Firms face an expanding set of digital transformation opportunities, including process automation, analytics platforms, customer experience systems, artificial intelligence applications, cloud migration, and ecosystem integration. These opportunities promise efficiency, growth, responsiveness, and innovation, but they also compete for limited financial, technical, managerial, and organizational resources. As a result, digital transformation is increasingly a problem of strategic selection rather than simply technological adoption. Many organizations struggle to distinguish between initiatives that are strategically essential, operationally attractive, technically fashionable, or politically sponsored. Without a structured prioritisation approach, firms may approve too many initiatives at once, spread scarce resources across weakly connected projects, and create initiative overload. This can produce fragmented execution, duplicated investment, capability strain, and weak alignment between digital spending and strategic objectives. This article develops the Digital Transformation Priority Framework as a practical decision framework for selecting and sequencing digital transformation initiatives. The framework evaluates initiatives through three pillars: strategic value, capability readiness, and implementation risk. It is designed to help managers make transparent prioritisation decisions when competing initiatives differ in expected value, organizational preparedness, and execution uncertainty. The article is based on a conceptual synthesis of peer-reviewed articles published across strategic management, information systems, project portfolio management, digital transformation, and decision sciences. It does not report new empirical data. Instead, it integrates existing research into a practical model that managers can use to structure judgment, compare alternatives, and communicate prioritisation decisions. The framework provides assessment criteria and scoring guidelines for each pillar, integrates the three assessments into a unified prioritisation logic, and demonstrates managerial application through a decision matrix. Five tables specify strategic value dimensions, capability readiness indicators, implementation risk categories, the integrated prioritisation model, and an example portfolio application. The article concludes that systematic prioritisation can improve resource allocation, reduce initiative fatigue, and ensure that digital transformation efforts are strategically focused and execution-ready.
Digital transformation has become a central managerial concern because firms increasingly rely on digital technologies to redesign business models, improve operations, engage customers, and renew organizational capabilities. Vial defines digital transformation as a process in which digital technologies trigger significant changes in organizational value creation, while Verhoef, Broekhuizen, Bart, Bhattacharya, Dong, Fabian, and Haenlein emphasize that digital transformation cuts across strategy, operations, customer interaction, and organizational design [1, 2]. This breadth creates a large and expanding menu of possible initiatives, from automation and analytics to digital platforms and new customer interfaces. The managerial problem is therefore not whether digital transformation matters, but which digital initiatives should receive priority under limited resources.
The difficulty of prioritisation arises because digital transformation initiatives vary in strategic relevance, capability requirements, and implementation uncertainty. Hanelt, Bohnsack, Marz, and Antunes Marante show that digital transformation requires coordinated strategic and organizational change, while Warner and Wäger argue that digital transformation depends on dynamic capabilities that evolve through ongoing strategic renewal [3, 4]. A firm may therefore face several attractive digital options, but each option may demand different levels of investment, managerial attention, technical infrastructure, and cultural adaptation. Without a decision framework, prioritisation may be driven by urgency, executive preference, vendor influence, or imitation rather than strategic reasoning.
Poor prioritisation can produce substantial organizational costs because digital transformation initiatives often compete for the same scarce resources. Chanias, Myers, and Hess demonstrate that digital transformation strategy-making requires difficult choices in pre-digital organizations, especially when legacy structures constrain strategic action [5]. Yeow, Soh, and Hansen similarly show that alignment with digital strategy is dynamic and requires continuous adjustment rather than isolated project approval [6]. When firms approve too many poorly aligned initiatives, they risk initiative overload, duplicated effort, conflicting implementation demands, and gradual strategic drift.
The objective of this article is to propose a rigorous yet practical decision framework that brings structure to digital transformation prioritisation. The Digital Transformation Priority Framework integrates three assessment pillars: strategic value, capability readiness, and implementation risk. This triadic logic is consistent with research showing that digital innovation involves both opportunity creation and organizational constraint, as illustrated by Svahn, Mathiassen, and Lindgren in their analysis of competing concerns in incumbent firms [7]. The framework therefore treats prioritisation as a disciplined managerial process that links strategic intent with execution capacity and risk awareness.
