How Innovators Are Synthesizing AI Strategies with Commercial Success

How Innovators Are Synthesizing AI Strategies with Commercial Success | ZDNet

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The so-called “subject matter expert” has been a staple for most companies since the invention of fire. Lately, however, with many simple and not-so-simple tasks being handed to AI, leaders are realizing that someone with a blend of technology and business acumen needs to be steering things in the right – and, hopefully, profitable – direction. Call it the renaissance technologist.

Also: AI has caused a renaissance of tech industry R&D, says Meta’s chief AI scientist

An analysis of companies posting the most AI jobs finds they “exhibited a higher demand for AI professionals combining technical expertise with leadership, innovation, and problem-solving skills, underscoring the importance of these competencies in the AI field,” according to a report out of the Organization for Economic Cooperation and Development (OECD). The researchers looked across 32,000 unique skills within leading economies.

The renaissance technologist -- be they inside or outside the IT department – is an important track for career development, especially among people designing, developing, and deploying AI-based systems. This role will ensure that AI is delivering what the business needs, while suppressing bias, addressing errors, and maintaining security. After all, you don’t want to be doing the wrong thing at subsecond speed.

The following are some of the elements that renaissance technologists need to surface in their businesses:

What business are we in, what do we want to be in, and how will AI help us get there?
What is the potential return on investment for this effort?
How will AI open new perspectives and ideas to people?
How will AI boost people’s career potential?
What are competitors missing with the AI opportunity?
Is the system taking in data relevant to the desired outcome?
Is the system producing insights relevant to the direction the business wants to take?

Technology managers and professionals are increasingly being called upon to answer such questions. “Beyond basic technical skills and AI management knowledge, broader subject matter expertise will remain essential across the various domains that make up the business world,” says Ted Lango , senior vice president at Intradiem.

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The emerging renaissance technologist “will need expertise in fostering an environment where AI tools and human workers complement each other to enhance productivity and employee well-being,” Lango urges. “Basic skills related to emotional intelligence and problem-solving, and the ability to communicate clearly, think critically, and work collaboratively will remain as important as ever.”

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Simply stated: less coding and development work, and more focus on the business. We’ve heard this prediction many times over the past couple of decades, but AI seems to be effectively picking up the slack of heads-down programming.

“The time spent doing non-value-added work to create software – like searching through internal knowledge systems – will be sped up through AI, so engineers can focus on more high-value tasks,” says Jonny LeRoy , chief technology officer at Grainger. For example, he relates, “Grainger’s most recent internal hackathon was centered on using AI to gain productivity. The winning team created a question-answering agent that retrieves relevant internal Grainger information from various systems including Jira, Confluence, and GitHub.”

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It’s important that technology teams understand the business context “so they can frame the problem they’re trying to solve with AI,” says LeRoy. “At Grainger, we encourage ‘ride-alongs’ with our sellers or customer service agents, so team members deepen their understanding of our customers, their wants and needs.”

There’s no question that technology professionals and engineers are using new AI tools to write code, debug, create APIs, and define test cases faster,” says Ilya Goldin , principal data scientist at Phenom. “But AI also frees up professionals to engage with their work in more meaningful ways through opportunities for creativity and innovation.”

Renaissance technologists, then, will lead the way with “idea generation, driven by the knowledge, skills, and abilities of creative personnel to produce novel solutions,” Goldin illustrates. “Generative AI can help with idea generation, finding highly complex problems that tech organizations can work to solve. It can also help with content generation in text, images, video, and code.”

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Going forward, success in the technology career arena requires “interdisciplinary learning,” says Lango. “Courses in AI and machine learning are fundamental, of course, but studies in psychology, human behavior, and ethics are also really relevant to the evolution of technology work.”

Training programs “should focus on developing a holistic understanding of how technology impacts human behavior and organizational culture,” Lango continues.

At Grainger, prompt engineer is a relatively new emerging job title, but AI is opening up a multitude of potential career paths. “We’re trying to do multiple things,” says LeRoy. “We have also hired great talent often using broader titles like machine learning scientist, and we’re teasing apart the ML-specific skills from the more engineering-oriented ones to allow focus for experts and fungibility for generalists. I expect to see more roles dedicated to quality, safety, and governance, which will require a fascinating set of skills to manage the emerging risks around bias, confabulation, and quality.”

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Across today’s companies, additional titles emerging include “AI ethics officer, human-tech integration specialist, and employee experience officer,” says Lango. “These roles focus on ensuring the ethical usage of AI, managing the integration of AI into workplaces, and enhancing employee experiences in increasingly tech-driven work environments.”

Renaissance technologists will help deliver breakthrough innovation, which “is a cognitively demanding task that human intelligence struggles to meet,” Goldin points out. “It takes strategic thinking to identify when creative solutions are needed, and to anticipate the consequences of a particular solution. With AI handling more of the idea and content generation, tech professionals can spend more time applying their abilities to elements such as problem identification and idea evaluation.”