Think Big Newsletter #16 - Think Big, Strategic AI Transformation Framework, Claude for Excel, Moat

AI, Innovation and Business Value

Welcome to the Think Big Newsletter, where we explore how business leaders can create value with AI and innovation. In each issue, I’ll share practical strategies, real-world case studies, and actionable frameworks that help you navigate the AI wave without getting lost in the hype.

I’d love to hear your thoughts, challenges, and suggestions for future editions.

In this issue:

1. Leadership Principles in the age of AI - Think Big

2. Business Value with AI - Strategic AI Transformation Framework

3. AI Tool Deep Dive - Claude in Excel

4. One New Term at a Time - Moat

#1 - Leadership Principles in the Age of AI

Think Big: from survival to strategic transformation in the age of AI

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.

Thinking big is not only about ambition - it’s also about timing

“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.

A painful lesson from Nokia: when incremental excellence wasn’t enough

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.

Déjà vu: AI as the next strategic inflection point

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.

Think Big in the AI era: strategy before scale

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.

For a deeper dive into how paradigm shifts play out and why timing matters more than most leaders think - I recommend this conversation between Marc Andreessen and Lenny Rachitsky. Andreessen draws a striking parallel: the PC industry was a text-prompt system for 17 years before taking a “left turn into GUIs and never looking back.” Then five years later, another left turn into web browsers. According to Marc we don’t yet know the shape and form of the ultimate AI products. The mistake is assuming the competition is between today’s chatbots and search engines - ”both the current chatbot companies and many new companies are going to figure out many kinds of user experiences that are radically different that we don’t even know yet.” The companies that win won’t be those executing best in today’s paradigm - they’ll be those who recognize when the paradigm shifts.

#2 - Business Value with AI

Strategic AI Transformation Framework

Drawing on insights from MIT Sloan’s Artificial Intelligence: Implications for Business Strategy program and Gartner research, this framework outlines key principles for successfully leading AI-driven transformation.

1. Lead with a strategic AI vision. AI adoption should be a top-down, strategic initiative championed by leadership. Define a clear vision of how AI aligns with your business goals, and ensure it continuously co-evolves with those goals. The most effective leaders treat AI as an opportunity to augment human capabilities, not a threat – setting a tone of innovation, learning, and long-term value creation.

2. Focus on business value and outcomes. Start with business challenges, not technology. Identify high-impact use cases where AI can either boost revenue, cut costs, or enhance customer experience, and prioritize these ROI-driven opportunities. The goal is to leverage AI for tangible competitive advantage rather than AI experimentation for its own sake.

3. Build strong data and technology foundations. Behind every successful AI initiative is a robust data and IT backbone. Ensure your data is high-quality, accessible, and representative – without this foundation, even the best models will falter (indeed, 85% of AI projects fail due to poor data quality or lack of relevant data).

4. Empower people and foster an AI-ready culture. Organizational transformation is crucial – AI success depends on your people embracing new ways of working. Invest in upskilling programs and cross-functional teams so employees at all levels have the skills and mindset to work alongside AI. As MIT experts note, starting with a healthy corporate culture greatly improves the odds of AI success.

5. Establish robust AI governance and ethics. Treat AI governance and ethical risk management as core leadership responsibilities, not afterthoughts. Develop clear principles and policies for responsible AI use – from data privacy and security to fairness, transparency, and compliance. A strong governance framework not only prevents missteps and bias, it also enables sustainable innovation.

Why this is also an ESG and leadership responsibility

As AI reshapes industries at speed, it also shifts the burden of adaptation from governments to organizations. The public sector often cannot match the pace of technological disruption, and so the responsibility to guide people through change increasingly falls on corporate leadership.

This isn’t just about managing risk - it’s about upholding trust and social license to operate. Whether it’s ensuring fair AI outcomes, investing in employee upskilling, or rethinking job roles in human-machine teams, these are not side concerns. They are core to long-term business sustainability and social equity.

In this light, AI transformation is also ESG in action: a matter of inclusive innovation, ethical governance, and resilient workforce strategy.

A final thought: from insight to ownership

Having lived through one wave of disruption, I’ve learned this: the winners are rarely those with the most features — but those with the clearest vision, the courage to act early, and the discipline to scale responsibly.

AI will not wait for perfect conditions. But it also doesn’t reward panic. What it demands is strategic clarity, leadership accountability, and thoughtful ambition - not hype or hesitation.

Whether you’re shaping a roadmap, framing your AI ambition, or questioning your organization’s readiness: now is the time to move. Think big. Act grounded. Own the transformation.

