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26 May 2026

How Artificial Intelligence Is Reshaping Innovation Management: Evidence from Pre- and Post-Generative AI Research

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1
Research Department, Universidad Tecnológica del Perú, Jirón Hernán Velarde 289, Lima 15046, Peru
2
Faculdad de Ciencias Biológicas, Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Mar-cos, Calle German Amezaga 375, Lima 15081, Peru
3
Faculdad de Administración de Empresas, Universidad de Tarapacá, Avenida 18 de Septiembre N° 2222, Arica 1000000, Chile
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Author to whom correspondence should be addressed.
Sci2026, 8(6), 122;https://doi.org/10.3390/sci8060122
This article belongs to the Special Issue Generative AI: Advanced Technologies, Applications, and Impacts
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Abstract

Artificial intelligence (AI) has become a central driver of transformation in innovation management, reshaping how organizations design strategies, develop offerings, and generate knowledge. This study examines how innovation management has evolved from the pre-ChatGPT era—characterized by analytics, automation, and decision support—to the post-ChatGPT period, marked by the widespread adoption of generative AI (GenAI) and human–AI collaboration. Using a structured literature review of Scopus-indexed studies published between 2020 and 2025, the paper identifies the following six dominant thematic dimensions of AI-enabled innovation management: strategic and business model innovation, product and service innovation, sustainability-oriented innovation, organizational agility and capabilities, human-centric innovation, and knowledge, learning, and research. The findings reveal a conceptual shift from efficiency-driven applications toward more creative, strategic, and collaborative uses of AI, with generative models acting as co-creators rather than mere analytical tools. The study contributes by synthesizing the fragmented literature into an integrative framework that captures this transition and by highlighting emerging research gaps, particularly in sustainability and human-centered innovation. Practical implications for managers and policymakers are discussed.

1. Introduction

In innovation management, artificial intelligence (AI) is increasingly applied to strategy, operations, organization, and research. Its capacity to process vast datasets, identify complex patterns, and automate routine tasks creates novel pathways to sustained competitive advantage.
How has generative AI (GenAI) transformed innovation management practices before and after ChatGPT? The distinction between the pre- and post-ChatGPT literature is meaningful because the launch of ChatGPT in late 2022 marked a paradigm shift in the accessibility, perception, and application of AI.
Before ChatGPT, AI in innovation management focused on analytics, prediction, and automation to boost efficiency and decision-making. These tools required expertise and aimed at data-driven optimization, automation, and knowledge management. Research then centered on integration issues, managerial adaptation, and readiness rather than creativity or collaboration.
Following the emergence of ChatGPT, GenAI is formally defined as a subset of AI based on deep learning models—such as Large Language Models (LLMs) and GPT-based systems—capable of producing novel content, including text, ideas, and designs, from natural language prompts. This development marked a shift from analytical tools to creative co-creators in innovation processes [1,2,3]. Unlike traditional AI, GenAI extends the role of AI beyond analytical functions, positioning it as a creative and strategic actor. Interest in AI grew as a co-creator of knowledge, enabling ethical, human–AI collaboration, and transforming innovation capabilities.
This evolution enables the following two interrelated concepts: human–AI collaboration refers to the operational interaction between humans and AI systems in the execution of innovation tasks, with AI functioning as analytical and creative support [1,4,5]. In contrast, human-centric innovation refers to a broader strategic and organizational perspective in which AI is aligned with human creativity, ethical considerations, and social value creation. Recent frameworks such as the Innovation Flow model [5] further advance this understanding by proposing structured processes for managing innovation through generative artificial intelligence in collaborative human–AI environments.
Human–AI collaboration is defined as symbiotic interactions where AI agents augment human decision-making and creativity through conversational interfaces and shared tasks [1,4], distinct from human-centric innovation, which prioritizes AI’s role in enhancing human creativity, entrepreneurial identity, and collaborative processes while reallocating cognitive effort from routine tasks to higher-value strategic activities [6,7]. Dividing the literature at this temporal boundary thus captures both chronological and conceptual transitions in AI’s innovation role.
Despite this transformation, the literature remains fragmented across six thematic di-mensions without an integrative framework connecting pre- and post-ChatGPT findings. This study addresses this gap through a structured literature review, synthetizing how the evolution from traditional AI systems to generative GenAI has reshaped innovation management across strategic and business model, product and service, sustainability, organizational agility and capabilities, human-centric, and knowledge, learning, and research dimensions.
The remainder of this paper is structured as follows. Section 2 presents the six main themes and theoretical foundations of AI in innovation management. It outlines how the field has evolved before and after the emergence of ChatGPT. Section 3 details the methodological approach adopted in this study, including the data collection process, inclusion criteria, and thematic analysis procedures. Section 4 reports the key results derived from the literature. It synthesizes findings across key AI impacts and themes. Section 5 offers critical discussion of the results. Finally, this study demonstrates that GenAI marks a conceptual turning point, evolving from efficiency tool to strategic co-creator across six innovation dimensions, addressing its theoretical and practical implications, as well as future research directions and limitations.

