Understanding AI adoption in business: Benefits, barriers, and future adoption intention - An empirical study of Indian businesses

Introduction

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, reshaping how business organizations plan, operate, and compete (Chen et al., 2023) [1] . From automating repetitive processes to enabling predictive decision-making, AI is increasingly embedded into core business functions such as marketing, finance, human resource management, supply chain, and customer service. Organizations ranging from micro-enterprises to large multinationals are experimenting with AI-enabled tools to improve efficiency, reduce cost, and sustain competitive advantage, yet the pace and depth of this adoption vary considerably with organizational size, sector, financial capacity and, importantly, the underlying readiness of the organization to absorb technological change (Jöhnk et al., 2021; Schwaeke et al., 2024) [2,3] . The Technology Acceptance Model (TAM) proposed by Davis (1989) [4] explains that perceived usefulness and perceived ease of use drive the acceptance of information technology, while Rogers (2003) [5] , through the Diffusion of Innovations framework, argues that relative advantage, compatibility, complexity, trialability and observability jointly determine the speed of adoption. The Technology-Organization-Environment (TOE) framework of Tornatzky et al. (1990) [6] extends this view by emphasizing that adoption outcomes depend jointly on technological characteristics, organizational readiness, and environmental conditions; a recent meta-analysis of twelve empirical studies spanning 3,398 respondents confirms that seven of the eight TOE factors exert a statistically significant effect on organizational AI adoption across industries (Pinto et al., 2025) [7] . For continued or sustained use, Bhattacherjee's (2001) [8] Expectation-Confirmation Model (ECM) further proposes that post-adoption continuance is governed less by initial attitude than by confirmed expectations and structural capability, a mechanism recently re-confirmed in AI-specific continuance contexts where confirmation, perceived usefulness and satisfaction jointly predict sustained use (Guo et al., 2026) [9] . Taken together, these frameworks suggest that a favourable attitude toward AI is necessary but not sufficient: organizations must also build the infrastructure, skills, culture and policy required to sustain AI use, a distinction Gallivan (2001) [10] captures as the difference between the decision to adopt a technology and its subsequent assimilation into routine practice. Empirical work supports this view across business functions. Chen et al. (2023) [1] linked firm-level AI adoption intensity to improved marketing performance; Smith et al. (2024) [11] found AI particularly suited to structured, data-intensive finance tasks, a conclusion reinforced by Roos et al. (2025) [12] , who identify suitable IT infrastructure, high-quality data, regulatory compliance and workforce upskilling as prerequisites for AI adoption in accounting functions such as invoice processing, anomaly detection and financial forecasting; Agarwal (2022) [13] reported expected gains in recruitment efficiency from AI use in human resource management; and Dora et al. (2022) [14] and Hangl et al. (2022) [15] identified organizational, technical and social factors shaping AI adoption in supply chains. At the organizational level, Jöhnk et al. (2021) [2] demonstrated that a positive attitude toward AI does not by itself guarantee readiness, and Dreyling et al. (2024) [16] proposed that AI adoption unfolds through discrete maturity stages requiring corresponding investment in infrastructure and governance. This staged conceptualization is echoed by Kalleparambil et al. (2025) [17] , who developed a tailored organizational-readiness model specifically for resource-constrained firms, arguing that AI readiness must be assessed and built dimension-by-dimension rather than treated as a single attitudinal threshold. Li et al. (2024) [18] linked technological readiness to willingness to use AI, while Binsaeed et al. (2023) [19] identified knowledge-sharing as an important enabler of adoption, and Lee et al. (2023) [20] confirmed that AI implementation affects organizational, technological and people-related