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The release of ChatGPT in November of 2022 led to the fastest public adoption of any new technology we’ve seen in a long time – maybe ever. However, many businesses are largely taking a “wait and see” approach, which will make it difficult to keep pace as technology develops.
In recent months, generative AI tools such as ChatGPT, Jasper, Midjourney and Rowi, and others, have demonstrated incredible breadth. For the first time, language models are passing Google’s recruitment exam for engineers, Wharton’s MBA exam, and the University of Minnesota Law School exam.
Perhaps even more impressive, however, are creative areas once thought to be the sole domain of the human mind – such as art, music and poetry – are being disrupted by automated systems capable of creating original works. And this is only the beginning. Generative AI tools are improving at such an astonishing rate that it won’t take long for us to consider these early versions of the technology.
The quality of these generative AI systems is primarily due to the incredible breadth of data and computing on which they are built. However, developing such sophisticated generative AI models requires vast amounts of data and money – available only to some of the world’s biggest and most powerful technology firms. While there are interesting reports that companies are finding innovative applications for generative AI platforms, most companies grapple with legitimate concerns regarding intellectual property, security, and overall quality.
While it is important for organizations to fully consider the implications of disclosing their intellectual property to these third-party systems and yet to be aware of ongoing quality concerns, they cannot overlook such important technological advances. Can do Although the concerns are valid, it is also important to recognize that they will soon be addressed. The technology is only going to get more sophisticated, and the longer they wait, the harder it will be to catch up.
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We have seen this pattern too many times; An innovation is unveiled, businesses widely acknowledge its disruptive potential and then refuse to engage with it because of some legitimate but ultimately – in the grand scheme of things – misplaced concerns.
For example, I can still remember when concerns about intellectual property, security, and privacy discouraged many organizations from using third-party email servers, which provided significant support for the development and operation of in-house email. dedicated resources. The same happened when personal mobile devices were initially banned from the workplace or were widely avoided when cloud technology was introduced. Now every company has a cloud strategy.
For large, legacy companies with significant investments in in-house, non-cloud native applications, the cost and challenges of starting the cloud journey were so daunting that they pushed on. It’s been years since AWS, Azure, and GCP became available, and yet many Fortune 500 companies are still in the early stages of adopting and strategically leveraging these services.
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For those making significant investments now, it would clearly have been cheaper, faster and better if this journey had started years ago. Ultimately, the time lost gives linear startups who embrace the cloud competitive ground and can scale more quickly.
Today, companies are once again faced with game-changing technology and yet the same 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 model is even more advanced, and the technology is expected to develop at an even faster rate than we’ve seen in the past.
While the need to address these concerns is legitimate, and the quality issues with these platforms are real, we have overcome such challenges countless times; We can expect that they will be resolved in this instance. In the meantime, I firmly believe that at least some small investment should be devoted to understanding the art of the possible and its limits, and working through intellectual property, security, and legal issues.
Throughout history, countless inventions have improved human productivity. Software engineers today are more productive than engineers decades ago. what changed? This was certainly not the capacity of the human mind. Instead, our increased productivity is thanks to new software engineering frameworks, platforms, and tools. AI tools represent the next giant 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 technology in its early stages, the most significant risk most tech companies run into is waiting too long and allowing the competition to get on board with the technology first.
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Start-ups are in a particularly advantageous position, as they have little to lose and much to gain by taking a bold risk on early AI adoption. However, large enterprises can start working with generative AI by finding low-risk use cases. They should also ensure that it is treated as a top priority for the legal and security teams and that the critical part is adequately communicated.
While the applicability of these techniques is broad, I recommend finding a practical, simple area to experiment with and learn from, then expand from there. Perhaps even host an in-house hackathon to see all the creative solutions your teams have come up with.
There are countless opportunities to experiment with generative AI in marketing, engineering, customer service, and many more business functions. It is wise to start small, being aware of the risks and taking steps to reduce them. However, it is important to start; Otherwise, you may run the risk of being left behind.