Artificial intelligence is increasingly positioned as a major technological development with significant organisational implications. It has been described as systems that “apply advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions” (Neumann, Guirguis and Steiner, 2024). Similarly, AI can be understood as “intelligent systems created to use data, analysis, and observations to perform certain tasks without needing to be programmed to do so” (Reim, Åström and Eriksson, 2020).
Organisations invest in AI because it offers “enormous potential for adding value and competitive advantage” (Reim et al., 2020) and is associated with cost reduction, improved productivity and faster decision-making. However, the advantages of AI “cannot be achieved without successful implementation” (Merhi, 2023), and many organisations struggle to adopt it effectively (Neumann et al., 2024).
AI in Practice
In organisational settings, AI is used to automate and enhance processes. Drawing on prior literature, (Neumann et al., 2022) note that AI application areas include process automation, predictive analytics, conversational agents, fraud detection, resource allocation, and supporting expert tasks. AI systems can process large and complex datasets and provide decision support (Reim et al., 2020).
Beyond operational efficiency, AI is described as a catalyst for business model innovation. It may enable organisations to refine or expand product portfolios and operate more efficiently to cut costs (Reim et al., 2020). In public sector contexts, AI has been associated with better decision-making and forecasting, improved communication, personalised services, and reduced administrative burdens (Neumann et al., 2024).
These applications explain why AI systems “offer organisations great benefits, leading decision-makers to invest more in these systems” (Merhi, 2022). Yet implementation outcomes vary significantly.
Implementation Is Not Automatic
AI is not a simple technology insertion. It is described as a highly complex, general-purpose technology with high implementation complexity that differentiates it from digital tools that are “easy-to-use and easy-to-deploy” (Neumann et al., 2024).
(Reim et al., 2020) argue that managers have little support from academia when attempting to implement AI in their organisation’s operations; this leads to an increased risk of project failure and suboptimal outcomes.
Successful implementation requires structured attention to organisational capabilities. (Reim et al., 2020) propose that organisations must:
Understand AI and organisational capabilities needed for digital transformation
Understand the current business model and potential for business model innovation
Develop and refine capabilities needed to implement AI
Reach organisational acceptance and develop internal competencies
This framing emphasises that AI adoption is strategic and organisational, not purely technical.
Critical Success Factors
Merhi (2023) identifies 19 critical factors influencing AI implementation and categorises them into organisation, technology, process, and environment. Using analytic hierarchy process, the findings indicate that “technology is the most significant of the four categories” (Merhi, 2022). Importantly, “ethics is the most crucial factor among all 19 factors” (Merhi, 2022).
(Neumann et al., 2022) similarly highlight that the importance of technological and organisational factors varies depending on the organisation’s stage within the adoption process, whereas environmental factors are generally less critical. They emphasise that adoption should be understood as “an ongoing process instead of a single point in time,” with stages described as assessed, determined, and managed.
Together, these findings suggest that AI maturity matters. Early-stage organisations may focus on experimentation. More advanced organisations require stronger strategic alignment, internal capabilities, and management support.
Limitations and Risks
AI implementation carries risks that extend beyond operational challenges. (Reim et al., 2020) identify transparency concerns, often described as the “black-box’ issue, where systems impair traceability. Lack of transparency can undermine trust among employees.
In public contexts, AI adoption has been linked to concerns that it may jeopardise privacy, reinforce inequalities, or even threaten democracy (Neumann et al., 2024). Privacy, legal, and ethical issues are therefore central considerations.
Merhi (2023) reinforces this by identifying ethics as the most crucial factor across all critical success factors. This indicates that performance gains alone are insufficient justification for AI deployment.
Before pursuing AI initiatives, pause and assess readiness. AI should not be adopted solely because of technological advancement or industry pressure. Instead, organisations should consider their stage of AI maturity, technological capability, organisational alignment, and ethical safeguards. Investment alone does not guarantee successful implementation. It requires deliberate strategy, capability development and attention to transparency and ethics (Merhi, 2022; Neumann et al., 2022; Reim et al., 2020).
AI Readiness in Practice: A Structured Reflection
AI adoption is described as an ongoing process rather than a single decision (Neumann et al., 2024). This activity helps you assess where your organisation currently stands and whether the necessary technological, organisational and ethical conditions are in place. It is designed to prompt honest reflection and practical next steps, regardless of sector or role.
Work through each section individually or with colleagues. For each area, record concrete evidence rather than assumptions. If you cannot clearly demonstrate capability, treat it as a development priority before expanding AI use.
This checklist is most effective when revisited periodically. AI capability develops over time. Regular review supports continuous learning and reduces the risk of costly failure.
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Focus Area
|
Reflective Questions |
What Evidence Do You Have? |
Next Steps |
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Clarity of Purpose |
What specific organisational problem is AI intended to solve? Is the relative advantage clear? |
Documented business case? Defined success measures? |
Refine purpose before investing further |
|
Technological Capability |
Do we have sufficient data quality, infrastructure and technical expertise? |
Audit of data readiness? Identified skill gaps? |
Address gaps before scaling |
|
Organisational Alignment |
Is AI aligned with strategic priorities and supported by leadership? |
Visible executive sponsorship? Dedicated resources? |
Strengthen strategic alignment |
|
Stage of Maturity |
Are we assessing, defining, or managed in our AI journey? |
Pilot projects only? Scaled use? Defined processes? |
Align ambition with maturity level |
|
Transparency and Trust |
Can AI outputs be explained to those affected? |
Clear communication processes? Training? |
Improve interpretability and dialogue |
|
Ethical Safeguards |
Have ethical risks such as privacy or inequality been explicitly reviewed? |
Documented ethical review? Governance oversight? |
Embed ethics into governance structures |