A famous provocation from 2013 by Professor Dan Ariely of Duke University, referring then to Big Data and today certainly transferable to Open Innovation, goes like this: “Big Data Open Innovation is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” Obviously it is a joke, but it perfectly captures the sense of the Imitation Game.
Open Innovation for medium and large-sized enterprises remains a central challenge, because business transformation is an imperative that cannot be fully accomplished only with internal projects. Resources, experiences, assets, and knowledge are limited by definition, and utilizing an appropriate mix of in-house and open innovation strategies potentially brings greater opportunities in terms of speed, learning curve, investments, and diversification. Obviously, the main challenge lies in the effective implementation of continuous and increasingly open innovation programs, which are becoming more multifunctional, all-encompassing, and central to the organization’s core business. The culture of open innovation must be nurtured and strengthened progressively as capabilities grow. The future is not predicted, it is built, and it is no longer enough to wait for others to build it and then replicate it. Not only because the time factor is increasingly a competitive lever, but above all because the future of companies will be increasingly personalized and hyper-specific.
In the era of real-time data, which feeds the corporate knowledge base and especially generative AI that thrives on data and its correlations, business transformation and innovation paths are destined to be increasingly tailored to individual organizations. The traditional opportunistic “wait and copy” of followers and late movers is destined to pay off less and less, especially in less regulated industrial sectors.
We are at the beginning of an epochal discontinuity, never experienced before by homo sapiens, marked by multiple generative AI technologies that are so close to the common man that they easily pass the famous Turing test on AI. It is no coincidence that today we speak of Autosapiens, a necessary neologism to represent non-biological entities capable of expressing capacities for action, learning, empathy, and mystification with the imperfection and ambiguity of their biological fathers (hallucinations and misunderstandings included).
A year ago, in January 2023, just two months after its introduction, ChatGPT reached 100 million users. In December of the same year, it recorded annualized revenues (the value of active subscriptions in the month multiplied by 12) of 2 billion dollars, only 14 months after launch. ChatGPT is just one of the first mass-market generative AI application services, with an initial penetration speed ten times that of Smartphones and a hundred times that of the Internet.
In reality, the methods, practices, techniques, technologies, and generative AI services for organizations (enterprise grade, with the relative requirements of capacity, security, and reliability) are already numbering in the hundreds and are proliferating thanks to the quantum leap made by Large Language Models (LLM) which are at the base of this epochal discontinuity, together with cloud computing, GPUs, and latest generation semiconductor nanotechnologies (3nm). And leveraging LLMs, Retrieval-Augmented Generation (RAG) techniques, and orchestrated multi-modal AI logics, Large Action Models (LAM) are also making headway, which can guide cyber-agents (Autosapiens) to make decisions and execute actions in the real world, adapting and evolving in full autonomy.
With generative AI, deep-tech is no longer a topic for an isolated research center of the socio-organizational continuum. It is naturally pervasive and cross-functional to all business functions and processes. Paradoxically, it is precisely the IT function that is touched the least, at the level of strategic and cultural discontinuity.
Therefore, skills and corporate maturity in the ability to involve actors (biological and non-biological) and innovative organizations for corporate innovation experiments with generative AI become a necessary premise to fully seize the deepest and most discontinuous business transformation opportunities that human society has ever experienced. In experiments, the main result is the leap in knowledge that then guides conscious industrial choices regarding investment, M&A, development, reorganization, and more.
The degree of maturity of Corporate Innovation is classifiable into 7 levels, as seen in the figure: Disconnected, Exposed, Theatre, Open, Embedded, Master, and Systemic.
The ladder metaphor in the infographic is intended to suggest a maturity model in which organizations can move from being unaware of the need to innovate (stage 0) to a stage where innovation is deeply rooted and systemic (stage 6 and beyond). Each step upward represents a deeper integration of innovation into the organizational culture and processes. Some level changes in the maturity model are not achieved only through experience and the success of project experiments, but require organizational discontinuities.
For example, the transition from “Theatre” (level 2) to “Open” (3) requires a jump at the functional level with specific representatives and mandates that dialogue with business functions to activate significant Open Innovation projects.
But it is the next step – from Embedded to Master – that allows organizations to seize the best business transformation opportunities with Generative AI. This level transition implies an organizational discontinuity at the level of the entire business. Innovation strategy becomes a validated cornerstone of corporate strategy; there is a widespread culture of innovation, venture building, innovation factories, and other programs designed to generate high-impact innovations based on intense investments.
The last step, which is neither necessary nor practicable for most organizations, is from Master (level 5) to Systemic (6), where the discontinuity is at the vision level: reaching far beyond the company perimeter to dialogue with industrial supply chains of interest and with society in a broader sense. These are the organizations of the country system or the pan-European socio-economic system that can successfully push into this dimension of support and circularity of innovation as a model for the sustainable development of human society and Autosapiens.
Equipping oneself adequately (Master level) to be able to play one’s business transformation matches (many) well with generative AI does not only constitute one of the various basic (necessary) premises but is an enabling condition to give continuity to the future of many organizations. In 5 years, the Enterprise Value of a non-AI-ready organization could be dramatically devalued by the Autosapiens agents of the financial system (Banks and Private Equity).