dimensions simultaneously. Extending the TOE lens to small and medium enterprises specifically, Sánchez et al. (2025) [21] integrate TOE with Diffusion of Innovations constructs to show that compatibility, complexity tolerance, and data competence jointly determine whether SMEs progress beyond early experimentation, while ul Haq et al. (2025) [22] demonstrate, in a Pakistani manufacturing sample, that AI adoption mediates the effect of technological readiness and leadership vision on firm performance. A recent bibliometric and systematic review of the SME AI-adoption literature by Yesuf et al. (2025) [23] similarly converges on three dominant thematic clusters, AI-driven innovation, entrepreneurial decision-making, and adoption barriers/enablers centred on organizational readiness, digital infrastructure, human capital and financial capability that closely mirror the constructs examined in the present study. Despite these benefits, AI adoption continues to be constrained by well-documented barriers. The OECD (2025) [24] and Gartner (2019) [25] identify cost, skill shortages, and difficulty finding appropriate use cases as recurring obstacles, particularly for small and medium enterprises; AICPA et al. (2026) [26] report persistent gaps in AIskilled talent and governance preparedness between early adopters and laggards; and, in the Indian context specifically, The Dialogue (2026) [27] identifies data-privacy concerns, limited technical expertise, and integration difficulties as significant barriers. A large-scale systematic review of 106 peer-reviewed studies similarly finds that compatibility, infrastructure, knowledge, resources, culture, competition, regulation and ecosystem support constitute recurring TOEbased clusters of enablers and inhibitors across the SME literature (Schwaeke et al., 2024) [3] . Focusing specifically on Indian micro, small and medium enterprises, Ganguly et al. (2025) [28] apply the Best-Worst Method to rank and interlink adoption barriers, finding that lack of top-management support and resistance to change function as organizational bottlenecks that, in turn, amplify technologyand environment-related barriers a pattern of interacting, rather than isolated, constraints that is highly consistent with the structural gap examined in the present study. Resistance to organizational change further compounds these constraints, though Li et al. (2023) [29] suggest its negative effects can be mitigated through leadership and highperformance work systems. In agriculture-adjacent sectors, Mishra et al. (2023) [30] similarly found implementation cost, infrastructure shortage and lack of standardization to be leading challenges, while Hirtranusi et al. (2026) [31] confirm, through a systematic review grounded in the TOE framework, that organizational readiness for AI is inherently multidimensional. India is undergoing rapid digital transformation, supported by improving infrastructure and growing AI literacy, yet there remains limited primary survey evidence that jointly examines, within a single Indian business sample, the ranked applications of AI, the distribution of businesses across adoption stages, the empirically measured magnitude of future intention, and the ranked barriers to adoption. Most recent contributions either analyse SMEs qualitatively across multiple countries (Schwaeke et al., 2024; Sánchez et al., 2025) [3,21] , model firm performance rather than continuance intention (ul Haq et al., 2025) [22] , or focus narrowly on barrier ranking without linking barriers to a measured attitude-capability gap (Ganguly et al., 2025) [28] . None of these studies simultaneously quantifies the numerical gap between perceived benefit and organizational readiness and tests which construct more strongly predicts continuance intention within the same sample the specific empirical contribution this study offers. Building on this gap, the present study was designed with four specific objectives: (i) to study the major applications of AI used by businesses; (ii) to examine the level of AI adoption among businesses; (iii) to study the future (continuance) intention of businesses to use AI, including whether perceived benefit or organizational readiness more strongly predicts this intention; and (iv) to study the constraints of using AI in business.