Digital transformation prioritisation is challenging because digital initiatives are rarely comparable on a single dimension. Nambisan, Lyytinen, Majchrzak, and Song argue that digital innovation reshapes innovation management by creating new forms of openness, recombination, and technology-enabled experimentation [8]. This means that one initiative may promise operational efficiency, another may create new customer value, and another may strengthen platform or data capabilities. A prioritisation framework must therefore evaluate different kinds of contribution without reducing all digital projects to short-term financial return.
Existing research also shows that firms differ substantially in their ability to execute digital initiatives. Li, Su, Zhang, and Mao show that digital transformation in small and medium-sized enterprises depends on capabilities, entrepreneurial action, and organizational adaptation [9]. Wessel, Baiyere, Ologeanu-Taddei, Cha, and Jensen further distinguish digital transformation from traditional IT-enabled organizational transformation, indicating that digital change often alters identity, routines, and value creation rather than merely improving existing processes [10]. Prioritisation must therefore consider readiness, because an initiative with high strategic value may fail if the organization lacks the skills, data maturity, governance, or change capacity to implement it.
Another difficulty is that traditional IT portfolio management and roadmapping approaches often emphasize project selection, cost control, or technological sequencing without fully capturing the strategic and organizational complexity of digital transformation. Kohli and Melville argue that digital innovation requires synthesis across technology, organizations, and value creation, while Canhoto, Quinton, Pera, Molinillo, and Simkin show that digital strategy alignment in smaller firms depends on dynamic capabilities rather than static planning [11, 12]. These insights suggest that prioritisation cannot be treated as a narrow project-ranking exercise. It must connect strategic value, capability development, and implementation feasibility.
A multi-criteria decision framework is therefore needed because digital transformation decisions involve competing objectives and uncertain consequences. Queiroz, Tallon, Sharma, and Coltman show that IT application orchestration capability can improve agility and performance, while Tallon, Queiroz, Coltman, and Sharma highlight the relationship between information technology and organizational agility [13, 14]. These studies imply that digital initiatives should be assessed not only by expected benefits but also by their contribution to adaptive capacity. The prioritisation challenge is thus to identify which initiatives should be executed immediately, which should be prepared through capability-building, and which should be deferred or rejected.
Strategic value assessment evaluates how strongly a digital transformation initiative contributes to the firm’s strategic objectives, competitive position, customer value, and future growth. Teichert’s review of digital transformation maturity indicates that maturity and transformation are linked to strategic direction, organizational capabilities, and management commitment [15]. In a prioritisation context, strategic value should not be defined narrowly as immediate financial return. It should include the extent to which an initiative advances strategic alignment, strengthens differentiation, improves scalability, deepens customer relationships, or enables future digital options.
The first dimension of strategic value is alignment with explicit business goals. Gökalp and Martinez emphasize digital transformation capability maturity as a structured way to assess whether organizations possess the capabilities required for digital transformation [16]. However, capability maturity should serve strategy rather than replace it, because firms may become efficient at implementing projects that are not strategically important. A high-value initiative should therefore demonstrate a clear connection to growth objectives, operational priorities, customer experience goals, platform strategy, or long-term competitive positioning.
The second dimension is value creation potential, including revenue growth, cost reduction, customer value, and data-enabled learning. Aras and Büyüközkan present digital transformation maturity assessment as a holistic process, while Bican and Brem connect digital business models, digital transformation, and sustainable value creation [17, 18]. These perspectives indicate that strategic value may arise from direct economic benefits as well as indirect benefits such as improved decision quality, stronger customer insight, or reusable digital infrastructure. Managers should therefore require evidence of value pathways rather than relying on broad claims that an initiative is innovative or technologically advanced.
