From Frameworks to Sense-Making: Agility Reframed for Real Decision Advantage

In today’s unpredictable business world, sticking to rigid frameworks often leads to inefficiency and missed opportunities. While processes like Agile promise adaptability, many companies find themselves bogged down by rituals that fail to deliver results. The solution? Shift from following frameworks to sense-making - a decision-making approach that prioritizes understanding complexity, experimenting, and responding effectively to change.

Key takeaways:

• Frameworks fall short in complex environments: They work in predictable scenarios but struggle with uncertainty.

• Sense-making focuses on better decisions: Probe, test, and adjust quickly to navigate ambiguity.

• Psychological safety is critical: Teams need open communication to share insights and act on them.

• AI accelerates the need for agility: Use data to inform decisions, but rely on human judgment to interpret it.

Dave Snowden - What Agile got Wrong, and Right!

Understanding Complexity Through the Cynefin Framework

Dave Snowden's Cynefin framework offers a fresh perspective on problem-solving by emphasizing that not all challenges can be tackled with the same methods. While many leaders default to analysis and planning, this framework highlights the importance of adapting approaches based on the nature of the problem. It moves away from rigid methodologies, helping organizations embrace a more flexible and effective decision-making process.

At its core, the Cynefin framework categorizes problems into distinct domains, guiding leaders to align their strategies with the specific challenges they face. This approach provides a structured way to navigate complexity while encouraging agility in leadership.

The 5 Domains of Cynefin

The Cynefin framework divides challenges into five domains, each requiring a unique strategy:

• Simple (or Obvious) domains: These involve clear cause-and-effect relationships, where best practices are well-established. For instance, a manufacturing process adhering to quality standards follows the straightforward sequence of sense → analyze → respond.

• Complicated domains: These require expertise and detailed analysis to uncover solutions. Engineering problems or financial modeling fit here, where specialized knowledge and a methodical sense → analyze → respond approach are essential.

• Complex domains: In these scenarios, cause and effect are only clear in hindsight. For example, shifting market trends or changing customer behaviors demand experimentation and adaptation, as traditional analysis often falls short.

• Chaotic domains: These call for immediate action to regain control. Situations like cybersecurity breaches or supply chain disruptions require leaders to act decisively, followed by sensing and responding.

• Disorder: This domain arises when it’s unclear which of the other domains applies. In such cases, organizations often fall back on familiar problem-solving methods, even if they’re not suited to the situation.

Why Frameworks Struggle in Complex Domains

Traditional frameworks often assume predictability and control, which works well in complicated domains where structured analysis can uncover solutions. However, in complex domains, outcomes are unpredictable, and the connection between actions and results only becomes apparent after the fact. This unpredictability makes rigid plans ineffective.

While detailed plans are useful in stable environments, they falter in rapidly changing contexts. In such cases, sticking to predefined methods can lead to missed opportunities as initial assumptions quickly become irrelevant. Instead, leaders need to focus on running small experiments, learning from outcomes, and adjusting strategies in real time. This iterative approach is key to navigating complexity.

Practical Insights for Business Leaders

For executives, understanding the Cynefin framework can reshape how decisions are made and resources are allocated. Recognizing whether a challenge is simple, complicated, complex, or chaotic allows leaders to tailor their strategies accordingly.

In complex settings, rather than relying on detailed business cases, leaders should prioritize small, safe-to-fail experiments that generate early insights. This approach reduces the risk of applying a one-size-fits-all methodology and enables more dynamic, responsive resource management.

Organizations that can quickly identify the nature of their challenges and adapt their methods are better equipped to handle uncertainty. By doing so, they position themselves to achieve lasting success while fostering both internal and external adaptability.

Building Inner and Outer Agility

Creating an agile organization goes beyond implementing frameworks and tools - it’s fundamentally about how people think, learn, and adapt. While processes provide structure, it’s the human element that determines whether an organization can truly navigate complexity and achieve meaningful outcomes. This interplay between inner agility (mindset, judgment, learning) and outer agility (methods, flow, scale) is what enables effective decision-making on a large scale. Let’s explore how these two dimensions work together to drive agility.

