Lean Decision Quality: from intuition to value-based decisions

Organizations have more data than ever before, but lack the confidence, time, and consistency to make decisions where it truly matters.
The number of strategic decisions entrusted to C-level executives has increased by 35% over the last five years, while perceived quality has dropped by 25% (BCG, 2024) — revealing a systemic crisis.
Fragmented processes, cognitive biases, and cross-functional delays reduce decision speed, with an average cost for “suboptimal” choices equal to 3–5% of annual EBIT (McKinsey, 2024).
A strategic decision-making cycle takes an average of 63 days, but drops to 45 in organizations that have adopted lean models (Gartner, 2025).
The solution is Lean Decision Quality (LDQ): an operational model born from the convergence of decision science, neuroscience, and Lean principles, designed to improve the quality, speed, and coherence of choices.
A practical evolution of Stanford’s Decision Quality, LDQ enables companies and teams to reduce bias and decision-making time by up to 40%, accelerating the transformation of data into action and alternatives into shared value (Stratego.life, 2024).
Adopting LDQ means enabling leadership that does not react, but orchestrates change — building trust, reducing waste, and ensuring that every strategic decision is guided by insight, not by habit or pressure.
In an era of increasing complexity, Lean Decision Quality represents the key to evolving from reactive systems to agile decision ecosystems.

Over the past decade, the corporate decision-making landscape has been transformed by increasingly volatile markets, exploding data, and shrinking response times.
Yet, many organizations still make decisions with tools designed for a stable world: hierarchical models, slow processes, and little transparency about underlying assumptions.
The result is a silent crisis in decision quality.
The lack of formalized decision processes — a condition that affects 78% of Italian companies — generates inefficiencies that translate into estimated losses of 2–5% of EBITDA margin each year (McKinsey, 2024).
Only 22% of Italian companies have structured processes, compared to an OECD average of 39%.
The use of decision canvases or equivalent tools remains at 9%, far below the 25% of international best practices, and systematic stakeholder engagement does not exceed 30%, compared to 55% among global leaders (Decision Intelligence Observatory – Polimi; Stanford DQ Lab; Stratego, 2024).
More data does not mean better decisions; on the contrary, informational overload often slows, disperses, and polarizes.
Real-world experiences demonstrate how better decision architecture can reverse this trend:

• In the pharmaceutical industry, Sanofi introduced Decision Canvases and cross-functional teams for managing clinical trial protocols: time-to-decision fell from 94 to 61 days, post-decision revisions dropped by 40%, and stakeholder involvement increased by 25% (MIT Sloan, 2023).
• In the banking sector, a major European bank established a Decision Office based on LDQ principles: in just six months, internal IT project escalations decreased by 52%, while the Decision Confidence Index rose from 6.3 to 8.1, reflecting a measurable boost in organizational trust.

The issue is not the lack of data, but the inability to interpret and act collectively.
Complexity grows faster than clarity; every function builds its own truth, and decision-making becomes a negotiation arena rather than a strategic alignment mechanism.

The origins of LDQ lie in the Decision Analysis studies developed during the 1970s and 1980s at Stanford University by Ronald A. Howard, father of Decision Quality (DQ).
Initially conceived as an engineering discipline of rationality — a method for reducing uncertainty and making decisions measurable, replicable, and transparent — it evolved when it met Lean Thinking, derived from the Toyota Production System.
In the 2010s, institutions like Stanford DQ Lab and MIT Sloan began experimenting with hybrid DQ–Lean models, giving rise to Lean Decision Quality (LDQ): a pragmatic evolution of decision science for organizations operating in high-complexity, high-variability environments.
Today, LDQ represents the convergence of neuroscience, lean thinking, and organizational design, translating decision science into a practical operating system for change leadership.

1. Excess data, lack of insight
Companies are surrounded by reports and dashboards but struggle to turn information into decisions. Each function produces its own analysis in isolation, leading to confusion rather than value.
2. Systemic cognitive bias
Traditional processes lack tools to identify and counteract biases such as anchoring, confirmation bias, or groupthink. As a result, 7% of potential ROI is lost to unchallenged group assumptions (McKinsey, 2023).
3. Organizational silos and slowness
Top-down processes, disjointed teams, and lack of a common decision language create delays and inefficiencies. Each business unit becomes an island, hindering execution speed.
LDQ integrates neuroscience, Lean principles, and organizational design to transform these barriers into operational levers.
With iterative cycles, visual tools, and clear roles, it evolves decision-making from episodic action into a continuous process of learning, alignment, and shared value.

LDQ combines decision science and Lean thinking to make complex organizational decision-making simple, measurable, and replicable.
At its core is the Decision Wheel — a cyclical structure guiding leaders through four main phases plus a final optional phase of consolidation and learning.
Each phase represents a distinct organizational learning moment: the goal is not only better decisions but stronger collective decision capability over time.

