Why We Must Focus on What AI Can Give Us Instead of Focusing on What It Could Take Away As we face a revolutionary technology, we should spend less time on what it will take away, and more time on how we will leverage it to create and accomplish more.
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ChatGPT's prototype launched less than half a year ago, shocking though it may seem. In the short time since, the platform has transformed how marketers, creators and inventive students view content creation. Depending on who you ask, and depending on how they are embraced, generative AI platforms like ChatGPT and Google's newly released Bard will either improve the way we do business or put us out of business.
One year ago, generative AI art approximating colleagues' facial characteristics littered our LinkedIn feeds. Now we're discussing the future of work as we know it … and it will likely be determined by those same behind-the-scenes algorithms.
I encourage you to consider not what generative AI can take away but what it can provide. While it's only natural for a technology that solves the unsolvable to cause a certain amount of trepidation, it's not a reason to try to ignore or suppress its capabilities. In fact, consider for a moment the major technological advancements of past centuries and the significant impact they had on GDP (Gross Domestic Product) over the years.
For example, the industrial revolution in the late 18th and early 19th centuries led to a massive increase in productivity and output in manufacturing industries, which contributed to the growth of GDP. Similarly, the advent of the internet and digital technologies in the late 20th century has revolutionized communication and information sharing, which has led to the growth of many new industries and businesses, contributing to an increase in GDP. As we once again face a revolutionary technology, I contend that we should spend less time on what it will take away, and more time on how we will leverage it to create and accomplish more.
AI has already enabled remarkable innovations
Enterprises handle a massive amount of data. From CRMs to APIs to consumer-facing technologies and beyond — everything in the enterprise tech stack generates an enormous volume of insight-rich data. Globally, The World Economic Forum predicts we will generate about 463 exabytes of data daily as early as 2025. For context, that's 1,000 bytes multiplied by a factor of six. In other words, it's more data than we as humans know what to do with or how to maximize the value of.
While our species is incapable of fully understanding data at this scale, much less extracting value from it, non-generative AI applications have been heavily leveraged to categorize, analyze, correlate and draw conclusions from such data at a phenomenal rate. Generally speaking, this level of efficiency-driving data science and machine learning was quickly embraced and adopted across numerous industries from business to healthcare. Because such non-generative AI was not seen as a threat to human competencies, but rather as a tool to help us achieve more, we have made tremendous progress by leveraging such technologies.
Such is not the case with this new form of generative AI that has now emerged, the capabilities of which are deemed to overlap a lot more with those of humans. In fact, generative AI is causing citizens and business leaders alike to be seemingly much more cautious about its applications and the threat that it may pose to jobs, industries and our current way of life than they were about its non-generative predecessor. While some of these concerns are certainly founded, it is imperative that we look not only at what we may lose but what we stand to gain by embracing this new technology — and even what we stand to lose by not embracing it.
AI has the potential to transform workplace tech
One example of how generative AI can change our lives, or at least the course of our workday, is its ability to transform our relationship with the everyday software around us.
Today, humans use software to accomplish or be more efficient at certain tasks. In most cases, we are required to physically interact with the software, digest information it gives us, make decisions on the tasks and strategies we will implement using said software, then, of course, use the software itself to execute those tasks. While ultimately the software is helpful, there is no ignoring that to reap its full benefit, we must invest time and effort into it which is taken away from other core parts of our day.
But consider for a moment a world where such a relationship is antiquated, and that software is no longer a tool that we have to spend time using, but rather a partner that will give us time back by doing things for us. Generative AI is one of the keys to realizing this new relationship with software. Time-consuming decisions and tasks across organizations and society are now automatically completed on our behalf, giving us time back to do more of what we are passionate about and great at. The efficiencies gained, not to mention the optimizations leveraged, will not only transform our outputs as a society, but the learnings and further innovations that will result will transform our economies, technologies and ways of life. This is where we at SOCi see software going and where we are investing our time and leveraging new AI technologies.
How do we move toward an AI-based future?
My answer to this question is simple: optimistically, but cautiously. Although I've discussed the positives of AI maturity at length, I must also emphasize what AI can't accomplish.
AI, generative or otherwise, is a powerful tool that can be leveraged, but it is seldom the end product. It is our responsibility to train the tool to be effective, to integrate it into workflows and processes that we need to achieve our goals and to continue to consider the needs of our customers. While the AI models flooding the market today are powerful, they still need direction, application and that "human touch" to be rendered into specific solutions suitable for our businesses.
It is also important to note that while AI may be leveraged to provide insights and complete certain tasks, it does not (yet) "think" as humans do. AI models are built to process data and deliver outputs but not to produce original thoughts and complex solutions. For the time being, humans will still be at the helm of crafting such strategies and solutions to larger societal or organizational challenges.
In the end, it will be the innovators amongst us that accept these challenges and embrace the benefits of AI that will dictate the advancements that we make and the transformations that our way of life will undergo. Our creators at SOCi are deeply passionate about being at the forefront of this movement and specifically about authoring the transition of the relationship our customers have with marketing software — from a tool that they use to accomplish meaningful tasks to a co-marketer who can execute on thousands of data-driven decisions and tasks — for them to deliver real-world results and give them time back to do what they are passionate about.