In 1945 (so the story goes, I wasn’t there), engineer Percy Spencer was working on radar technology when he noticed that the chocolate bar in his pocket had melted. Putting 2 and 2 together led Spencer to the invention of the microwave oven, a device that would change both Spencer’s career and the world of cooking in ways no one could have predicted.
What’s that got to do with generative AI?
Well, right now I think most people using AI are making the same mistakes Percy Spencer did in thinking his technology was best for radar, when it was actually best for something completely different.
In short, I don’t think most people really understand the true potential of the AI technology they have at their fingertips.
Most see generative AI just as a tool for creating written content… blogs, emails, social media posts, etc.
Yawn.
I’d like to spend some time here today explaining my opinion that generative AI’s most powerful applications are not for writing tasks; that they actually lie hidden beneath the surface, waiting to be discovered by innovative and curious folks.
Beyond content creation
At its core, generative AI is a prediction machine, attempting to forecast the next word, pixel, or data point based on patterns it has learned.
You know the trick that Gmail does when it guesses the second half of the sentence you’ve started writing? That’s essentially what generative AI is doing, on steroids, (Yes, I’m doing the technology a massive disservice with that simplification, but it serves a purpose).
Because AI is so good at its parlour trick of guessing the next word, and because it is so impressive to all of us who have spent a lifetime labouring away at the manual process of writing, we get blinded by the trick. So blinded that we don’t scratch any deeper to see if AI has more impressive tricks up its sleeves/GPUs.
Ironically, I think written content generation might be the least impressive trick in generative AI's repertoire.
In the same way that Percy Spencer’s tinkerings in one area led to magic in another place, I’d like more people to know that the true power of generative AI isn’t in mimicking human writing.
Generative AI has many abilities to process, twist, remix, categorise and transform vast amounts of content and data in ways that most people have not yet discovered or expected.
The six faces of generative AI
To really grasp the potential of generative AI, I’d encourage you to look beyond just written content generation.
Here are 5 other major use cases that showcase AI’s massive versatility and power:
- Extraction: Pulling specific information from large datasets.
- Summarisation: Condensing lengthy content into concise overviews.
- Re-writing: Adapting existing content for different purposes or audiences.
- Classification: Categorising data and then uncovering the value in that data.
- Question Answering: Providing insights by combining information from multiple sources.
In the same way as the microwave changed not just cooking but food production, packaging, and even social habits, I expect that in the coming years these applications of AI applications will ripple through entire industries; reshaping processes and business models.
"Woah, this writing malarky sure is sweaty business."
Breaking new ground: Innovative AI applications
To demonstrate that the real excitement in generative AI comes when you discover its less obvious applications, here are some real life examples from clients of mine that go beyond the typical AI use cases for writing:
- Extraction: I have helped people use a bespoke AI automation to extract key information, data points, and clauses from complex legal documents in seconds, a task that previously took employees over 1,000 hours annually.
- Summarisation: Teams are using generative AI to summarise lengthy financial reports and earnings calls, generating concise summaries and highlighting key financial metrics, market trends, and potential risks. This allows them to quickly grasp essential information from massive documents that previously took days to read and understand.
- Re-writing: As a beautiful example, educators are using generative AI to automatically re-write comms for families in the primary languages that they know the parents and grandparents speak in that student’s home. The AI adapts the content to suit the family's language preferences and nuances, ensuring that important messages resonate with caregivers in their mother tongue, while maintaining accuracy and brand consistency.
- Classification: Many businesses I work with are using generative AI to classify the results of customer surveys they run. In minutes they are classifying results and pulling deep insights from both qualitative and quantitative survey results; tasks that previously took them days of painstaking manual analysis.
- Question answering: I see many companies integrating generative AI into their CRM systems to provide intelligent question-answering assistants for customers that can access and combine information from multiple databases, past interactions, and product knowledge bases to provide comprehensive and context-aware responses to customer inquiries. (It must be noted that AI chatbots are an advanced use case that holds significant dangers if executed poorly, like made up answers that can severely frustrate the customer and significantly damage the company’s reputation).
Widening the AI horizon
Despite these really powerful capabilities, many businesses I see out there just remain focused on using AI for basic content generation.
That narrow vision annoys me.
But I get where it comes from… the tech is still so new that most people have had very limited exposure to AI's full potential (if any).
And media storylines mainly highlight the most obvious and easily understood uses of AI, leading to a self-fulfilling prophecy where businesses only explore what they've been told is possible.
It’s a real shame, because businesses that limit their view of AI to basic writing use cases are really missing out on its massive power and capability across so many other processes, workflows and pain points in their company.
To really find the true potential of generative AI, I recommend that business leaders do all they can to create a culture of experimentation and curiosity.
How to do that? Well, you can experiment with approaches like…
- Encouraging hands-on experience with AI tools across ALL departments.
- Brainstorms or hackathons to collect a wide grouping of specific business challenges that AI might (or might not) be able to help with. Then experiment.
- Creating cross-functional teams to explore AI applications.
- Remaining open to unexpected outcomes and serendipitous discoveries.
I think that by adopting these approaches and mindsets, companies increase their chances of stumbling upon their own "microwave moment" – a real step-change application of AI that transforms their business in unforeseen ways.
Conclusion: Embracing the unexpected
The microwave oven wasn't the result of a directed search for a new cooking method, but an unexpected outcome of openness to new possibilities.
In the same way, the most transformative applications of generative AI may not be the ones you set out to create.
They may emerge from unexpected corners of your business; from the curious explorations of employees empowered to experiment with these new tools, stumbling onto use cases for AI that they hadn’t expected or even hoped for.
As business leaders, our task is not just to implement AI in ways we already understand, but to create an environment where AI's potential can reveal itself.
The future of AI in business isn't just about doing what we do now, faster or cheaper.
It's going to be about uncovering entirely new ways of operating, new products, and new services that we can't yet imagine.
Like Percy Spencer with his melted chocolate bar, let's be ready to notice and act on the unexpected.
Shameless plug: If you’re confused, bemused or enthused by any of the above, I lead companies to do everything I’ve described in this article. Get in contact, and I’ll be happy to discuss the possibilities.
Shameless acknowledgement: Like most of my articles, this one was inspired by the thinking of someone I respect and follow. In this case it was the constantly impressive and thought provoking Christopher S. Penn, in this article he wrote recently on his own site.
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