People Pillar: Navigating Stakeholders & Teams in the Age of AI

🔑 Why People Still Matter Most

Artificial Intelligence is transforming how we work — from automating manual tasks to generating entire product experiences. But no matter how powerful the technology, products don’t succeed because of AI alone. They succeed because of the people behind them.

The People Pillar is designed to help Product Managers (PMs) map and navigate these human dynamics in the AI era. Before you decide what to transform (Process) or with what enablers (Power), you must understand who matters most and how they shape outcomes.

🧭 Targets & Transformers

The People Pillar divides stakeholders into two groups:

Targets → The external and internal people you aim to understand and influence:

Users – must want to adopt.

Buyers – must agree to fund.

Deciders – must prioritze and commit.

Influencers – must be heard.

Transformers → The people who turn ideas into reality inside your organization:

Owner

Organizer

Creator

Implementer

Tester

Maintainer

This simple structure helps PMs answer:

Who matters most in this situation?

How does AI change their role?

What strategies should I use to engage them?

🎯 Targets: The Interconnected Network

1. Users

Users are always the anchor. Users determine whether an AI initiative actually solves a real pain or becomes a shiny but irrelevant feature. Their adoption (or rejection) ultimately defines success. Without user value, no case stands.

Case in point: In 2023, Duolingo integrated GPT-4 into its premium service to create more natural, personalized practice sessions. Users welcomed the improvements, but the company made sure to keep educators in the loop — balancing user delight with responsible oversight. (OpenAI Blog, 2023)

💡 PM takeaway: Anchor AI initiatives in real user pains — personalization, clarity, or speed — not just in technology potential. To do so i.e. truly capture the user's underlying pains, PM can / should turn to AI — clustering behaviors, identifying hidden needs, and predicting future segments.

2. Deciders

Deciders — often executives, department heads or product leaders — control prioritization and approvals as well as weighing priorities against risks. They want confidence that AI fits into strategy and compliance.

Case in point: In 2023, Coca-Cola partnered with OpenAI and Bain to power AI-driven creative campaigns. The decision wasn’t driven by hype alone — buyers saw the efficiency gains and innovation value for marketing budgets. TheDrum.Com, Feb 2023

💡 PM takeaway: For deciders, AI is not just a feature — it’s a risk, an investment, and a strategic bet.

3. Buyers

Buyers hold the purse strings. They think in terms of trade-offs and opportunity costs. AI can help PMs model ROI, simulate cost reductions, and present pricing strategies.

Case in point: Amazon’s recommendation system drives an estimated 35% of its sales Amity, 2023. Without convincing buyers of this ROI, those investments would never have scaled.

💡 PM takeaway: Anchor buyer conversations in cost vs. value, not just excitement about AI.

4. 📣 Influencers

Influencers aren’t always formal decision-makers. They’re respected voices — senior engineers, designers, or community advocates — who can make or break AI adoption.

Case in point: In 2024, IKEA piloted AI-powered design assistants to help customers explore room layouts. By including in-house designers and community testers early, IKEA turned potential skeptics into advocates. Forbes, 2024

💡 PM takeaway: Involve influencers early — if ignored, they become blockers; if engaged, they become champions.

🛠️ Transformers: The Stakeholders Who Make It Real

Even with Targets aligned, nothing ships without Transformers:

Owners & Organizers: ensure initiatives align with business strategy and timelines.

Creators & Implementers: design, build, and integrate AI systems.

Testers & Maintainers: safeguard quality, compliance, and sustainability over time.

While Targets shape demand and approval, Transformers execute and AI is transforming their workflows too:

Owners use AI to prioritize backlogs with business value scores.

Organizers rely on AI tools like Notion AI to manage projects.

Creators use GitHub Copilot or Figma AI to accelerate design and coding.

Implementers deploy with AI-assisted pipelines.

Testers reduce QA overhead with automated test suites.

Maintainers apply predictive AI monitoring for system health.

Case in point #1 – Continuing from above: With GitHub Copilot Enterprise, creators (developers) quickly saw productivity gains. But implementers warned about integration complexity, and maintainers flagged monitoring burdens at scale. The lesson? Transformers don’t just “execute” — they shape feasibility and sustainability.

💡 PM takeaway: Treat transformers as partners, not just executors. Involve them early to spot risks and design realistic rollouts.

Case in point #2: Atlassian’s AI features reduce manual backlog grooming and bug triage, freeing teams for higher-value work Atlassian Blog, 2023.

💡 PM takeaway: Mapping how AI augments each Transformer role helps PMs identify quick wins and adoption barriers.

🧩 Real-Life Scenarios

The People Pillar comes alive when you apply it to real PM challenges. Here are two everyday situations:

Scenario 1: Requesting Budget for an AI Pilot

You want to launch a support chatbot.

User: Customers want faster responses. But do you frame your case around efficiency gains or delight?

Decider: The COO wants ROI. Do you present bold forecasts for approval, or conservative ones to protect credibility?

Buyer: The Head of Support controls budget but fears hidden costs. Do you frame the chatbot as a cost-saver or a long-term investment?

Influencer: An engineer’s prototype is clunky. Do you champion their effort, or propose another vendor?

👉 Learn how to balance agendas in our [Stakeholder Alignment Playbook].

Scenario 2: Drafting a New Feature

You’re proposing AI-driven personalization in your app.

User: AI clustering reveals both clear demand and an unexpected new segment. Do you pivot, or stay the course?

Decider: The VP of Product asks: “Why this over other priorities?” Do you reframe around strategic impact, or risk being cut?

Buyer: Infrastructure costs raise concerns. Do you ask for upfront funding, or propose phased delivery?

Influencer: A designer argues their approach is better. Do you align with them or defend your own vision?

👉 Practice trade-offs in our [Feature Prioritization Quick Win Pathway].

💡 Want more? These are just two examples. In practice, PMs juggle dozens of such dilemmas every week. Explore the full guide with 5 Stakeholder Scenarios Every PM Faces with AI in our Vault.

👩‍💼 The PM as People Translator

The People Compass positions Product Managers as translators between two forces:

Targets, who define the “why” of transformation (value, adoption, ROI, governance).

Transformers, who determine the “how” (feasibility, sustainability, scale).

Without this translation, AI projects stall in pilots, meet resistance, or burn out teams. With it, PMs turn human dynamics into allies of transformation.

Therefore, a PM's most important skill to master is translating AI insights and human needs:

Interpret data without losing empathy.

Align stakeholders with conflicting agendas.

Coach teams in responsible AI adoption.

This is where PMs differentiate themselves from AI: leading with context, judgment, and human connection.

🚀 Next Steps

📥 Download the AI Stakeholder Map Template.

🛠️ Try the Stakeholder Alignment Playbook.

🎯 Explore the Quick Win Pathways to practice real-world trade-offs.

Because in the end, AI won’t convince your stakeholders — you will.