The third dimension is strategic leverage, meaning the degree to which an initiative enables other digital initiatives, accelerates organizational learning, or strengthens long-term transformation capacity. Vaska, Massaro, Bagarotto, and Dal Mas show that digital transformation and business model innovation are closely connected, and Saarikko, Westergren, and Blomquist argue that digitally conscious firms must treat transformation as a strategic managerial responsibility [19, 20]. In this framework, initiatives with high strategic leverage should receive stronger consideration even when short-term benefits are modest, because they may create foundational capabilities for future transformation. Table 1 defines the dimensions and metrics for assessing the strategic value of digital transformation initiatives.
Table 1. Strategic Value Assessment Dimensions: Criteria, Metrics, and Scoring Guidelines for Digital Transformation Initiatives
Strategic value dimension | Core assessment question | Evidence requirements | Suggested metrics | Low score guideline | Medium score guideline | High score guideline |
Strategic alignment | Does the initiative directly support stated business priorities? | Strategy documents, executive priorities, transformation roadmap, business unit objectives | Alignment with strategic goals, executive sponsorship, relevance to priority markets or processes | Weak or indirect connection to strategy | Supports one recognized strategic objective | Directly advances multiple high-priority strategic objectives |
Competitive impact | Does the initiative strengthen differentiation or market position? | Competitor analysis, customer expectations, platform or ecosystem analysis | Differentiation potential, speed-to-market effect, ecosystem positioning | Mainly imitates competitors without clear advantage | Improves parity or modest differentiation | Creates meaningful competitive advantage or market positioning |
Revenue and growth potential | Can the initiative generate new revenue or expand existing value streams? | Business case, market estimates, customer demand evidence, pricing assumptions | Expected revenue growth, market expansion, monetization potential | Limited or unclear growth pathway | Moderate growth potential with plausible assumptions | Strong growth potential supported by clear value pathway |
Cost and productivity value | Can the initiative improve efficiency, quality, or scalability? | Process analysis, cost baseline, productivity data, operational bottleneck evidence | Cost reduction, cycle-time reduction, error reduction, scalability improvement | Minor operational benefit | Noticeable efficiency or quality improvement | Major productivity, scalability, or cost advantage |
Customer value contribution | Does the initiative improve customer experience, trust, access, or service quality? | Customer journey analysis, service data, feedback, retention evidence | Customer satisfaction, retention, personalization, service responsiveness | Minimal customer-facing value | Improves selected customer touchpoints | Substantially improves customer value and experience |
Data and learning value | Does the initiative generate reusable data, insight, or learning capacity? | Data architecture review, analytics use cases, knowledge reuse plan | Data asset creation, analytics potential, learning loops, decision quality | Produces little reusable knowledge | Generates useful but limited data or insight | Creates reusable data assets and organizational learning capability |
Strategic leverage | Does the initiative enable future digital initiatives or transformation pathways? | Dependency map, capability roadmap, architecture review | Platform reuse, capability spillover, integration potential | Standalone project with little future leverage | Enables some related initiatives | Foundational initiative that unlocks multiple future initiatives |
Time-sensitive strategic relevance | Is there a strategic reason to act now rather than later? | Market timing, regulatory timing, competitor movement, customer urgency | Urgency, window of opportunity, delay cost | Can be delayed with little consequence | Delay has moderate opportunity cost | Immediate action is strategically important |
Capability readiness assessment evaluates whether the organization has the technical infrastructure, digital skills, data foundations, governance routines, and cultural preparedness required to implement a digital transformation initiative successfully. Kraus, Jones, Kailer, Weinmann, Chaparro-Banegas, and Roig-Tierno show that digital transformation research increasingly emphasizes organizational adaptation rather than technology adoption alone [21]. This means that readiness must be assessed before prioritisation decisions are finalized, because an initiative with high strategic value may still be premature if the organization lacks the ability to execute it. In the Digital Transformation Priority Framework, readiness functions as a feasibility filter that distinguishes immediately actionable initiatives from those requiring capability-building first.