Inner Agility: A Mindset for Adaptation

Research from McKinsey on "Leading with Inner Agility" highlights that the most effective leaders in uncertain environments share key mental traits that help them make sound decisions under pressure. Inner agility is built on three pillars: curiosity that fuels ongoing learning, emotional regulation that keeps clarity intact during chaos, and openness to learning, which allows leaders to adjust their thinking as new information arises. These traits are critical in navigating unpredictable scenarios where flexibility is essential.

Leaders with strong inner agility don’t try to eliminate uncertainty by overanalyzing. Instead, they embrace ambiguity and make provisional decisions, ready to adjust as needed. They ask smarter questions, challenge their own assumptions, and remain open to shifting direction when evidence points to a better path.

This mindset is particularly important in complex situations where cause and effect often become clear only in hindsight. Leaders with mental agility can experiment decisively without being tied to rigid goals or outdated mental models. This enables them to pivot quickly, cut losses on failing initiatives, and reallocate resources effectively based on emerging opportunities rather than past decisions.

Psychological Safety: The Missing Link

While individual adaptability is crucial, it’s not enough on its own. Amy Edmondson’s research on psychological safety reveals the importance of a supportive environment in fostering organizational sense-making. Without psychological safety, critical information often gets suppressed, and decision-making suffers. If team members fear repercussions for speaking up, sharing bad news, or admitting mistakes, the organization loses access to valuable insights it needs to handle complexity.

Psychological safety creates the conditions for collective sense-making to thrive. In such environments, team members feel comfortable sharing early signals, questioning assumptions, and engaging in constructive debates that lead to better decisions. This is especially vital in complex domains, where success depends on gathering and acting on observations from across the organization.

The impact of psychological safety is measurable. Teams that operate in safe environments learn faster, identify problems earlier, and adapt more effectively to change. They also take smarter risks - engaging in calculated experiments that are essential for navigating complexity.

For leaders, fostering psychological safety isn’t just about being approachable. It’s about creating a culture where sharing problems is encouraged, learning from failure is expected, and challenging the status quo is everyone’s responsibility. This cultural foundation enables faster and better decision-making across the organization.

Scaling Agility Through Governance

The challenge for large organizations is translating individual and team-level adaptability into enterprise-wide agility. This requires rethinking governance to focus on guardrails instead of gatekeepers.

Traditional governance models are built on predictability and control, relying on detailed plans, stage-gate approvals, and strict adherence to processes. While effective in stable and straightforward scenarios, this approach often fails in complex environments where solutions emerge through experimentation and adaptation.

RESTRAT offers a different perspective on governance for complexity: context-sensitive governance. Instead of rigidly controlling actions, this approach focuses on ensuring the quality of decision-making processes. It involves setting clear criteria for when to pivot or continue, creating feedback loops to surface critical signals quickly, and maintaining transparency about successes and failures.

This governance model prioritizes learning over compliance. It emphasizes shorter decision cycles, regular check-ins, and mechanisms to integrate new insights into strategic plans. It also normalizes failure as part of the learning process, incorporating it into how success is measured and communicated.

Effective governance also requires flexible resource allocation. Instead of locking in annual budgets, organizations should be able to quickly redirect resources based on shifting market conditions, customer needs, or competitive pressures. This adaptability ensures that strategic priorities remain aligned with real-time insights.

The goal of this governance approach isn’t just to move faster but to make smarter decisions. By focusing on decision quality rather than rigid adherence to processes, organizations can align their actions with evolving priorities, ultimately improving ROI and building resilience. Teams are empowered to experiment within clear boundaries, while leaders maintain oversight to decide where to invest or adjust course as needed. In complex environments, this kind of governance ensures that agility leads to better outcomes, not just faster processes.

Leadership and Portfolio Decisions: Outcomes Over Outputs

Shifting from a rigid adherence to frameworks to a focus on sense-making transforms how leaders approach portfolio management and strategic decision-making. This change calls for a fresh look at governance and how resources are allocated.

Focus on Outcomes, Not Process Compliance

Jonathan Smart, in his book Sooner Safer Happier, highlights a key principle: optimize for outcomes, not rituals. Too often, organizations measure agile maturity by how well they follow certain processes, rather than by the value they deliver to customers or the business.

