What Drives AI Value in Organisations - The Central Role of Modernisation and Workflow Integration

Abstract

Despite substantial investment in artificial intelligence (AI), organisations continue to experience highly uneven returns, with many failing to scale beyond pilot implementations. This paper synthesises insights from HBR Analytic Services’ report What Drives AI Value: Why Modernization and Workflow Integration Matter with peer-reviewed literature in information systems, digital transformation, and socio-technical theory to explain the drivers of AI value creation. It argues that AI value is not primarily determined by algorithmic sophistication, but by three interdependent organisational conditions: (1) organisational readiness, including leadership capability and structural alignment; (2) workflow integration, whereby AI is embedded directly into operational decision-making processes; and (3) data infrastructure modernisation, enabling interoperability, scalability, and real-time data access. The analysis further shows that AI value emerges through recursive socio-technical transformation rather than linear technological adoption. The paper concludes that organisations that successfully scale AI are those that treat it as a systemic capability requiring coordinated change across technology, processes, governance, and organisational behaviour.

1. Introduction

Artificial intelligence (AI) has rapidly moved from experimental adoption to a central pillar of digital transformation strategies across industries. Yet despite widespread investment, organisations continue to report uneven and often disappointing returns from AI initiatives. Empirical studies suggest that while AI can significantly enhance productivity, decision-making speed, and innovation capacity, these benefits are not automatic and tend to cluster in firms with strong complementary capabilities (Brynjolfsson and McElheran, 2016; Ransbotham et al., 2020). This divergence has prompted growing academic and practitioner interest in understanding what actually drives AI value at scale.

The HBR Analytic Services report, What Drives AI Value: Why Modernization and Workflow Integration Matter (HBR Analytic Services, n.d.), argues that AI value creation depends less on the sophistication of models themselves and more on the degree to which organisations modernise their digital infrastructure and integrate AI into operational workflows. This perspective aligns with a broader body of research in information systems and digital transformation that emphasises AI as a socio-technical capability rather than a standalone technological artifact (Orlikowski, 1992; Vial, 2019).

From a theoretical standpoint, the challenge of realising AI value can be situated within long-standing observations about the productivity paradox of information technology. Early work by Brynjolfsson (1993) and subsequent studies highlighted that investments in IT do not translate directly into measurable productivity gains unless accompanied by complementary organisational change. This idea has been reinforced in the context of AI, where Brynjolfsson, Rock and Syverson (2017) describe a “productivity J-curve” in which performance gains are delayed until firms reorganise processes, skills, and decision rights around new technologies.

More recent empirical research further supports the claim that organisational readiness is a key determinant of AI success. For example, McAfee and Brynjolfsson (2012) show that data-driven decision-making is strongly associated with improved performance, but only when firms restructure workflows to ensure that data insights directly influence operational decisions. Similarly, Agarwal et al. (2010) argue that IT value is contingent on organisational capabilities such as process integration, managerial coordination, and cross-functional alignment, all of which become even more critical in AI-enabled environments.

In parallel, digital transformation scholarship has increasingly emphasised the importance of infrastructure modernisation. Westerman, Bonnet and McAfee (2014) argue that digital maturity depends on the integration of digital technologies into both customer-facing and internal operational processes, requiring firms to modernise legacy systems and data architectures. Vial (2019), in a comprehensive systematic review of digital transformation research, similarly concludes that value creation arises from the interplay between technological change, organisational structure, and value generation mechanisms, rather than from technology deployment alone.

Within this context, AI introduces additional complexity because it is fundamentally dependent on high-quality data pipelines, interoperable systems, and continuous feedback loops embedded in operational environments (Davenport and Ronanki, 2018). Without modernised infrastructure, AI systems often remain confined to pilot projects or isolated analytics functions, limiting their ability to influence real-time decision-making. This helps explain the frequent observation that organisations become stuck in “pilot purgatory,” where promising proofs of concept fail to scale into enterprise-wide capabilities (Ransbotham et al., 2020).

Taken together, these strands of literature converge on a consistent conclusion: AI value is not primarily a function of algorithmic advancement but of organisational transformation. The HBR Analytic Services report reinforces this view by emphasising that modernisation of data systems and deep integration of AI into workflows are the primary mechanisms through which value is realised (HBR Analytic Services, n.d.). This paper builds on that insight by situating it within established academic research on digital transformation, socio-technical systems, and IT value creation, arguing that AI should be understood as an embedded organisational capability rather than a discrete technological innovation.

2. AI Value as a Function of Organisational Readiness

A growing body of research in information systems and digital transformation suggests that the value derived from artificial intelligence (AI) is fundamentally contingent on organisational readiness. Rather than being an inherent property of AI technologies, value emerges from the alignment between technological capabilities and the organisational context in which they are deployed (Orlikowski, 1992; Vial, 2019). This section elaborates this argument by integrating insights from the HBR Analytic Services report What Drives AI Value: Why Modernization and Workflow Integration Matter (HBR Analytic Services, n.d.) with peer-reviewed literature on IT value, dynamic capabilities, and socio-technical systems.

