Scaling AI Responsibly: Innovation, Governance, and Trust
This TNCR Executive Research project was shaped by input from Research Contributors Kiran Palla, Paul A. Mohabir, Tim Ryan, Harsha Bellur, Rocky Vienna, and Lonnie Snyder. Their suggested areas of inquiry converged around a central executive challenge: how organizations can scale AI quickly while maintaining governance, accountability, security, and trust. Their input included AI’s impact on executive decision-making, workforce trust, AI governance, agentic AI security, AI agent access controls, and safe AI use without exposing sensitive data.
In this poll, we’re exploring how technology leaders are balancing innovation speed with the controls, ownership models, and organizational readiness needed to support responsible enterprise AI adoption.
Research Questions
- 1. Who has primary ownership for AI governance and accountability in your organization today?
- 2. What are the biggest barriers to scaling AI responsibly across your organization?
- 3. How confident are you in your organization’s ability to manage the risks associated with agentic AI or autonomous AI systems?
- 4. Which risks concern you most as AI adoption scales across the enterprise?
- 5. What advice would you give technology leaders trying to scale AI responsibly without slowing innovation?
- 6. May we attribute your response to you when sharing these insights in an article or research summary?
- 7. How would you characterize your organization’s current approach to enabling AI innovation while managing governance and risk?
Participate in this research →
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