This process-first mindset shows its flaws in portfolio management. Many leaders approve projects based on detailed plans and adherence to stage-gate processes, even when market conditions have shifted dramatically since those plans were created. While this approach may work in stable environments, it falters in complex scenarios where solutions emerge through trial and error.

Forward-thinking organizations flip this script. They focus on clear, measurable outcomes - like customer satisfaction, revenue growth, market share, or cost savings - and give teams the freedom to decide how to achieve these goals. Frameworks provide structure, but sense-making provides direction. Frameworks ensure consistent collaboration and communication, while sense-making allows teams to adapt based on what they learn.

This shift is especially evident in how portfolios are prioritized. Instead of ranking projects based on initial business cases or adherence to planning templates, leaders evaluate initiatives by their potential to deliver measurable results quickly. For example, projects that test critical assumptions within 90 days take precedence over those requiring 18 months to show value.

This outcome-driven mindset also reshapes how resources are allocated. Instead of locking in annual budgets tied to detailed project plans, leaders keep funding flexible. Teams that show strong results and learning progress receive more investment, while those struggling to deliver are redirected or intervened with sooner.

Faster Pivots and Better Alignment

Focusing on outcomes accelerates decision-making and ensures initiatives align with strategic goals. Sense-making allows organizations to make critical scale-or-terminate decisions earlier in the investment cycle. Traditional portfolio management often falls victim to the sunk cost fallacy, where failing projects continue to receive funding due to prior investments. Leaders skilled in sense-making recognize that in complex environments, decision quality matters more than process speed.

Short feedback loops and early detection of success or failure indicators are key. Organizations that embrace this approach hold regular portfolio reviews centered on learning rather than status updates. They establish clear criteria for when to pivot, persist, or end initiatives.

This approach has measurable benefits. Companies that adopt outcome-focused portfolio management experience shorter decision cycles, fewer wasted investments, and more funding directed toward initiatives that create value. They also respond faster to market changes, customer feedback, and competitive pressures.

Alignment improves as well. When teams are clear about the outcomes they’re working toward, they make better day-to-day decisions. They can adjust their methods based on new information without waiting for formal approvals. This combination of autonomy and accountability enables faster execution and better results.

Instead of focusing on compliance with portfolio processes, leaders design governance systems that enhance decision-making quality. This includes defining investment themes tied to strategic outcomes, creating feedback mechanisms to identify critical signals early, and maintaining the flexibility to reallocate resources as conditions change.

Framework Compliance vs. Sense-Making Comparison

The contrast between these two approaches becomes clearer when examining leadership practices:

Aspect	Framework Compliance	Sense-Making Approach
Decision Criteria	Process adherence	Outcome achievement, learning speed
Resource Allocation	Fixed annual budgets	Flexible funding based on evidence
Success Metrics	Milestone completion	Customer value, business impact, adaptability
Risk Management	Upfront planning, stage gates	Early experimentation, quick adjustments
Portfolio Reviews	Status updates	Learning-focused, outcome-driven reviews
Team Autonomy	Follow prescribed methods	Freedom to achieve defined outcomes

Sense-making doesn’t eliminate structure or accountability - it simply redirects them toward outcomes rather than activities. Teams still need clear goals, regular check-ins, and consistent communication. The difference lies in what gets measured and how decisions are made.

Leaders who embrace this shift report several advantages. They make faster decisions by focusing on evidence rather than rigid processes. Alignment improves as teams understand the "why" behind their work, not just the "how." Organizations also become more resilient, as teams learn to adapt their methods while staying focused on desired results.

This transformation requires bold leadership. It means accepting that teams might not follow prescribed methods perfectly, as long as they deliver results and demonstrate learning. It also means shifting from controlling activities to enabling outcomes. Most importantly, it recognizes that culture trumps strategy and technology if signals are ignored - even the best frameworks won’t help if an organization lacks the cultural conditions for effective sense-making.

As complexity and change accelerate, this focus on outcomes becomes even more critical. The ability to sense, interpret, and act on weak signals often determines whether initiatives succeed or drain resources. Leaders who master this skill build organizations that don’t just survive uncertainty - they thrive in it.