The central proposition advanced by the HBR report is that organisations realise significantly higher AI value when they possess modernised digital infrastructure and when AI systems are embedded directly into end-to-end business workflows. This aligns closely with the notion that IT value is not realised through investment alone, but through complementary organisational changes that enable technologies to be effectively absorbed and operationalised (Brynjolfsson and Hitt, 2000; Bharadwaj, 2000). In this sense, organisational readiness functions as a mediating condition between AI deployment and performance outcomes.

Empirical research on IT productivity consistently supports this view. Brynjolfsson and Hitt (2000) demonstrate that information technology investments are strongly associated with productivity gains only when firms simultaneously invest in organisational capital, including training, process redesign, and decentralised decision-making. Similarly, Aral, Brynjolfsson and Wu (2012) find that data-driven management practices improve firm performance, but only when embedded within structured decision processes that allow information to directly influence operational actions. These findings suggest that AI systems, which are even more dependent on data quality and process integration than traditional IT systems, are likely to exhibit similar or stronger complementarities.

A key dimension of organisational readiness is digital infrastructure modernisation. Legacy systems, fragmented data architectures, and siloed databases limit the scalability and reliability of AI systems by constraining data accessibility and interoperability (Davenport and Ronanki, 2018). Ross, Beath and Quaadgras (2013) argue that firms with strong “digital platforms” are better able to integrate data across functions, thereby enabling faster and more consistent exploitation of analytics and AI capabilities. This infrastructural foundation is essential for moving from isolated AI experiments to enterprise-wide deployment.

Organisational readiness also includes the ability to integrate AI into workflows in a way that meaningfully alters decision-making processes. Wamba et al. (2017), in a systematic review of big data analytics capabilities, show that firms achieve superior performance when analytics are embedded into operational routines rather than confined to separate analytical units. This is consistent with the HBR Analytic Services finding that AI generates the most value when it is “in the flow of work,” directly shaping decisions rather than producing insights that require manual interpretation or transfer across organisational boundaries (HBR Analytic Services, n.d.).

From a theoretical perspective, this embedding process can be understood through the lens of dynamic capabilities theory. Teece (2007) argues that competitive advantage arises from a firm’s ability to sense opportunities, seize them, and reconfigure resources accordingly. AI can enhance these capabilities, but only when organisations possess the structural flexibility to incorporate AI-generated insights into decision cycles. Without such flexibility, AI outputs remain underutilised, regardless of their technical sophistication.

Moreover, socio-technical systems theory provides a useful framework for explaining why organisational readiness is so critical. As Trist and Bamforth (1951) originally observed, and as later developed by Orlikowski (1992), organisational performance depends on the joint optimisation of social and technical systems. In the context of AI, this means that algorithms, data pipelines, decision rights, incentives, and workflows must be designed as an integrated whole. Failure to achieve this alignment often results in what has been described in practice as “pilot stagnation,” where promising AI initiatives fail to scale beyond experimental settings (Ransbotham et al., 2020).

Finally, recent large-scale empirical studies reinforce the importance of organisational maturity in AI adoption. Kane et al. (2019) find that digitally mature firms are significantly more likely to succeed in scaling AI across business functions, largely because they have already established the governance structures, data standards, and cross-functional collaboration mechanisms required for effective deployment. In contrast, less mature firms tend to deploy AI in isolated use cases that fail to generate system-wide impact.

In summary, the literature strongly supports the proposition advanced in the HBR Analytic Services report: AI value is a function of organisational readiness, particularly in terms of infrastructure modernisation and workflow integration. Rather than treating AI as a standalone technological intervention, firms must view it as an organisational capability that depends on the alignment of technical systems, data architecture, and operational processes.

3. Workflow Integration as the Primary Value Lever

A consistent finding across both practitioner reports and academic literature is that the principal determinant of artificial intelligence (AI) value is not the sophistication of models, but the extent to which AI outputs are embedded into organisational workflows. The HBR Analytic Services report, What Drives AI Value: Why Modernization and Workflow Integration Matter (HBR Analytic Services, n.d.), emphasises that AI delivers the greatest impact when it is operationalised “in the flow of work,” directly shaping decisions, actions, and task execution rather than functioning as a detached analytical capability. This section develops that argument by situating workflow integration within research on decision automation, digital work design, and socio-technical alignment.

At a foundational level, workflow integration determines whether AI systems remain advisory tools or become decision-shaping technologies. Early work in decision support systems already highlighted the importance of embedding analytical tools into organisational routines rather than treating them as standalone systems (Alter, 1977). However, the emergence of AI significantly raises the stakes of integration because AI systems increasingly perform not only descriptive analytics but also predictive and prescriptive functions that can directly trigger or automate actions.

