CxO AI Concerns Vary by Job Title

As always is the case with any information technology deployment in an enterprise, CxOs have distinctly different concerns about using generative artificial intelligence, C-suite surveys generally suggest. 

CEOs might generally face issues understanding the potential applications of generative AI across the organization and making informed decisions about where to invest. CEOs also will be concerned with the strategic alignment of generative AI initiatives with overall business goals and objectives; impact on company culture and employee morale due to potential job displacement or skill gaps or the long-term sustainability and scalability of generative AI solutions. 

For CEOs, return on investment often is an issue as well. 

Chief marketing officers especially might have issues with evaluating potential applications and making informed decisions about how generative AI can be used to improve marketing campaigns, create personalized content, and generate creative assets.

CMOs also must evaluate potential for inauthentic or misleading content generation that could damage brand reputation. Measurement and attribution of marketing campaigns that involve AI-generated content can also be concerns. 

As you would guess, CIOs and CTOs have other top concerns about security, privacy, documenting the logic, integration with existing systems, total cost of ownership, vendor management and employee training. Regulatory compliance and ethical concerns also exist. 

Issue	CIO Concerns	CTO Concerns
Security and Data Privacy	Maintaining data security and user privacy with large data sets.	Ensuring robust security infrastructure can handle the demands of generative AI models.
Transparency and Explainability	Difficulty in understanding and explaining AI outputs for decision-making and compliance.	Mitigating the "black box" nature of complex models and building interpretability into the development process.
Integration and Interoperability	Integrating generative AI with existing IT infrastructure and ensuring compatibility across different systems.	Managing the technical complexity of integrating AI models into applications and workflows.
Cost and Return on Investment (ROI)	Justifying the cost of acquiring, developing, and maintaining generative AI systems against potential benefits.	Balancing technical feasibility with cost-effectiveness and demonstrating clear ROI for proposed AI projects.
Skill Gap and Workforce Management	Identifying and acquiring the necessary talent to manage, operate, and maintain generative AI solutions.	Addressing potential job displacement and reskilling existing personnel to adapt to a changing technological landscape.
Vendor Management and Long-Term Support	Evaluating and selecting reliable vendors for AI solutions and ensuring ongoing support and maintenance.	Ensuring the chosen technology stack can be sustained and adapted to future advancements in the field.
Ethical Considerations and Regulatory Compliance	Mitigating potential bias in AI outputs and ensuring ethical and responsible use of the technology.	Addressing evolving regulatory frameworks and complying with data privacy regulations in different jurisdictions.

Of course, those include the typical concerns CxOs have about any proposed new information technology. 

CxO Role	Concerns
CEO	Strategic Fit: Does the technology align with the overall business strategy and objectives? Return on Investment (ROI): Can the technology demonstrably improve profitability, growth, or other key metrics? Competitive Advantage: Can the technology create a sustainable edge over competitors? Risk Management: What are the potential risks associated with deploying the technology, and how can they be mitigated? Change Management: How will the technology impact the organization's culture, workforce, and existing workflows?
CIO	Security and Data Privacy: Can the technology be implemented securely and meet data privacy regulations? Integration and Interoperability: Can the technology integrate seamlessly with existing IT infrastructure and systems? Scalability and Performance: Can the technology handle the current and future needs of the organization? Total Cost of Ownership (TCO): What are the upfront and ongoing costs associated with acquiring, deploying, and maintaining the technology? Vendor Management: Does the chosen vendor have a good reputation, strong support infrastructure, and a clear roadmap for future development?
CTO	Technical Feasibility: Can the technology be implemented effectively given the organization's technical capabilities and resources? Technical Debt and Complexity: Will the technology introduce technical debt or create additional complexity for the IT team? Standardization and Maintainability: Can the technology be easily standardized and maintained within the existing IT environment? Performance and Scalability: Can the technology meet the organization's performance and scalability requirements? Future-Proofing: Is the technology based on a future-proof architecture that can adapt to evolving industry standards?
CMO	Customer Impact: How will the technology impact the customer experience and overall marketing effectiveness? Data and Analytics: Can the technology generate valuable customer insights and improve marketing ROI? Brand Reputation: Could the technology potentially damage the brand's reputation if not implemented or managed effectively? Agility and Time to Market: Can the technology help the marketing team be more agile and bring new products or services to market faster? Measurement and Attribution: Can the impact of the technology be effectively measured and attributed to marketing efforts?