AI has moved from experimental playground to boardroom priority. Yet for many executives, the challenge isn't whether to invest in AI - it's how to build a balanced portfolio that delivers both quick wins and transformative breakthroughs.

This strategic framework will help you create a balanced AI portfolio that drives immediate value through core improvements, expands your business through growth initiatives, and positions you for the future through breakthrough innovations.

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Betting the company vs. playing it safe: Why you need both?

Every executive faces the same tension: delivering quarterly results while investing in technologies that may not pay off for years. With AI, this challenge intensifies - the potential is enormous, but the path forward often unclear.

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Think of AI as a general-purpose technology, like electricity or the internet. The challenge isn't just implementing AI - it's discovering which business problems it solves best in your specific context. This requires moving beyond isolated pilots to a systematic portfolio approach that manages risk while aligning with strategic objectives.

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Three types of AI bets every company must make

Smart AI strategies don't bet everything on one initiative. They spread investments across three categories, each serving different purposes and time horizons.

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The 70-20-10 rule

A balanced AI portfolio follows this allocation:

• 70% in Core AI - Proven applications optimizing core operations for reliable returns
• 20% in Growth AI - Adjacent applications expanding market reach and creating new value
• 10% in Breakthrough AI  - Bold bets on transformative technologies that could redefine everything

These ratios are starting points. A startup might go 20-50-30 for aggressive growth, while a regulated bank might prefer 80-15-5 for stability. What matters is intentional allocation aligned with your strategy.

Every project must map to a clear business objective(s), e.g. cost reduction, growth, risk mitigation, or customer satisfaction. If an idea doesn't serve a priority, drop it.

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Core AI: Your engine for excellence (70%)

These AI projects apply proven technologies to existing processes, delivering predictable ROI within months - your safe bets that generate reliable returns and fund everything else.

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Example: JP Morgan's COIN (Contract Intelligence) reviews commercial loan agreements in seconds instead of 360,000 hours annually. This isn't revolutionary - it's AI doing existing work better, faster, cheaper. Classic core project: proven technology, reliable returns, building foundation for future capabilities.

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These projects also serve as learning labs where your organization builds confidence and develops AI capabilities. Sequence them strategically: the first reveals data quality issues, the second tests change management capabilities, each building on previous lessons.

Target processes with high repetitive volumes or significant labor costs for quick wins that generate momentum. But here's the key insight: prioritize foundational use cases that unlock multiple opportunities. An AI-powered customer data platform doesn't just improve marketing - it enables personalization, predictive analytics, and risk assessment across the enterprise. These foundational projects deserve priority even with modest immediate ROI, as they create infrastructure for exponential value.

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Growth AI: Creating new value and revenue streams (20%)

Growth AI projects enhance offerings or create adjacent revenue streams - your moderate-risk investments that expand market reach and capture new opportunities.

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Example: Netflix's recommendation engine wasn't about making streaming more efficient (that's core optimization). It created new value by keeping users engaged longer, reducing churn, and commanding premium pricing. The AI made their existing service stickier and more valuable - classic growth project that expands the business.

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Success requires shared infrastructure, not isolated solutions. Build a central data platform with robust governance that multiple initiatives can leverage. This reduces costs, accelerates development, and ensures consistency.

These projects demand close collaboration between technology and business teams. What's technically possible matters less than what customers will actually pay for. Run pilots with key clients to validate concepts before scaling.

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Breakthrough AI: Betting on transformative technologies (10%)

Breakthrough AI projects are your high-risk, high-reward bets on transformative technologies - autonomous systems, breakthrough generative AI applications, or entirely new business models. Bold investments where most will fail, but one success could secure your future leadership.

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Example: Lovable built an AI that creates complete software applications from natural language descriptions - achieved in months, not years. They're not making coding better; they're eliminating it. This is classic breakthrough AI: a bold bet that could obsolete entire industries. Most companies attempting this will fail, but winners redefine everything.

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Think like a venture capitalist: small, staged bets across multiple projects. Expect failures - that's the nature of breakthrough innovation. Set clear milestones for continued funding and be ready to pivot or terminate underperforming initiatives.

The goal isn't immediate ROI but learning, capability building, and positioning for disruption. Consider innovation labs or startup partnerships to access cutting-edge capabilities without disrupting operations.

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Why ROI calculations kill breakthrough innovation?

Different project types need different evaluation frameworks. Traditional ROI kills breakthrough innovation; absent metrics waste core project resources.

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• Core AI: Standard financial metrics—payback period, NPV, IRR. Clear business cases competing for funding with predictable returns.
• Growth AI: Balanced scorecards mixing financial returns with strategic indicators like market share or customer satisfaction - evaluating both immediate value and expansion potential.
• Breakthrough AI: Technical feasibility, learning value, and transformative potential matter more than near-term financials. Let technologists, not just business leaders, assess these opportunities.

Across all categories - core, growth, and breakthrough - prioritize projects building foundational capabilities. Infrastructure investments, like robust data pipelines, reusable AI models, scalable platforms might seem expensive initially but become invaluable when enabling dozens of future initiatives. Make "unlocking potential" a key evaluation criterion across your entire portfolio.

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The organizational structure that makes or breaks AI success

Business units excel at core improvements but rarely produce breakthrough innovations. R&D generates breakthrough ideas but may lack business grounding.

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Where you place AI innovation shapes what gets built.

For breakthrough AI, create a centralized innovation unit with corporate funding, protected from quarterly pressures. Give it mandate to explore, freedom to fail, and direct leadership access. But maintain strong business connections to ensure relevance.

Core and growth AI projects belong with the business units that benefit from them. This ensures alignment, accountability, and adoption. Without business ownership, you get "successful pilots" that never scale.

Consider a hub-and-spoke model: a central AI center provides expertise and infrastructure while business units own initiatives. This balances technical depth with business relevance.

Your governance should evolve with maturity. Early adoption requires central coordination to build capabilities. As you mature, distribute ownership of core and growth initiatives while the center focuses on breakthrough AI and cross-functional opportunities.

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From strategy to action: Your AI portfolio roadmap

An AI portfolio is like an investment portfolio: diversified across core, growth, and breakthrough initiatives, regularly rebalanced based on performance and market conditions.

With thoughtful structure, you generate reliable returns today while positioning for tomorrow's breakthroughs.

Start by categorizing current initiatives: Which are core? Growth? Breakthroughs? Map everything to clear business objectives. Question any project that doesn't fit the framework or serve strategic priorities.

Remember: capability building matters as much as project delivery. Each initiative—whether core, growth, or breakthrough - should advance your learning journey, developing infrastructure, talent, and processes for long-term success.

The path forward requires courage to invest in uncertainty while maintaining operational excellence. With a balanced portfolio, clear governance, and appropriate metrics, you'll drive both immediate value and long-term transformation.

Ready to optimize your AI innovation portfolio? Transformation begins with understanding where you are and charting where you need to go.