2. Literature Review

With the ongoing advancement of computer technology, digitalization and networking have emerged as prevailing trends in contemporary society [8]. AI has emerged as a transformative force reshaping business innovation across multiple disciplines. The literature reveals diverse impacts ranging from business model redesign to innovation offerings, sustainability-driven practices, organizational agility, human-centered innovation, and knowledge creation. To clarify and organize these findings, this study introduces six major themes.

2.1. Strategic and Business Model Innovation

A dominant stream of research highlights AI’s role in driving BMI and strategic transformation.
Effective use of AI drives not only process and product innovations but also transforms entire business models [9]. From its early stages as an emerging technology, AI has matured into a fundamental driver of business decision-making, supporting firms as they address environmental complexity [10].
SMEs have been a central focus, with AI-based self-assessment tools supporting their innovation capacity across strategy, portfolio management, and alliances [2]. Furthermore, AI enhances open innovation processes, extending both outside-in and inside-out innovation practices [11].
Sjödin et al. (2023) [12] examine the potential of AI to empower circular business model innovation (CBMI) for industrial manufacturers. In applied contexts, GPT-based real estate consulting tools illustrate how AI fosters new business solutions and entrepreneurial opportunities, such as property design and conversion options [13].
While not all pre-ChatGPT abstracts directly focused on business model innovation (BMI), the work on strategic foresight [14] and the rationalizing influence on decision-making [15] establishes the groundwork for AI’s role in strategic transformation.
Collectively, these findings demonstrate that AI plays a pivotal role in reshaping business strategies and models, especially for SMEs and in dynamic industries.

2.2. Product and Service Innovation

Another large body of work demonstrates AI’s impact on product and service development.
Emerging studies emphasize that AI is most widely applied in the development stage of the innovation process, with limited but growing application in ideation and commercialization stages [16]. AI supports ideation and idea screening, enhancing the creativity and innovation potential of concepts; however, its impact on generating top ideas is mixed and does not yet do a good job of selecting ideas [17].
In agriculture, AI prototypes such as the solar dryer dome for post-harvest coffee innovation demonstrate how AI can be embedded in physical technologies to increase efficiency and sustainability [18].
The role of AI in the innovation process is a central focus in the pre-ChatGPT literature. Brem et al. [19] introduce a framework in which AI plays dual roles as the originator and facilitator of innovation and examines its contribution to new product development. Truong and Papagiannidis [20] review how AI can augment innovation managers in their tasks across the four stages of the process.
The evidence positions AI as a catalyst for new product development and service refinement, especially where customer-centric and technology-driven innovation converge.

