Insight
The Role of Artificial Intelligence in Accelerating Innovation
October 24, 2024
Tina Asmuth Chatalas
This article is the first in a two-part series from an interview conducted by NextAccess Senior Partner Tina Asmuth Chatalas with Dr. Ted Ladd on how generative AI (gen AI) is changing innovation. Gen AI doesn’t just help with the “WHAT” of innovation, but also the “HOW”. The ability to effectively integrate gen AI into innovation processes may well become a defining factor in speed to market and corporate success.
Ladd is a professor of Entrepreneurship and Innovation at Hult International Business School, instructor of Platform Entrepreneurship at Harvard University and a veteran of seven Silicon Valley start-ups.
Innovation has long been the lifeblood of competitive advantage, allowing companies to stay relevant in a rapidly changing marketplace. The combination of intuition, experience, and occasionally luck often determined the next great breakthrough. Today, the rise of artificial intelligence (AI)—and particularly generative AI (gen AI)—is transforming this process and changing the blend of art and science. According to Dr. Ted Ladd, professor of entrepreneurship and innovation at Harvard and Hult, this shift is poised to change the way companies approach innovation across multiple dimensions and democratize it across company ranks, thereby accelerating results.
Traditional Approaches to Innovation - Epiphanies and HIPPOs
Traditionally, innovation within organizations came from two primary sources: the entrepreneurial epiphany and the wisdom of senior management. Innovation in startups often sprang from a founder’s eureka moment - a personal frustration that sparked a business idea. In larger corporations a detailed innovation roadmap process with numerous checkpoints often culminated in what Ladd refers to as the “HIPPO Principle", where the “Highest Paid Person’s Opinion” made the final strategic call.
The challenge with these approaches is they don’t embed innovation as an organization’s cultural norm, and leave the impression that some people are more “innovative” than others. How to change this? Use AI and gen AI to apply a more rigorous, data-driven approach that includes a wider set of inputs and voices in the process.
Enter AI and Gen AI: Broadening the Lens for Hypotheses Generation
The introduction of gen AI changes the game across numerous areas of the innovation process by allowing more inputs from more places. In the initial phase of innovation, identifying the right “problem to solve” is critical, as it drives all future thinking. Gen AI allows the exploration of a broader set of hypotheses and to test them more quickly.
As Ladd explains, “You can use gen AI to develop the assumptions and what has to be true. The way you can do this is simply by saying, ‘take this particular idea and give me 5 hypotheses that I would need to test’. You can even ask gen AI to determine what thresholds you should expect for these hypotheses.” It then becomes an iterative process between gen AI and other research inputs to arrive at the best areas for innovation exploration.
Gen AI’s Role in Idea Generation and Refinement: Large Language Models (LLMs) and Small Language Models (SLMs)
During innovation exploration and refinement, gen AI excels at both divergent thinking (generating lots of ideas) and convergent thinking (narrowing down to the most promising options). By analyzing vast datasets and recognizing patterns the human eye might miss, gen AI can propose numerous possibilities in minutes. Ladd advocates for a two-pronged approach: using large language models (LLMs) to cast a wide net, and small language models (SLMs) for more focused insights.
Large language models (LLMs) allow you to analyze huge amounts of data across the company’s ecosystem. Ladd says, “Don’t just confine yourself to customer inputs and ideas. Look at all the other players in the company’s ecosystem that might have a valid, useful opinion on what customers might want.”
However, you have to be careful when checking LLM outputs. Ladd explains, “An LLM contains all the knowledge the AI engine could scrape from everywhere. It includes reputable material from academic journals, The New York Times and The Wall Street Journal. However, LLMs also grab text and images from People Magazine and the Inquirer. So, peer-reviewed facts sit next to opinions from the Kardashians. An LLM is excellent at reflecting global information, but it should never be assumed to be consistently accurate.”
Equally important is the strategic use of small language models (SLMs). Unlike their larger counterparts, these models can be tailored to specific organizational contexts and customer segments. They can also incorporate proprietary data and institutional knowledge in a more secure setting. This customization allows for more focused and relevant insights, effectively cutting through the noise that often plagues the larger LLMs that can also have structural bias. Combining the wider lens of the LLM with the focused insights of the SLM is a formidable combination for finding new areas of opportunity.
LLM Definition:
Gen AI systems trained on the internet and vast amounts of data and can understand, process and generate human language. They are computing intensive and harder to customize. Particularly helpful for divergent thinking.
SLM Definition:
Gen AI models that are tailored on specific domain knowledge, organizational contexts, information and customer segments. Great ways to leverage LLM insights into your corporate context for convergent thinking.