Empirical evidence supports the claim that embedding analytics into workflows is strongly associated with performance gains. McAfee and Brynjolfsson (2012) demonstrate that firms adopting data-driven decision-making outperform peers, but only when organisational processes are redesigned to ensure that data insights are systematically incorporated into operational decisions. Without such redesign, data remains underutilised, regardless of its quality or availability. Similarly, Brynjolfsson, Hitt and Kim (2011) show that IT-enabled productivity improvements are significantly larger in firms that reorganise business processes around new technologies, reinforcing the importance of structural alignment between technology and workflow design.

The HBR report’s emphasis on workflow integration aligns closely with this evidence, particularly in its observation that AI value is often lost when outputs are delivered through disconnected dashboards or siloed analytics teams (HBR Analytic Services, n.d.). In such cases, AI generates insights but fails to influence behaviour at the point of decision-making. This phenomenon is consistent with what Marchand, Kettinger and Rollins (2000) describe as the “information management gap,” where organisations possess analytical capability but lack the processes to translate information into action.

From a process perspective, workflow integration involves redesigning task sequences so that AI outputs are embedded directly into operational steps. Davenport (2018) distinguishes between “automation of tasks” and “augmentation of decision-making,” noting that both require deep integration into business processes to be effective. In automation contexts, AI systems may directly execute decisions (e.g., fraud detection systems blocking transactions), while in augmentation contexts, AI informs human decisions at critical workflow junctures. In both cases, integration into the workflow determines whether AI has tangible operational impact or remains informational.

A growing body of research in business process management further reinforces this view. van der Aalst (2016) argues that modern organisations increasingly rely on data-driven process optimisation, where event logs and real-time analytics are used to continuously refine workflows. AI enhances this capability by introducing predictive intelligence into process flows, enabling dynamic adjustments rather than static process execution. However, such benefits only materialise when AI is tightly coupled with process execution systems such as enterprise resource planning (ERP) and customer relationship management (CRM) platforms.

The importance of integration is also evident in studies of digital platforms and enterprise architecture. Ross, Beath and Quaadgras (2013) emphasise that firms with modular, well-integrated digital platforms are better positioned to embed advanced analytics and AI across multiple business functions. In contrast, fragmented IT architectures create barriers to integration, limiting AI’s ability to influence end-to-end processes. This reinforces the HBR report’s claim that legacy system fragmentation is a key inhibitor of AI value realisation.

From a socio-technical perspective, workflow integration can be understood as the alignment of technical systems with organisational routines, roles, and incentives. Orlikowski (2000) argues that technology-in-practice emerges through the recurrent enactment of technology within work routines. In the context of AI, this implies that value is not embedded in the algorithm itself but in how it is routinely used, interpreted, and acted upon within workflows. If AI outputs are ignored, overridden, or bypassed, then no value is realised regardless of technical accuracy.

More recent empirical research supports this interpretation. Ransbotham et al. (2020) find that organisations achieving “AI scaling” are those that integrate AI into multiple stages of the value chain, particularly in operational and customer-facing processes. Conversely, organisations that confine AI to isolated experimentation environments struggle to achieve measurable business impact. This distinction highlights that integration is not merely a technical challenge but an organisational scaling problem.

Importantly, workflow integration also reshapes decision rights within organisations. As AI systems increasingly generate recommendations or automate decisions, firms must redefine who is responsible for final decisions and how exceptions are handled. This aligns with research by Mikalef and Gupta (2021), who argue that AI-driven value creation depends on organisational agility and the ability to reconfigure decision structures in response to technological capabilities. Without such reconfiguration, AI outputs may be ignored due to lack of trust, unclear accountability, or misaligned incentives.

In summary, the literature strongly supports the proposition that workflow integration is the primary lever through which AI generates value. The HBR Analytic Services report correctly identifies that AI must be embedded directly into operational processes to have meaningful impact (HBR Analytic Services, n.d.). Academic research further demonstrates that such integration requires not only technical connectivity but also redesign of processes, decision rights, and organisational routines. AI value, therefore, emerges not at the point of model development, but at the point where predictions and recommendations are translated into action within everyday work.

4. The Importance of Data Infrastructure Modernisation

A substantial body of research in information systems and digital transformation identifies data infrastructure as a foundational determinant of artificial intelligence (AI) value creation. While AI capabilities are often framed in terms of algorithms and modelling sophistication, both academic and practitioner literature consistently emphasise that the quality, accessibility, and integration of underlying data systems are the primary constraints on performance (George, Haas and Pentland, 2014; Kitchin, 2014). The HBR Analytic Services report, What Drives AI Value: Why Modernization and Workflow Integration Matter (HBR Analytic Services, n.d.), reinforces this perspective by arguing that organisations cannot scale AI without modernised, interoperable, and well-governed data infrastructures.

At a conceptual level, AI systems are only as effective as the data ecosystems that support them. Machine learning models require large volumes of high-quality, well-structured, and timely data to generate reliable outputs. However, many organisations continue to operate with legacy IT environments characterised by siloed databases, inconsistent data standards, and fragmented ownership structures. These limitations constrain the ability of AI systems to access and synthesise information across business functions, thereby reducing predictive accuracy and limiting generalisability (Davenport and Harris, 2007).

