Within a handful of years, artificial intelligence has moved from speculation about what machines might one day accomplish to an everyday instrument of management, disrupting manufacturing, banking, healthcare, public administration and the creative professions (Füller et al., 2024; Haefner et al., 2023; Kim et al., 2022). The call for papers that opened this special issue framed the transformation around a demanding proposition, that artificial intelligence does not merely add another tool to the managerial repertoire but reshapes competition, redraws the boundaries of strategy formulation and reorganises the structures through which firms divide and coordinate work (Shrestha et al., 2019; Haefner et al., 2023). The scholarship that this issue joins has begun to specify the conditions under which that promise is realised. Against this background, the special issue advances a single thesis that unites its 11 contributions: the next wave of innovation will be defined less by the autonomy of machines than by the quality of the partnership between human judgement and computational capacity. The call for papers named this idea with the vocabulary of Society 5.0, describing the transformation of the person into a human + entity, whose cognitive reach technology extends rather than displaces (Bartoloni et al., 2022; Laviola et al., 2024).
The articles honour that vocabulary while subjecting it to empirical scrutiny. They anchor their claims in the field's established apparatus, from the technology-organisation-environment framework and the technology acceptance model through the unified theory of acceptance and use of technology to dynamic capabilities and the componential theory of creativity. They also draw on a deliberately varied methodological repertoire, spanning participant observation, controlled experiment, multiple and single case study, cross-country survey, structural equation modelling and event study. Three levels of analysis structure the contributions, set out in sequence by the call: the individual, the enterprise and the nature of the technology itself. The discussion that follows traces that ascent.
At the most intimate level of analysis four studies examine how people think alongside intelligent systems, and their shared discovery is that the benefits of the technology are neither automatic nor monotonic but depend on the manner in which the human being remains cognitively engaged. Generative artificial intelligence and decision-making:evidence from a participant observation with latent entrepreneurs (Bastone et al., 2026) enters this terrain through people who have never founded a venture yet must reason through an entrepreneurial task with a conversational tool at their side. Through participant observation reinforced by a post-observation survey and analysed inductively, the authors trace three patterns of interaction, an oscillation between taking knowledge from the machine and giving knowledge to it, an assertion of human leadership over the conversation when its answers disappoint and an alternation between treating the system as an operational instrument and embracing it as a source of learning. The account locates agency firmly on the human side of the exchange and furnishes some of the clearest evidence in the issue for the human + paradigm, showing that generative intelligence supports rather than supplants the reasoning of the inexperienced decision-maker.
Where that first study observes behaviour, Human-AI synergy: finding cognitive balance in idea generation for product innovation (Cristofaro and Giardino, 2025) measures it. A controlled experiment with 123 product innovators contrasts 3 regimes of assistance and uncovers a relationship shaped like an inverted U, in which moderate assistance maximises cognitive engagement and yields the greatest number of original and feasible ideas, while excessive assistance invites automation bias, dampens originality and inflates confidence beyond what the evidence warrants and the absence of assistance leaves creativity hostage to the limits of unaided cognition. The contribution reframes the technology as a cognitive amplifier whose value depends on calibration, converting a general intuition about balance into an actionable guideline for structuring the dose of intelligence that a creative task can profitably absorb, and it extends the componential theory of creativity into a human and machine setting (Garbuio and Lin, 2021). The professional stakes of that calibration rise sharply in Toward human + medical professionals: navigating AI integration in healthcare to enhance human expertise (Perillo et al., 2026), which carries the individual lens into a domain where error carries a human cost. Reading cases that range from an oncology support chatbot to robot-assisted surgery through a framework attentive to physical, digital and social complexity, the authors distinguish augmentation from automation and argue that the future of the clinician lies in the former, since artificial intelligence enhances decision-making and widens access most safely when the professional remains the locus of clinical responsibility (Cannavale et al., 2022). The study demonstrates that augmentation is a design choice rather than a technological inevitability.