2.3. Sustainability-Oriented Innovation

Sustainability has become a critical lens in AI and innovation research. By leveraging AI, SMEs adopt energy-efficient processes, integrate renewable energy solutions, and implement effective carbon-reduction strategies [21]. AI further enables companies to directly and indirectly control emissions as they pursue net-zero targets [22].
AI promotes green innovation performance both directly and indirectly, through exploratory and exploitative supply chain innovation [3]. Studies consistently show that AI enables firms to proactively adapt to regulatory and market demands, manage resources, reduce waste, and align operations with sustainability imperatives [23]. AI also supports sustainable development goals (SDGs), for example, by improving sustainable management practices and promoting quality education [24].
This is the theme that receives the least explicit emphasis in the pre-ChatGPT literature reviewed. However, related underlying concepts, such as AI reducing the cost of laborious tasks and promising to lower uncertainty [20], are precursors to later AI applications focused on sustainability through efficiency, resource management, and waste reduction.
This literature highlights AI as a strategic enabler that drives eco-innovation and sustainability transformations across industries.

2.4. Organizational Agility and Capabilities

A recurrent theme in the literature highlights how AI enhances organizational agility by streamlining operations and improving decision-making.
Managerial studies suggest that AI enables decentralization and adaptability, transforming organizational structures and managerial decision-making [25]. Kosta et al. [26] found that AI technologies improve operational efficiency and increase customer engagement.
Pereira et al. [27] mention work that can be automatized; moreover, research into GenAI agents reveals their potential to reshape decision-making in organizational innovation performance [1].
The transformative role of LLM-powered models in advancing business intelligence and inventory management [28]. In supply chain management, AI reduces costs, enhances product forecasting accuracy, and increases overall efficiency [29].
This theme is strongly supported by the earlier literature. Ref. [30] focus on how AI reshapes companies and how innovation management is organized and consider the transformation toward the digital organization of innovation. Pietronudo et al. [15] explicitly find that AI is renewing the organization of innovation and has a rationalizing influence on decision-making in innovation management. Truong and Papagiannidis [20] position AI as an enabler that can boost innovation managers in dealing with tasks throughout the innovation process.
Collectively, these contributions position AI as a cornerstone of organizational agility and operational excellence.

2.5. Human-Centric Innovation

Although much of the literature focuses on efficiency and strategic transformation, several studies emphasize the human dimension of AI-enabled innovation.
Beyond strengthening firm performance, AI amplifies creativity, an essential dimension of entrepreneurial performance [31]. AI has been found to reshape entrepreneurial identity and creativity, challenging traditional notions of how entrepreneurs perceive themselves and their innovative capacities. Entrepreneurs are increasingly relying on AI to generate data-driven insights, anticipate future trends, and customize customer interactions [4].
Integration of design thinking and AI fosters human-centered innovation by promoting engagement, responsiveness, and collaboration [6]. Complementing these approaches, ref. [5] propose the Innovation Flow framework, which offers a practical structure for orchestrating human–AI collaborative innovation processes powered by GenAI. Case studies in emerging economies highlight how AI-enabled design thinking supports social innovation, bridges socio-economic gaps, and generates grassroots entrepreneurial opportunities [32].
By automating routine and repetitive work, AI creates greater opportunities for employees to engage in the creative aspects of their organization’s functions [7].
Although heavily focused on efficiency, the pre-ChatGPT works addressed implications for human workers and creativity: Haefner et al. [30] discuss the possibility of replacing human organizations and outline a framework for the extent to which AI can replace humans. Pietronudo et al. [15] explicitly list AI as one of the four main influences, augmenting creativity.
These findings underscore the need to balance technological advances with human values, culture, and creativity to ensure inclusive and meaningful innovation outcomes.

