Technologists may be very adept at creating highly innovative AI projects. But the key is how early they are able to hand off their innovations to bossiness leaders, who are then charged with delivering on its potential business processes. Getting the most out of AI? It all starts with inspired leadership that recognizes how it can benefit the customer. As with all technologies, it’s all ultimately about the customer.
Solving the customer’s needs should be part of the package right from the beginning, says Dr. Andrew Ng, founder of DeepLearning.AI and adjunct professor at Stanford University. “So double check, is this idea even technically feasible, and then talk to prospective customers to make sure there is a market need,” he explained in a recent webinar.
Ng recounts how he “used to spend a long time working on a project myself, before bringing on a CEO. But we realized that bringing on a leader at the very beginning reduces a lot of the burden of having to transfer knowledge — or having a CEO come in and having to revalidate what we discovered. We learned it’s much more efficient to bring the leader at the very start. Then we spent three months, six, two-week sprints, to work with them to build a prototype as well as do deep customer validation.”
The lesson here is that business leaders and managers need to get involved with AI efforts early to help bring innovation to the customer. “Current AI development is largely being steered by technologists,” observes Reggie Townsend, vice president of data ethics for SAS. “Not only that, but technologists also are frequently driving the conversation around AI.”
It’s important “that people of different backgrounds assume stronger roles in AI, generally, to make sure we stay focused on the practical risks and opportunities.” Townsend continues. “We need a diverse set of voices at the table who can think about the macrolevel implications of AI beyond the specific applications of the technology.”
Technology companies, at the forefront of such efforts, are paving the way for collaboration that connects AI development to the customer. “For us, leadership in AI initiatives is shared by technical and non-technical roles,” Artem Kroupenev, vice president of strategy at Augury. “Technical roles encompass data scientists, engineers, and AI researchers, who are instrumental in the development and refinement of our AI technologies. Meanwhile, non-technical roles in product management design and business strategy ensure technological advancements align with our business objectives and market needs.”
There are risks of having AI initiatives remaining too long within the technological domain. “Like any person with expertise or skills in a particular area, technologists are going to have blind spots,” says Townsend. “They are trained on precision and mathematical accuracy but the application of AI in the real-world can get messy quickly. Being technically accurate does not always lead to ideal outcomes in areas like justice, well-being and equity. Imprecision and bias are ingrained in humanity, and we need domain experts to sort through the mess to uncover risks and opportunities.”
Augury undertakes a three-step process to ensure the effectiveness of AI in the enterprise: “normalize, socialize, and productize,” says Kroupenev. “During the normalize phase, we ensure universal access to AI tools and provide clear usage guidelines. In the socialize phase, we share success stories and best methods, fostering a culture of learning and collaboration.”
Finally, he says, “in the productize phase, we integrate AI usage into our standard processes and team structures. This phase also involves continuous improvement, as we learn and iterate to enhance AI’s impact on our work.”
As AI drives more business decision-making, new roles will evolve, Kroupenev predicts. “We’ve already begun this process by establishing a cross-functional GenAI leadership team. These roles bridge technical understanding with business implications and use cases. Non-technical roles like AI product managers, who take ownership of AI products, and AI business strategists, who integrate AI initiatives into business strategy will be vital.”
There also be a rise in demand for “hybrid socio-technical roles.,” says Townsend. “People with some expertise in AI development, but also with the knowledge and historical perspective of a sociologist, will be highly valuable. AI career opportunities are open to more than just technologists but require a baseline understanding of what AI is and what it isn’t, the practical risks and the opportunities.”