The individual level closes with AI adoption drivers and innovative work behaviors (Vandana et al., 2026), which asks what turns a willing employee into an innovative one. Building on the unified theory of acceptance and use of technology and testing the model with partial least squares structural equation modelling on responses from 278 employees and managers in information technology firms, the authors show that performance expectancy and training both drive adoption, that training also operates through a mediating channel and that personal innovativeness conditions the strength of the relationship, with adoption in turn feeding innovative work behaviour. The finding restores the human being to the centre of the adoption story and reminds managers that the diffusion of intelligent tools is a matter of expectation, capability and disposition rather than mere availability, echoing the warning that individual productivity does not translate automatically into organisational benefit. Taken together, these four studies compose a coherent argument that value accrues neither to the person who surrenders judgement to the algorithm nor to the person who refuses it, but to the one who sustains an engaged, leading and appropriately sceptical partnership with it.
When the lens widens from the individual to the organisation, the returns to artificial intelligence come to depend on a deliberate realignment of strategy, structure, roles and collaboration rather than on procurement alone and four studies portray an enterprise recomposed around the technology. Redefining identity: corporate evolution in the AI era (Nevi et al., 2025) confronts the most existential of these questions. Through interviews with artificial intelligence leaders in financial firms listed on the Italian FTSE MIB, the authors show that intelligent systems have become a strategic asset that reshapes corporate identity at the level of content and client relationship, so that firms find themselves serving a new kind of customer, described with the coined figures of the Promptumer and the Promptvestor and operating through intelligent ecosystems, dashboards and virtual agents that extend organisational boundaries. The study proposes an AI signature as a novel dimension of identity analysis and offers managers a corporate identity canvas to navigate the tension between who a company is today and who its technology invites it to become, suggesting in a striking formulation that firms may soon be valued less for what they sell than for how their systems reason, act and converse.
Adoption, however, is never uniform, and technological, organizational and environmental determinants influencing the intention to adopt GEN-AI of public sector organizations. A cross-country comparative analysis (Mercuri et al., 2025) demonstrates how profoundly context conditions it. Integrating the technology, organization and environment framework with the technology acceptance model and testing the combined model on more than 1,000 public sector employees across Italy and Spain, the authors reveal that the same determinants pull with different force in different national settings, technological and organizational factors dominating in Italy while environmental pressure, and citizen expectation in particular, drives adoption in Spain. The study answers the call's question about whether adoption drivers differ across contexts with a firm affirmative and cautions against universal prescriptions. The market's verdict on this activity is the subject of when do AI investments pay off? Evidence from stock market responses to innovation and alliances (Laviola et al., 2025), which applies event study methodology to a sample of investment announcements from firms listed on the S&P 500 index between 2020 and 2024 and reaches a sobering conclusion, that announcing an artificial intelligence investment, even one carrying genuinely innovative attributes, does not by itself move the market and that investors reward the technology only when it is pursued through strategic alliances. The study integrates classic theories of innovation management with the discipline of firm valuation and cautions executives that the value of intelligence is realised through the relationships within which it is embedded rather than through the announcement of capability alone.
For the youngest firms the challenge is not valuation but survival at scale, and Harnessing AI-capabilities for startup scalability: unlocking potential through AI-driven innovation ecosystems and AI-infrastructure readiness (Chotia et al., 2025) traces the path from capability to growth. Surveying 274 decision-makers in artificial intelligence intensive firms in the United States of America and modelling the relationships with partial least squares structural equation modelling, the authors show that AI-enhanced decision-making, AI-powered entrepreneurial agility and ethical governance all feed an intelligent innovation ecosystem, which in turn cultivates the infrastructure readiness that underwrites both internal and external scalability. By positioning the ecosystem and readiness as sequential mediators the study extends dynamic capabilities theory into the age of artificial intelligence, and it specifies in empirical terms the organisational readiness on which the returns to the technology are known to depend. Across these four studies the enterprise appears as a system in motion, its adoption filtered through national and organisational context, its identity renegotiated as machines begin to reason, its market value conferred on collaboration rather than mere capability and its scale achieved through ecosystems and readiness rather than through technology alone.
