Generative AI Hype
As generative AI has taken the business world by storm, many are still wary of its actual application. Like blockchain, Web3, and the metaverse before it, generative AI hype has taken off so quickly that it’s important to remember that we’re still in the early days of the technology, and no one can accurately predict where we’re going.
Built on large language models (LLMs), generative AI has been improving for more than a decade. It became a household word a year ago with Open AI’s ChatGPT release. A recent study from MIT Technology Review looks at generative AI deployment. While the hype is high, some executives liken it to the beginning of the internet. There is much to like about how AI can help us and just as much to worry about. Some key takeaways from the article include prudence, partnering, democratization, workforce impact, and regulatory questions.
Almost universally, AI is expected to be transformative, with less than five percent of the respondents thinking it will not impact their business. However, less than 10% stated they have implemented a generative AI project. And when companies are ready to test the waters, three-quarters plan to work with a partner.
The opportunity to tap into the benefits of generative AI will not be limited to enterprise-level companies. Small companies can just as easily deploy a use case. Much like cloud computing enabled small and mid-size businesses without the high level of investment in hardware, so too will generative AI.
The impact on jobs at small and large organizations is yet to be determined. There is a high likelihood that some jobs will be eliminated. Still, there is also a belief that many new jobs will be created, especially in light of the ethical and risk factors associated with the technology. As with any new technology, regulations will soon follow, and based on what we’ve seen to date, generative AI will need quick action to limit the risks. Forty percent of respondents said that regulatory uncertainty is their top concern for adoption.
To help with deployment, the article lays out strategies to guide your path.
Pace for success
Be deliberate in your deployment. This is important at the outset with the regulatory landscape shifting. It’s best to start slowly with the adoption of new technology and be prepared to pivot. Once the environment settles, adoption will grow exponentially.
Partner up to build capabilities for scale
The ecosystem for generative AI is growing. Enterprise-level organizations are focusing on it as new start-ups enter the space. Look for synergies with other companies to help you, and if you’re in the AI sector, be clear in communicating how you can enable others.
Workforce shifts are not predestined
Don’t fall for the “sky is falling” unemployment forecasts. The top companies will consider implementing generative AI to increase employee engagement and customer satisfaction. It could mean reskilling or upskilling roles that are displaced. It does mean being open and honest with your workforce and collaborating on what the future will look like.
New laws and institutions are needed to manage AI risk, but it will take time to establish
From copyright issues to privacy concerns, the regulatory environment must address outdated laws and discuss new legislation. Content creators will need updated licensing and revenue models; consumer usage will inform new business models.
Regulators are right to consider new and updated laws and legislation for AI, but these should be specific and informed by a broad stakeholder community. In the meantime, mounting legal clashes over IP and copyright could subside as industries and tech companies work together on new licensing and revenue models. In what may be AI’s “Napster moment,” the current period could give rise to new business models and consumer norms. This may also be an opportunity to fundamentally revisit some assumptions and norms in areas like copyright.
Venture Capital dollars are flowing into AI, and it will be interesting to see which companies succeed over time.
We recommend reading the full report to see examples and dive deeper into these areas. And for more articles on technology, HR, recruitment, and executive learning, visit our blog.