Each of the five SLGTC domains is measured through three underlying variables. Together, the fifteen variables provide the measurement structure underpinning the five domains and inform ORAVORN's assessment and recommendations. The cited sources establish underlying theoretical relationships that SLGTC applies or extends to organisational AI transformation.
Strategy
Strategy asks whether the organisation knows what it is trying to achieve with AI and whether that ambition matches its actual capacity to deliver.
Strategic Clarity draws on the sensing and seizing dimensions of Teece's dynamic capabilities framework (Teece, Pisano & Shuen, 1997; Teece, 2007), which describes a firm's capacity to identify opportunity and organise to act on it. The variable examines whether that capacity is evident in organisational practice, not whether, for instance, a strategy document exists.
Competitive Positioning draws on Miles and Snow's (1978) argument that organisations pursuing different strategic orientations require different organisational arrangements. The Defender, Creator and Pacer postures echo this typology: a Defender uses AI to protect and strengthen an existing position, a Creator uses AI to build new market space, and a Pacer uses AI to keep pace with competitors rather than lead. SLGTC's Creator overlaps with Miles and Snow's Prospector, and its Pacer overlaps with Analyzer. Reactor is deliberately omitted as a posture. The variable compares the posture an organisation declares with what its current AI activity is mainly for. Where no posture has been settled, the variable is not assessed before any AI engagement, and is read as Absent once AI activity is under way.
Capacity-Ambition Alignment applies Barney's (1991) resource-based view, which grounds competitive strategy in the resources a firm actually holds, alongside Hamel and Prahalad's (1989) concept of strategic intent, which recognises that ambition may legitimately exceed currently available resources. The variable assesses whether that gap, when present, has been recognised and deliberately sequenced rather than left unacknowledged.
Leadership
Leadership asks whether the leadership team has the orientation, capability and credibility to lead an AI transformation.
AI Literacy at Leadership Level extends upper echelons theory (Hambrick & Mason, 1984; Hambrick, 2007), which holds that organisational outcomes reflect the background, values and cognitive frames of the people leading the organisation. The variable examines whether leadership's cognitive frame is informed by sufficient understanding of AI's capabilities and limitations to exercise judgement, rather than delegating that judgement to technical teams by default.
Leadership Alignment draws on Amason's (1996) distinction between cognitive conflict, associated with improved decision quality, and affective conflict, associated with poorer decision quality, in top management teams. This provides grounding for treating leadership division as a meaningful variable.
Human-AI Decision Culture draws on research documenting two opposing failure patterns in how people respond to algorithmic input: Dietvorst, Simmons and Massey (2015) documented algorithm aversion, the tendency to abandon an algorithm after a single visible error even when it outperforms a human forecaster. Logg, Minson and Moore (2019) documented the converse pattern, algorithm appreciation, under different conditions. Together, these studies establish that responses to algorithmic input can become distorted in either direction. SLGTC extends this insight to the way leadership teams interpret, challenge and use AI-generated input. They do not establish that leadership teams who interrogate AI output achieve measurably better organisational performance.
Governance
Governance asks whether the organisation has the structures, policies and accountability mechanisms to deploy AI responsibly and sustainably.
Ethics and Principles Framework draws on two closely related but distinct instruments: the OECD's Recommendation of the Council on Artificial Intelligence (OECD, 2019, revised 2024) and the European Commission's Ethics Guidelines for Trustworthy AI (High-Level Expert Group on Artificial Intelligence, 2019), which together identify closely related principles associated with trustworthy AI, including fairness, transparency, accountability, privacy and forms of human oversight. The variable examines whether an organisation has translated these into its own specific, testable commitments, rather than citing the frameworks generally.
Risk Architecture is informed by the risk-based structure of the EU Artificial Intelligence Act (Regulation (EU) 2024/1689), supported by Marcinek et al.'s (2024) account of its regulatory architecture. The variable examines whether an organisation has established a comparable classification system for its own AI applications, independent of whether it is formally subject to the Act.