Business Outcomes in the AI-Driven Future

AI is speeding up decision-making like never before. Companies that can quickly interpret signals and adapt their strategies are gaining a clear edge. But this isn’t just about adding more data to existing processes - it’s about reshaping how businesses handle complexity. To keep up, organizations need new ways of making decisions that prioritize agility and focus on outcomes.

AI's Impact on Complexity and Decisions

AI is flooding every part of a business with data - whether it’s customer insights or market trends - that used to take months to gather. While this creates exciting opportunities, it also brings new challenges. Traditional systems like quarterly reviews or annual planning cycles often can’t keep up with rapidly shifting market dynamics and evolving customer preferences. What once provided structure can now cause delays.

The best organizations turn these challenges into advantages by mastering the art of sense-making. While AI doesn’t replace human judgment, it amplifies the need for sharper decision-making. In complex situations, the connection between cause and effect often becomes clear only in hindsight. AI helps by speeding up feedback loops, making it easier to identify what’s working and what isn’t.

Take portfolio management as an example. Instead of waiting for scheduled reviews to assess performance, leaders can now track key metrics - like customer engagement or satisfaction - on an ongoing basis. These real-time signals help identify which projects are thriving and which are struggling, enabling faster and more informed decisions.

This shift also calls for a rethink of governance. Traditional risk management focuses on avoiding mistakes through rigid planning. In contrast, AI-driven governance emphasizes spotting issues early and responding quickly. The goal isn’t to avoid all errors but to address problems swiftly and minimize their impact.

Metrics for Measuring Sense-Making Success

When organizations embrace sense-making, they see measurable improvements across the board. Decision-making speeds up as teams move away from lengthy debates toward rapid testing and learning. Better signal detection reduces wasted investments and ensures resources are allocated more effectively.

Portfolio efficiency also gets a boost. More investments are directed toward initiatives that genuinely add value for customers. Leading companies report that a larger share of their spending directly supports strategic goals, reflecting a refined ability to separate impactful projects from less effective ones.

Customer responsiveness improves as well. By interpreting customer signals more effectively, businesses can bring new products and features to market faster. AI helps spot emerging trends, while strong sense-making processes ensure timely and strategic action.

Additionally, companies with robust sense-making capabilities are more resilient in the face of disruptions. They recover faster from market shocks by quickly identifying new opportunities and reallocating resources to meet shifting demands. This flexibility helps sustain growth even in uncertain times.

Future Outlook: Scaling Sense-Making for Advantage

The future belongs to organizations that can detect subtle signals and act on them before their competitors. Internal agility - fostering a culture of learning, judgment, and adaptability - drives external agility in processes and execution. As AI continues to speed up change, this ability becomes increasingly critical.

To stay competitive, businesses must embed sense-making into their core operations. This means building systems that automatically highlight key signals, creating decision-making processes that match AI’s speed, and cultivating a culture where teams feel safe to experiment and learn from mistakes.

Modern governance systems act as guardrails, enabling quick but responsible decision-making. They establish clear investment priorities tied to strategic goals, incorporate feedback loops to catch early signals, and remain flexible enough to adapt as conditions change.

Success requires balancing AI’s ability to generate insights with human expertise in interpreting them. Companies that thrive will use AI to identify patterns and opportunities while ensuring their teams have the skills and confidence to act decisively. A culture of psychological safety - where employees feel comfortable sharing insights - is essential. Without it, critical signals might be overlooked, leading to poor decisions.

This transformation demands investments in both technology and people. AI systems must be capable of processing massive amounts of data and identifying meaningful patterns. At the same time, leaders need to contextualize these insights and create environments where teams can experiment and adapt quickly.

Conclusion: From Frameworks to Sense-Making

The way businesses operate has undergone a seismic shift. While frameworks offer a sense of structure, they simply can’t keep up with the complexity of today’s markets. In a world where AI speeds up the pace of change, having clear direction is far more critical than rigid adherence to structure.

Take recent examples like ING Bank, which slashed project cycle times by 28%, or Unilever, which saved $120 million by reducing stranded investments. These successes highlight the power of enabling teams to act quickly and decisively, leading to tangible results[1]. Such outcomes challenge traditional governance models, urging leaders to prioritize adaptable, results-driven strategies over outdated, process-heavy approaches.

