindset: As AI proliferates, organizations will benefit from bringing on individuals who are willing to learn and curious about the new opportunities AI presents. 

It’s imperative to build diverse teams in order to effectively drive AI initiatives. Diverse teams are well-equipped to problem-solve, innovate, and think creatively when it comes to leveraging AI. A diverse talent base also helps companies understand and connect with customers on a deeper level. Organizations need to stay accountable and walk the talk when it comes to diversity by regularly measuring it within the organization and setting diversity-related hiring goals for leadership. It’s just like any other business goal -- metrics and results matter. 

Quoting Stephenson again, “Interesting things happen along borders -- transitions -- not in the middle where everything is the same.” We’re transitioning into a world in which AI touches everything. No one knows how this will look in five or 10 years, but if it’s anything like the innovation we’ve seen so far, we can bet it will be interesting. Every company is striving to succeed in the intelligent systems economy, and the ones that will are those who get two things right: data and people.