Gen AI Enables Ideas From Everyone
Perhaps the most exciting change in integrating gen AI into the innovation process is shifting who contributes and when. No longer is creativity the domain of a select few; it's a company-wide endeavor. Although cross-functional participation has long been an important component, gen AI allows insights from an even broader group than the traditional functional areas.
For example, customer service representatives, sales personnel, and other client-facing staff all have firsthand knowledge about customer needs and pain points. However, due to the historical challenges of capturing this information, it was not always included at scale. Now, with gen AI, these interactions can be systematically tracked, included, and analyzed to elevate these insights and potentially uncover groundbreaking innovation opportunities that might otherwise remain hidden. The junior employee with a kernel of an idea can see it nurtured and developed by gen AI, potentially blossoming into the next big thing.
Innovation with Gen AI Leads to Better Ideas, Faster
With gen AI, the opportunities for innovation multiply. The benefits are clear: faster, more cost-effective market research, more accurate identification of customer pain points, and a proliferation of diverse ideas. What once took weeks or even months to analyze can now be done in hours. For companies fighting to stay ahead in a fast-paced market, this speed is a clear advantage.
““Gen AI is a probabilistic prediction machine. It excels at finding patterns, but struggles with genuine novelty. True breakthroughs often come from human creativity and lateral thinking, areas where AI remains limited.”
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The Human-AI Partnership: Finding the sweet spot with this Probabilistic Prediction Machine
Despite its potential, gen AI is not a silver bullet. “Gen AI is a probabilistic prediction machine” Ladd cautions. “It excels at finding patterns, but struggles with genuine novelty. True breakthroughs often come from human creativity and lateral thinking, areas where AI remains limited.” Therefore, gen AI’s role in innovation should complement, not replace, human creativity.
The challenge, then, lies in finding the sweet spot between AI's analytical power and human creativity and intuition. Gen AI can suggest countless iterations of an idea, but it requires human intelligence to push those ideas into uncharted territory. It's a delicate dance, one that organizations are still learning to master. Some companies hesitate, concerned about data confidentiality or the specter of job displacement. But forward-thinking firms are tackling these issues head-on, implementing secure gen AI infrastructures and viewing gen AI as a tool for enhancement rather than replacement.
Defining Factor in Speed to Market and Corporate Success
AI is accelerating the innovation process, and the future will be shaped by those who can effectively nurture human creativity while harnessing gen AI's potential. It's a future where the next big idea could come from anywhere in the organization, powered by gen AI but sparked by human ingenuity. In practice, this means that gen AI doesn’t just help with the “what” of innovation but also the “how.” By aiding in multiple aspects of the innovation process, including hypothesis testing and idea creation and refinement, gen AI enables organizations to iterate faster and smarter.
The question will no longer be whether or not to use gen AI, but how to use it best. As we move forward, the ability to effectively integrate gen AI into innovation processes may well become a defining factor in speed to market and corporate success. How organizations realign to take advantage of this new opportunity for collaboration is the subject of our next article: Part II: How Artificial Intelligence Is Reshaping Organizational Collaboration and Culture in Innovation.
Want to explore ways to foster more innovation at your company? Please reach out to Tina at tina.chatalas@nextaccess.com
Tina Asmuth Chatalas is a Senior Partner at NextAccess, a boutique strategy consulting firm that delivers advanced go-to-market solutions for executive leaders. Our team of experienced operators has a track record of leading successful sales, marketing, and innovation transformations at global companies across industries. At NextAccess, we bring a unique mix of strategic insight, operating experience, analytical rigor, and sophisticated AI capabilities to drive tangible results for our clients. We cut through the noise to grow revenue and margin by harnessing the best in artificial – and human – intelligence.
Tina has led growth strategy and innovation projects for Fortune 500, middle market companies, and start-ups in the US, Europe, and Asia. Her passion is finding untapped opportunities at the intersection of consumer insights, market trends, and corporate strategy.
Dr. Ted Ladd is a professor of Entrepreneurship and Innovation at Hult International Business School, an instructor of Platform Entrepreneurship at Harvard University, and a veteran of seven Silicon Valley start-ups, including the IPO of Palm Inc, where he did the first acquisition of an App Store and an ebook reader. Ted also worked on smartwatch software that was acquired by Google, rebranded WearOS, and currently powers watches from over 40 brands. He is a board member of an electric utility, a venture studio, an economic development agency, and an environmental non-profit.
His research, detailed at ORCID, Google Scholar, and his Forbes column, focuses on how AI impacts the strategy of multi-sided platforms. His most recent book, Innovating With Impact, was published by the Economist.