Empirical research supports the claim that data infrastructure maturity is strongly associated with analytics and AI performance. Bharadwaj, El Sawy and Pavlou (2013) argue that digital business strategy depends on the development of scalable digital platforms that integrate data across organisational boundaries. Such platforms enable firms to reuse data assets, reduce redundancy, and support cross-functional analytics applications. Similarly, Hsu et al. (2008) demonstrates that firms with higher data integration capability are more likely to derive competitive advantage from analytics investments, particularly in dynamic and uncertain environments.

The HBR report emphasises that modernisation is not simply a matter of upgrading hardware or migrating to cloud systems, but involves fundamentally re-architecting how data flows through the organisation (HBR Analytic Services, n.d.). This includes establishing unified data models, standardising data definitions, and enabling real-time data access across systems. These capabilities are critical for AI because many advanced applications—such as predictive maintenance, fraud detection, and personalised recommendation systems—require continuous streaming data rather than static datasets.

Cloud computing has emerged as a central enabler of data infrastructure modernisation. Armbrust et al. (2010) highlight that cloud platforms provide scalable storage and computing resources that significantly reduce the cost of data-intensive applications. More importantly, cloud architectures support the decoupling of data storage from application logic, enabling greater flexibility in how data is accessed and used by AI systems. This architectural shift is a key enabler of enterprise-wide AI deployment, as it allows organisations to centralise data governance while decentralising analytics capabilities.

However, infrastructure modernisation is not solely a technical challenge; it is also an organisational and governance issue. Khatri and Brown (2010) emphasise that effective data governance is essential for ensuring data quality, consistency, and accountability across the enterprise. Without clear governance structures, organisations often struggle with duplicated datasets, inconsistent definitions of key metrics, and unclear data ownership—all of which undermine AI model reliability. The HBR report similarly highlights governance fragmentation as a major barrier to scaling AI initiatives beyond pilot phases (HBR Analytic Services, n.d.).

From a socio-technical perspective, data infrastructure functions as the connective tissue between organisational units and technological systems. Orlikowski and Iacono (2001) argue that technology should be understood not as isolated artefacts but as embedded configurations of systems, practices, and organisational arrangements. In the context of AI, this implies that data infrastructure is not merely a technical layer but a socio-technical system that shapes how information is produced, shared, and interpreted across the organisation.

A key insight from the literature is that AI value creation depends on the degree of data interoperability across systems. Ross, Beath and Quaadgras (2013) describe the importance of establishing “data liquidity,” where information can flow seamlessly across organisational silos. Without such liquidity, AI systems are forced to operate on partial or inconsistent datasets, reducing their ability to generate robust predictions. This fragmentation is particularly problematic in large enterprises with multiple legacy systems acquired through mergers and acquisitions, where data integration challenges are both technical and organisational in nature.

Furthermore, modern AI systems increasingly rely on real-time data processing capabilities. Traditional batch-processing architectures are often insufficient for use cases such as autonomous systems, dynamic pricing, or real-time fraud detection. This shift has led to the adoption of streaming data architectures, which allow continuous ingestion and processing of data. Akidau et al. (2015) highlight that stream processing frameworks enable low-latency analytics, which are essential for time-sensitive AI applications. However, implementing such architectures requires significant investment in infrastructure modernisation and organisational capability development.

The importance of infrastructure modernisation is also closely linked to the concept of “technical debt.” Fowler (2009) defines technical debt as the long-term cost of maintaining suboptimal system architectures. In the context of AI, legacy systems that accumulate technical debt can severely limit an organisation’s ability to deploy scalable machine learning models. The HBR report implicitly reflects this issue by noting that organisations with outdated infrastructure often face high integration costs and slow deployment cycles, which reduce the overall return on AI investment (HBR Analytic Services, n.d.).

In summary, the literature strongly supports the argument that data infrastructure modernisation is a prerequisite for effective AI value creation. The HBR Analytic Services report correctly identifies that organisations must move beyond fragmented, legacy systems toward integrated, cloud-enabled, and well-governed data ecosystems. Academic research further demonstrates that such modernisation enhances data quality, interoperability, and scalability, all of which are essential for reliable and impactful AI deployment. Without this foundation, AI initiatives are likely to remain isolated, brittle, and difficult to scale across the enterprise.

5. Organisational and Managerial Implications

The preceding analysis suggests that artificial intelligence (AI) value is not primarily determined by algorithmic sophistication, but by organisational readiness, workflow integration, and data infrastructure modernisation. Taken together, these findings imply that AI should be understood less as a discrete technological investment and more as a catalyst for broad organisational transformation. The HBR Analytic Services report, What Drives AI Value: Why Modernization and Workflow Integration Matter (HBR Analytic Services, n.d.), emphasises that leaders must actively redesign systems, processes, and operating models to unlock AI value at scale. This section develops the managerial implications of this perspective by synthesising research on digital transformation leadership, organisational design, and socio-technical change.