2.6. Knowledge, Learning and Research

Finally, AI is transforming the landscape of knowledge and research. Systematic reviews chart the evolving intersection of AI and a firm’s knowledge and innovation [33]. AI usefulness is also evaluated using a toolbox that draws on ten popular organizational theories [34].
Systematically reviewing a study provides a structured analysis of the evolving research dimensions of AI-enabled BMI, distinguishing between static and dynamic perspectives [35].
AI’s ability to handle data and reduce uncertainty—critical for knowledge and strategic decision-making—is a recurring topic in the pre-ChatGPT literature. Mühlroth and Grottke [14] present an AI-based model for strategic foresight and the automated detection of emerging topics and trends, functioning as an early warning system to identify emerging technologies. Truong and Papagiannidis [20] state that AI promises to lower uncertainty with more accurate predictions.
These contributions establish AI not only as a practical enabler but also as a subject of knowledge creation and theoretical inquiry.

Synthesis

The literature consistently affirms AI’s multi-faceted role in innovation, ranging from strategic and business model redesign to product and service innovation, sustainability transformations, organizational agility, human-centered processes, and knowledge creation.

3. Materials and Methods

3.1. Dataset Collection

The dataset was compiled from the Scopus database, known for its comprehensive coverage of the peer-reviewed academic literature in business and management. The search was conducted using the query TITLE-ABS-KEY (“artificial intelligence in business innovation management”), restricted to documents (articles) classified under Business, Management, and Accounting. The only conference included was a literature review—”The Impact of Artificial Intelligence on Innovation Management”. The primary focus was on the post-ChatGPT period (2023–2025), during which 209 records were initially identified. A supplementary search covering the pre-ChatGPT period (2021–2022) was also performed, yielding 65 articles that were included to contextualize the evolution of AI’s role in innovation management.
After screening for relevance and quality, 33 articles for the period 2023–2025 were retained for the final analysis, and 15 articles for the period 2020–2022. The number was lower due to lower production during this period, as shown in Table 1. Studies were excluded if they were not open access, if the role or benefits of AI were not clearly defined, if they were primarily attributed to other digital technologies, or if they discussed AI in only a general or peripheral manner without direct application to innovation management.
Table 1. Number of documents by year.

3.2. Thematic Analysis

The thematic coding procedure followed an inductive–deductive approach in the following three stages: open coding of all extracted findings, axial grouping of semantically similar codes (pattern identification), and selective coding to consolidate six recurrent clusters (cluster consolidation and validation). This process enabled the identification of coherent thematic domains that reflect how AI influences innovation management across strategic, creative, sustainability, operational, organizational, human-centric, and knowledge dimensions.

4. Results

Table 1 shows that until 2023 the number of articles kept steady, after which there is an exponential growth attaining 120 studies in 2025.
Table 2 synthesizes 33 post-ChatGPT studies (2023–2025) across the six thematic dimensions.
Table 2. Novel findings of AI in business innovation management: the post-ChatGPT literature (2023–2025).
The distribution of the reviewed papers across the six thematic areas reveals varying levels of scholarly attention. Strategic and Business Model Innovation and Organizational Agility and Capabilities were the most represented themes—the former reflecting strong academic interest in how AI reshapes strategic decision-making, business models, and open innovation, and the latter highlighting AI’s influence on managerial transformation and operational efficiency. Creativity, Ideation, and Entrepreneurial Transformation and Sustainability and Green Innovation received comparatively less attention, while Human-Centric Innovation is emerging as a promising yet underexplored area of research. Collectively, these clusters demonstrate that the post-ChatGPT literature deepens established domains of AI-enabled innovation while expanding the scope of cognitive collaboration between humans and intelligent systems. Table 2 summarizes the core impact of AI on innovation management as described in these pre-ChatGPT documents.

