AI as an innovation machine? Hold that thought.
Artificial intelligence certainly will surface insights not apparent to mere mortal minds. But AI has its limits, and it’s debatable whether AI can re-create that magic mix of inspiration and serendipity from which creativity and innovation springs. AI helps spur new lines of thinking — and, heck, discoveries uncovered with AI may be serendipitous events themselves — but people working with the technology need the freedom and encouragement to innovate.
The bottom line is that companies with the most innovation are those with supportive cultures, a study out of McKinsey finds. What companies with the most growth “have in common is an innovation culture,” according to Matt Banholzer, a partner with McKinsey, in a recent webcast. “We were shocked at the distance between the top and bottom performers, which was as much as a 1,000-percent-plus difference. Those with strong innovation cultures are much more likely to report that their products and services lead their industries and that their organizations are best in class in the speed of new product development.”
Many leaders were also working with generative AI one or two years before ChatGPT hit the scene, Banholzer continues. However, what sets an innovative powerhouse apart is there is a greater willingness to challenge AI output — just as they can challenge management decisions.
“When gen AI spits out an answer, top innovators ask, “Is this a useful answer?” says Banholzer. “Top innovators challenge assumptions and assertions, embrace uncertainty, and enable iterative development.”
There are many functions and task that should be automated, he says. However, the top-performing companies “understand the limitations of the tool. Just like you don’t use a hammer to turn a screw, you don’t ask gen AI questions that are best answered in other ways.”
Innovation, by its very nature, often arises out of serendipitous encounters, discoveries, or inquiring minds that connect two unrelated disciplines. Such as engineers from separate parts of the company running into each other in the lunchroom, and realizing they’re working on the same problem. Can AI help achieve this in a more systematic way? Opinions from across the industry are mixed.
“While AI has proven valuable in expanding, optimizing, and cross-pollinating existing ideas, its ability to systematize the generation of entirely new concepts remains unproven,” says Tommi Vilkano, director of RELEX Labs at RELEX. “Early signs are promising, but more evidence is needed to confirm its impact on accelerating true innovation and scientific discovery.”
Others feel AI can catalyze the innovation process. “AI can drive stage-gate decision processes in evaluating new ideas, categorizing their value, determining like use cases and helping to drive investment decisions,” says Bob Lamendola, senior VP of technology and head of Ricoh’s North America Digital Services Center. “The human capacity to process the same level of information with equal accuracy falls short of what AI models can produce. In all cases, properly trained AI models can use empirical data and volumes of unstructured information to help present innovation alternatives from learned information.”
AI can help systemize any process, “and that includes innovation processes, too,” agrees Adrian McKnight, chief digital officer at WNS. “Systemization and a structured approach are the antithesis of innovation, which springs from the randomness of ideas and cross-ideation of approaches, products and experiences across industries. Yet, AI can be a great enabler.”
The enablement comes in the form of “rapidly analyzing the innovation pipeline to discover windows of opportunities,” McKnight continues. “Algorithms can collect and connect relevant data on innovation, spot patterns of innovative impact, and may even be trained with automated foresight to uncover ‘unknown unknowns.’”
Natural language processing may enable AI to actively participate in innovation, helping “workers from diverse backgrounds can engage with data more intuitively,” says Sumeet Arora, chief development officer for ThoughtSpot. “The conversational approach reduces the need for technical expertise. What would have traditionally taken hours, days, or weeks for data analyst teams to examine vast amounts of data and share actionable answers will take minutes. AI increases the speed of action, empowering a broader audience to make data-driven decisions, leading to a strong data culture.”
Natural language processing “can immediately assist in drafting business plans by providing templates, suggesting improvements and even predicting potential challenges,” Lamendola agrees. In addition, “AI algorithms can simulate different business scenarios, offering insights into the most viable strategies. And predictive analysis can be used to assess risk and aid in contingency planning.”
AI also plays a role as a neutral party in the innovation and design process. “Entrepreneurs, for example, often become emotionally invested in their ideas, which can sometimes lead to confirmation bias,” says McKnight. “Generative AI can offer objective assessments, and simulate various customer personas, providing realistic market testing and interactive feedback.”