Materials and Methods

Research design and approach

This study used a cross-sectional, quantitative survey design with both descriptive and correlational analysis. The descriptive analysis covered the applications of AI, the level of AI adoption, and the constraints to AI use through frequency and percentage analysis, The correlational analysis examined the relationship between perceived benefit, organizational readiness, and continuance intention through meanscore/ranking analysis supported by Pearson's correlation analysis. The overall design draws on the Technology-Organization-Environment framework (Tornatzky et al., 1990) [6] and the Expectation-Confirmation Model (Bhattacherjee, 2001) [8] as guiding theoretical lenses, consistent with recent TOE-based empirical designs used to study AI adoption in SME and business-survey contexts (Pinto et al., 2025; Sánchez et al., 2025; ul Haq et al., 2025) [7,21,22] .

Population, sampling and sample size

The study population included businesses that were evaluating or already using AI, ranging from businesses with no adoption to those with organization-wide adoption. Data were collected through a structured questionnaire survey administered to 100 business respondents (n = 100), with responses anonymized and recorded using a respondent identifier (R001-R100).

Data collection

Primary data were collected using a structured, self-administered questionnaire covering respondents' views of AI benefits, organizational readiness, adoption level, future intention, and constraints to adoption. Data were collected in Pune City, Maharashtra, between 17 April 2026 and 16 May 2026. Secondary data were drawn from peerreviewed journal articles and institutional survey reports (OECD, Gartner, AICPA and CIMA) relating to AI adoption in business, as detailed in the literature reviewed above.

Instrument and measurement scale

The questionnaire contained 25 items, including 5-point Likert-scale items (1 = Strongly Disagree to 5 = Strongly Agree), single-choice items, and multi-select items. Attitudinal and perception-based items were measured on the 5-point Likert scale, while single-choice and multi-select items used nominal/ordinal categories appropriate to each question (e.g., AI adoption stage, biggest barrier to adoption, likeli hood of increasing AI use). The item structure was informed by constructs used in recent TOE-and ECM-based AI-adoption instruments (Bhattacherjee, 2001; Tornatzky et al., 1990; Pinto et al., 2025) [8,6,7] , adapted to the applications, adoption-stage, and barrier categories most frequently reported in the Indian and international SME literature (Ganguly et al., 2025; Sánchez et al., 2025) [28,21] .

Constructs of the study

Three main constructs were used for the correlation analysis. Perceived Benefit was computed as the mean of four items covering efficiency, decision-making speed, productivity, and overall business efficiency. Organizational Readiness was computed as the mean of six items covering infrastructure, employee skills, overall preparedness, organizational culture, policies/processes, and employee willingness to learn. Continuance Intention, the dependent construct, was computed as the mean of two items measuring intention to continue using AI and likelihood of recommending AI to other businesses.

Statistical analysis

Data on the major applications of AI used by businesses, the level of AI adoption among businesses, and the constraints of using AI in business were analysed using frequency and percentage analysis. For the future (continuance) intention of businesses to use AI, mean scores were computed for the Continuance Intention items and ranked, and construct-level means and standard deviations were computed for Perceived Benefit, Organizational Readiness, and Continuance Intention. Pearson's product-moment correlation coefficient (r) was computed to examine the strength and direction of association between Perceived Benefit, Organizational Readiness, and Continuance Intention, and the resulting matrix is additionally visualised as a correlation heat-map (Figure 6) to aid interpretation. Mean Organizational Readiness was further cross-tabulated against AI adoption stage to examine whether readiness varies systematically with adoption maturity. Correlations are reported as descriptive associations consistent with the cross-sectional design and do not imply causation, in line with recent TOE-based survey studies that similarly report correlational rather than causal evidence (Pinto et al., 2025; ul Haq et al., 2025) [7,22] . All descriptive and correlational computations were performed using standard spreadsheet and statistical software, and all figures reported were cross-verified against the underlying respondent-level survey dataset (n = 100) prior to reporting.

Results

Major applications of AI used by businesses

Table 1 presents the major functional areas in which AI is applied by the surveyed businesses, based on a multi-select item; consequently, percentages do not sum to 100. Supply chain and logistics was the most widely reported application (40%), followed closely by sales forecasting and human resource management (36% each). Finance and accounting and production/operations were each reported by 30% of respondents, followed by data analysis and decision-making (27%), customer service (25%), and marketing and advertising (23%), which ranked last. The concentration of AI use in supply chain and logistics, sales forecasting, and human resource management indicates that businesses currently perceive the greatest value from AI in operational and managerial functions rather than customer-facing functions such as marketing and customer service. This pattern aligns with Dora et al. (2022) [14] , who identified organizational readiness and management support as critical success factors in food-supply-chain AI adoption, and with Agarwal (2022) [13] , who linked HR-focused AI adoption to expected gains in recruitment efficiency.

