Between the 20+ conferences I’ve attended this year, dozens of conversations with leading AI experts, and a number of briefings released, I’ve been exposed to a serious amount of enlightening viewpoints when it comes to the trends that will fuel AI’s trajectory next year.
While there’s a lot to talk about, here are the six AI trends I’m watching the most.
1. Increased Attention to Simple Usability
Top engineering teams like GitHub have shared how they prioritize AI projects based on how likely the new feature will impact the model’s ease of use. If a new feature requires developing new behaviors, they immediately deprioritize it. In 2024, other companies are likely going to follow suit.
The more intuitive an AI tool is (think: ChatGPT) the easier it is to adopt.
In 2024, we’re going to continue to see tech evolve, but I suspect that most companies are going to spend the majority of their time answering questions, like:
- How do we make this easier to consume?
- How do we make it safer to consume?
- How do we defend against it?
- How do we adapt skills-based training?
2. Maturing Cyber Solutions for Generative AI
As LLM-powered applications go mainstream, the demand will grow for specialized cybersecurity solutions to defend against risks like prompt injections. Ensuring guardrails (the set of filters, rules, and tools that sit between inputs, the model, and outputs) are in place when designing models will also continue to be a priority for teams.
I also expect to see widespread development of training to help individuals identify more sophisticated AI-based phishing attacks.
3. Widespread Investment in Skills-Based Learning Across Enterprises
During FORTUNE Brainstorm Tech 2023, Vinod Khosla unpacked his prediction that “80% of 80% of jobs will be eliminated” due to AI. What he means by this is that the majority of content across the majority of jobs are likely to be affected by AI-based transformation.
And this shouldn’t come as a major shock—workforce transformation isn’t new. If we look at logistics and supply chain professionals, for example, you’ll see they’re doing very different things today than they did even five years ago.
We’re already seeing how AI will enable us to do things we haven’t done yet. In one scenario, we could develop an AI-based health and wellness coach. You could have a daily conversation about your health with this automated system, but it would still be overseen by your care provider who was trained to use the system to enhance their overall quality of care to you.
Skills will come and go, but the workforce will stay, and it’s critical to continually reorient them to the most needed skills. In this Harvard Business Review article, they call out that the average half-life of a skill is now less than five years. For certain tech skills, it’s less than two. Organizations that want to pull ahead will have to invest in tech and processes that enable continuous up-skilling and re-skilling.
4. Faster, Cheaper, and Safer Is Only the Start of the Automation Journey
Every time we have a digital revolution, people start to care about making processes and technology faster, cheaper, and safe. For example, the industrial supply chain has focused on reducing the cost of shipping, increasing the amount of widgets produced in a day, and reducing average delivery time.
I suspect the same is going to be true with AI transformation.
Once we get past the initial fear of “automating away everything,” we’re going to realize that there are a lot of different and better ways to do things that weren’t possible before AI. Here’s one example where access to 3D printing and generative patterns allows on-demand creation of customizable parts with better strength and performance for specific tasks.
5. Multimodal AI Applications
Multimodal AI is a completely new form function that is going to lead to a slew of meaningful applications. With multimodal AI, different data types (images, text, speech) are combined to achieve different and better performance.
This function will allow us to maximize the use of AI and enterprise content. The way we convey information is naturally multimodal—we have PDFs, charts, tables, and visuals. With this format of AI applications, we’re able to build models that can consume information the way we typically do.
Introducing combined data types will also require us to manage risk in new ways. An image on its own and text on its own may be harmless, but when put together it could be offensive. (Ex. A picture of a tall woman and the word giraffe).
6. Tighter Legislation Will Impact Decision Making
Although 2024 is probably too soon for us to see AI legislation officially come down in the U.S., legal precedents are going to become more prevalent. We’ve seen this already with Rite Aid being banned from using facial recognition software and UnitedHealth facing a class action lawsuit over algorithmic care denials.
With these instances in the news and laws like the EU AI Act getting finalized, enterprises are finally going to have a window into how they should go about managing their risks more effectively.
The AI prototypes we’ve seen over the last year have dazzled us with their potential, but the real challenge lies in their deployment in real-world scenarios. If 2023 was the year of learning and planning, 2024 will be the year of execution.
The past 12 months may have seemed overwhelming, but we’re still early in the race. The AI digital transformation will take a few years—and there’s a lot of opportunity to create value using more traditional automation approaches.
About the Author: Cal Al-Dhubaib is a globally recognized data scientist and AI strategist in trustworthy artificial intelligence, as well as the founder and CEO of Pandata, a Cleveland-based AI consultancy, design, and development firm.
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