Technical readiness refers to the extent to which the organization’s architecture, data environment, systems integration, cybersecurity controls, and operational infrastructure can support the proposed initiative. Correani, De Massis, Frattini, Petruzzelli, and Natalicchio demonstrate that digital strategy implementation requires learning from transformation projects and adapting organizational processes during execution [22]. A firm should therefore assess whether existing systems can be integrated, whether data are available and reliable, and whether the initiative requires major technical remediation before implementation. This assessment prevents firms from selecting projects that appear attractive on paper but become stalled by hidden infrastructure constraints.
Organizational readiness includes managerial commitment, cross-functional coordination, available talent, change management capacity, and decision authority. Hess, Matt, Benlian, and Wiesböck argue that digital transformation strategy requires deliberate choices about technology use, value creation, structural change, and financial aspects [23]. These choices cannot be enacted without managers who can coordinate competing units, allocate resources, and resolve implementation conflicts. Capability readiness therefore includes not only technical resources but also the organizational capacity to absorb change without weakening ongoing operations.
Cultural readiness refers to whether the organization is prepared to adopt new digital routines, accept data-driven decision-making, and tolerate the learning required by transformation. Danesh, Ryan, and Abbasi show that project portfolio management benefits from multi-criteria decision-making because portfolio choices involve competing factors and managerial judgment [24]. In the same way, readiness scoring should translate qualitative judgments about skills, culture, and change capacity into comparable evidence for prioritisation. Table 2 outlines the capability readiness assessment criteria and their indicators.
Table 2. Capability Readiness Assessment Framework: Organizational, Technical, and Cultural Readiness Dimensions
Readiness dimension | Core assessment question | Key indicators | Evidence requirements | Low readiness condition | Moderate readiness condition | High readiness condition |
Technical infrastructure | Can current systems support the initiative? | System scalability, cloud capacity, integration readiness, cybersecurity baseline | Architecture review, system audit, infrastructure capacity assessment | Major technical gaps block implementation | Some upgrades or integration work required | Infrastructure is largely ready for deployment |
Data maturity | Are required data available, reliable, and governed? | Data quality, interoperability, ownership, metadata, privacy controls | Data inventory, quality reports, governance documentation | Data are fragmented, unreliable, or inaccessible | Usable data exist but require cleaning or integration | Data are reliable, accessible, and governed |
Digital skills | Does the organization have the expertise required to execute and sustain the initiative? | Technical talent, analytics skills, product management, vendor management | Skills inventory, staffing plan, training assessment | Critical skills are absent | Skills are partially available but need reinforcement | Required skills are available internally or through reliable partners |
Managerial commitment | Is there active sponsorship and decision support? | Executive sponsorship, budget commitment, governance participation | Leadership approvals, budget records, steering committee evidence | Weak sponsorship or unclear ownership | Sponsorship exists but decision rights are incomplete | Strong ownership and active executive support |
Change capacity | Can the organization absorb the initiative without overload? | Workload, competing projects, implementation bandwidth, process disruption tolerance | Portfolio review, resource allocation data, operational workload assessment | Organization is already overloaded | Change capacity exists but is constrained | Organization has clear capacity for implementation |
Cross-functional coordination | Are required units aligned around implementation? | Business–IT collaboration, process ownership, escalation mechanisms | Governance map, stakeholder analysis, operating model review | Units are fragmented or resistant | Coordination mechanisms exist but are immature | Strong cross-functional collaboration is established |
Cultural preparedness | Are employees prepared to adopt new digital routines? | Openness to experimentation, trust in data, learning orientation, resistance levels | Employee feedback, change readiness survey, adoption history | High resistance or low digital confidence | Mixed readiness with targeted support needed | Strong cultural support for digital adoption |
Vendor and partner readiness | Are external dependencies manageable? | Vendor capability, contract flexibility, support quality, interoperability | Vendor evaluation, service-level agreements, dependency analysis | High dependence on weak or uncertain partners | Partners are available but require close management | Reliable partners and clear external support are in place |
Implementation risk assessment evaluates the uncertainty that may prevent a digital transformation initiative from achieving its intended strategic value. Marttunen, Lienert, and Belton show that multi-criteria decision analysis benefits from careful problem structuring, especially when decision problems involve multiple methods, criteria, and stakeholder concerns [25]. Digital transformation risks are similarly multidimensional because they may arise from technical complexity, organizational resistance, regulatory exposure, financial uncertainty, or external dependency. A prioritisation framework must therefore assess not only expected benefits and readiness but also the likelihood and impact of implementation failure.
