Moving Past the Shiny Newness of Generative AI

A Google survey found that around 40% of executives said there is an urgent necessity to adopt generative artificial intelligence (AI). However, many of these same executives did not even know whether their companies are ready to adopt the technology. Sixty-two percent of executives said they do not think their companies have the AI skills they need for a successful deployment.

While some of the latter perception may relate to executives not really knowing what kind of expertise they need, it is no less of a concern regarding their preparedness. Still, a separate Google survey also found that more than half of developers share the worry that their companies lack critical skills for generative AI deployment.

The Future Workplace Needs More Than Just Implementation

While deploying generative AI solutions and having the right engineering and computer science skills may be a top concern, it may not be the most relevant. Companies will also need to consider how they employ generative AI in jobs to ensure it is a value-add. Once they have a better idea of how to employ the technology efficiently, they will be able to adapt and redesign jobs around that. Currently, 62% of total work time involves language tasks that could be impacted by generative AI.

It is even estimated that large language models (LLMs) — the neural networks behind OpenAI’s ChatGPT and Google’s LaMDA — could impact 40% of all working hours. However, the counterbalance to the concept of cutting such a large portion of the work required to do a task is that it could potentially make the same space more competitive for smaller firms. The critical question for companies to ask, then, is how they will repurpose that time to increase the value of their products. Those who see LLMs as a replacement for human labor rather than as a labor-saving device could risk making themselves vulnerable to savvier competition.

There Is More to Generative AI Than Function Alone

The generative AI market is expected to grow to $1.3 trillion by 2032, compared to $40 billion in 2022. Much of that initial growth may simply be in the hardware required to power and train the technology, which could account for $247 billion of that $1.3 trillion. However, along with that growth comes a host of problems related to cost and environmental impacts.

Alongside environmental responsibility, generative AI deployments will need to account for ethical and reputational risks associated with both privacy concerns on the back end and disinformation concerns on the front end. It will be essential for any company considering generative AI to step into the space with both eyes open and to keep them open as it moves forward.

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