Your action step

This week, ask yourself Mika’s three foundational questions:

1. Where will AI change how we create value?

2. Which decisions should machines increasingly support or automate?

3. What parts of our operating model will no longer make sense when intelligence becomes cheap and ubiquitous?

Write down your answers - even rough ones. The act of articulating them reveals whether you’re thinking incrementally or thinking big.

The shift and the role of leadership. Image generated with the help of Castifai.com

#3 - AI Tool Spotlight: Claude for Excel

Claude for Excel: when AI meets the world’s most powerful business tool

When Mika writes about paradigm shifts - about recognizing when optimization stops being enough - he’s describing something happening right now in one of the most consequential business tools ever built: Excel.

Anthropic just embedded Claude directly inside Microsoft Excel. Not as a separate app you paste data into. As a native sidebar with complete awareness of your workbook - every tab, every formula, every cell reference.

The 10-minute financial model

In the video below, Nate B. Jones built an 11-tab rent-vs-buy financial model - with sensitivity analysis, opportunity cost comparisons against S&P 500 returns, and dynamic tax calculations across every U.S. zip code - in 10 minutes. The time it takes to make a good cup of coffee.

This isn’t incremental improvement. This is what Mika calls above a strategic inflection point.

Claude doesn’t just help write formulas (any AI can do that now). It reasons about the entire structure of multi-tab workbooks. When the context window maxed out mid-build, Claude examined the existing tabs, inferred what was missing, and continued building without explicit instructions. That’s the Opus 4.5 model understanding architecture, not just executing commands.

Norway’s sovereign wealth fund (I actually met with them a few times as AWS Innovation program lead in the Nordics) has been using Claude in Excel during the enterprise beta. Their estimate: 213,000 hours saved. That’s real institutional proof, not a demo.

What I like about Claude for Excel

• The data partnerships. Through MCP (Model Context Protocol - see issue 12), Claude pulls from LSEG for live market data, Moody’s for credit ratings across 600 million entities, S&P Capital IQ for financials, FactSet and Morningstar for research, Pitchbook for private company intelligence. Any language model can help you write a SUMIF formula - that’s table stakes. What a generic model can’t do is pull this morning’s pricing from the London Stock Exchange, cross-reference against current Moody’s credit ratings, check fundamentals from S&P, and update your comparable company analysis in one workflow.

• The change trail. Every AI action gets logged transparently. This matters because finance models get audited, reviewed by skeptical colleagues, handed to successors who need to understand logic. The ability to demonstrate how AI-assisted changes were made is the difference between a tool you can deploy and a liability compliance will never approve.

• Local file support. Microsoft Copilot for Excel requires files saved to OneDrive with autosave enabled. Many finance teams hate this - they want control over when work gets saved. Claude works with local files. A product decision that meets users where they are.

Limitations to understand

Claude struggles with certain specialized data that isn’t easily publicly available - benchmark datasets and niche research may require manual pasting. Charting is functional but not beautiful; if you want truly polished visualizations, expect five minutes of formatting work. Context windows on complex builds can max out, requiring you to clear the chat and ask Claude to infer the build plan from existing tabs. These are errors you can live with when you’ve compressed weeks of work into minutes, but worth knowing.

As the nate B. Jones (another ex-AWS incidentally) puts it in the video below: “The question is not are the models good enough. The question is who controls the workflows, who owns the data relationships, who gets embedded in places where real work happens.”

Try it yourself

Claude for Excel is available now on the Pro tier ($20/month). The add-in installs as a native sidebar inside Excel. For finance leaders asking Mika’s foundational questions - where will AI change how we create value? - this is a concrete place to start experimenting.

#4 - One New Term at a Time: Moat

A sustainable competitive advantage that protects a business from competitors, borrowed from the defensive trenches around medieval castles.

In the AI era, the moat question has become urgent: what advantages actually hold when AI capabilities are rapidly commoditizing?

Traditional moats - proprietary technology, specialized expertise, information asymmetry - erode faster when AI can replicate capabilities in months rather than years. The new moats are emerging around data (unique datasets that can’t be easily replicated), network effects (platforms that get smarter as more users join), and speed of learning (organizations that can iterate faster than competitors).

Warren Buffett popularized the term for investing. Now every leader needs to ask: in a world where AI makes intelligence cheap and ubiquitous, what’s your moat?

The key insight is that companies that win won’t be those with the best AI - AI capabilities are converging. They’ll be those with advantages AI can amplify but not replace.

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