Technical risk includes integration complexity, system fragility, cybersecurity exposure, data migration problems, and architectural incompatibility. De Almeida, Ekenberg, Morais, Mohamed, and Alencar emphasize that risk, reliability, and maintenance decisions require structured multicriteria models because technical decisions often involve uncertainty and trade-offs [26]. In digital transformation, technical risks are especially important when initiatives require integration across legacy systems, customer-facing platforms, cloud infrastructure, and data pipelines. A project with high value but extreme technical complexity may need phased implementation rather than immediate full-scale deployment.
Organizational risk includes user resistance, unclear process ownership, weak governance, insufficient training, and disruption to existing routines. Salo, Keisler, and Morton argue that portfolio decision analysis supports improved resource allocation, which is directly relevant when managers must distribute limited resources across competing initiatives [27]. Implementation risk should therefore be assessed in relation to organizational capacity, because even a technically feasible initiative may fail if staff are unprepared, incentives are misaligned, or decision rights are unclear. Risk scoring should help managers decide whether to proceed, redesign, pilot, delay, or reject an initiative.
External and financial risks include vendor lock-in, regulatory exposure, data privacy obligations, cost escalation, uncertain return, and dependency on partners or platforms. Mardani, Jusoh, Nor, Khalifah, Zakwan, and Valipour show that multiple criteria decision-making techniques are widely used when decision-makers must compare alternatives across complex and uncertain criteria [28]. For digital transformation prioritisation, risk should be scored by likelihood and impact, then interpreted alongside strategic value and readiness. Table 3 categorises implementation risks and their potential impact on digital transformation projects.
Table 3. Implementation Risk Assessment: Risk Categories, Likelihood, Impact, and Mitigation Strategies
Risk category | Typical risk source | Likelihood indicators | Impact indicators | Prioritisation implication | Mitigation strategy |
Technical complexity | Integration across legacy systems, cloud platforms, data pipelines, or external applications | Many interfaces, unstable systems, limited documentation, complex migration requirements | Delays, system failures, cost escalation, service disruption | High technical risk may require piloting or phased implementation | Conduct architecture review, prototype integrations, sequence technical dependencies |
Data risk | Poor data quality, weak governance, missing data, privacy constraints | Incomplete data inventories, inconsistent definitions, limited ownership | Weak analytics outputs, compliance failures, poor decision quality | High data risk may require data foundation work before project approval | Improve data governance, clean data sources, define ownership and access controls |
Cybersecurity and privacy risk | Exposure of sensitive data, insecure APIs, weak access control | High data sensitivity, external access, weak controls | Breaches, penalties, reputation loss, operational disruption | High security risk may reduce priority unless controls are strengthened | Perform security assessment, privacy impact review, access control design |
Vendor dependence | Proprietary systems, limited switching options, weak vendor support | Restrictive contracts, closed architecture, limited interoperability | Lock-in, rising costs, reduced strategic flexibility | High vendor risk may require alternative sourcing or modular design | Negotiate flexibility, require interoperability, create exit strategy |
Regulatory exposure | Sector-specific compliance, data protection, audit requirements | Ambiguous regulations, cross-border data flows, regulated processes | Legal penalties, implementation delays, forced redesign | High regulatory risk may require compliance review before prioritisation | Involve legal and compliance teams early, document controls, monitor regulatory change |