Accountability Structures applies Jensen and Meckling's (1976) analysis of agency costs, which describes the risks that arise when a principal delegates authority to an agent without adequate alignment of interests or clarity of responsibility. The variable treats diffuse accountability for AI decisions as a condition where delegated authority and responsibility have become misaligned, extending agency logic to AI governance specifically.
Transformation
Transformation asks whether the organisation is structurally and operationally capable of embedding AI in ways that produce sustained value rather than isolated pilots.
Operating Model Readiness extends the socio-technical systems insight established by Trist and Bamforth's (1951) study of technological change in coal mining, which showed that the effects of new technology depend on the social and work system surrounding it. The variable examines whether an organisation's roles, workflows and coordination arrangements can accommodate AI, since an operating model built for human-executed, informally coordinated work may resist AI integration through structural incompatibility regardless of the technology's own quality.
Data Maturity draws on the CMMI Institute's Data Management Maturity Model (2014), which conceptualises enterprise data capability across related organisational, process and technical dimensions. SLGTC applies this logic by treating data readiness as a condition supporting AI investment, rather than assuming it can always be developed independently alongside deployment.
Capability Pipeline applies Argote and Miron-Spektor's (2011) organisational learning research, which establishes experience as an important source of organisational knowledge and examines how that knowledge is created, retained and transferred. The variable applies this to AI capability specifically: an organisation relying solely on externally acquired AI capability, without building capability internally through practice, may struggle to develop the routines that sustain capability over time. The variable also looks at whether the managers who run day-to-day operations have the AI literacy to translate opportunities into practice, and whether the organisation has a deliberate approach to building AI fluency across the workforce over time.
Change Management
Change Management asks whether the organisation has the human and cultural conditions for AI change to be adopted, sustained and built upon.
Psychological Safety Around AI extends Edmondson's (1999) finding, that a shared belief in safety for interpersonal risk-taking was associated with learning behaviour, which in turn mediated team performance. The variable extends this to circumstances where raising concerns, admitting uncertainty, or flagging AI-related errors involve comparable interpersonal risk.
Communication Architecture draws on Kotter's (2012) change leadership work, which places considerable emphasis on sustained and appropriately sequenced communication in building support for change. The variable examines whether leaders say a consistent thing about AI, including what is not yet known, and whether the people most affected by an AI change hear about it first and through the right channel, rather than relying on ad hoc messaging.
Adoption Infrastructure draws on Rogers' (2003) diffusion of innovation research, which describes how innovations spread through a social system via identifiable adopter categories and influence pathways, and Davis' (1989) Technology Acceptance Model, which identifies perceived usefulness and perceived ease of use as important determinants of individuals' intentions to use and subsequent adoption of a technology. The variable examines whether an organisation has established support mechanisms such as training, peer champions and feedback loops, rather than assuming adoption will follow automatically once a tool is available.
References
Amason, A. C. (1996). Distinguishing the effects of functional and dysfunctional conflict on strategic decision making. Academy of Management Journal, 39(1), 123–148.
Argote, L., & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123–1137.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
CMMI Institute. (2014). Data Management Maturity (DMM) Model, Version 1.0. CMMI Institute.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126.
Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383.
European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.
Hambrick, D. C. (2007). Upper echelons theory: An update. Academy of Management Review, 32(2), 334–343.
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. Academy of Management Review, 9(2), 193–206.
Hamel, G., & Prahalad, C. K. (1989). Strategic intent. Harvard Business Review, 67(3), 63–76.
High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI. European Commission.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm. Journal of Financial Economics, 3(4), 305–360.
Kotter, J. P. (2012). Leading change: With a new preface by the author. Harvard Business Review Press.
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation. Organizational Behavior and Human Decision Processes, 151, 90–103.
Marcinek, K., Stanley, K. D., Smith, G., Cormarie, P., & Gunashekar, S. (2024). Risk-based AI regulation: A primer on the Artificial Intelligence Act of the European Union. RAND Corporation.
Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure, and process. McGraw-Hill.
OECD. (2019, revised 2024). Recommendation of the Council on Artificial Intelligence. OECD/LEGAL/0449.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Teece, D. J. (2007). Explicating dynamic capabilities. Strategic Management Journal, 28(13), 1319–1350.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38.