A first implication is that AI adoption requires a shift in managerial mindset from technology deployment to capability building. Rather than treating AI as an IT initiative, organisations must frame it as a strategic transformation that reshapes how decisions are made and how work is executed. Westerman, Bonnet and McAfee (2014) argue that digitally mature firms succeed because leaders focus on integrating digital technologies into core business processes rather than isolating them within innovation units. This aligns with the HBR report’s argument that AI value emerges only when it is embedded across workflows and supported by modernised infrastructure (HBR Analytic Services, n.d.).

Leadership plays a central role in enabling this shift. Kane et al. (2015) find that digital transformation success is strongly associated with leadership commitment, particularly when senior executives actively champion cross-functional integration and organisational change. In the context of AI, this implies that executives must move beyond sponsorship to active involvement in redesigning decision rights, incentives, and operational structures. Without such engagement, AI initiatives are likely to remain fragmented and fail to scale beyond pilot projects.

A second implication concerns organisational structure and governance. AI systems often cut across traditional functional boundaries, requiring coordination between IT, operations, marketing, and analytics teams. This creates governance challenges related to accountability, data ownership, and decision authority. Khatri and Brown (2010) emphasise that effective data governance is essential for ensuring consistency, quality, and trust in organisational data assets. In AI contexts, governance must extend beyond data stewardship to include model oversight, ethical review, and lifecycle management of algorithmic systems.

The HBR report similarly highlights that fragmented governance structures are a major barrier to scaling AI across enterprises (HBR Analytic Services, n.d.). In many organisations, AI initiatives are distributed across departments without central coordination, leading to duplicated efforts, inconsistent standards, and limited interoperability. To address this, firms increasingly adopt hybrid governance models that combine centralised standards (for data and infrastructure) with decentralised experimentation (for use cases and applications), allowing both control and innovation.

A third implication relates to workforce transformation and skill development. AI adoption changes the nature of work by automating routine tasks and augmenting decision-making processes. This requires organisations to invest in reskilling and upskilling employees to work effectively alongside AI systems. Brynjolfsson and McAfee (2014) argue that technological progress increasingly complements rather than replaces human labour, but only when workers are equipped with the skills needed to interpret and act on machine-generated insights. Similarly, Raisch and Krakowski (2021) emphasise the importance of “complementarity thinking,” where human and machine capabilities are deliberately designed to enhance one another.

From a managerial perspective, this implies that workforce strategy must evolve in parallel with AI deployment. Organisations that fail to invest in human capability development risk creating a “capability gap,” where AI systems produce insights that employees cannot effectively interpret or operationalise. This gap significantly reduces the realised value of AI investments, regardless of technical performance.

A fourth implication concerns process redesign and organisational agility. AI systems are most effective when embedded within processes that are themselves adaptive and data-driven. Teece (2007) highlights that dynamic capabilities—particularly the ability to sense and seize opportunities and reconfigure resources—are essential for sustaining competitive advantage in rapidly changing environments. AI can enhance these capabilities by enabling real-time decision-making, but only if organisations are structurally flexible enough to incorporate AI outputs into operational workflows.

This has important implications for process ownership and design authority. Traditional process models, which are often rigid and hierarchical, may constrain the effective use of AI. Instead, organisations must adopt more modular and iterative process designs that allow continuous refinement based on AI-driven insights. van der Aalst (2016) shows that process mining and data-driven workflow optimisation can significantly improve efficiency, but only when organisations are willing to continuously adapt process structures.

A fifth implication relates to organisational culture and trust in AI systems. The successful adoption of AI depends not only on technical integration but also on the willingness of employees and managers to trust and act on AI-generated outputs. Davenport and Ronanki (2018) note that resistance to AI often arises when systems are perceived as opaque or when accountability structures are unclear. Building trust requires transparency in model design, explainability of outputs, and clear delineation of human and machine responsibilities.

Ransbotham et al. (2020) further find that organisations that successfully scale AI tend to invest heavily in change management and communication strategies that emphasise augmentation rather than replacement. This cultural framing is critical in reducing resistance and encouraging adoption of AI-enabled workflows. The HBR report reinforces this point by highlighting that AI value is maximised when users perceive it as embedded support within their daily work rather than as an external or disruptive system (HBR Analytic Services, n.d.).

Finally, there are strategic implications for how organisations evaluate AI investments. Traditional return-on-investment (ROI) frameworks may underestimate AI value because they fail to capture indirect benefits such as improved decision quality, faster cycle times, and enhanced organisational learning. Brynjolfsson, Hitt and Kim (2011) argue that productivity gains from IT often manifest in complementary improvements that are difficult to measure using conventional accounting metrics. As a result, managers should adopt broader evaluation frameworks that incorporate strategic flexibility, process efficiency, and capability development.

In summary, the managerial implications of AI adoption extend far beyond technology implementation. The literature strongly suggests that AI success depends on leadership commitment, governance redesign, workforce transformation, process agility, cultural adaptation, and expanded investment evaluation frameworks. The HBR Analytic Services report appropriately emphasises that AI value is realised only when organisations modernise infrastructure and embed AI into workflows (HBR Analytic Services, n.d.). Academic research further demonstrates that this requires a coordinated transformation of organisational systems, structures, and capabilities, rather than isolated technological deployment.

