How to Prioritize People in AI Implementation

AI won't fix broken product decisions. But it can amplify good ones. I met a product leader recently who spent months using AI tools to build new product ideas, but still couldn't answer a simple question: "Which features should we prioritize next?" This isn't uncommon. We're all overloaded by tools now. However, it's common for product teams to have strong AI capabilities that don't translate to better decisions. After helping numerous product and UX leaders navigate this challenge, here's what separates success from failure: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 Define what specific product decisions you need to improve first. A CPO I work with narrowed their focus to just user onboarding decisions. This clarity made their AI implementation 3x more effective than their competitor's broader approach. 𝟮. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗶𝗿𝘀𝘁 Document your current decision-making process before implementing AI. What criteria matter most? What trade-offs are acceptable? These guardrails ensure AI serves your product strategy, not replaces critical thinking. 𝟯. 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽 The best product teams use AI to expand options, not narrow them. They still validate AI recommendations through direct customer conversations. AI can spot patterns but can't understand the "why" behind user behaviors. 𝟰. 𝗕𝘂𝗶𝗹𝗱 𝗔𝗜 𝗹𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 Your entire team doesn't need to become AI experts. But product managers should understand enough to critically assess AI outputs. Focus training on interpretation skills, not just tool mechanics. 𝟱. 𝗔𝘂𝗴𝗺𝗲𝗻𝘁 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 Instead of replacing human judgment, first use AI to enhance it. Look for places where your team is constrained by time or resources, not expertise. Flexible consulting partnerships can be more effective than massive AI investments or new full-time hires. It depends on your timeline, budget, and executive buy-in. The right external partner can help you integrate AI incrementally while preserving your team's core decision-making strengths. What's your biggest challenge in integrating AI into product decisions? Has your team found the right balance?

Your Leadership Blueprint for the Future 🔛 If you're an executive grappling with the fast-paced evolution of Tech, AKA #ai, you're far from alone. But while some see a challenge, I see an unprecedented opportunity. #GenerativeAi isn't just the future—it's your next competitive advantage. As someone who has spearheaded major technological integrations at AT&T, embracing AI today is not an option but an imperative. >>Key Leadership Strategies in the AI Era 1. "Active Listening: Your Secret Weapon in AI Adoption" Begin by conducting internal audits or surveys to understand the current perception of AI within your organization. Address concerns openly in town-hall meetings. 2. "AI: Augmenting Human Excellence, Not Replacing It" Implement pilot projects that clearly show how AI can improve but not replace human tasks. 3. "A Vision Well Communicated is a Vision Half Realized" Develop a transparent roadmap for AI adoption and share it across all organizational levels. 4. "Collective Learning: The Cornerstone of AI Success" Organize regular training sessions and encourage cross-functional teams to collaborate on AI projects. 5. "Human Potential: The X Factor in Your AI Strategy" • Celebrate and reward creativity, problem-solving, and other uniquely human skills that AI can't replace. >> Reshaping Corporate Roles for an AI-Driven World • "From Rote to Remarkable: Entry-Level Roles Reimagined" Invest in training programs that allow entry-level employees to upskill and take on more creative or strategic roles. • "Middle Management: Your New Role as the Talent Nurturer" Pivot from task managers to talent developers, focusing on guiding teams to maximize the use of AI tools effectively. • "Senior Leaders: Data-Driven Culture Architects" Lead by example. Utilize AI to make informed decisions and set a precedent for a data-driven culture. >> Organizational Structure: The New Shape of Success • "Flat is the New Up: Why Project-Based Teams are Tomorrow's Winners" Move toward a more agile structure that encourages rapid decision-making and adaptation. • Strategic Partnerships: Your Path to AI Superiority "Don't Just Compete, Dominate: Partner to Innovate" Seek partnerships with AI solution providers or academic institutions to stay ahead of the curve. This tech shift and paradigm change will redefine leadership, organization, and strategy. The AI revolution is already here—how you respond today will determine where you stand tomorrow. Are you leveraging AI to solve real-world problems, or are you still in the exploratory phase? •••••••••••••••••••••••••••••••••••••••••••••• Mariana Saddakni, ★ Digital Product Innovation, Operational Mastery, and Customer Experience Excellence ★ Former Global Head of Product and Customer Experience, AT&T– Fractional Executive, Service Industry Growth and Retention Expert 🌐 Let's connect! ••••••••••••••••••••••••••••••••••••••••••••••

I’m excited to be filming my new Udemy course on “AI for People Managers” aimed at folks who aren’t necessarily AI experts but want to help their teams use AI ethically and effectively. The great Allie K. Miller suggests that you encourage your people to experiment with AI for ~10 hours a week. This means you have to do more than offer begrudging permission. You need to demonstrate curiosity and excitement— even if you’re still learning too. Here are ten things people managers should know about AI experimentation: 1. Set clear rules upfront about what data your team can and can’t feed into AI tools, because nothing kills an AI experiment faster than a data privacy violation. 2. Frame AI as your team’s new super-powered assistant, not their replacement, so people get excited about what they can accomplish rather than worried about their jobs. 3. Start small with low-risk experiments like brainstorming or first drafts, because you want people building confidence with AI, not stress-testing it on your most important projects. 4. Make it totally okay for people to share when AI gives them weird or unhelpful results, since learning what doesn’t work is just as valuable as discovering what does. 5. Teach your team that getting good AI results is all about asking good questions, and yes, “prompt engineering” is now a legitimate workplace skill worth investing in. 6. Always have someone double-check AI outputs before they go anywhere important, because even the smartest AI can confidently give you completely wrong information. 7. Keep an eye out for AI responses that might be unfair to certain groups of people, since these tools can accidentally bake in biases that you definitely don’t want in your work. 8. Let AI inform your team’s decisions but never make the final call itself, because human judgment still needs to be the ultimate decision-maker. 9. Stay curious about new AI developments and limitations because this technology changes faster than your smartphone updates, and what’s true today might not be tomorrow. 10. Track more than just “how much time did we save” and also measure whether people are actually doing better, more creative work with AI as their sidekick. Let me know if you’re as excited about this topic as I am (and yes, I am learning alongside you too)! #ai #leadership #managers

Lately, I’ve been asked an interesting question about AI adoption. Specifically, how should an organization implement AI tools without instilling fear, but instead align with the organization’s mission and vision? Whenever I discuss AI adoption, two thoughts always come to my mind immediately: 1️⃣ Transparency in AI Usage: As executives, we must be transparent about our future use of AI within our business. Transparency aligns with our commitment to ethical practices and strengthens employees’ trust. It’s important to demystify the use of AI, ensuring everyone understands its applications and benefits. Together, we can build a foundation of trust that forms the bedrock of our AI journey. 2️⃣ Employee Inclusion in Decision-Making: Our greatest asset is our people. In the AI adoption era, including employees in decision-making is critical to building trust. They bring invaluable insights, diverse perspectives, and a deep understanding of our operations. By involving our teams, we tap into a wellspring of collective intelligence that propels us ahead. Let’s foster a culture where everyone feels empowered to contribute to the decisions that shape our AI strategy. By combining transparency in AI usage with inclusive decision-making, we are not just adopting technology; we are building a culture of innovation, trust, and shared success. The convergence of transparency and employee inclusion isn’t just a strategy; it’s an organizational mindset.