Innovation is routinely invoked as a cure-all in the social change and organisational strategy space. It appears in grant applications, corporate mission statements, public sector reform agendas, and community-sector roadmaps. Yet the term often functions as a placeholder rather than a practice. In this episode, Amir—an innovation practitioner based in Stockholm with experience across education, startups, large corporates, nonprofits, and Amazon Web Services—offers a grounded account of what innovation is, what it is not, and how it can be operationalised as a disciplined approach to creating real-world value.
The conversation is particularly useful for changemakers, organisational leaders, and community builders who are trying to move from good intentions to measurable outcomes. It also provides a practical lens for navigating the current wave of artificial intelligence (AI) adoption, which is increasingly treated as synonymous with innovation, despite being only one possible enabler within a broader innovation system.
Defining Innovation as Value Creation, Not Ideation
Amir begins by addressing a core challenge: innovation has become a word that “can mean nothing and anything”. His definition pushes against the popular tendency to equate innovation with creativity, brainstorming, or “having a great idea”.
In this framing, innovation must satisfy two criteria.
First, it must create value in the world. That value can take different forms depending on organisational purpose. For private firms, it may be revenue, market growth, or competitive advantage. For public institutions and nonprofits, it may be improved services, community wellbeing, or mission-aligned impact. The common thread is that innovation must be demonstrably consequential beyond the organisation’s internal enthusiasm for novelty.
Second, innovation is not an accident. It requires intentionality and systematisation. A one-off successful idea—even if valuable—is not, in Amir’s view, innovation as a capability. Innovation becomes meaningful when organisations develop the capacity to repeatedly produce value under changing conditions. That capacity depends on structures, frameworks, and practices that guide people through uncertainty and complexity.
This definition has implications for the social impact sector, where “innovation” is sometimes used to describe pilot programs that never scale, prototypes that never reach users, or technology adoption that is not connected to outcomes. Amir’s insistence on value creation and repeatability shifts the emphasis from novelty to effectiveness.
The Real Divider Is Not Organisational Size, but Culture
A common assumption is that innovation is easier in startups and harder in large institutions. Amir complicates this story. Based on his experience across multinationals and smaller organisations, he argues that size is less determinative than culture.
He points to Amazon as an example of an organisation that, despite its scale, deliberately cultivates a “day one” mindset—an orientation that treats the work as if it is still at the beginning, requiring ongoing curiosity, responsiveness, and iterative problem-solving. In contrast, some organisations—regardless of size—become anchored to tradition and prior success. Past wins can create an implicit belief that current approaches are sufficient, reducing the perceived need to experiment or adapt.
For organisations working in social change contexts, this is a crucial lesson. Many community-sector organisations are structurally constrained by funding cycles, risk management, and limited resourcing. Yet within those constraints, culture still matters: whether teams feel safe to propose change, whether learning is rewarded, and whether innovation is treated as a shared responsibility rather than a specialised role.
Working Backwards: Starting With the Customer, Not the Solution
One of the most actionable contributions in the episode is Amir’s explanation of Amazon’s “working backwards” methodology, which he describes as foundational to disciplined innovation.
The method begins with three clarifying questions:
- Who is the customer?
The key is specificity. “Everyone who uses our service” is too broad to anchor innovation decisions. Innovation requires a clearly defined group with particular needs and constraints. - What is the customer problem or missed opportunity?
Most customers have multiple problems. The innovation task is to choose which problem matters most to address now. - What is the most important benefit in that space?
This forces prioritisation. It also prevents innovation efforts from drifting into feature accumulation or internally-driven preferences.
This approach reverses a pattern common in both corporate and social impact settings: starting with a technology, a capability, or an attractive idea, and then searching for a problem it might solve. Working backwards makes the problem and the user the organising centre, and only then asks what must be built—whether new partnerships, capabilities, operating models, or technologies—to deliver the intended benefit.
A striking insight in the conversation is Amir’s observation that even well-established teams frequently lack alignment on these basic questions. In other words, organisations can operate for years without a shared answer to “who exactly are we serving?” and “what problem are we prioritising?”. Innovation processes that begin with ideation can unintentionally conceal this misalignment; working backwards exposes it early, when it is still relatively low-cost to resolve.
A Practical Example: Scaling Anti-Bullying Impact Through Digital Mechanisms
Amir illustrates the approach through pro bono work with a Swedish nonprofit addressing bullying in schools. The organisation’s goal was to scale impact—a familiar ambition in the nonprofit sector, often constrained by the need for more staff, more funding, and more delivery capacity.
Rather than jumping immediately into building a digital product, the team first clarified strategic focus: who, precisely, the “customer” was in this system. In the context of school bullying, plausible candidates include students, parents, teachers, school leadership, municipal authorities, and policymakers. Each implies different forms of value and different mechanisms for scaling.