Organizational resistance | Employee concern, workflow disruption, low trust in digital systems | Low adoption history, weak communication, strong informal resistance | Low usage, productivity loss, implementation failure | High resistance may require change readiness work before rollout | Use change management, training, co-design, communication, local champions |
Financial uncertainty | Unclear return, cost escalation, underestimated maintenance burden | Weak business case, uncertain benefits, hidden operating costs | Budget overrun, opportunity cost, abandoned project | High financial risk may lower priority or require staged investment | Use milestone funding, benefit tracking, sensitivity analysis |
Strategic execution risk | Misalignment between project design and evolving strategy | Unclear ownership, shifting priorities, weak governance | Strategic drift, duplicated initiatives, diluted transformation focus | High strategic risk may require governance clarification before approval | Align with transformation roadmap, define ownership, review portfolio fit |
The Digital Transformation Priority Framework begins with the identification of candidate initiatives and then evaluates each initiative through strategic value, capability readiness, and implementation risk. Kabir, Sadiq, and Tesfamariam show that multi-criteria decision-making methods are useful when decision-makers must evaluate alternatives across technical, economic, and organizational criteria [29]. This logic is appropriate for digital transformation because no single criterion can determine priority. A project should be prioritized when it is strategically valuable, sufficiently ready for execution, and exposed to an acceptable level of implementation risk.
The first step is initiative definition, which requires managers to specify the problem addressed, expected value pathway, affected stakeholders, required capabilities, timeline, dependencies, and decision owner. Khin and Ho show that digital technology and digital capability influence organizational performance through digital innovation, which implies that initiative definition must connect technology use with capability development and performance outcomes [30]. Poorly defined initiatives should not proceed to scoring because vague project descriptions can inflate value claims and obscure execution requirements. A clear initiative profile is therefore the entry condition for meaningful prioritisation.
The second step is pillar scoring, where managers assign structured scores for strategic value, capability readiness, and implementation risk using evidence rather than informal preference. Priyono, Moin, and Putri show that firms follow different digital transformation paths depending on business model conditions and contextual constraints [31]. This supports the need for a flexible framework rather than a universal ranking formula. In practice, two firms may evaluate the same initiative differently because one has stronger data infrastructure, greater change capacity, or a more urgent strategic need.
The third step is aggregation into a composite priority score, with weighting adapted to strategic orientation. Eller, Alford, Kallmünzer, and Peters show that digitalization creates benefits but also antecedents and challenges for small and medium-sized enterprises, reinforcing the importance of context-sensitive assessment [32]. A growth-oriented firm may assign greater weight to strategic value, while a risk-sensitive firm may increase the weight of implementation risk or capability readiness. This weighted approach allows the framework to remain structured without forcing all organizations into the same prioritisation logic.
The decision logic of the framework is not to reward the highest value initiative automatically, but to identify the most appropriate managerial action. An initiative with high strategic value, high readiness, and low risk should be advanced as a priority project, while an initiative with high value but low readiness should be deferred into a capability-building pathway [1, 16]. An initiative with moderate value and high risk should be redesigned, piloted, or rejected unless it supports an essential strategic dependency. Table 4 presents the proposed Digital Transformation Priority Framework integrating the three assessment pillars.
