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Neither hope nor denial are strategies for managing AI risk.
AI adoption is accelerating faster than any previous technological shift. The opportunities for creating competitive advantage are huge – but so are the risks of new, faster routes to reputational damage.
GuardrAIls bridges the gap between innovation and reputation management by providing actionable, non-negotiable guardrails for ethical AI together with a framework for managing the Seven Pillars of AI Reputational Risk. With incisive case studies from companies such as Air Canada, Coca-Cola and Google Photos, together with concrete decision frameworks, crisis playbooks and a pragmatic 90-day action plan, it enables leaders to adopt AI aggressively but responsibly, and turn potential crises into competitive advantage.
Discover how to put in place effective AI governance, not as a technical or legal exercise but as a critical marketing and brand-protection discipline, so that you can actively maintain brand authenticity and stakeholder trust as you chart the path into the future.
Craig McDonogh is an accomplished marketing executive and AI ethicist with more than 30 years of experience in global B2B software. He has led marketing teams and strategy at companies including ServiceNow, BMC and Perspectium, and more recently at AI-driven organizations such as Forma.ai and Imgix. He is an advisor on AI Ethics to the University of San Francisco Digital Marketing Program, and publishes a daily newsletter on responsible AI at ethicore.substack.com
Craig combines a technical understanding of how AI systems work with deep experience in the ethics of AI, advising on issues such as fairness, transparency, accountability, and sustainability. Recognized as both a SaaS marketing leader and trusted voice on AI responsibility, he helps organizations adopt AI in ways that build trust, minimize risk, and drive long-term value.
Foreword: Making “humans in the loop” more than just a catchphrase ix
A note on AI use xi
Introduction: What is ethical AI? 1
Part I Understanding AI reputation risk
1 Why reputation risk is different with AI 7
2 Mapping your AI risk landscape 19
3 IP infringement and carbon footprint 37
4 Protecting vulnerable populations 49
Part II Operational risk management
5 Establishing governance that works 65
6 Content, creativity and copyright 79
7 Customer interactions 97
8 Marketing operations and advertising 111
9 Bias, fairness and testing 123
Part III Crisis and recovery
10 Prevention and early warning 139
11 Crisis response playbook 153
12 Recovery and rebuilding trust 167
Part IV AI Wagons Ho!
13 When AI isn’t the answer 177
14 The future of AI reputation 197
15 Your 90-day action plan 207
Appendices 223
Appendix A AI reputational risk self- assessment 225
Appendix B Essential vendor questions 231
Appendix C Stakeholder communication templates 237
Appendix D Key resources 245
Appendix E Industry- specific quick reference 251
Notes 257
Acknowledgments 263
About the author 265
Index 279