Here is an approach that should work - a. Engage with stakeholders to better understand their concerns around AI. What does "uncertain outcomes" mean to them? Is it about data-privacy, impact of outcome, quality of outcome, cost of outcome or just lack of talent to pick an AI project b. Build Confidence with small wins through projects that have lower cost/gestation and can impact at least one business metric c. Socialize the impact with the stakeholders so that the initiative gains confidence and trust d. Mitigate Risks and Scale - Once there is confidence and appreciation of the impact, pick up larger initiatives while keeping risks under control - strict data privacy protocols, proven models, invest in or hire the right talent

Getting stakeholders on board with AI can be tricky, especially when results aren’t instant. To really connect, I’d focus on showcasing AI’s value—concrete ROI speaks volumes. Imagine presenting scenarios where AI insights drive cost savings or refine operations. Those real-world examples make AI tangible and ground stakeholders in the benefits. It’s not about selling hype; it’s about making AI’s impact real, giving everyone a clear view of what’s in it for them and why it matters.

After leading AI initiatives for a decade, here’s my strategy to get stakeholder buy-in: - Start with narrowly defined use cases that clearly show ROI. - Choose high-impact, low-complexity projects first to prove value and earn trust for larger ones. - Avoid AI use cases dependent on datasets from multiple internal or external groups—data access delays are common and costly. - Prioritize clean, complete data and strong MLOps. Embrace iterative improvement through continuous testing, learning, and user feedback. - Build confidence by sharing success stories from similar projects. -Finally, build trust by being transparent about challenges and plans to overcome them.

Securing stakeholder buy-in for AI initiatives requires clear communication and alignment with their goals. Start by demonstrating the potential value of AI through data-driven insights and case studies relevant to their interests. Involve stakeholders early in the process to gather input and address concerns, fostering a sense of ownership. Provide regular updates and showcase quick wins to build confidence. Lastly, emphasize the importance of collaboration and ongoing support to ensure a shared vision and commitment to the initiative's success.

To secure stakeholder buy-in after years in AI, my approach is to - Start with well-defined use cases where AI can show clear ROI. - Focus on high-impact, low-complexity projects that build trust, proving value before scaling up. - Avoid AI cases needing data from multiple internal/external sources to sidestep delays. - Prioritize clean data, comprehensive MLOps, and iterative improvement—testing, learning, and refining based on user input are key. - Share success stories to demonstrate potential and build trust. Be transparent about challenges and limitations while showing plans to address them.