Thematic Distribution and Evolution Analysis

Quantitative analysis (according to Table 4) reveals a clear paradigm shift across the 48 studies reviewed. The pre-ChatGPT literature (n = 15 studies, Table 3) is dominated by efficiency-driven themes (67%), where the trend is leaded by organizational agility and capabilities (40%, n = 6). On the other hand, post-ChatGPT research (n = 33, Table 2) transitions toward strategic and creative themes powered by strategic and business model (30%, n = 10) and human-centric (18%, n = 6), with sustainability remaining underexplored (12%, n = 4).
Table 3. Sample of findings of AI in business innovation management: the pre-ChatGPT literature (up to 2022).
Table 4. Thematic evolution across AI eras (pre- vs. post-ChatGPT).
Qualitative synthesis demonstrates GenAI’s evolution from analytical enabler [20,30] to strategic co-creator [6,9]. Pre-ChatGPT studies emphasize AI reducing uncertainty and labor costs, while post-ChatGPT research reveals GenAI enabling business model reinvention, entrepreneurial identity transformation, and human–AI symbiosis across the innovation funnel.

5. Discussion

5.1. Strategic and Business Model Innovation

The 30% dominance of strategic and BMI of post-ChatGPT studies (Table 4) confirms GenAI’s evolution from analytical support to active business model reinvention [9,35]. This aligns with dynamic capabilities theory, where GenAI augments sensing [10], seizing [11], and transforming [2] activities. However, this concentration reveals research imbalance: while 10/33 post-ChatGPT studies address strategic and BMI, sustainability-oriented innovation receives only 12% attention, signaling missed opportunities for circular economy integration.

5.2. Product and Service Innovation

Ref. [17] document GenAI’s “mixed impact on top ideas and poor idea selection”, exposing fundamental limitations challenging the co-creator narrative. Algorithmic biases in training datasets systematically undervalue unconventional concepts, while lack of Human-In-The-Loop (HITL) validation fails to filter commercially unviable ideas [16]. These constraints confine GenAI to development-stage augmentation (15% of studies of post-ChatGPT studies, Table 4) rather than front-end creativity, necessitating hybrid HITL architectures for breakthrough innovation [19].

5.3. Sustainability-Oriented Innovation

Despite representing only 12% (n = 4) of post-ChatGPT studies (Table 4), sustainability reveals GenAI’s most critical trade-off. While enabling emissions reduction through supply chain optimization and net-zero strategies [21,22], training GPT-4 generates carbon emissions equivalent to 300 transatlantic flights, creating “carbon debt” that undermines net-zero claims [3]. This contradiction demands Life-Cycle Assessments (LCAs) and energy-efficient architectures to validate sustainability credentials beyond surface-level efficiency gains [23], highlighting why this remains underexplored despite regulatory pressures.

5.4. Organizational Agility and Capabilities

Studies of this dimension declines from 40% pre- to 21% post-ChatGPT (Table 4), transitioning from cost-reduction [30] to collaboration enabler [25]. GenAI democratizes analytics access but introduces governance risks: over-reliance on unverified outputs and ethical blind spots in decentralized decision-making [1]. These challenges remain undertheorized, requiring AI governance frameworks beyond technical proficiency.

5.5. Human-Centric Innovation

Human-centric innovation rises from 13 to 18% (Table 4), marking GenAI’s most transformative shift. Rather than replacement, GenAI reallocates cognitive effort from routine to creative tasks [7], reshapes entrepreneurial identity [4], and enables design thinking symbiosis [6]. However, longitudinal evidence on cultural and organizational impacts remains scarce, representing the framework’s most promising research frontier.

5.6. Knowledge, Learning and Research

AI’s dual role—as a research object [33] and a research instrument [34]—fundamentally reshapes innovation scholarship. Yet, methodological rigor lags: transparency standards, bias mitigation protocols, and validation procedures for AI-assisted bibliometric and foresight tools remain undeveloped [14]. This meta-level gap threatens the reliability of GenAI-driven research synthesis.