6. Discussion

The preceding sections collectively suggest a consistent and increasingly well-supported explanation for why artificial intelligence (AI) generates uneven value across organisations: value is structurally dependent on organisational readiness, data infrastructure modernisation, and workflow integration. The HBR Analytic Services report, What Drives AI Value: Why Modernization and Workflow Integration Matter (HBR Analytic Services, n.d.), synthesises this practitioner insight by arguing that AI impact is maximised when embedded into modernised systems and end-to-end workflows rather than deployed as isolated analytical capability. The academic literature broadly converges with this view, but it also introduces important nuances and tensions that merit closer examination.

A first key insight is that AI value creation should be understood as a socio-technical phenomenon rather than a purely technological one. This perspective is strongly supported by foundational work in information systems, which emphasises that outcomes arise from the interaction between technology, organisational structures, and human practices (Orlikowski, 1992; Orlikowski, 2000). In this framing, AI systems do not produce value autonomously; rather, value emerges through their embeddedness in organisational routines, decision processes, and governance structures. The discussion across Sections 2–5 reinforces this point by showing that infrastructure, workflows, and managerial practices are not secondary considerations but primary determinants of realised value.

However, while there is broad agreement on the importance of organisational context, the literature diverges on the degree to which AI requires radical organisational transformation versus incremental adaptation. One stream of research, aligned with the productivity paradox literature, argues that substantial organisational redesign is necessary before technology-related productivity gains materialise (Brynjolfsson and Hitt, 2000; Brynjolfsson, Rock and Syverson, 2017). This perspective implies that AI value is delayed and contingent on deep structural change. In contrast, other studies suggest that firms can realise incremental gains through more modular adoption strategies, particularly when deploying AI in narrowly defined tasks or decision domains (Davenport and Ronanki, 2018). This tension highlights an important boundary condition: the scope of AI deployment (narrow task automation versus enterprise-wide transformation) likely determines the extent of required organisational change.

A second area of synthesis concerns the role of data infrastructure as both an enabler and a constraint on AI value. As discussed in Section 4, modernisation of data systems is consistently identified as a prerequisite for scalable AI deployment (George, Haas and Pentland, 2014; Kitchin, 2014). Yet, the literature also suggests that infrastructure alone is insufficient. Bharadwaj, El Sawy and Pavlou (2013) argue that digital infrastructure must be coupled with organisational capabilities such as agility, governance, and strategic alignment. This introduces an important conceptual distinction between “digital readiness” and “digital capability”: the former refers to technical foundations, while the latter refers to the ability to operationalise those foundations effectively. The HBR Analytic Services report implicitly supports this distinction by emphasising that modernisation must be accompanied by workflow integration to generate value (HBR Analytic Services, n.d.).

A third theme emerging from the discussion is the centrality of workflow integration as the primary mechanism through which AI translates into measurable performance outcomes. The literature suggests that AI systems create value only when they alter the timing, location, or quality of decision-making within organisational processes (Alter, 1977; McAfee and Brynjolfsson, 2012). However, this raises a critical implementation challenge: integrating AI into workflows often requires reconfiguring decision rights and organisational boundaries. As Ransbotham et al. (2020) observe, organisations that successfully scale AI tend to redesign processes so that AI outputs are not merely advisory but embedded in operational execution. This reinforces the idea that workflow integration is not a technical integration problem alone, but a governance and behavioural change challenge.

Importantly, the discussion also highlights a potential paradox in AI adoption. While AI promises to enhance efficiency and decision quality, its value depends on organisational willingness to redistribute authority between humans and machines. This creates what Raisch and Krakowski (2021) describe as the automation–augmentation paradox: organisations must simultaneously leverage AI for automation while preserving human oversight to ensure accountability and adaptability. This tension is particularly relevant in high-stakes domains such as finance, healthcare, and logistics, where full automation may be neither feasible nor desirable.

Another key insight relates to heterogeneity in AI adoption outcomes across firms. Even among organisations with similar technological access, outcomes vary significantly due to differences in organisational design, leadership capability, and cultural readiness (Kane et al., 2019). This suggests that AI should not be treated as a uniform productivity enhancer but rather as a capability amplifier whose effects depend on pre-existing organisational conditions. In this sense, AI may widen performance disparities between firms rather than narrowing them, reinforcing concerns about digital inequality and competitive divergence.

At a broader theoretical level, the discussion suggests that existing models of IT value creation remain relevant but require extension to account for the unique characteristics of AI systems. Unlike traditional IT systems, AI systems are adaptive, probabilistic, and increasingly autonomous. This introduces additional complexity into integration processes, as outputs are not deterministic and may evolve over time. As a result, organisational readiness must now include not only structural and infrastructural factors, but also capabilities related to model monitoring, interpretability, and continuous learning.