Table 4. Digital Transformation Priority Framework: Integration of Strategic Value, Capability Readiness, and Implementation Risk into a Unified Prioritisation Model
Framework stage | Managerial purpose | Required assessment inputs | Decision output | Recommended managerial action |
Initiative identification | Establish a clear candidate portfolio of digital initiatives | Initiative description, strategic objective, business owner, expected users, estimated resource need | Defined initiative profile | Include only initiatives with clear purpose, scope, and ownership |
Strategic value scoring | Assess contribution to business strategy and value creation | Strategic alignment, competitive impact, customer value, revenue potential, productivity value, strategic leverage | Strategic value score | Prioritize initiatives with direct strategic contribution and strong value pathway |
Capability readiness scoring | Assess whether the organization can execute the initiative | Infrastructure readiness, data maturity, skills, change capacity, governance, culture, partner readiness | Capability readiness score | Advance ready initiatives; defer high-value initiatives that require capability-building |
Implementation risk scoring | Assess uncertainty and possible negative consequences | Technical complexity, data risk, vendor dependence, regulatory exposure, resistance, financial uncertainty | Implementation risk score | Reduce, redesign, pilot, or defer initiatives with excessive risk |
Weighting and aggregation | Convert pillar scores into a transparent priority score | Pillar weights, scoring scale, evidence notes, managerial assumptions | Composite priority score | Rank initiatives while making assumptions explicit |
Decision classification | Translate scores into managerial categories | Composite score, pillar pattern, strategic dependencies | Priority category | Classify initiatives as advance, prepare, pilot, defer, redesign, or reject |
Portfolio balancing | Check whether selected initiatives create a coherent transformation portfolio | Resource capacity, initiative dependencies, risk concentration, capability load | Balanced digital portfolio | Avoid overload and ensure sequencing across strategic, technical, and organizational constraints |
Review and adjustment | Update priorities as strategy, readiness, and risk conditions change | Performance data, capability progress, risk events, strategic shifts | Updated prioritisation decision | Re-score initiatives periodically and adjust resource allocation dynamically |
Figure 1 presents the Digital Transformation Priority Framework as an integrated decision model linking strategic value, capability readiness, and implementation risk to transparent prioritisation outcomes.
Figure 1. Digital Transformation Priority Framework for Selecting and Sequencing Digital Transformation Initiatives Based on Strategic Value, Capability Readiness, and Implementation Risk
The decision matrix translates the framework into a practical tool for comparing digital transformation initiatives. Hanelt, Bohnsack, Marz, and Antunes Marante emphasize that digital transformation involves strategic and organizational change, which means managerial application must go beyond ranking isolated technologies [3]. The matrix requires managers to score each initiative across strategic value, capability readiness, and implementation risk, then combine these scores into a transparent priority classification. This makes prioritisation discussable across strategy, IT, operations, finance, and business units.
The matrix can use a simple five-point scale for each pillar, where higher strategic value and higher readiness increase priority, while higher implementation risk reduces priority. Danesh, Ryan, and Abbasi show that project portfolio management often benefits from multi-criteria scoring approaches because they allow decision-makers to compare alternatives systematically [24]. A practical scoring formula can assign positive weights to value and readiness and a negative or inverse weight to risk. This approach does not eliminate judgment, but it disciplines judgment by requiring managers to explain why an initiative receives a particular score.
Weighting should reflect the firm’s strategic context rather than remain fixed across all situations. For example, a firm pursuing rapid growth may assign greater weight to strategic value, while a regulated organization may assign greater weight to risk reduction and compliance readiness [25, 28]. A firm with weak digital foundations may temporarily weight capability readiness more heavily to avoid overcommitting to initiatives that exceed its execution capacity. The framework therefore supports both comparability and strategic adaptation.
The decision matrix also helps managers distinguish between “do now,” “prepare first,” and “do not pursue” initiatives. A high-value initiative with weak readiness may be important but should be preceded by data governance, skill development, or infrastructure modernization [15, 22]. A lower-value initiative with high readiness and low risk may be useful as a quick win, but it should not crowd out initiatives with stronger strategic leverage. Table 5 illustrates the decision matrix application with a hypothetical portfolio of digital transformation initiatives.