1. Define the decision perimeter — Clarify objectives, boundaries, risks, and priorities.
2. Identify strategic decisions — Distinguish what’s strategic from what’s tactical and engage stakeholders early.
3. Generate value alternatives — Create at least three “what-if” scenarios, stimulating divergent and creative thinking.
4. Analyze, compare, and select — Evaluate trade-offs, risks, and expected returns, documenting everything in a Decision Record.

Consolidation and learning follow: communicating results and reflecting collectively to transform experience into organizational knowledge.
Five operational principles make LDQ practical and scalable:

• Visual canvases to map decision quality and flow
• Shared accountability and empowerment
• Cognitive diversity as a driver of insight
• Bias mitigation tools to limit distortions
• Lean thinking to remove friction and waste

Measured results:

• +70% clarity of objectives
• +40 points in cross-functional engagement
• –40% fewer post-decision revisions

Organizations adopting LDQ report tangible and sustainable financial improvements within 12 months.
The model operates along three key dimensions that together define Decision ROI: time, trust, and strategic coherence.
1. Time
LDQ reduces the duration of strategic decisions by 30–40% (Gartner, 2025), freeing up managerial bandwidth and preserving capital.
Example – Enel: Decision time dropped from seven months to under four, with tripled what-if scenarios and a +30% improvement in capital allocation efficiency (Stratego.life, 2024).
2. Organizational trust
Transparency and collaboration increase decision confidence by 35%, while post-decision revisions drop 40% (Stratego.life & HBR Analytics, 2024).
3. Strategic coherence
Alignment between strategy and execution improves by 25% (McKinsey, 2024).
Example – Nestlé: Integration of LDQ and Decision Intelligence with Deloitte increased scenario testing from 3 to 10 per product launch, cutting time-to-market by 25% and post-launch revisions by 30% (Deloitte, 2024).
In short: LDQ transforms decision quality into measurable competitive advantage — less time wasted, higher trust, and more consistent decisions generating superior returns.

Adoption of LDQ remains uneven across countries.
Only 9% of Italian firms have formally integrated LDQ tools, compared to 27% in the UK and 34% in the US (Stanford DQ Lab, 2024).
Dedicated Decision Offices exist in 8% of Italian organizations versus 29% in North America (Polimi, 2025).
The gap stems less from technology and more from cultural and structural factors — hierarchies, low accountability, and intuition-based decision-making.
In more mature ecosystems, Decision Quality is recognized as a strategic asset, not just an efficiency lever. Continuous measurement and learning make decision quality a pillar of competitiveness, resilience, and strategic alignment.

Over the last decade, digitalization has made companies increasingly data-driven — dashboards, KPIs, and analytics abound.
Yet, visibility rarely translates into better or faster decisions. Data has become the end, not the means: analysis for knowing, not for acting.
The result? Growing informational productivity but stagnant decision productivity.
LDQ marks the shift to a decision-driven model — moving from indicator accumulation to scenario building, simulation, and explicit trade-off evaluation.
Data-driven asks: “What happened?”
Decision-driven asks: “What should we do now — and why?”
LDQ provides a shared grammar and practical tools for transforming insights into agile, transparent, and shared decisions.
Quality is no longer about knowing everything — it’s about deciding better and faster when it matters most.

The convergence of Decision Intelligence and Simulative AI greatly amplifies LDQ’s potential.
Advanced algorithms can now simulate hundreds of scenarios, assess risks and returns in real time, and suggest optimized solutions to complex problems.
But without a clear decision framework — criteria, responsibilities, and strategy — even the best AI yields misaligned outcomes.
True value comes from integration: LDQ defines how to decide, Decision Intelligence defines what to decide.
Key findings:

• –18% in decision costs (Deloitte, 2024)
• +22% in forecast accuracy (PwC, 2025)
• 42% of enterprises integrating LDQ with Decision Intelligence by 2025, cutting insight-to-action time to under 14 days (Gartner, 2025; Forrester, 2024)

To sustain improvement, LDQ introduces quantitative metrics to monitor decision process maturity.
The Decision Quality Scorecard — now an international benchmark — measures five core areas:

• Strategic consistency: ≥ 85% of decisions aligned with company values
• Efficiency: < 45 days per strategic decision
• Participation: ≥ 60% stakeholder involvement
• Analytical quality: ≥ 3 simulated alternatives per decision
• Confidence: Decision Confidence Index ≥ 8/10

This transforms decision quality from an abstract concept into a measurable strategic capability.

Lean Decision Quality is not just a methodology — it’s a new organizational competence for competing amid uncertainty.
Adopting LDQ step by step builds a mindset that turns decision-making from episodic acts into structured, shared, self-improving processes.
In an environment defined by speed, complexity, and pressure, decision quality becomes the foundation for trust, execution discipline, and resilience.