Level of AI adoption among businesses

Table 2 shows that partial adoption in some functions was the most common adoption stage (27%), followed by exploring/evaluating (22%). Full adoption across the organization and pilot projects were each reported by 20% of businesses, while 11% reported no adoption. In total, 89% of businesses reported some level of AI engagement. To understand adoption maturity more clearly, mean Organizational Readiness was cross-tabulated against adoption stage (Table 3). Readiness rose monotonically from 2.88 (no adoption) to 2.91 (exploring/evaluating), 3.33 (pilot projects), 3.93 (partial adoption), and 4.02 (full adoption). This pattern is consistent with the TOE framework (Tornatzky et al., 1990) [6] and with Dreyling et al. (2024) [16] , and suggests that deeper organizational readiness accompanies, and is likely necessary for, advancement to more mature adoption stages rather than adoption maturity being driven by attitude alone.

IV

Future (continuance) intention to use AI

Continuance Intention was measured through two items: intention to continue using AI, and likelihood of recommending AI to other businesses. The recommend-AI item (mean = 4.01) ranked ahead of the continue-using item (mean = 3.64), yielding an overall Continuance Intention mean of 3.83 (an intention index of 76.6%). Table 5 shows that Organizational Readiness correlated more strongly with Continuance Intention (r = 0.42, moderate positive) than did Perceived Benefit (r = 0.31, weak-to-moderate positive), while Perceived Benefit and Organizational Readiness were themselves positively correlated (r = 0.61). This finding is consistent with Bhattacherjee's (2001) [8] Expectation-Confirmation Model and with Jöhnk et al. (2021) [2] , both of which indicate that post-adoption continuance is driven more by structural, confirmed capability than by initial attitude. In addition, 75% of respondents indicated they were likely or very likely to increase AI use in the next two to three years, while only 12% considered this unlikely.

Constraints of using AI in business

High cost of implementation was the leading constraint, reported as the single biggest barrier by 25% of businesses (Table 6). Data privacy and security concerns ranked second (19%), followed by resistance to change (17%), lack of skilled personnel (14%), lack of management support (13%), and inadequate IT infrastructure (12%).

Notably, concern about data privacy and security was far more pervasive than its 19% ranking suggests: 55% of all respondents reported being very or extremely concerned about data privacy and security, indicating that this concern cuts across the sample rather than being confined to a minority, and persists even among businesses at higher levels of adoption. These findings align with the OECD (2025) [24] and Gartner (2019) [25] , both of which identify cost and skill-related barriers as leading obstacles to enterprise AI adoption, and with The Dialogue (2026) [27] , which similarly found data-privacy and security concerns to be a leading barrier among Indian businesses. The third-ranked position of resistance to change is consistent with Li et al. (2023) [29] , who identified employee resistance as a distinct barrier mitigable through lead-ership and work-system interventions. Using an independently collected sample of Indian MSMEs and the Best-Worst Method rather than simple frequency ranking, Ganguly et al. (2025) [28] similarly place cost-and management-related barriers ahead of purely technological ones and, importantly, show that these barriers are interlinked rather than independent a structural insight that helps explain why, in the dataset used in this study, data-privacy concern remains widespread (55%) even though it was named the single biggest barrier by only 19% of respondents: it appears to function as a background constraint that compounds the effect of other, more visibly ranked barriers rather than acting in isolation. The association between adoption barriers and adoption stage was examined but was not found to be statistically significant in the underlying dataset and is therefore reported descriptively.

