What Small and Mid-Size Companies Need to Know About Generative AI in 2024

Navigating the AI Landscape

As an employee, it can often be confusing to navigate the current tech landscape when new, fancy AI features are popping up everywhere like daisies. Microsoft Outlook is releasing CoPilot, Adobe has released Adobe Firefly, and even productivity tools like Notion seem to incorporate GenAI wherever they can.

Feeling overwhelmed by all the AI jargon? I've got a PowerPoint deck here that has demystified 'ChatGPT' for a mid-sized German company, and it can do the same for you.

What is Generative AI?

In general terms, Artificial Intelligence (AI) aims to replicate aspects of natural intelligence in machines and has been a research field since 1956. Within this field, Machine Learning stands as a subset focused on teaching machines to find specific solutions to problems based on data, rather than pre-programmed rules. These solutions are stored in what are commonly called 'models.'

Digging deeper, Deep Learning is a type of Machine Learning that employs artificial neural networks to create more complex structures for data processing. Generative AI is a specialized subset of Deep Learning; it's responsible for creating models that can generate new data similar to the data they were trained on.

There are different types of generative deep learning models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Recursive Neural Networks (RNNs). These were particularly relevant when I wrote my master's thesis back in 2019.

One eye-catching example is the website 'This Person Does Not Exist,' which generates unbelievably realistic images of non-existent people. Here's an image of a person who does not exist:

The Rise of Large Language Models

Today, the most relevant deep learning models are Large Language Models (LLMs) and Diffusion Models, such as DALL-E 2 and Midjourney. LLMs are massive models trained primarily on text data scraped from the Internet.

As language models, they operate by taking an input text and successively predicting the next word. Notable examples include GPT-4 used in ChatGPT, Google's PaLM used in Bard, and Meta's LLaMa, as well as BLOOM, Ernie 3.0 Titan, and Claude.

Looking to the future, LLMs are becoming increasingly commoditized. Even now, as software companies develop new GenAI capabilities, it's becoming essential to compare different language models based on pricing and performance. As mentioned in the introduction of this article, they are popping up like daisies!

So, with all these advancements and the rising prominence of LLMs, you might be wondering who's actually harnessing the power of generative AI today and which industries stand to benefit the most. Let's dive into that next.

Which Companies Are Using Generative AI and What Industry Will Benefit the Most?

The transformative power of generative AI isn't some distant dream; it's redefining entire industries right now. McKinsey's report on the economic potential of generative AI provides the following overview:

As you can see, Banking could gain an astronomical $200 to $340 billion annually with full adoption of generative AI. But it doesn't stop there. High tech, life sciences, retail, and consumer packaged goods are also poised for colossal shifts, potentially amassing an additional $400 to $660 billion annually.

Their latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually —by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. That's not only impressive, but game changing!

New Possibilities for Knowledge Work: How Can Midsize Companies Keep Up with Generative AI?

In the grand scheme of things, LLMs like GPT-4 can be seen as the tip of the iceberg. Wherever language and interaction are at play, the possibilities are nearly endless. This is why this technology is slated to have its most substantial impact on knowledge work—activities involving decision-making and collaboration—which have traditionally been resistant to automation.

It is clear that not every organization has the budget to onboard a GenAI specialist or a pricey consultant. But let's be clear—this shouldn't prevent you from leveraging the immense benefits of generative AI. There are a lot of different resources out there. Some of my favorites are Matt Wolfe's YouTube Channel or the Superhuman Newsletter by Zain Kahn.

That's why I'm on a mission to arm you with the actionable insights and resources you need to sift through the buzz and tap into the real potential. To that end, I'm offering a complimentary PowerPoint presentation in my member exclusive area —one that I've previously used to bring a German trade company up to speed on the topic.

In subsequent posts, I'll break down how small and midsize companies can seamlessly integrate generative AI across various roles and functionalities. Expect deep dives into AI chatbots, intelligent agents, automated workflows, and even hands-on code examples that aren't just flashy but actually useful.

So, if tech innovations like these get your gears turning as much as they do mine, stick around. There's a lot more to uncover, and I can't wait to share it with you.

Conclusion

• Generative AI, a specialized area in deep learning, is being led by innovations in Diffusion Models and Large Language Models.
• Based on McKinsey's findings, the industries set for major change include banking, high tech, life sciences, retail, and consumer goods.
• The democratization of GenAI is underway, making it accessible for companies of all sizes.
• For small and midsize companies, you can stay up-to-date with Matt Wolfe's YouTube channel, Zain Kahn's Superhuman Newsletter, or right here on this blog. Stay tuned for upcoming posts that will delve into its practical applications, including AI chatbots and intelligent agents.
• Want to dive deeper? Download the free PowerPoint presentation that accompanies this blog post and stay ahead of the GenAI curve.