Understanding the AI Ecosystem in 2024
Ever since the release of ChatGPT 3.5 in November 2022, the generative AI race, and the many conversations we need to have about potential impacts, AI has dominated the business side of the news cycle. A 2023 report by the MIT Sloan School of Management found that “more than 50% of companies with more than 5,000 employees were using Al.” This statistic is even more telling considering the significant increase from just six years before, in 2017, when only 6% of companies reported using AI in any capacity.
Is AI a new phenomenon? The simplest answer is no, it’s not. In 2011, Apple brought digital assistants based on natural-language interfaces to the everyday consumer in the form of Siri. Today, nearly all (97%) of mobile users use some kind of AI-powered voice assistant. But the form of AI that can respond to a prompt with something new, like an image, a body of text or a pie recipe in the style of Shakespeare, is new. This kind of AI, Generative AI (GenAI), was released to the general public for the first time in 2022 as ChatGPT 3.5.
Fast forward to now, and we find ourselves in a market where the loudest voices are speaking about AI as if it were a certainty — the safest bet for tech there ever was, and something that’s going to change everything, all at once. Maybe you would wholeheartedly agree, if only you had a firmer grasp of what AI even is and what it’s meant to do. Don’t worry, you aren’t alone. For all the technical explanations about AI out there, the general business discourse hasn’t fenced it in with agreed upon definitions or categories yet. In fact, many businesses are still struggling to grapple with the enormous implications it brings to the table.
"By focusing on people who’ve been there and done that already, companies artificially shrink the pool of talent they have access to and find themselves in fierce competition for the apparently scarce developers they need. Instead, focusing on key skills and assessment-based evaluations allows those qualified candidates to show what they’re capable of. We’re in the early days of this specialization, and some of the strongest practitioners have yet to join the field.”
Jason Wodicka, Principal Engineering Advocate at Karat
What is holding businesses back?
Lack of Skilled Talent to Develop and Implement
Companies may not have the in-house expertise to build and deploy AI models. Generative AI requires a specialized skill set, including data science, machine learning and expertise in generative modeling techniques. However, AI isn’t as new as the news cycle would lead many to believe — in fact, experienced tech professionals skilled in all areas of AI creation, training and implementation have been part of technology teams for years. However, finding these qualified candidates can be difficult, time-consuming and costly. Machine learning engineers, for example, make 10% more on average than the average tech professional.
Cost to Build and Train
AI models are expensive to develop and train. These powerful bodies of work are not just a “set it and forget it” technology; rather, the value of these systems comes in their ability to evolve over time. The computational resources required to train a large model can be significant, and the data required to train the model can also be expensive to acquire and annotate.
Lack of Clear Business Case
Company leadership may be grappling with how AI can be used to create value for their business. The newness of the technology as a viable tool means there is little precedent for strategy to lean on, and there are still many unanswered questions about the longer-term impacts of adoption. As we’ll cover much more in this eBook, nearly every implementation requires work from existing employees and often new expert headcount, an expensive series of decisions that must be prioritized against other business needs.
Where and How is AI Being Used in Businesses Today?
For many of us, Generative AI models like ChatGPT and Google Gemini are the Paul Revere to your proverbial sleeping town of AI awareness. But as we mentioned before, AI, and precursors to AI, have been used in businesses for over a decade. AI has many different use cases, and can tackle a myriad of fascinating challenges. As you head into the next era of technical recruiting, it is critical for you to have a strong understanding of what sort of projects tech professionals who specialize in AI related skillsets might be working on.
These are just a few examples, and the applications of AI are constantly expanding. As the field continues to develop, we can expect to see AI play an even larger role in our lives.