Discussion

The results indicate that businesses are attitudinally ready for AI but remain organizationally behind the central attitude-capability gap this study set out to examine. Businesses reported high perceived benefit (3.74) and high continuance intention (3.83), yet organizational readiness (3.49) lagged both, and it was organizational readiness rather than perceived benefit that more strongly predicted continuance intention (r = 0.42 versus r = 0.31). This pattern is consistent with the Expectation-Confirmation Model (Bhattacherjee, 2001) [8] , which holds that sustained use is governed by confirmed, structural capability rather than initial enthusiasm, and with the TOE framework (Tornatzky et al., 1990) [6] , which frames adoption outcomes as jointly determined by technological, organizational and environmental conditions. It also corroborates Jöhnk et al. (2021) [2] , who found that a favourable attitude toward AI does not, by itself, guarantee organizational readiness, and Dreyling et al. (2024) [16] , whose maturity-stage conceptualization is directly reflected in the monotonic rise of readiness scores from 2.88 at no-adoption to 4.02 at full adoption observed here. The magnitude of the readiness-continuance association (r = 0.42) is also broadly consistent with Guo et al.'s (2026) [9] recent structural-equation evidence that confirmation and perceived usefulness proxies for realised, rather than merely anticipated, capability are stronger predictors of AI continuance intention than initial attitude, reinforcing the view that the dataset used in this study's 0.25point benefit-readiness gap is a meaningful early-warning indicator rather than a statistical artefact. The concentration of AI applications in supply chain and logistics, sales forecasting, and human resource management suggests that businesses currently derive the greatest perceived value from AI in operational and managerial functions rather than customer-facing activities. This is consistent with Dora et al. (2022) [14] and Hangl et al. (2022) [15] on supply-chain AI adoption, and with Agarwal (2022) [13] on HR-focused adoption, and may reflect the relatively structured, data-intensive nature of these functions, echoing Smith and Ayele's (2024) [11] observation that AI is best suited to structured tasks while human judgment remains important elsewhere. The comparatively lower ranking of marketing and customer service is noteworthy given field-experimental evidence that generative AI conversational assistants can raise customer-support agent productivity substantially, particularly for less-experienced staff (Brynjolfsson et al., 2025) [32] ; this divergence suggests that the productivity gains documented in large, technologically mature firms have not yet been widely realised or perceived by the smaller and mid-sized businesses in the sample used in this study, consistent with Ortal and Magpantay's (2025) [33] finding that generative AI chatbot adoption in customer support depends on service-quality and service-satisfaction determinants that resource-constrained organizations may not yet have in place. This functional concentration also mirrors Sánchez et al.'s (2025) [21] observation that SMEs internationally tend to favour simpler, off-the-shelf AI solutions targeted at operational efficiency and customer-service automation over more strategically ambiguous, judgment-intensive functions, suggesting that the pattern observed in the Indian sample examined in this study reflects a broader, cross-national tendency rather than a sector-specific idiosyncrasy. The persistence of high implementation cost, data-privacy concern, and resistance to change as leading constraints regardless of adoption maturity supports the view, advanced by the OECD (2025) [24] , Gartner (2019) [25] , and The Dialogue (2026) [27] , that these barriers are structural rather than transitional. The finding that 55% of respondents reported high data-privacy concern, considerably exceeding its 19% ranking as the single biggest barrier, indicates that privacy concern operates as a pervasive undercurrent across the sample rather than a decisive constraint for only a subset of businesses. This aligns with Mishra et al. (2023) [30] , who similarly identified cost and infrastructure shortages as dominant, cross-cutting barriers in an Indian agri-business context, and reinforces Hirtranusi et al.'s (2026) [31] conclusion that organizational readiness for AI is inherently multidimensional, requiring alignment across infrastructure, competencies, and governance rather than resolution through any single intervention. Most directly, the barrier ranking obtained here cost, followed by privacy/security, followed by resistance to change closely parallels the interlinked barrier structure reported by Ganguly et al. (2025) [28] among Indian MSMEs, who found using the Best-Worst Method that lack of top-management support and change resistance function as upstream organizational bottlenecks that intensify downstream technological and environmental barriers; this convergence across two independently collected Indian business samples strengthens confidence that the barrier hierarchy identified in the present study is a genuine, generalizable feature of the Indian business environment rather than a sampling artefact. It is also consistent with the broader cross-country evidence assembled by Schwaeke et al. (2024) [3] , whose systematic review of 106 studies similarly clusters cost, infrastructure, knowledge and cultural resistance as recurring TOE-based barrier categories across SME contexts worldwide. Overall, the findings suggest that AI adoption strategy should shift emphasis from cultivating positive attitudes which, at 3.74, are already comparatively strong toward deliberately closing the 0.25-point organizational-readiness gap through investment in infrastructure, staff training, data governance, and change management, consistent with Li et al.'s (2023) [29] finding that leadership and work-system interventions can mitigate resistance to change. This recommendation aligns with Kalleparambil and Akoum's (2025) [17] tailored readiness model, which argues that resource-constrained firms should assess and build readiness dimension-by-dimension (technological, organizational, and data-governance readiness) rather than treating a single favourable attitude score as evidence of preparedness, and with ul Haq et al.'s (2025) [22] finding that AI adoption's effect on firm performance operates through, rather than around, organizational readiness and leadership vision. Practically, businesses beginning or scaling their AI journey may benefit from sequencing adoption toward functions where AI currently delivers demonstrable value (supply chain, sales forecasting, HR) to build organizational momentum before extending AI into functions where perceived value and readiness are comparatively lower.

