Sometimes the best insights come from people who've lived through what the rest of us are only reading about. This issue features a guest article.
Mika Lampinen and I worked together when I led the innovation program at AWS Nordics, where he was an Enterprise Service Manager. But his story starts earlier - and it's why I asked him to write this piece.
Mika spent over five years at Nokia during the smartphone era, leader of the MeeGo Middleware development. He was there for the N9, managing teams of 180 people and budgets of €20+M, building what Nokia believed would be the future of mobile computing. A bit before that iPhone had happened. He watched from the inside as a company doing everything right - by the old rules - lost to a paradigm shift.
Since then, Mika has led large-scale transformations at Nordea, led agile consulting at CGI Finland, developed a productized digital transformation planning and execution framework as lead transformation program manager at Tieto, and is now Danske Bank, where he leads business critical (development) initiatives. He also recently completed MIT Sloan's Artificial Intelligence: Implications for Business Strategy program.
When Mika shared his thinking with me, I realized it speaks to both leadership and business value. So we're doing something different this issue - a combined piece that flows from the leadership mindset (Section 1) into a practical transformation framework (Section 2).
I'll let Mika take it from here.
"Think Big" is often interpreted as a call for bold ambition: bigger bets, faster execution, louder vision. Ambition certainly matters. But in my experience, it is rarely the differentiator.
What separates winners from laggards is not who had the biggest aspirations, but who recognized when the rules of the game had changed - and acted accordingly.
Timing is an underappreciated leadership skill. Move too early, and you burn credibility and capital before the organization is ready. Move too late, and even exceptional execution may no longer matter. In periods of incremental change, optimization and patience are virtues. In periods of paradigm shift, they quietly become liabilities.
Thinking big, therefore, is not just about seeing a bigger future. It is about understanding when incremental improvement stops being sufficient, and when leaders must shift from optimizing the current model to inventing the next one.
AI is forcing exactly this kind of leadership test today.
I lived through the rise and fall of Nokia's smartphone platform development - from Symbian to MeeGo. At its peak, Nokia was executing exceptionally well within the existing paradigm: feature-rich devices, rapid incremental innovation, and continuous improvement of what had already made the company successful. Phones evolved into always-online pocket computers, step by step.
Then Apple introduced the first iPhone - not as a feature-maximized device, but as a "Minimum Lovable Product". Fewer features, but a radically different user experience, ecosystem logic, and platform model. It wasn't a better phone in the old sense - it was a different category of product. Google followed with Android. The paradigm shifted.
Nokia's response was not a lack of effort or competence. It was something more structural: optimizing the old model while the market had already moved to a new one. Too much was done right - but too late, and in the wrong frame of reference.
The lesson is uncomfortable, but clear:
Paradigm shifts do not punish incompetence - they punish incrementalism.
When I look at the current wave of artificial intelligence, the pattern feels uncomfortably familiar.
AI is advancing at a pace that puts unprecedented pressure on organizations' ability to adapt - not just technologically, but strategically. Capabilities that once took years to diffuse are now becoming broadly accessible in months. Decision-making, knowledge work, customer interaction, and even core business processes are all being reshaped simultaneously.
This creates a natural sense of Fear of Missing Out. Leaders see competitors experimenting, vendors promising breakthroughs, and headlines declaring winners and losers. The instinctive response is often to launch pilots, accumulate use cases, and "do something with AI" - fast.
But history suggests this is precisely where many organizations go wrong.
Like smartphones before them, AI capabilities can be incrementally layered onto existing products, processes, and operating models. And for a time, that may look like progress. Yet the real disruption does not come from adding intelligence to the old model - it comes from redefining how value is created, decisions are made, and work is organized.
AI is not just another efficiency lever. It is a force that changes the economics of cognition itself. That makes it a strategic inflection point - one where optimizing the current model for too long risks repeating the same mistake: doing the right things, in the wrong paradigm.
One of the reasons Amazon's Think Big leadership principle remains so relevant is its emphasis on vision and ambition - not merely incremental improvements. In the context of AI, this distinction matters. Many organizations are moving quickly - launching pilots, enabling tools, encouraging experimentation. Activity is high. Yet activity alone does not constitute strategy.
Thinking big in the AI era means resisting the temptation to scale before you understand what you are scaling toward. It requires leaders to ask foundational questions:
Where will AI change how we create value?
Which decisions should machines increasingly support or automate?
What parts of our operating model will no longer make sense when intelligence becomes cheap and ubiquitous?
One of my key takeaways from the MIT Sloan course Artificial Intelligence: Implications for Business Strategy was how consistently real-world cases demonstrated a clear pattern: organizations that approach AI primarily as a technology deployment effort struggle to move beyond experimentation. The ones that succeed frame AI as a strategic leadership challenge - one that touches governance, learning, organizational design, and long-term competitive positioning.
This is where Think Big becomes operational. Strategy must come before scale. Governance must evolve with innovation. And leaders must design not just for near-term efficiencies, but for systemic change in how their organizations learn, decide, and compete.
Only then does it make sense to ask how fast to move.
A NOTE FROM AMIR: What Mika describes above is the leadership mindset. But mindset alone doesn't transform organizations. In the next section, Mika shares a practical framework for turning strategic clarity into action - drawing on MIT Sloan research and Gartner insights.