Abstract
Research on artificial intelligence (AI) in communication has increasingly examined how automation and data-driven systems reshape professional practice. Prior foundational work such as Zerfass et al.’s (2018) strategic communication and Getchell et al.’s (2022) AI in business communication has identified shifts in practitioner roles, datafication, and emerging ethical dilemmas. Recent studies on generative AI extend these debates, showing how algorithmic content production affects persuasion, attitudes, credibility, and public trust (DeVasto & Palmer, 2024). Situated within this context, Karen E. Sutherland’s Artificial Intelligence for Strategic Communication emerges as one of the first full-length, empirical, practitioner-focused monographs in the field.
Sutherland’s book offers a distinctive combination of theoretical framing, empirical data, and hands-on tools. A close reading reveals strengths and limitations across multiple dimensions, including conceptual contribution, methodological rigor, empirical breadth, technical treatment, ethical depth, practical transferability, pedagogy and accessibility, and reproducibility. The book excels at translating established adoption theories into the domain of strategic communication. The blended use of Diffusion of Innovation and TAM3 in Chapter 2 is illuminating, helping to interpret variance in practitioners’ uptake and attitudes, and providing a coherent framework for structuring the proposed model for practice. This theoretical integration is leveraged consistently throughout the text to explain findings and organize practical guidance. Where the book is less ambitious is in engaging adjacent theoretical literatures, such as cultural theories of mediation, organizational learning, or critical algorithm studies, which could have foregrounded power, discourse, and institutional dynamics more explicitly. By prioritizing adoption frameworks, the author narrows the lens in a productive but somewhat conventional way, ensuring clarity at the cost of broader interdisciplinary insight.
Methodologically, the book is commendable for its transparency. Sutherland clearly describes the mixed-methods approach, including interview protocols, survey design, sample demographics, and data analysis steps, which strengthens confidence in the empirical claims (e.g., Chapter 2). Sutherland also acknowledges a key limitation: most resources underrepresent the perspectives of those directly shaping and experiencing AI in strategic communication.
While the book reports both frequency statistics and qualitative perceptions, it occasionally conflates descriptive insights with causal language, so claims of “impact” should be interpreted as inferential rather than experimentally established (Chapter 14). Greater attention to potential response biases, such as self-selection or early-adopter effects, and to coding procedures, including inter-rater reliability and coding frame examples, would have increased reproducibility.
The empirical breadth of the book is a strong point, with interviews and surveys providing rich, practitioner-grounded examples that enliven practical recommendations. Nonetheless, the sample appears weighted toward organizations already experimenting with AI, which risks overstating readiness and underrepresenting contexts where infrastructure, regulation, or cultural attitudes constrain adoption (e.g., Iran, Afghanistan). Global perspectives, particularly from Global South or low- and middle-income contexts (e.g., Iraq, Brazil, Ukraine,) where data access, regulatory regimes, and linguistic diversity shape AI use differently, are relatively thin. This limitation affects the book’s applicability in contexts with fewer resources or different regulatory environments.
A persistent challenge in this field is the rapid pace of technological change, which means that platform-level tool recommendations and feature-specific guidance can become obsolete almost as soon as they are published. The book attempts to mitigate this vulnerability by grounding its advice in transferable frameworks, defined workflow stages, and practical checklists (Chapters 9 and 12). These elements help readers generalize beyond any single product. However, the book would benefit from a more explicit focus on vendor-agnostic evaluation criteria and on the broader lifecycle management of AI tools, both of which would further insulate its guidance from becoming outdated. This issue is most visible in Chapters 6–8, where detailed descriptions of tools such as ChatGPT, DeepSeek, Sora, Runway, and Midjourney risk losing relevance as their capabilities rapidly evolve. Although Sutherland consistently emphasizes underlying principles rather than specific features, the speed of innovation in generative AI means that any fixed catalogue of tools will have limited long-term durability.
The book’s ethical analysis is one of its strongest contributions. Chapter 3 stands out for its integrated discussion of bias, privacy, misinformation, accountability, and governance, weaving academic literature with practitioner concerns in a grounded, practical way. It culminates in a usable ethics policy template that clearly bridges abstract principles and organizational decision making. However, the same strength highlights areas for further development. The treatment of regulatory contexts is relatively thin, given the book’s high-level focus on ethics. A deeper engagement with instruments like the GDPR (General Data Protection Regulation), extending beyond privacy to data rights, automated decision-making, and accountability, would place the guidance more firmly within legal realities practitioners face. Adding content-liability and platform-governance models would also illuminate how ethical decisions interact with institutional constraints.
The volume provides substantial practical guidance, but its ethical framework would be more cohesive if ethical principles were more consistently translated into socio-technical practices. Concrete mechanisms such as participatory data set curation or community-driven auditing would clarify how commitments to fairness and accountability are operationalized. This gap is closely related to the book’s limited engagement with critical perspectives on algorithmic governance, surveillance capitalism, and platform power. Because these structural forces shape the conditions under which ethical choices are made, addressing them directly would strengthen and unify the book’s applied ethics. Within this context, the book’s pragmatic contributions are its strongest asset. The guides, checklists, and stage-based model offer a clear pathway for organizational AI adoption, and the practical treatment of prompt engineering, editorial workflows, and fact-checking is particularly effective. However, some recommendations, such as continuous monitoring and rigorous evaluation regimes, assume levels of expertise and resources that may be unavailable to smaller organizations or non-specialist teams.
The book’s pedagogical value reinforces its practical orientation. Sutherland’s clear, precise prose makes complex concepts accessible without oversimplification, while templates and procedural guidance support both classroom and professional training. This instructional strength would be further enhanced by integrated case studies that trace ethical and practical decisions across the workflow, using before-and-after examples with sample prompts, intermediate outputs, and final edited products.
In sum, Artificial Intelligence for Strategic Communication delivers a holistic, empirically grounded, and theoretically informed account of how AI is transforming communication practice. Sutherland not only maps current uses and challenges but also offers tools, processes, and models with immediate real-world application. While there are areas where more critical or Global South perspectives could enrich the analysis, the book stands as one of the most comprehensive and practice-oriented scholarly treatments of AI in communication available to date.