Theoretical and practical implications

Theoretically, this study contributes to the technology-adoption literature by empirically testing, within a single Indian business sample, the relative predictive strength of perceived benefit versus organizational readiness a comparison that recent TOE-based studies (Pinto et al., 2025; Sánchez et al., 2025; ul Haq et al., 2025) [7,21,22] have examined mainly through adoption or performance outcomes rather than through continuance intention specifically. By explicitly quantifying the gap between perceived benefit and organizational readiness (0.25 points) and linking it to differential correlation strength with continuance intention, the study offers a replicable, low-cost diagnostic that future TOE-and ECM-based studies could adopt to benchmark the attitude-capability gap across other sectors and regions. Practically, the findings imply that business managers and policymakers should treat AI adoption strategy as a readiness-building exercise rather than a technology-procurement exercise: phased-cost pilots and transparent data-governance policies directly target the two leading constraints identified here, structured change-management programmes address the third-ranked constraint (resistance to change), and sequencing adoption toward already high-value functions (supply chain, sales forecasting, HR) builds the organizational momentum that Dreyling et al. (2024) [16] and Kalleparambil et al. (2025) [17] identify as a precondition for progressing to full, organization-wide adoption.

Limitations

The findings should be considered in light of several limitations. First, the study relies on self-reported perceptions collected through a structured questionnaire rather than independently verified adoption behaviour, which may introduce social-desirability bias, particularly for organizational-readiness and continuance-intention items. Second, the sample of 100 respondents, drawn from a single survey instrument administered in one city, may not generalize across industries, firm sizes, or regions; larger, multi-region samples of the kind assembled by Schwaeke et al. (2024) [3] and ul Haq et al. (2025) [22] would strengthen external validity. Third, the cross-sectional design captures perceptions at a single point in time and can establish association but not causal direction between organizational readiness and continuance intention, a limitation shared by most TOE-based survey studies in this literature (Pinto et al., 2025) [7] and one that longitudinal designs are better positioned to resolve. Finally, the association between adoption barriers and adoption stage was examined but was not found to be statistically significant in the underlying dataset and should there-fore be interpreted with caution rather than generalized to other business contexts.

Conclusion

This study examined the applications, adoption maturity, continuance intention, and constraints associated with AI use among 100 Indian businesses currently evaluating or using AI. AI adoption was found to be concentrated in operational and managerial functions, particularly supply chain and logistics, sales forecasting, and human resource management, a pattern consistent with international SME evidence (Sánchez et al., 2025; Schwaeke et al., 2024) [21,3] . Although 89% of businesses reported some level of AI engagement and continuance intention was generally high (76.6%), organizational readiness consistently lagged behind perceived benefit, and it was organizational readiness not perceived benefit alone that more strongly predicted businesses' intention to continue using AI, corroborating recent TOE-and ECMbased evidence (Pinto et al., 2025; Guo et al., 2026) [7,9] . High implementation cost, data-privacy and security concerns, and resistance to change emerged as persistent, structural constraints that do not appear to resolve with adoption experience alone, and their ranking and interlinkage closely mirror independent evidence from Indian MSMEs (Ganguly et al., 2025) [28] . These findings confirm a measurable attitude-capability gap and suggest that sustained AI adoption depends less on cultivating enthusiasm than on the deliberate, phased building of organizational infrastructure, skills, and governance, consistent with tailored readiness models proposed for resource-constrained firms (Kalleparambil et al., 2025; ul Haq et al., 2025) [17,22] . Future research employing larger, multi-region, multi-instrument and longitudinal designs is needed to establish whether these relationships generalize across industries and to test the causal direction between organizational readiness and continuance intention.

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