More than a decade ago, in an article in the Harvard Business Review, Thomas Davenport declared that data scientist was the “sexiest job of the 21st century.” Today, in the era of generative artificial intelligence (AI), is the “prompt engineer” about to take on this title?
What is already certain is that it is one of the most fashionable professions. Prompt engineering is about getting the best and most relevant responses from generative AI tools. This is both a conversational and programmatic activity, with prompts integrated into the code.
And with the global buzz around generative AI, prompt engineers are in high demand. And good profiles are rare. Recruiting prompt engineers is therefore not within everyone’s reach. “I think most people who hire in this space are stealing skills from competitors,” notes Greg Beltzer, head of technology at RBC Wealth Management.
“Today, a good prompt engineer costs more than a data scientist”
I had the opportunity to speak with Mr. Beltzer at the recent Salesforce conference in New York, where he spoke about the challenges of training in the AI era. “Today, a good prompt engineer costs more than a data scientist,” he observes. “It’s extremely difficult to find someone who has experience. You won’t find someone who has more than five years of experience. At most you can get two or three years, but it’s difficult to find”.
Beltzer continues: “There is a dire need to train people in prompt engineering. But is it a science? Is it an art? Will we build more tools? The good news , is that once the tools are in place, it may be easier to train artificial intelligence models using prompt in a systematic and programmatic way,” he adds.
However, until robust and useful tools are available, prompt engineering will remain a challenge. Even with tools, Beltzer says it’s important to note that this skill set goes beyond technical acumen. Additionally, it is still too early to determine exactly what experience and know-how are best suited for a prompt engineer.
“We are looking for people who are on the business side and who have a technical bent”
For example, Mr. Beltzer doesn’t think it makes sense to train a data scientist or other IT professional to adopt prompt engineering skills: “A lot of those skills have to be adapted to the business context. You have to think like the user to help do prompt engineering – it’s not just code, it’s not just development. It’s about a set of technical business skills that are also creative.”
He notes that some of the people coming into this field aren’t necessarily technicians: “They’re writers,” he observes. “They just know how to write. And that’s part of it.”
RBC keeps an eye on its internal talent, with an emphasis on combining business acumen and technical acumen, says Beltzer, “We’re really looking for people who are more often on the business side and have a penchant for the technique. And I don’t want to go any further than that until the tooling is a little more advanced.”
Let’s wait for the tools
The level of investment in AI and generative AI companies over the past year “is also going to shape the type of talent we will have,” Beltzer says. “By then, the market for talent will be very tight. If you look at the turnover rate within these booming companies, you’ll see that they can set their price.”
At RBC, once a very conservative company, change has become the rule, starting with the increasing adoption of cloud-based capabilities and services. “Once we moved to the cloud, we did 25 updates a year,” Beltzer says. “Which in the financial services industry is crazy: the industry average is one update per year. We have a great team of business professionals and IT people, and we can do evolve the platform very, very quickly.”
At the same time, Mr. Beltzer doesn’t think his organization will go 100% AI anytime soon. While AI can help developers and salespeople do 80% of their tasks, the other 20% requires human intervention, he says: “I think AI is real. But I think we have still work to do for commercial viability in my sector.”
AI capabilities are useful for employees who speak directly to customers
For example, RBC uses generative AI to help contact centers. “We have some good use cases, but it’s about reducing costs rather than generating revenue,” Beltzer says. “But it’s a good start.”
On a broader level, AI may never completely replace humans in the wealth management industry, he adds. What we’ve found is that people don’t want to talk to a machine that’s saying, “Everything’s going to be okay.”
AI capabilities, however, are useful in assisting employees who speak directly to customers. “More and more people are using wealth management, because we have more assets,” says Beltzer. “So they will be able to serve their clients with more technology – making sure that this box is checked or that this formality is done for them. This is where we need to develop. This way, advisors will be able to concentrate on relationship with the customer and ensuring that what they have invested in will meet their long-term goals.”
As an IT leader, “our challenge is to make systems more scalable and more efficient,” says Beltzer. “I need to make sure employees can do more of what they love and cut out activities that don’t add value.”
Source: “ZDNet.com”