The HBR Analytic Services report contributes to this evolving understanding by framing AI value as dependent on both modernisation and workflow integration, effectively bridging infrastructure-focused and process-focused perspectives (HBR Analytic Services, n.d.). However, the academic literature suggests that this relationship is dynamic rather than linear. For example, improvements in workflow integration may drive further infrastructure investment, while enhanced infrastructure may enable more sophisticated forms of workflow redesign. This recursive relationship implies that AI value creation is better understood as an iterative transformation process rather than a one-time implementation outcome.

In summary, the discussion reveals three central conclusions. First, AI value is fundamentally socio-technical and depends on the alignment of technology, organisation, and human practice. Second, organisational readiness—including infrastructure modernisation and workflow integration—is a necessary but not sufficient condition for value realisation. Third, AI adoption creates new organisational tensions, particularly around governance, decision rights, and human–machine complementarity. While the literature broadly supports the claims advanced in the HBR Analytic Services report, it also indicates that AI value creation is more complex, recursive, and uneven than linear adoption models suggest.

7. Conclusion

This paper set out to examine what drives AI value in organisations, using HBR Analytic Services’ report What Drives AI Value: Why Modernization and Workflow Integration Matter as a foundation and situating its claims within established academic literature. Across the preceding sections, a consistent conclusion emerges: AI value is not inherent in the technology itself but is produced through the alignment of organisational readiness, workflow integration, and modernised data infrastructure.

First, organisational readiness is a fundamental precondition for AI value creation. The literature shows that firms realise benefits from AI only when leadership commitment, governance structures, and organisational capabilities are sufficiently mature to support technological absorption. Without this readiness, AI initiatives tend to remain fragmented and confined to experimental contexts, limiting their strategic impact.

Second, workflow integration is the primary mechanism through which AI generates measurable value. AI systems create impact only when they are embedded into operational processes and directly influence decision-making. The evidence suggests that organisations achieve significantly higher returns when AI is integrated into end-to-end workflows rather than isolated in analytics functions or advisory dashboards. This requires not only technical integration but also redesign of decision rights, incentives, and process structures.

Third, data infrastructure modernisation provides the technical foundation upon which AI systems depend. Legacy systems, fragmented data architectures, and weak governance structures constrain scalability and reduce model reliability. Conversely, modern, interoperable, and well-governed data ecosystems enable AI to function effectively across organisational boundaries and support real-time, enterprise-wide deployment.

Importantly, the analysis demonstrates that these three dimensions are mutually reinforcing rather than independent. Infrastructure modernisation enables deeper workflow integration; workflow integration exposes further infrastructure limitations; and organisational readiness determines whether either can be effectively implemented. As a result, AI value creation should be understood as a recursive socio-technical transformation process rather than a linear implementation journey.

The study also highlights a broader theoretical implication: traditional models of IT value creation remain relevant but insufficient for explaining AI outcomes without extension. Unlike conventional information systems, AI systems are adaptive, probabilistic, and increasingly autonomous, introducing new challenges related to governance, trust, and decision authority. These characteristics intensify the importance of organisational design and amplify performance differences between firms with varying levels of digital maturity.

In conclusion, the findings suggest that AI does not generate value through deployment alone. Instead, value emerges when organisations systematically reconfigure their infrastructure, workflows, and governance structures to incorporate AI into everyday operations. Firms that fail to undertake this holistic transformation risk remaining stuck in cycles of experimentation without scalable impact, while those that succeed are likely to establish durable competitive advantage through deeply embedded AI capabilities.

8. References

Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R.J., Lax, R., McVeety, S., Mills, D., Perry, F., Schmidt, E. and Whittle, S. (2015) ‘The Dataflow model’, Proceedings of the VLDB Endowment, 8(12), pp. 1792–1803.

Agarwal, R., Sambamurthy, V. and Stair, R.M. (2010) ‘Research commentary: The evolving relationship between general and specific IT use: An empirical assessment’, Information Systems Research, 21(4), pp. 754–769.

Alter, S. (1977) ‘A taxonomy of decision support systems’, Sloan Management Review, 19(1), pp. 39–56.

Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I. and Zaharia, M. (2010) ‘A view of cloud computing’, Communications of the ACM, 53(4), pp. 50–58.

Aral, S., Brynjolfsson, E. and Wu, D.Y. (2012) ‘Three-way complementarities: Performance pay, data analytics, and information technology’, Management Science, 58(5), pp. 913–931.

Bharadwaj, A.S. (2000) ‘A resource-based perspective on information technology capability and firm performance’, MIS Quarterly, 24(1), pp. 169–196.

Bharadwaj, A., El Sawy, O.A. and Pavlou, P.A. (2013) ‘Digital business strategy: Toward a next generation of insights’, MIS Quarterly, 37(2), pp. 471–482.

Bostrom, R.P. and Heinen, J.S. (1977) ‘MIS problems and failures: a socio-technical perspective’, MIS Quarterly, 1(3), pp. 17–32.