Table 5. Decision Matrix Application: Example Portfolio Prioritisation Using the Digital Transformation Priority Framework
Hypothetical digital initiative | Strategic value score | Capability readiness score | Implementation risk score | Interpretation of score pattern | Priority classification | Managerial recommendation |
Enterprise data governance platform | 5 | 3 | 3 | High strategic value with moderate readiness and moderate risk; foundational for later analytics and AI initiatives | Prepare and advance in phases | Approve phased implementation with capability-building milestones |
Customer personalization engine | 5 | 4 | 3 | High value and strong readiness, with manageable risk if data controls are adequate | Advance now | Prioritize for near-term execution with privacy safeguards |
Legacy enterprise resource planning replacement | 4 | 2 | 5 | Important but readiness is low and risk is very high because of complexity and disruption | Defer and redesign | Conduct architecture review and staged modernization plan before approval |
Robotic process automation for back-office tasks | 3 | 5 | 2 | Moderate value, high readiness, and low risk; suitable for quick operational benefit | Select as quick win | Implement selectively while monitoring process standardization benefits |
AI-based demand forecasting | 4 | 3 | 4 | Strong potential but data maturity and model risk require careful piloting | Pilot first | Launch controlled pilot with data quality and model governance checkpoints |
Digital supplier collaboration portal | 4 | 3 | 3 | Good strategic value and moderate readiness; risk depends on partner adoption and integration | Conditional advance | Proceed if supplier onboarding and integration requirements are confirmed |
Blockchain traceability system | 2 | 2 | 4 | Low current strategic value, low readiness, and high uncertainty | Reject or defer | Do not prioritize unless regulatory or customer requirements change |
Employee digital learning platform | 3 | 4 | 2 | Moderate value, strong readiness, and low risk; supports broader capability-building | Advance as enabler | Approve as a supporting initiative for transformation capability development |
Figure 2 illustrates how the decision matrix translates pillar scores into practical managerial actions for different digital transformation initiative profiles.
Figure 2. Decision Matrix for Managerial Prioritisation of Digital Transformation Initiatives Using Value–Readiness–Risk Profiles
The Digital Transformation Priority Framework is conceptual and requires empirical validation across different industries, firm sizes, and transformation contexts. Vial’s review indicates that digital transformation research remains broad and fragmented, which creates opportunities for more cumulative and testable frameworks [1]. Future research should examine whether the proposed scoring pillars predict implementation success, portfolio coherence, and realized transformation value. Action research and longitudinal case studies would be especially useful because prioritisation decisions unfold over time rather than in a single planning event.
A second limitation is that the framework relies on managerial judgment when assigning scores to strategic value, capability readiness, and implementation risk. Marttunen, Lienert, and Belton note that problem structuring is central to effective multi-criteria decision analysis because decision quality depends on how criteria and alternatives are defined [25]. In practice, managers may overstate strategic value, underestimate risk, or misjudge readiness because of optimism bias, political sponsorship, or incomplete evidence. Future development should therefore include calibration guidelines, scoring workshops, inter-rater comparison, and governance mechanisms to improve scoring reliability.
A third limitation is that the framework evaluates initiatives individually before considering deeper interdependencies across the transformation portfolio. Portfolio decision analysis shows that resource allocation decisions can be improved when interdependencies, constraints, and portfolio-level effects are explicitly considered [27]. Future versions of the framework could integrate decision support software, dynamic resource allocation models, dependency mapping, and scenario analysis. This would help firms update priorities as capabilities improve, risks change, and strategic conditions evolve.
Digital transformation prioritisation is a strategic management problem because firms must choose among many possible initiatives while operating under limited resources and organizational constraints. The Digital Transformation Priority Framework developed in this article provides a structured way to compare initiatives through strategic value, capability readiness, and implementation risk. By combining these pillars, the framework helps managers avoid ad hoc selection, initiative overload, and weak alignment between digital investments and strategic goals.
The framework’s main contribution is practical clarity. It gives decision-makers a common language for discussing why one initiative should advance, why another should be prepared through capability-building, and why another should be deferred, redesigned, piloted, or rejected. This transparency is especially important when digital transformation decisions involve multiple business units, competing priorities, uncertain benefits, and uneven readiness.
Firms that adopt structured prioritisation are more likely to convert digital ambition into coherent execution. The framework does not remove managerial judgment, but it makes judgment more disciplined, evidence-based, and reviewable. Used consistently, it can improve resource allocation, reduce transformation fatigue, and ensure that digital initiatives are selected because they are strategically meaningful, organizationally feasible, and responsibly managed.
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Institute for Management, Business, and Accounting Studies
