Keeping Humans at the Center of Innovation

Re-Asserting the Indispensable Role of Human-Derived Insights in the Age of AI

March 7, 2024
By Tom Carmona
This image was created with the assistance of DALL·E 2

Why would one lean heavily on an autonomous innovation process predicated on language models and algorithms trained on potentially outdated or untargeted insights…Perhaps it goes without saying, but the forefront of market insights lies in the markets themselves, which are composed not of datasets, but rather of people living in the present moment.

In recent discussions on innovation methodology, a narrative has emerged promoting the concept of autonomous innovation processes at the expense of traditional frameworks like Design Thinking and Lean Startup. Our perspective, however, diverges markedly, advocating instead for continuous innovation, an approach that leverages automation but still underscores the critical role of human interaction, creativity, and leadership throughout the innovation journey. It’s a deliberate choice that not only leverages technology to augment human capabilities but also firmly upholds human-to-human interaction as the fundamental driver of insights throughout the innovation process. Also, importantly, it’s a rejection of the mutual exclusion between new technologies and tested innovation methodologies and frameworks. 


Upholding the Double Diamond

Amid voices claiming that the double diamond model is obsolete, we would like to assert its relevance. The double diamond is not “dead,” as some have suggested. Instead, it remains a vital underpinning for innovation processes. The model’s divergent and convergent phases still provide the cadence for thoughtful innovation processes, even as technology allows us to compress timelines and tackle more projects simultaneously. Also, the early emphasis on empathy in the double diamond should be seen as a strength and not a weakness. Any attempts to incorporate insights into feasibility and viability early in the innovation process should be additive and not dilutive to the customer focus in the innovation process. Also, any attempt to achieve greater efficacy and scale in the discovery process should work toward the goal of elevating human perspectives, not sidelining them.


The Primacy of Human Interaction

Human-to-human interaction should still be the first and most important activity in the innovation process, and technology should be designed to enhance the output from that interaction. Direct engagement with individuals—be they customers, experts, or team members—unlocks a wealth of unscripted, unprompted ideas. This mode of interaction is unparalleled in its ability to reveal hidden needs, unanticipated challenges, and novel perspectives. The mechanization of insight generation cannot capture the depth of understanding and spontaneous innovation that springs from genuine human conversation.


Empathy and Serendipity at the Core of Discovery

At the heart of discovery lie empathy and serendipity, principles that underscore the importance of engaging with real people. This approach to discovery is about venturing beyond predefined objectives to uncover insights we didn’t initially seek. It’s in these unanticipated moments, facilitated by active and strategically guided listening, that true innovation often finds its spark. Empathy ensures that our solutions resonate deeply with those they are intended to serve, while serendipity opens the door to groundbreaking ideas born from the richness of human interaction. Our openness to unforeseen insights stems from the unalterable truth that often you don’t know what you don’t know, and often we need other people to illuminate those paths without the constraints of closed-ended conversations that could bias what might otherwise be a more unadulterated live feed into someone’s perspective.


The Limitations of Autonomous Innovation: The Recursive Trap and the Need for Fresh Insights

As we move forward in our own journey exploring the relationships between technology and human reason and creativity in the innovation process, we remain committed to the integrity and strategic value of research. An over-reliance on machines for generating human insights poses a significant risk to the depth and breadth of our understanding. The question arises: why would one lean heavily on an autonomous innovation process predicated on language models and algorithms trained on potentially outdated or untargeted insights?

Autonomous innovation processes, heavily reliant on machine learning, can inadvertently become trapped in a cycle of recursive and self-referential outputs. This cycle limits the strategic value of the research by failing to advance real knowledge and insights about an industry or customer persona. Machines, for all their processing power and pattern recognition capabilities, operate within the confines of their training data. If this data lacks the latest, nuanced human-derived insights, the resulting innovations risk detachment from current realities and emerging trends. Perhaps it goes without saying, but the forefront of market insights lie in the markets themselves, which are composed not of datasets but rather of people living in the present moment.

Incorporating AI-driven insights into our innovation work carries the risk of creating a feedback loop where AI models recycle existing knowledge without contributing new understandings. This recursive trap can stifle innovation, leading to solutions that may be technically sound but lack relevance or fail to address emerging challenges and opportunities. The dynamic nature of industries and the ever-evolving preferences of customer personas demand insights that are current and forward-looking, something that primarily retrospective analysis fails to provide.

The antidote to the limitations of machine-generated insights is a strategic emphasis on human-derived intelligence. By prioritizing interactions and engagements that yield fresh, real-time insights from primary sources, organizations can build a growing repository of proprietary knowledge. This knowledge, reflective of the latest trends, challenges, and opportunities in an industry, becomes a valuable asset in training AI models. Instead of relying on generalized, retrospective data, these models are informed by up-to-date, specific, and strategically relevant insights.


Conclusion: Balancing Human Insight with Supportive Technology

Our commitment to continuous (but not fully autonomous) innovation is defined by a balanced approach that values human insight above all. While we embrace technologies to enhance our processes, we do so with the understanding that technology is an enabler of, and not a replacement for, human creativity and intuition. The tested principles in guiding innovation frameworks like Lean Startup and Design Thinking among others, coupled with our focus on empathy through direct human engagement, ensure that our innovation processes are not only efficient but deeply human-centric. By thoughtfully integrating automation in ways that support further human interpretation and leveraging technology to streamline operational aspects, we can enhance our capacity for innovation without losing sight of the human experiences that drive it. This approach positions us to create solutions that are not only technologically advanced but also profoundly resonant with the people they aim to serve, embodying the truly lasting spirit of innovation.


Prior to joining ID8, Tom co-founded, a workforce intelligence tool that, following a merger with Hitch, was acquired by ServiceNow. He has also invested in several successful ventures, some of which where he took an active role as an operator. Tom holds an MBA with a specialization in entrepreneurial management from the University of Wisconsin and lives in Nashville with his wife and three children. Before entering the business world Tom completed both the Peace Corps and Teach For America programs. In his spare time he enjoys arguing about NBA history with his friends.





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