6. Conclusions

This study demonstrates that AI has fundamentally transformed innovation management, with GenAI marking a clear conceptual and practical turning point. By distinguishing between the pre- and post-ChatGPT literature, the paper shows how AI has evolved from a primarily analytical and efficiency-oriented tool into a strategic, creative, and collaborative actor within innovation systems. The identification of six thematic dimensions provides an integrative framework that consolidates fragmented research and clarifies how AI influences innovation across strategic, operational, human, and knowledge-based domains.
Theoretically, this review contributes a comprehensive categorization of AI’s roles in innovation management, highlighting that AI extends beyond efficiency gains to reshape business models and organizational capabilities. It also underscores emerging concepts (e.g., AI as co-creator) that theory should incorporate. Practitioners can leverage AI to accelerate prototyping, drive smarter supply chain innovations, and enable more agile business model experimentation. Research implications: our findings suggest that researchers should carefully ensure that proposed future research directions genuinely address unmet needs rather than reiterating known topics.
This study presents several limitations that should be acknowledged. First, the dataset is restricted to the Scopus database, which, although comprehensive, may exclude relevant contributions indexed in Web of Science, Google Scholar, preprint archives or domain-specific repositories. This database selection may introduce coverage bias, particularly in emerging or interdisciplinary AI research streams.
Second, the search query employed (“artificial intelligence in business innovation management”) may be overly restrictive, potentially omitting studies addressing AI-enabled innovation under alternative terminologies (e.g., digital innovation, machine learning applications, or intelligent systems). This limitation affects the recall and representativeness of the sample.
Third, the exclusion of non-open access articles introduces a systematic selection bias, favoring more accessible publications and potentially underrepresenting high-impact or subscription-based research.
Fourth, the study is subject to publication bias, as AI-related research—particularly in the post-ChatGPT period—tends to emphasize positive outcomes such as efficiency, creativity, and performance gains, while underreporting failures, negative externalities, or implementation challenges.
Fifth, the use of qualitative thematic analysis involves a degree of researcher subjectivity in coding and interpretation, despite the application of structured procedures.
Finally, the cross-sectional nature of the review limits the ability to capture temporal dynamics and causal relationships in the evolution of AI-enabled innovation processes.
Future research should examine how AI-enabled innovation evolves over time, particularly in relation to learning effects, capability development, and organizational adaptation across pre- and post-generative AI contexts. More empirical and mixed-method studies are also needed to explore human–AI collaboration within innovation teams, especially at the micro level of cognition, decision-making, and creativity. Another important avenue concerns the governance of AI-driven innovation systems, with greater attention to trust, transparency, accountability, and ethical risks. Comparative studies across industries, regions, and institutional contexts would help clarify how regulation, digital infrastructure, and innovation ecosystems shape AI adoption and outcomes. In addition, more research is needed on the negative and unintended consequences of AI in innovation, including bias amplification, overreliance on algorithmic outputs, and the possible homogenization of creative processes.
A promising avenue for future research concerns the role of AI in transforming useful knowledge—both propositional (scientific) and prescriptive (applied)—into sustainable innovation. Drawing on Joel Mokyr’s concept of the economics of useful knowledge, this line of inquiry could examine how AI systems facilitate the conversion of scientific discoveries into practical, value-creating innovations. Key research questions include: how does AI accelerate the translation of scientific knowledge into practical innovation? and which sectors or innovation ecosystems demonstrate greater efficiency in this conversion process? Addressing these questions would deepen the understanding of AI’s cognitive and systemic contributions to innovation and help identify the conditions under which digital intelligence most effectively transforms knowledge into sustainable economic and social outcomes.

Author Contributions

Conceptualization, J.J.C.P.; methodology, J.J.C.P., C.E.B.M. and C.R.V.; validation, J.J.C.P., C.E.B.M., R.E.A.P. and N.G.Q.Q.; formal analysis, J.J.C.P., C.E.B.M., R.E.A.P. and N.G.Q.Q.; investigation, J.J.C.P. and C.E.B.M.; data curation, J.J.C.P. and C.R.V.; writing—original draft preparation, J.J.C.P. and C.R.V.; writing—review and editing, J.J.C.P., C.E.B.M., R.E.A.P. and N.G.Q.Q.; visualization, J.J.C.P. and C.E.B.M.; supervision, J.J.C.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data supporting reported results are included in the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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