Why Funding Products (Not Projects) Matters Even More in the Age of AI

Organizations are pouring unprecedented resources into Artificial Intelligence, chasing the promise of revolutionary efficiency and competitive advantage. The hype is immense, and the investment is staggering. Yet, behind the headlines and optimistic forecasts, a troubling reality is emerging: AI initiatives are failing at an alarming rate, leaving executives wondering why their bets aren't paying off.

This disconnect creates an AI paradox. The technology itself is more capable than ever, but the potential for transformative digital transformation is being squandered. The root of the problem isn't a failure of algorithms or data science; it's a failure of the operating model. Most organizations are attempting to build the future of AI on the crumbling foundation of a rigid, outdated, project-based funding system. This approach is fundamentally mismatched with the iterative, experimental nature of AI development. To truly unlock business value, a shift is required—from funding temporary projects to funding durable products.

This post reveals five critical takeaways that explain why a product-centric operating model is no longer optional in the age of AI.

1. Your AI Initiative Has an 80% Chance of Failure Under a Traditional Project Model

Let's start with a stark reality check. Research cited by Harvard Business Review reveals that AI project failure rates are as high as 80%, nearly double that of traditional IT projects. This isn't an anomaly; it's a direct consequence of a fundamental mismatch between the work and the model used to manage it.

Traditional projects work like building a house with a fixed blueprint. Success is measured by adhering to the "iron triangle" (the rigid constraints of time, cost, and scope). Digital and AI development, however, is a process of continuous discovery and adaptation, where customer needs and technical possibilities evolve rapidly. When AI initiatives are forced through project frameworks, teams become focused on delivering pre-planned features rather than solving the underlying customer problem.

"Operating a project model on a digital product is like trying to fit a square peg in a round hole. The 'iron triangle' of time, cost, and scope fails when customer needs evolve continuously and competitive advantage requires constant innovation."

2. AI Creates a New, Costly, and Invisible Form of Technical Debt

The technical debt created by AI is not like traditional software; it is not a one-time fix. It is a continuous, invisible tax on the model's value. This isn't about messy code; it's about the inherent decay of the model's value over time. The core problems include:

• Model Deterioration & Statistical Drift: An AI model's predictions naturally become less accurate as the real world changes. The patterns it learned no longer perfectly match the present.
• Data Drift: The underlying data feeding the model changes in structure or statistical properties, eroding performance.
• Evolving APIs: The ecosystem of data sources, tools, and platforms that the AI relies on is constantly changing, creating integration and maintenance challenges.

A project-based model, with its defined end date and temporary team, is fundamentally unequipped to handle this reality. Once a project is "done," the team disbands, and there is no one with long-term ownership to perform the continuous monitoring, retraining, and feature refreshing required to preserve the AI's value. A product model, which establishes a durable, long-term team, is the only way to manage this ongoing technical debt and protect the organization's AI investment.

3. The "New" Solution Is Actually Decades Old and Proven by Industry Giants

The idea of organizing around products isn't a fleeting trend born from the digital age; it has a long and proven history of transforming industries. Its principles are rooted in a century of management innovation focused on a single idea: organizing work around the value delivered to the customer.

A Historical View of Product Thinking

• 1931 - Procter & Gamble: Neil McElroy's "Brand Men" memo created the first product management role to give a single product line dedicated, focused ownership.
• 1940s - Toyota's Production System: Introduced lean principles and a relentless focus on customer-driven production and waste elimination.
• 2001 - Agile Manifesto: Codified the modern principles for today's product operating models, prioritizing customer collaboration and responsiveness to change.

This model's power is demonstrated by its transformational success at some of the world's most dominant companies:

• Amazon Prime: A masterclass in the product model's three pillars: Product Strategy (identified shipping costs as the core customer problem), Product Discovery (extensive experimentation), and Product Delivery (empowered teams iteratively improving fulfillment).
• Microsoft: Achieved a 60% reduction in manual work and 20% faster feature delivery.
• JPMorgan Chase: Generated $1.5 billion in business value from its AI-enabled product operations.

4. Funding Products Isn't a Blank Check, It's a Strategic Shift to "Spend Envelopes"

A common misconception is that shifting to product funding means handing over a blank check with no accountability. The reality is a strategic shift from funding inflexible, time-bound projects to funding durable, capacity-based teams organized around customer value streams. The journey of TD Bank provides a practical, phased model for this transition:

The Phased Transition Model

1. Stage 1: Smart Funding: Work is chunked into shorter 3-6 month increments with simplified business case justifications. This de-risks the transition by proving the value of shorter feedback loops.
2. Stage 2: Spend Envelopes: The organization evolves to allocating a yearly budget to a specific product line or customer journey. The product owner, accountable for business outcomes, manages and allocates those funds. This approach is often governed by a "Shark Tank" model, ensuring every dollar is aligned with top-level priorities.

5. This Isn't Just an IT Change; It's a C-Suite Strategy for Long-Term Value

The move to a product operating model is more than an IT process improvement; it aligns the entire organization with a foundational shift in the global economy—moving from an era of shareholder capitalism (short-term profits) to stakeholder capitalism (long-term value creation). Stakeholders, including customers, employees, and investors, demand that companies demonstrate long-term value through KPIs related to talent, innovation, society, and governance.

A product model is the natural organizational structure to execute this modern strategy. By funding a continuous value stream over its entire lifecycle, it creates the perfect framework for a long-term, value-focused, and AI-enabled organization. Highly mature product-led companies achieve 60% greater total returns to shareholders and 16% higher operating margins compared to their project-based peers.

Stop Piloting AI and Start Funding Value

To unlock the true, transformative potential of AI, organizations must break free from the constraints of project-based thinking. The project model is a relic of a pre-digital era and is actively sabotaging AI initiatives. Success in this new age requires a fundamental shift to a product operating model.

This is a shift from managing costs to managing value. It means funding durable teams organized around customer outcomes, not temporary projects measured by arbitrary deadlines. The organizations that master this will define their industries for the next decade.

The question is no longer if your organization needs to make this shift, but whether you'll make it before it's too late.