Скачать или смотреть AI Strategy That Drive REAL Business Outcomes

In this episode, I speaks with Shaista, a global program leader at Philips with deep experience in AI-driven digital transformation. We break down how to connect AI strategy to real business results—from forming the right cross-functional teams to building governance, prioritizing use cases, and handling the human side of decision-making.

You’ll hear practical frameworks for AI readiness, data and platform choices (build vs buy vs partner), roles and skills for AI execution, and how to move from prototype to productization—without losing speed, safety, or value.

What You’ll Learn

AI strategy, not buzzwords: Tie AI to revenue, cost, risk, and customer outcomes

Everyday AI vs transformation: Efficiency wins vs step-change impact
How to start: Core team design—data, design, business, and engineering working as one
Governance that scales: Playbooks, guardrails, AI readiness and completeness references
Prioritization that works: Pick solvable, high-value use cases before moonshots
Build / buy / partner: Where to differentiate, where to leverage the ecosystem
Data realities: Latency, context, and dashboards that link work to outcomes
People and decisions: Escaping the “consensus conundrum,” making tough calls with clarity
Roles and skills: What great AI strategists do and why they’re rare (and highly paid)
Healthcare lessons: Balancing innovation with safety and compliance in regulated environments

🎯 Who This Is For
Business and tech leaders building an AI roadmap
CIO / CDO / CPO / Head of Data & AI and product leaders
AI Strategists, Program Managers, PMs, Architects
Teams moving from POCs to production and scale
✅ Actionables From The Episode
Form a core AI team across business, data, design, and engineering
Define the “why/what/where/how” in an AI strategy doc (goals, opportunities, priority matrix)
Start with smaller, winnable projects to earn trust and fund the next wave
Establish AI governance (guardrails, playbooks, decision rights) without killing agility
Clarify build vs buy vs partner for each capability; protect proprietary value
Instrument dashboards that map team and program metrics to business outcomes
Invest in AI strategist capability (internal or hire) to translate tech → value
Commit to continuous learning—the pace of change demands it

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