What kinds of new jobs are on the horizon as artificial intelligence takes hold in organizations? It may seem to be mainly a technology realm, and many fret that business-side roles will be supplanted. However, it’s just as likely that AI will catalyze many new business opportunities which fuse business and technology skills. Importantly, it will be businesspeople — not just technologists — stepping up and taking leadership of their company’s AI directions.
Examples of business-tech fusion roles emerging include “prompt engineers, linguistics experts, AI quality controllers, AI editors and decision engineers,” Lan Guan, global data and AI lead for Accenture, observes. The rise of AI in organizations means “you’ll need experts to codify how decisions get made – this is where you’ll also find sociologists, and decision engineers.”
Going up the corporate ladder a few rungs, a C-suite position likely to grow into prominence is that of chief AI officer or CAIO, says Sharad Varshney, CEO of OvalEdge. “This position will likely straddle technical and non-technical concerns, and the appointee will be responsible for coordinating technical implementation, cost-to-efficiency, and cultural impact.”
Still, industry leaders acknowledge there are still areas in which that technology managers need to stay in charge. “While AI is beginning to influence every aspect of architecture, it’s still down to experienced IT professionals to introduce AI solutions into a company,” says Varshney. “The complexity of the technology requires an acute knowledge of how AI functionality will respond to the business environment.”
Ultimately, AI leadership will come from both technical and non-technical roles. On the technology side, “roles such as AI ethics engineers and AI trust officers may come into being, creating and maintaining AI systems that are ethical, transparent, and trustworthy,” says Derrick Magdefrau, operations manager at the AI Lab at Armanino LLP. “AI Translator roles may also become essential, acting as intermediaries between the technical AI teams and the business stakeholders, interpreting and communicating complex AI concepts in an accessible manner.”
On the non-technical side, “we may see the rise of AI business strategists who specialize in aligning AI capabilities with business goals and strategies,” Magdefrau continues. “AI user advocates could play a vital role in representing the interests of users within AI development processes, ensuring that AI systems are not only effective but also user centric.”
Similarly, an increase in AI legislation may lead to the need for AI legal consultants, specializing in navigating the evolving landscape of AI laws and regulations,” Magdefrau says.
In addition to roles currently associated with AI — data scientists, data engineers, and business analysts — product managers and designers will be leveraging AI in their work. “Product managers, for instance, play a crucial role in guiding the AI solution’s development, aligning it with business objectives, and ensuring it meets market needs,” says Magdefrau. “UX/UI designers are also pivotal as they ensure the AI solution is user-friendly and intuitive, ultimately driving adoption and satisfaction among end users.”
How should such new or re-purposed roles be created? In areas where AI shows most promise, “companies should start by decomposing existing jobs into underlying bundles of tasks,” Guan advises. “Then assess the extent to which AI might affect each task — fully automated, augmented, or unaffected.”
AI and related analytical initiatives may be heavy with technology, but the business needs to get out in front of these efforts. AI transformation “is like any big transformation: lead from the top, have a strategy and precondition your systems and data to ensure you are prepared,” Guan advises. “This means understanding how uses of AI impact your business and where you expect to get value.”
There are substantive risks with relying too heavily on technologists leading AI efforts as well. If an organization’s primary technical resources are AI engineers, “then everything looks like a machine learning problem,” says Magdefrau. “This perspective can often lead organizations to overlook simple solutions to basic problems. For instance, a technical team might spend weeks optimizing an AI model to raise the model’s accuracy from 97% to 98%. While this 1% performance improvement is potentially significant from a technical standpoint, it could be irrelevant to the business in terms of the overall value it contributes, wasting valuable time and resources.”
Customer service is an area that is increasingly tied to AI, which calls for knowledgeable business oversight. “As job tasks evolve for agents, a standard process is continuing to validate the output, such as getting unbiased information on products and pricing,” Guan says. “Business leaders are responsible for ensuring AI does not create unacceptable risk. The right skills, infrastructure and governance processes ensures uses of AI are tracked, risk is mitigated and outcomes are monitored over time. ”This means providing awareness and training across the enterprise, to many roles. “The right skills – beyond technical deployment – are critically important to getting the value you need out of it. It is also key to using AI responsibly.”
Follow me on Twitter.