While Generative AI is disrupting the business world, executives are still struggling to fully grasp it. Here’s what you need to know to avoid being left behind.
Generative AI is evolving at record speed since the release of Chat GPT, MidJourney, Dall-E and similar tools, while at the same time becoming more and more democratic and accessible to everyone. To have a sense of this phenomenon it is enough to look at the numbers:
But while new Generative AI models are being designed and more and more people start adopting them, executives are still trying to learn the technology’s business uses and risks. Here’s the ultimate guide on the best Generative applications to invest in, and the essential knowledge you should have on the topic.
1: How to increase the amount of data at your disposal with Gen AI
Predictive analysis needs data, and a lot of it. To put it simply, for a prediction to be accurate there should be enough historical data, and the data should be representative of the problem you’re trying to solve. This causes many issues, since in theory you could only predict the chances of an event happening if the same event has already happened, and therefore there is historical data about it.
So, how do you overcome this stalemate? Here’s where Gen AI gets into the picture.
Advanced Generative AI models can create synthetic data that helps fill in the gaps where limited or incomplete data exists, therefore overcoming prediction problems and expanding your analytics possibilities.
2: Why you should know the difference between supervised and unsupervised learning
There are two main ways in which machines learn: supervised and unsupervised. As explained by IBM, the difference is substantially the use or not of labeled data. In supervised learning, the algorithm learns from the labeled dataset by iteratively making predictions on the data and adjusting for the correct answer. In unsupervised learning, the algorithm works on its own to capture patterns in the data.
Generative AI models typically employ the latter, as unsupervised learning allows them to generate new sample data based on learned patterns. As an executive, it is important that you have at least a basic understanding of the difference between these two approaches, to be able to make informed decisions about which one to employ and which datasets to provide for the model’s training.
3: Take your time to identify Generative AI Applications
The world of business is already being strongly impacted by Generative AI models, and this trend will keep growing. According to Bolton Consulting Group, some of the most common Generative AI use cases include:
- Expanding labor productivity
- Personalizing customer experience
- Accelerating R&D through generative design
- Supporting customer service
But there are many more, and leaders everywhere are already testing new applications in different business areas. Since every company is different and has its own risks, strengths and weaknesses, it is crucial to take your time to identify the most useful areas of applications for your business.
4: Learn the basics of neural and deep learning
While it is true that details on which training approach and algorithms to use are responsibility of the technology team and data scientists, good leaders should have a basic understanding of how Generative models work.
As explained by MIT, the best-performing AI systems in the last 10 years are the ones employing deep learning. Neural networks are designed to mimic the human brain’s interconnected network of neurons; they are organized into layers of nodes and they’re “feed-forward,” meaning that data moves through them in only one direction. Deep learning refers to the method of training neural networks using multiple node layers to pass data from base to top layers. The data is then assembled in complex ways and deeply transformed during each step.
5: Align Generative AI applications to your business problems
With so many Generative AI use cases, it is easy to get lost in the endless possibilities and want to try everything at once. Executives must remain focused and work to identify their company’s most urgent problems, understand how to address them using Generative AI models, and design a strategy with clear steps.
In order to be able to act quickly and adapt to different needs, it is crucial to prioritize the speed of implementation. This will allow you to respond in a quick and efficient way to urgent issues, new needs and upcoming opportunities.
6: Understand the iterative nature of Gen AI
When adopting new technologies, one must be ready to face obstacles, uncertainty and risks. Some of the most common issues include:
- Unreliability / inexplicability of the model
- Existing processes don’t allow to effectively use the new approach
- Lack of data
- Performance or scaling difficulty
- Unclear Generative AI use cases
Instead of fighting them, embrace them and develop a flexible and responsive workplace approach to change, especially technological ones. In order to do so, having contingency plans in place can prove very useful in mitigating potential risks and ensuring the smooth integration of Generative AI models into existing workflows.
Keypoints When Employing Gen AI To Open Up New Growth Horizons
The six takeaways we have discussed in this article are the main aspects companies should focus on and have clear in mind when approaching Gen AI. Even if executives aren’t supposed to know the technical and scientific details, having a basic and broad understanding of the topic will allow them to make smarter, more informed decisions and have a wider horizon.
The generative AI development company will deeply transform and disrupt virtually all industries, and to remain competitive is it imperative to keep pace:
“To be an industry leader in five years, you need a clear and compelling generative AI strategy today.”