“The only way to see what’s coming is to look back at how things have changed through times of uncertainty and volatility.”
– A Futurist Outlook
Let’s begin in the mid-2000s, when disruption generally occurred from known competitors delivering new innovations to tap into customer value through creative product or packaging designs. Then came the 2010s, when disruption emerged from the rapid digitalization created by startups, challenging their preconceived notions of how offerings could be designed and packaged for customers. For instance, Uber digitized taxi dispatches, and Spotify redefined accessibility to music.
We are now in the post-pandemic era, where disruption emanates from both within and outside industries. Coming off a period of uncertainty and lack of control, enterprises of all sizes are focused on diversification to strengthen their competitive positions, blurring industry lines in the process. Pulling together disparate signals of change has burdened innovation leaders with information overload that can cloud their judgments. While disruption is inevitable, its implications are becoming more ambiguous as unlikely players will be competing in your industry. Apple is competing alongside media and financial companies, and Tesla is accelerating private sector space exploration.
Shifting the Corporate Innovation Value Chain
The corporate innovation value chain generally consists of idea generation, development, and implementation. Just as important, and often overlooked, is the innovation direction that occurs before idea generation. Innovation direction largely deals with how corporate leaders prioritize where to innovate, and what innovation value needs to be realized and captured. Defining the innovation direction at the onset will help disseminate the urgency for innovation activities further down the corporate innovation value chain more effectively.
Defining the innovation direction at an organizational level drives the innovation mandate and purpose. As ambiguity is inherently subjective, innovation leaders will have to de-risk the impacts of any biases, and resistance from stakeholders as some may disagree with your convictions on how the competitive landscape is being implicated. We believe that leveraging technologies like AI could help with aligning innovation efforts and bring focus to tackling disruption in a world of information overload.
The Role of AI in Corporate Innovation
In the past year, the discussion has been around how the recent developments in AI, specifically Generative AI, can impact the overall corporate innovation value chain. Generative AI can create new thoughts or ideas based on the information and data within vast models. We tested this by asking ChatGPT, “What innovation direction should be taken by automotive companies?”. It recommended a list of opportunities most automotive leaders would be familiar with: electrification, autonomous vehicles, connected vehicles, and others.
What is clearly lacking is the discernment in understanding the context of the industry and extrapolating what adjacent or radical opportunities might be considered, as per the Tesla example. It is clear that Generative AI currently is limited in its capability to be ‘innovative’ rather than relying on a retrospective view of what opportunities could be most impactful for a given industry. How might we challenge industry borders and leverage existing organizational resources to diversify?
At its core, AI delivers rapid analysis of data and information. Synthesizing vast amounts of internal and external data helps people find and access key information easily. This is normally performed through a mixture of descriptive, diagnostic, predictive, and prescriptive AI models. These capabilities are more impactful in the short term for corporate innovation leaders compared to Generative AI.
The Capabilities & Risks of AI in Defining an Innovation Direction
AI brings automation to synthesizing vast data points, identifying relationships between various nodes, and understanding how those nodes are related to each other. This automation of decision-making through the use of AI is referred to as cognitive automation. This might be manifested in Knowledge Graphs, a visual representation of a network of real-world signals such as events, situations, or concepts to describe the relationship between key industry-level signals.
Knowledge Graphs and similar tools help decision-makers quickly understand the implications of distant or immediate signals and how they might interplay with one another. For instance, automotive companies such as Renault could collect disparate data points on electrification trends within mobility and smart city use cases. Descriptive and diagnostic AI models will portray and validate the causality of these trends and their corresponding impact on the industry. Prescriptive and predictive models will then rapidly produce various innovation opportunities and their potential expected returns. Innovation leaders will use these outputs to help declutter the breadth of information they are concerned with.
As automation through AI is focused on delivering convenience through summarized information, leaders must be conscious of potential correlation bias when forming judgments on limited or incomplete information. While AI helps comprehensively analyze signals, it may lack the contextual understanding of how these signals impact one another. When applying cognitive automation in defining an organization’s innovation direction, we still see the importance of empowering executives to layer modeled outputs with nuance. Plotting strategic triggers throughout the innovation journey will help innovation leaders embed adaptability effectively within teams.
Defining an Innovation Direction through Cognitive Automation
Defining an innovation direction could be achieved through a retrospective analysis of the competitive landscape, strategic foresight development, or a combination of both. These scientific & speculative methods aim to reduce ambiguity and accelerate decision-making based on trends and driving forces identified. In a world of information overload, how do we make sense of it all? Especially as industry lines are getting blurred, the impacts of new technologies are more volatile, and market reactions are uncertain.
We propose first leveraging the descriptive, diagnostic, predictive, and prescriptive capabilities of AI for the data analysis and synthesizing stage. Then tap into the experience and talent of an organization’s innovation team to effectively navigate ambiguity and achieve innovation objectives. Only through years of embedded nuance and industry know-how will innovation teams be able to put forward an innovation direction that would face less scrutiny from adjacent stakeholders or business units. Reasoning and leadership skills from industry or technology experts will still be superior to any recommendations churned by AI models.
Foundry 415 monitors how AI is being employed throughout the corporate innovation value chain. We often see positive results when its capabilities are married with the intuition and ‘gut feeling’ from internal industry experts and leaders. In today’s world of continuous disruption, information overload, and ambiguous implications, focusing on defining an innovation direction is most impactful for any corporate innovation venture.
Mallek Aminullah is Sr. Associate, Corporate Innovation Services at Foundry 415 Innovation Group.
