Here’s how I approach aligning stakeholders when managing AI projects, based on my experience: 1️⃣ Set Crystal-Clear Goals: Establish realistic outcomes and align them with AI capabilities—clarity is key to trust. 2️⃣ Foster Transparency: Share regular updates, challenges, and progress through data-backed insights to keep everyone in the loop. 3️⃣ Collaborate Actively: Involve stakeholders in critical decisions to ensure shared ownership and seamless alignment. My work with AI at Appétit has taught me that engagement + clarity = success! 🚀

Getting stakeholders on board starts with a thorough planning workshop to capture their inputs, this process should take as long as needed to ensure alignment. Use a DoR (Definition of Ready) checklist to document requirements, clarify expectations, and validate readiness, adding structure and transparency. A PoC is equally critical, serving as a practical demonstration of feasibility and value. It not only highlights risks and gaps early but also helps secure stakeholder confidence by turning abstract ideas into tangible outcomes. These steps ensure clarity, alignment, and a strong foundation for success.

AI is the newest and more transformative recent technology in business and this requires education and adaptation for all stakeholders. My view is that business governance should set a long-term path on how AI will deliver productivity increase in the business. A tailor-made dashboard should set 3 timelines : 5-year, 1-year and monthly plan with active monitoring. As each business is unique, so will be the measurements required. In general, performance should be expected in better sales/CRM, operations, customer service, insightful finance reports, intelligent market research. Monthly metrics are advisable for any business and an external or internal AI expert should take a leading role in facilitating all implementations.

To manage AI performance expectations effectively, I focus on clear communication and collaboration. First, I set measurable objectives that align with business goals, emphasizing that AI is a tool to assist, not replace, human efforts. I simplify complex concepts to ensure everyone understands both the potential and limitations of the technology. I deliver results incrementally, sharing early wins and demonstrating how small improvements lead to significant outcomes. Transparency is key—I’m upfront about challenges like data quality or model accuracy and explain how we’re addressing them. By involving stakeholders throughout the process, seeking their input, and providing regular updates, I build trust and ensure alignment.

Scale down. Be less ambitious. Appeasing stakeholders is not the same as building state of the art technology. In other words, even state of the art technology may sometimes not appease stakeholders. Stakeholders want to see output, not understand it. Show them what they want, even if it’s a short term heuristic. Then continue working on actual performance improvements.