Rethinking the MVP
'Why AI Demands a Return to “Old School” Product Strategies'
A month ago, a girl serving in the bar, asked me, 'You're Old School House?*' I was marginally surprised by the somewhat rhetorical question. Before I could reply, she said she'd observed me dancing. This made me laugh, and I had to agree to be labelled 'Old school'. Better than 'old fool', I guess.
*For the uninitiated, this doesn't mean I was a derelict building where kids used to be educated.
Anyway, I have adopted this nomenclature for everything I do. And indeed I am 'Old School' in business, strategy, operations and change management.
However, I have to point out, that being 'old school' doesn't stop you dancing to the latest popular trends or fashions. With the added advantage that you have the experiences to compare.
This was, of course, a digression. What I want to share is my view that AI is encouraging or necessitating a return to 'Old School' business methodologies.
And I am arguing that we are going to see a shift in the Agile / MVP methodologies.
The Solo-Preneur's Tool That Grew Too Big
MVP shines in small, agile environments where rapid market testing is essential. However, its widespread adoption by larger organizations has revealed significant limitations. When enterprises with established market positions and complex products adopt MVP methodologies wholesale, they risk more than they gain.
Consider this: While a solo entrepreneur can pivot quickly based on market feedback, larger organizations need to consider brand reputation, existing customer bases, and complex stakeholder relationships.
The "fail fast" mentality that serves startups well can become a liability at scale.
The Hidden Costs of 'Moving Fast'
The promise of faster development through MVP methodology often proves illusory. Technical debt accumulates silently, like interest on a high-risk loan. Teams end up spending more time fixing foundational issues that could have been avoided with proper initial planning.
What's more concerning is how MVP has become a convenient excuse for cutting corners in user experience design. The result? Products that technically "work" but fail to delight or retain users. In today's market, that's a death sentence for any product.
The Game Changer: It's AI
Artificial intelligence isn't just another feature to add to your product roadmap—it's fundamentally changing how we approach product development. AI-driven products require:
These requirements align more naturally with traditional business methodologies than with rapid MVP iterations.
You can't "move fast and break things" when dealing with AI systems that need reliable, well-structured data and careful governance.
The Program Management Renaissance
The MVP era coincided with a devaluation of program management expertise. Many organisations replaced structured development approaches with loose agile frameworks, often led by product managers with limited program management experience.
This shift has created significant blind spots in:
As AI projects grow in complexity, these gaps become increasingly apparent and costly.
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The Case for Old School Methodologies
What does “old school” really mean? It’s about re-embracing a methodical approach that values comprehensive planning, robust governance, and strategic clarity. Traditional frameworks—like stage-gate models, program management offices, and rigorous market analysis—offer something the MVP mindset often lacks: long-term vision. Rather than launching the bare minimum, we focus on ensuring a product’s architecture, user experience, and strategic positioning are solid from the start.
Strategic Planning in the Age of AI
AI development demands a return to robust strategic planning. Success in this new era requires:
These elements aren't old school hangovers —they're essential foundations for successful AI implementation.
Complexity in the AI Era
AI has become more than just a buzzword—it’s the linchpin connecting product evolution with customer engagement. Intelligent features learn from user behavior, predicting needs and preferences. This complexity demands rigorous knowledge management, data governance, and a sturdy strategic backbone. Releasing half-baked, “good enough” solutions in this environment is risky. Customers expect personalization and reliability powered by AI, and they notice when you’re simply patching problems on-the-fly.
This fundamental shift means that product teams must think beyond the initial release and consider the entire product evolution lifecycle from day one.
The implications are significant:
First, product architecture must be designed with continuous learning and adaptation in mind. Second, customer engagement strategies must be integrated into the core product development process rather than treated as a separate function. Third, data collection and analysis capabilities must be robust enough to capture and interpret complex patterns of user behaviour.
This convergence highlights another limitation of the MVP approach. When every customer interaction potentially shapes the product's evolution, launching with minimum functionality may mean missing crucial learning opportunities.
A more comprehensive initial release, supported by sophisticated data collection and analysis capabilities, often provides richer insights for future development.
The Path Forward: A Balanced Approach
The solution isn't to abandon MVP principles entirely but to integrate them thoughtfully with traditional business methodologies. Here's what organisations should consider:
Looking Ahead
As AI continues to reshape product development, organisations that maintain high standards in strategic planning and program management will have a significant advantage. The era of using MVP as an excuse for rushed, poorly planned development is coming to an end.
Success in the AI age requires a return to quality—in planning, in execution, and in delivery.
Traditional business methodologies, far from being outdated, are proving to be essential foundations for innovation in the AI era.
The question isn't whether to use MVP methodologies, but rather how to balance rapid development with proper planning and execution. As we move forward, the winners will be those who can harness the best of both worlds—the speed of modern development approaches with the rigour of traditional business methods.
The author is a organisation development strategist focusing on the intersection of AI and business methodologies.
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