Why Your "Agile" Process Is Actually Killing Innovation
The hidden reason your tech debt keeps growing
After more than 20 years in digital, I observe a recurring paradox that spans the entire tech ecosystem: companies invest massively in technology only to find themselves, 18 months later, with technical debt, obsolete products, and exhausted teams. This phenomenon affects startups and large corporations alike, regardless of their industry or technological maturity. The problem isn’t budget, talent, or even strategy. It’s our mass production culture applied to technological innovation.
We’ve transposed 20th-century industrial methods to the 21st-century digital economy. Result: we optimize for delivery speed and feature volume, at the expense of intrinsic quality and sustainability.
This approach generates a form of collective amnesia: we no longer know what it means to create something durable—we only know how to deliver.
Each sprint becomes a race against time where the objective is to check boxes in a backlog, not solve real problems. Each added feature accumulates technical debt that we systematically postpone to the “next sprint.” And when real technological disruption arrives—generative AI, cloud transformation, migration to new paradigms—we discover that we’re paralyzed by our own tech stack.
The irony is striking: the more we “deliver,” the less durable value we create. As Sandrine Olivencia emphasizes in “Build to Sell,” the fundamental difference lies in the very conception: creating assets versus creating deliverables. An asset generates value over time; a deliverable consumes resources to be maintained.
Why We Miss the Innovation Trains
This mass production culture explains why so many companies miss major technological transformations. According to a 2024 MIT study, 95% of AI pilots fail not for technical reasons, but for organizational and cultural reasons. Companies attempt to “plug” generative AI onto technical architectures and processes designed for mass production.
The result is predictable: AI amplifies existing dysfunctions. If your code is fragile, AI will generate even more fragile code. If your processes are chaotic, AI will automate chaos. If your culture favors quantity over quality, AI will produce bad solutions faster.
This reality affects all technological revolutions. Cloud transformation, migration to microservices architectures, adoption of DevOps methodologies: each time, the companies that fail are those trying to apply their old paradigms to new technologies.
Craftsmanship at Scale: A Necessary Alternative
Faced with these observations, it’s time to experiment with another model: craftsmanship at scale. This isn’t a romantic return to the past, but a conscious strategic trade-off that combines artisan excellence with modern industry’s deployment capacity.
Craftsmanship at scale rests on a simple principle: every person in the organization develops the ability to deliver quality on the first try. This means prioritizing intrinsic quality, care in execution, and eliminating voluntary technical debt. But unlike traditional craftsmanship, this approach must be designed to function at large scale, with reproducible processes and shared standards.
Concretely, this translates into several fundamental organizational changes. First, a redesign of performance metrics: instead of measuring only velocity (how many features delivered per sprint), we must integrate durable quality indicators that reveal the real health of our systems:
Rework rate: the percentage of code that must be rewritten within 6 months of its delivery
Flaky test rate: the proportion of tests that fail intermittently, revealing architectural fragility
Small change lead time: the time needed to deploy a minor modification to production, an indicator of accumulated complexity
Bug resolution time: the ability to quickly fix dysfunctions
Maintenance ease: the simplicity with which new developers can understand and modify existing code
Adaptability to changes: the system’s resistance to functional evolution
These metrics tell a different story from traditional dashboards. They reveal whether we’re building durable assets or accumulating technical debt disguised as productivity.
Training Craftspeople at Scale: From Production Flow to Learning Flow
But metrics and processes aren’t enough. You don’t scale craftsmanship with KPIs—you scale it by training craftspeople. In this model, the primary asset isn’t velocity but heritage: code, standards, and especially team expertise. This is where the real transformation lies.
The software craftsperson develops five fundamental skills that transcend technologies: real customer sense (starting from usage needs, not the backlog), quality at source (making defects appear early, stopping flow to learn), improvement science (formulating a gap, testing a countermeasure, measuring, standardizing), design for change (prioritizing changeability over completeness), and workshop work (pair programming, living standards, explicit transmission).
This training cannot be delegated to occasional training sessions. It requires structured, cadenced learning, inspired by traditional apprenticeship but adapted to modern constraints. Technical dojos (4-6 week cycles on concrete problems), engineering katas (90-minute weekly sessions to strengthen a precise technical gesture), and rotating pairing become rituals as important as stand-ups.
Mentoring takes on strategic dimension. We must identify our “workshop masters” (leads who guarantee standards), our “journeymen” (who carry daily quality and train), and our “apprentices” (who gain autonomy). This hierarchy isn’t status-based but functional: it organizes knowledge transfer.
More crucially: we must install a continuous learning culture where learning becomes a flow pulled by problems, not an “HR option.” This means protected and cadenced time, retrospectives at source after each incident, and the right to stop production to make a recurring defect visible.
ROA versus ROI: Changing Economic Paradigm
This transformation also requires a change in economic paradigm. We must shift from short-term Return on Investment (ROI) logic to sustainable Return on Assets (ROA) logic. ROI measures the immediate profitability of an investment; ROA measures an asset’s capacity to generate value over time.
A study by Elder Research published in 2024 shows that companies adopting “Return on Adoption” metrics (a variant of ROA applied to innovation) achieve results 40% superior over 3 years compared to those remaining focused on quarterly ROI. These companies invest more in team training, code quality, and system architecture. In the short term, they seem less “efficient.” In the medium term, they become indispensable.
AI as Revealer and Amplifier
The emergence of generative AI makes this transformation even more urgent. AI won’t replace developers, but it will amplify their strengths and weaknesses. A team that masters craftsmanship at scale will use AI to accelerate the creation of durable assets. A team trapped in mass production will use AI to produce technical debt faster.
The first feedback is enlightening. Companies succeeding in their generative AI adoption are those that first invested in their codebase quality, team training, and process clarity. AI then becomes a multiplier of their existing excellence.
In this context, trained craftspeople become essential: they know how to use AI as an exploration and generation tool, while maintaining their critical capacity to validate, refactor, and secure the produced code. They master “AI poka-yoke”: prompt templates, acceptance checklists for generated code, lints and policies that block generated code without safeguards.
A Strategic Leadership Challenge
Implementing craftsmanship at scale isn’t simple. It requires alignment at the strategic level, a complete redesign of incentive systems, and much pedagogy to evolve mindsets. It’s a leadership challenge that requires vision, patience, and the ability to resist short-term pressures.
But the stakes are worth it. In a world where AI will become omnipresent, companies that have developed a culture of technical excellence will have a decisive competitive advantage. They’ll be able to adopt new technologies without fearing amplification of their dysfunctions. They’ll be able to innovate sustainably rather than suffer the planned obsolescence of their own creations.
The Moment of Truth
We’re at a pivotal moment. Companies that will succeed in their transformation over the next 3 years will be those that had the courage to question their mass production culture to adopt craftsmanship at scale. This isn’t just a technical question—it’s a cultural and strategic question that engages the very future of innovation in our organizations.
The challenge is before us. It’s up to us to meet it, with the artisan’s rigor and the industrialist’s ambition.
Sources:
“Build to Sell: The Lean Secret to Crafting Irresistible Products” - Sandrine Olivencia, Flavian Hautbois, Caroline Besnard (June 2024)
“Why 95% of AI pilots fail and how to succeed” - LinkedIn/MIT Study (2024)
“Moving Beyond ROI: Return on Adoption” - Elder Research (2024)