The third level turns from the actors to the technology itself and to the distinction the call drew between traditional and generative artificial intelligence, rule-based systems excelling in structured settings with well-defined decisions while generative systems produce novel content and thereby feed the creativity on which innovation depends, at the cost of interpretability and control (Garbuio and Lin, 2021). Because the effects of the technology on the innovation process are stage-dependent and cumulative, gains realised at the front end of ideation carrying forward into development, testing and post-launch learning, the character of the intelligence deployed matters for the value it creates. Genius innovation management – a typology of GenAI use in innovation management (Mix et al., 2025) offers the issue's most systematic map of that terrain. Through a multiple case study spanning thirteen organisations of varied industry and size, the authors identify the motives that draw firms toward generative intelligence, catalogue the user, technology, company and society related challenges they must negotiate and derive a typology of four postures, the explorers who experiment at the edges, the technology specialists who master the tools, the human-centred visionaries who keep the person at the heart of the design and the combiners who weave the two commitments together. The typology gives managers and scholars a shared language for describing where an organisation stands and where it might travel.
Value is not the exclusive concern of the firm that seeks profit, and Surfing artificial intelligence: creating value in mission-driven contexts (Anzivino and Olivieri, 2025) carries the enquiry into the third sector, a domain that innovation management has too often neglected. Through 16 semi-structured interviews with foundation presidents, project and operations managers and digital specialists, analysed with an established interpretive method, the authors show that non-profit organisations deploy artificial intelligence chiefly to pursue marketing communication objectives, to strengthen fundraising and to deepen stakeholder engagement, and their framework distinguishes the strategic from the operational role of the technology while insisting that value creation in a mission-driven setting rests on a collaboration between computational capacity and human capital every bit as delicate as the one observed in commercial firms. The making of value returns to the world of products in AI in food product innovation and development: a Latin American empirical study in the coffee sector (Perez-Perez, 2026), which unites the creative promise of generative intelligence with the tacit wisdom of consumer co-creation. In a case study of a Colombian family coffee company more than a century old, the authors run a creativity workshop that pairs the SCAMPER technique with a generative tool and engages 140 young consumers in the reinvention of a heritage brand, human intelligence sustaining the emotional bond between the brand and its public while the machine optimises the tangible links in the value chain. By extending the componential theory of creativity into a hybrid human and machine model, the study supplies a practical template for organisations that must renew themselves across generations without severing the identity that made them worth renewing. Together these three studies establish that the character of the technology conditions but never determines the outcome and that the making of impactful value depends on how deliberately the human contribution is placed in tension with the generative machine.
Read as a whole, the 11 studies describe an ascent through the three levels of analysis and meet at the summit in the vision the call named Innovation 5.0 (Troisi et al., 2024), a model of partnership in which the person, extended into a human + entity, collaborates with intelligent systems to pursue change that is not merely efficient but impactful. The individual studies establish that the partnership succeeds only when human cognition stays engaged and in command, the enterprise studies that it must be embedded in governance, national context, strategic alliance and ecosystem readiness before it yields value, and the technology studies that generative intelligence multiplies creative possibility yet requires typological clarity, ethical purpose and human facilitation to convert possibility into worth.
For the practising manager, the message is concrete. Artificial intelligence should be dosed rather than maximised, embedded in training, leadership and the cultivation of personal innovativeness, pursued through alliances and ecosystems rather than announced in isolation, and governed with attention to ethics, sustainability and the shifting identity of the firm, prescriptions that echo and extend the strategic realignment the wider literature now demands and that specify the organisational readiness on which its returns depend. The contributions gathered here also open an agenda, inviting longitudinal work on how the balance between engagement and reliance shifts as users mature, comparative work that reaches beyond the European and North American settings studied here, deeper enquiry into the governance of collaborative artificial intelligence and the accountability of boards for decisions increasingly shaped by algorithms and a critical vigilance toward the dark and unexpected sides of the technology from bias and hallucination to the erosion of skills.
Ultimately, the contributions to this special issue suggest that the future of AI-enabled innovation will not be decided by the sophistication of algorithms alone but by the capacity of individuals, organisations and institutions to govern the relationship between human judgement and artificial intelligence in ways that are creative, responsible and socially meaningful.
The scientific coordination of this special issue was supported by the Cultural Association Knowmedtech (Italy).