Brynjolfsson, E. (1993) ‘The productivity paradox of information technology’, Communications of the ACM, 36(12), pp. 67–77.

Brynjolfsson, E. and Hitt, L.M. (2000) ‘Beyond computation: Information technology, organizational transformation and business performance’, Journal of Economic Perspectives, 14(4), pp. 23–48.

Brynjolfsson, E. and McElheran, K. (2016) ‘The rapid adoption of data-driven decision-making’, American Economic Review, 106(5), pp. 133–139.

Brynjolfsson, E., Hitt, L.M. and Kim, H.H. (2011) ‘Strength in numbers: How does data-driven decisionmaking affect firm performance?’, SSRN Working Paper.

Brynjolfsson, E., Rock, D. and Syverson, C. (2017) ‘Artificial intelligence and the modern productivity paradox’, NBER Working Paper No. 24001.

Brynjolfsson, E. and McAfee, A. (2014) The Second Machine Age. New York: W.W. Norton.

Davenport, T.H. (2018) The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Boston: MIT Press.

Davenport, T.H. and Harris, J.G. (2007) Competing on Analytics: The New Science of Winning. Boston: Harvard Business School Press.

Davenport, T.H. and Ronanki, R. (2018) ‘Artificial intelligence for the real world’, Harvard Business Review, 96(1), pp. 108–116.

Fowler, M. (2009) ‘Technical debt quadrant’, martinfowler.com.

George, G., Haas, M.R. and Pentland, A. (2014) ‘Big data and management’, Academy of Management Journal, 57(2), pp. 321–326.

HBR Analytic Services (n.d.) What Drives AI Value: Why Modernization and Workflow Integration Matter. Harvard Business Review.

Hsu, C.C., Kannan, V.R., Tan, K.C. and Leong, G.K. (2008) Information sharing, buyer-supplier relationships, and firm performance. International Journal of Physical Distribution & Logistics Management, 38(4), pp. 296–310.

Kane, G.C., Palmer, D., Phillips, A.N., Kiron, D. and Buckley, N. (2015) Strategy, Not Technology, Drives Digital Transformation. MIT Sloan Management Review and Deloitte.

Kane, G.C., Palmer, D., Phillips, A.N., Kiron, D. and Buckley, N. (2019) The Technology Fallacy. Cambridge, MA: MIT Press.

Khatri, V. and Brown, C.V. (2010) ‘Designing data governance’, Communications of the ACM, 53(1), pp. 148–152.

Kitchin, R. (2014) ‘Big data, new epistemologies and paradigm shifts’, Big Data & Society, 1(1), pp. 1–12.

Marchand, D.A., Kettinger, W.J. and Rollins, J.D. (2000) Information Orientation: The Link to Business Performance. Oxford: Oxford University Press.

McAfee, A. and Brynjolfsson, E. (2012) ‘Big data: The management revolution’, Harvard Business Review, 90(10), pp. 60–68.

Mikalef, P. and Gupta, M. (2021) ‘Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study’, Information & Management, 58(1), 103434.

Orlikowski, W.J. (1992) ‘The duality of technology’, Organization Science, 3(3), pp. 398–427.

Orlikowski, W.J. (2000) ‘Using technology and constituting structures: A practice lens for studying technology in organizations’, Organization Science, 11(4), pp. 404–428.

Orlikowski, W.J. and Iacono, C.S. (2001) ‘Desperately seeking the “IT” in IT research’, Information Systems Research, 12(2), pp. 121–134.

Raisch, S. and Krakowski, S. (2021) ‘Artificial intelligence and management: The automation–augmentation paradox’, Academy of Management Review, 46(1), pp. 192–210.

Ransbotham, S., Kiron, D., Gerbert, P. and Reeves, M. (2020) ‘Expanding AI’s impact’, MIT Sloan Management Review, 61(3), pp. 1–9.

Ross, J.W., Beath, C.M. and Quaadgras, A. (2013) ‘You may not need big data after all’, Harvard Business Review, 91(12), pp. 90–98.

Teece, D.J. (2007) ‘Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance’, Strategic Management Journal, 28(13), pp. 1319–1350.

Trist, E.L. and Bamforth, K.W. (1951) ‘Some social and psychological consequences of the longwall method of coal-getting’, Human Relations, 4(1), pp. 3–32.

Vial, G. (2019) ‘Understanding digital transformation: A review and a research agenda’, The Journal of Strategic Information Systems, 28(2), pp. 118–144.

van der Aalst, W.M.P. (2016) Process Mining: Data Science in Action. 2nd edn. Berlin: Springer.

Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R. and Childe, S.J. (2017) ‘Big data analytics and firm performance’, Journal of Business Research, 70, pp. 356–365.

Westerman, G., Bonnet, D. and McAfee, A. (2014) Leading Digital: Turning Technology into Business Transformation. Boston: Harvard Business Review Press.

Contact

Reach out via email for inquiries.

Email

Subscribe to newsletter

info@grcadvisory.ch

© 2025. All rights reserved.