In uncertain times, making better decisions trumps sticking to processes for the sake of it. Businesses that focus on outcomes rather than rituals consistently outperform those stuck in the routine of following frameworks. The key difference? Their ability to read the signals, adjust strategies, and ensure their actions align with their goals.

This is where leadership plays a pivotal role. Just as inner agility helps individuals adapt quickly, strong leadership creates an environment where teams feel safe to take smart risks and innovate. Leaders must move away from rigid oversight and instead design governance systems that act as flexible guidelines. These guardrails should empower teams to make decisions independently while still aligning with the broader strategy[1].

Psychological safety is a cornerstone of effective sense-making. When teams feel secure, they’re more likely to surface critical insights, enabling collective problem-solving. Organizations that prioritize transparency, open communication, and calculated risk-taking often allocate more resources to high-value initiatives and handle market disruptions with greater resilience. This shift in decision-making isn’t just a tweak - it’s a complete rethinking of how businesses operate, and it’s essential for long-term success.

As AI accelerates the flow of business signals, the edge goes to organizations that can interpret and act on them quickly - without compromising safety[1]. The future belongs to companies that blend inner agility - the ability to learn, think critically, and adapt - with outer agility in execution. This balance allows leaders to harness AI insights for timely, strategic decisions while relying on human judgment to navigate the complexities that machines can’t fully grasp.

Moving from framework compliance to sense-making isn’t just a procedural change; it’s a whole new way of operating in an unpredictable world. Businesses that embrace this shift will be better equipped to spot new opportunities, meet customer demands, and deliver measurable results. The real question is: Will your organization lead this transformation, or will it struggle to catch up?

FAQs

How does the Cynefin framework support better decision-making in complex business environments?

The Cynefin framework offers a way for organizations to make smarter decisions in challenging and unpredictable business environments. Instead of sticking to rigid, step-by-step methods, it emphasizes flexible strategies that adapt to change. In situations where cause and effect only become clear after the fact, Cynefin suggests a probe-sense-respond approach. This involves trying small experiments, observing the patterns that emerge, and adjusting quickly based on what proves effective.

What sets Cynefin apart from traditional models is its acknowledgment of the uncertainty and ever-changing nature of complex systems. By focusing on learning and staying adaptable, organizations can react more quickly to changing conditions, avoid unnecessary effort, and achieve better results - like faster course corrections, fewer wasted investments, and stronger returns. This framework gives leaders a practical way to tackle complexity while keeping their eyes on results that matter.

Why is psychological safety essential for improving sense-making and decision-making in teams?

Psychological safety plays a key role in helping teams make sense of complex situations and make better decisions. When team members feel safe to share their ideas, observations, and concerns without fear of judgment, it creates an open environment where critical information is not held back. This openness is essential for understanding complicated scenarios and making well-informed choices.

When teams are encouraged to voice different perspectives, they can work together to explore challenges, identify solutions, and adapt to shifting circumstances more effectively. This collaborative approach leads to quicker adjustments, smarter risk management, and stronger alignment with organizational goals. In uncertain or rapidly changing environments, psychological safety acts as a cornerstone for sound decision-making and the ability to respond with agility.

How can businesses combine AI and human judgment to navigate complexity and make better decisions?

Businesses can effectively blend AI-driven insights with human expertise by establishing distinct roles for each. AI shines in processing massive datasets and spotting patterns, while humans bring context, creativity, and critical thinking to interpret those findings and guide strategic decisions. This partnership creates a balance between the precision of data and the depth of human intuition.

To make this collaboration seamless, companies should define clear goals, align AI outputs with key decision points, and promote a team culture rooted in cooperation. While AI can speed up decision-making processes, human oversight remains crucial, especially when dealing with uncertainty or complex situations requiring nuanced judgment. By combining AI's efficiency with human insight, businesses can become more agile, minimize risks, and achieve tangible results.

Related Blog Posts

• Scaling Agile for Growth: Delivering the Right Value at the Right Time

• Beyond Frameworks: Embedding Agility That Improves Profitability and Reduces Risk

• Sustainable Agility: Building Organizations That Adapt, Compete, and Win

• Inner Agility: The Hidden Enabler of AI and Enterprise Transformation