Why Your People Won't Adopt Gen AI as Quickly as they Adopted the Video Call

As a technology enthusiast and a firm believer in the potential of generative AI, I frequently find myself pondering why the adoption of this fantastic technology seems to be lagging behind the near-instantaneous embrace of relatively recent tech such as video calling and meetings. In a tech-driven moment where generative AI adoption and return-on-investment (ROI) are high on the list of c-suite conversation, what sets the two technologies apart in the eyes of users and implementers?

The Shift: Essential vs. Nice-to-Have

The past year has revolutionised the accessibility and belief we have in gen AI technology. But not everyone is convinced or experimenting at the rate we might expect. Whilst video meetings went from optional to essential in the blink of an eye, thanks in part to the disruptive force of the pandemic, gen ai is currently perceived as nice-to-have, nice-to-adopt, and nice-to-keep by the lucky few. I think it's fair to say there isn't the same imperative or consistent top down communication stating that we have to change our ways of working or we won't survive. In the pandemic, we adapted to the technology and our circumstances out of necessity, realising that our behaviour had to shift to stay alive and to stay in business. The urgency of the situation made video calling a non-negotiable and now it's very hard to envisage a working world without it. I'm curious to both influence and observe whether the power of the technology, the messaging from leaders, competitor pressure, or other contributory factors may make gen AI essential or whether it will remain a nice-to-have.

Generative AI: A Different Challenge

In contrast to video calling, generative AI, with its vast capabilities and transformative potential, occupies a different space in our tech and working world. Although it holds promise for revolutionising processes and driving efficiency, its current status leans more towards the realm of a "nice-to-have" than an essential tool we cannot live without. As an expensive unknown to many, commitment is somewhat tentative and a dip of the toe isn't providing organisations with the statistical relevance of findings required to make sweeping substantial investments. And that applies to those with a structured approach to testing and a measurement framework in place. Many organisations are handing this powerful technology to individuals and expecting them to navigate its complexities independently. There is an expectation that it's intuitive and they will know where it excels and where it doesn't. Yet, the reality is far from being that simple.

Breaking the Communication Barrier

One of the primary obstacles hindering the rapid adoption of generative AI is the communication barrier. While we have been interacting with search engines and virtual assistants for years (i.e. Google, Amazon's Alexa), the shift to conversing with AI in a more natural tone remains a challenge for many. Our ingrained habits of engaging with technology in a specific manner pose a barrier when attempting to embrace the conversational nature of generative AI. Even those who have undertaken some prompt engineering training, myself included, still find themselves airing towards simple prompts and wondering why the results are not perfect first time.

Focusing on Experiences Over Application Capabilities

I have to admit that conversations purely on out-of-the-box technological capabilities have got a bit tiresome and are not sufficient to change our behaviours. Instead of an app for this, and a chat bot for that, what truly excites me is envisioning the remarkable experiences we are now able to create with the support of this technology. To successfully integrate generative AI into our workflows, we need to draw our focus away slightly from what the technology can do, to the people experiences we aim to deliver. It's about identifying existing pain points and processes ripe for automation, areas that can benefit from augmentation, and tasks that are better left untouched or removed completely. Impacting the human experience and quality we can deliver when we are focused on what truly matters is likely to cascade to people's sense of worth which in turn will impact productivity and ROI (not necessarily the other way around).

Maximising Generative AI: A Community Effort

To unlock the full potential of generative AI and expedite its adoption, my experience tells me that we must foster communities of users who share similar needs and challenges. Only through encouraging collective learning and collaboration can repetitive practices be streamlined and efficiencies unlocked. On that topic of learning, I am regularly asked what skills should we be instilling in our people as we increase our reliance on generative AI. At this moment in time, I keep coming back to critical thinking and patience. I see these as indispensable qualities that will ensure we continue to challenge the quality of outputs from this technology, and that will keep us from losing faith in its potential as it rapidly advances to meet our heightened levels of expectation.

Embracing Change and Evolution

If nothing else sticks, let it be this. Implementing generative AI is not a one-time endeavor, nor a plug and play, nor a current (at the time of writing) essential. It's a journey that demands attentive audience engagement, a north star vision of what success looks like for the individual (what's in it for me) and the organisation as a whole, and continuous encouragement to experiment and learn. By reflecting on current practices and reimagining workflows, we open ourselves up to a raft of opportunities to do more, do better, or do different. For example, what would I do differently if generative AI gave me more time back in my day? What could I do better than I'm doing today if I applied generative AI to my work? Through challenging ourselves to change our daily experiences and ways of working, we may just unlock the productivity and ROI benefits our leaders are desperately searching for.

So in essence, the disparity in adoption rates between video meetings and generative AI boils down to urgency, necessity, and a clear call-to-action from the top. While video calling quickly became a staple in our digital toolset, generative AI requires a concerted effort to bridge the gap between people and technical capabilities, as well as the best practical applications. By embracing a community-driven approach, focusing on experiences, and continuously evolving our practices, we can pave the way for generative AI to become an indispensable and essential asset in the way we get work done.

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Credits

Author: Rob Anderton

Editorial: OpenAI. (2024). ChatGPT 4o [Large language model]. https://chat.openai.com/chat

Images: OpenAI. (2024). ChatGPT 4o [Large language model]. https://chat.openai.com/chat