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The release of ChatGPT in November of 2022 prompted the fastest public adoption of any new technology we have seen in a long time — perhaps ever. Many businesses, however, are largely taking a “wait and see” approach, which will only make it harder to keep pace as the technology evolves.
In recent months, generative AI tools like ChatGPT, Jasper, Midjourney and Rowy, and others have demonstrated incredible breadth. For the first time, language models are passing Google’s hiring test for engineers, Wharton’s MBA exams, and Minnesota University’s Law School exams.
Perhaps even more impressive, however, is how quickly creative fields once thought to be the sole domain of the human brain — like art, music and poetry — are being disrupted by automated systems capable of creating original works. And this is only just the beginning. Generative AI tools are improving at such a stunning rate that it won’t be long before we consider these early versions of the technology primitive.
The quality of these generative AI systems is mainly due to the incredible breadth of data and computing they’re built on. However, developing this kind of sophisticated generative AI model takes a significant amount of data and money — the kind only available to a handful of the world’s largest and most powerful technology firms. While there are interesting reports of companies finding innovative applications for generative AI platforms, most companies have largely remained on the sidelines as they grapple with legitimate concerns regarding intellectual property, security and overall quality.
While it’s important for organizations to fully consider the implications of disclosing their intellectual property to these third-party systems and be aware of ongoing quality concerns yet to be addressed, they also can’t afford to ignore such important technological breakthroughs. Though the concerns are valid, it’s also important to recognize that they will likely be addressed soon. The technology is only getting more sophisticated, and the longer they wait, the harder it will be to catch up.
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We’ve seen this pattern play out plenty of times; an innovation is unveiled, businesses widely acknowledge its disruptive potential and then refuse to engage with it due to some valid but ultimately — in the grand scheme of things — misplaced concerns.
For example, I can still recall when concerns regarding intellectual property, security and privacy discouraged many organizations from using third-party email servers, who instead devoted significant resources to developing and operating in-house email. The same happened when personal mobile devices were initially banned from the workplace or when cloud technology was introduced, then widely avoided. Now every company has a cloud strategy.
For large, legacy companies with significant investments in in-house, non-cloud native applications, the costs and challenges of starting the journey to the cloud were so daunting that they pushed it off. It’s been years since AWS, Azure and GCP have been available, and yet there are many Fortune 500 companies in still just the early stages of adapting and strategically leveraging these services.
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For those making significant investments now, it obviously would have been cheaper, faster, and better if that journey had started years ago. Ultimately, time wasted yields competitive ground to the leaner startups that embraced the cloud and can move more quickly.
Today, companies are once again faced with a game-changing technology and yet have similar concerns regarding intellectual property, ownership, security, legal and compliance. The difference this time, however, is that the scale, sophistication and openness of the new AI models are even more advanced, and the technology is expected to evolve at an even faster pace than we have seen in the past.
While the need to address these concerns is valid, and quality issues with these platforms are real, we’ve overcome such challenges countless times over; we can expect they will be solved in this instance. In the meantime, I firmly believe at least some small investment should be dedicated to understanding the art of the possible and its limitations and working through the intellectual property, security, and legal issues.
Throughout history, countless inventions have improved human productivity. Software engineers today are more productive than engineers from decades ago. What changed? It certainly wasn’t the capacity of the human brain. Instead, our heightened productivity is thanks to new software engineering frameworks, platforms, and tools. AI tools represent the next major leap in this journey. Just imagine what an AI engine that can pass college-level exams can do when it’s purpose-built to help software engineers write code.
While there are risks associated with the technology in its early stage, the most significant risk most tech companies face is waiting too long and allowing the competition to onboard the technology first.
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Start-ups are in a particularly advantageous position, as they have much less to lose and much more to gain by taking a bold risk on early AI adoption. However, large enterprises can begin dabbling with generative AI by finding low-risk use cases. They should also ensure that this is considered a top priority for legal and security teams and adequately communicate the significant stakes.
While the applicability of these technologies is broad, I recommend finding a pragmatic, simple area to begin experimenting and learning, then expand from there. Perhaps even host an in-house hackathon to see all the creative solutions your teams think up.
There are countless opportunities to experiment with generative AI across marketing, engineering, customer service, and many business functions. While being conscious of the risks and taking steps to mitigate them, it makes sense to start small. However, getting started is important; otherwise, you may risk getting left behind.