The solution direction they developed centred on supporting school staff and educators responsible for monitoring and addressing bullying, while also acknowledging sensitivities such as privacy, case management ethics, and the importance of detecting cultural patterns rather than merely responding to individual incidents. The example demonstrates how innovation is often less about a single clever solution and more about choosing the right leverage point in a complex system.
Decision-Making Under Disagreement: Reflection, Mechanisms, and Reversibility
Innovation work reliably produces disagreement. Diverse perspectives are valuable, but they can also lead to stagnation when teams cannot decide. Amir offers two complementary strategies.
The first is reflective facilitation: explicitly naming when a team is stuck and prompting them to adopt a decision mechanism rather than continuing an unproductive debate. Decision mechanisms vary—board governance, executive authority, team consensus rules—but the critical point is that innovation requires decisions, not only discussion.
The second strategy draws on a well-known Amazon mental model: the distinction between two-way door and one-way door decisions. Many decisions are reversible. Teams can lower the stakes by designing experiments that allow learning without major irreversible commitment. Where decisions are reversible, speed matters; experimentation is preferable to perfectionism. Where decisions are irreversible—large investments, major hires, entering new geographies—deliberation must be more careful.
This distinction provides a practical antidote to risk paralysis, especially in mission-driven organisations where reputational concerns can discourage experimentation. It also introduces a disciplined approach to “moving fast”: moving fast where the cost of being wrong is low, and slowing down where the cost is high.
AI as an Innovation Co-Pilot: Speed, Scale, and New Prototyping Capacity
The conversation then shifts to AI, which Amir frames as an accelerant rather than a replacement for innovation practice.
His account of using large language models as an “innovation co-pilot” is notable for its pragmatism. Rather than treating AI as a novelty, he positions it as a tool that can support the steps within structured innovation: ideation, prioritisation, drafting, analysis, and rapid iteration. He also highlights a rapidly changing frontier: coding agents and prototype generation, which can reduce the time between idea and user testing from months to hours.
For changemakers, this is not simply about productivity. It changes the economics of experimentation. If a team can produce a functional prototype quickly, it becomes more feasible to test options early, gather feedback, and avoid over-investing in a single path based on internal assumptions.
Amir also introduces two critical caveats.
- AI must be taught your context and intent.
Like any collaborator, it requires guidance. The quality of outcomes depends on the quality of inputs, references, and constraints. - The human role must be designed, not assumed.
As AI can perform more tasks, organisations must explicitly decide where human judgment is essential—ethically, strategically, and culturally. The “human in the loop” question becomes central to responsible innovation design, especially when AI systems are embedded in services that affect people’s lives.
Sweden, Sustainability, and Cross-Disciplinary Collaboration
Amir’s reflections from living in Sweden add another layer: how an ecosystem that normalises sustainability and supports accessible education can shape professional practice.
He describes using Sweden’s free higher education opportunities to deepen his sustainability knowledge and then participating in an initiative—Global Green Action Day—that brings together sustainability, regulation, circular economy, systematic innovation methodologies, and AI. The model he describes is instructive: rather than hosting a hackathon without preparation, the program invested in two months of structured learning so participants could contribute meaningfully on the day, reducing the time spent “levelling the playing field” and increasing the quality of solutions developed.
This approach reflects an important principle: complex problem-solving requires capacity building. Innovation is not only a moment of creativity; it is a process of education, shared language, and cross-disciplinary translation.
A Leadership Lesson: Hold the Vision, Loosen the Grip on the Path
In concluding, Amir offers a leadership principle with direct relevance to organisations navigating uncertainty: hold a strong vision of the outcome, but remain flexible about how to get there.
This orientation rejects two common traps. One is drifting without a clear destination. The other is clinging rigidly to an initial strategy, even when evidence suggests a need to adapt. Innovation, as presented in this episode, becomes a practice of disciplined commitment: clarity about purpose, humility about methods, openness to learning, and a willingness to commit wholeheartedly once a decision is made—even if the path was not one’s personal preference.
Why This Matters for Changemakers
For readers working in social change, the episode offers a coherent alternative to “innovation theatre”—activity that appears innovative but does not generate durable impact. It centres innovation as a capability built through cultural design, structured methods, and responsible use of tools such as AI.
The implications are practical:
- Start with the people you serve, and define them precisely.
- Choose the problem intentionally, not opportunistically.
- Design for learning early, using reversible experiments.
- Treat AI as an accelerator for iteration, not a substitute for judgment.
- Build innovation as a repeatable mechanism, not a one-off project.
Innovation, in this framing, is not a label. It is a discipline. And for organisations trying to create meaningful change in complex systems, discipline is often what turns aspiration into outcomes.
