Strategy Principles for Innowaring
Creating Focus through Making Choices
This post is part of a series outlining my principles-first approach to AI-Native Product and Systems Innovation.
The first post describes the origin of the Innowaring idea after I published my book, Becoming a Software Company.
The second post describes the Innowaring Concepts, the operating model dimensions for AI-Native innovation.
The third post describes the principles for the Teaming dimension of Innowaring.
This post describes the principles for the Strategy dimension of Innowaring.
Product innovation thrives when it’s laser-focused on solving specific, high-impact customer problems. That focus doesn’t emerge from executing a fixed plan or checking boxes. It comes from making deliberate choices that position you to win in your target market. Those choices define your strategy.
There’s no shortage of thinking on strategy—what it is, how to build one, which frameworks to use, and how to execute it effectively. My approach is shaped by the work of Richard Rumelt, Roger Martin, and Chet Richards. Rather than reinvent the wheel, I aim to apply their insights to shape the Innowaring practice.
AI has thrust enterprise software into a high-stakes, fast-evolving landscape. Customer expectations are soaring, underlying business models and enterprise use cases are still taking shape, and technology is advancing at breakneck speed. In this environment, reacting blindly to threats and opportunities is a recipe for missteps. What organizations need is a clear, adaptable strategy.
That’s where the Innowaring approach comes in. What follows is a set of strategic principles—designed for clarity and action—that help enterprise teams navigate the complexity of AI-native product innovation with confidence and focus.
Principle: Creating Focus
A good strategy is a focusing device for minds, individual effort, and team collaboration.
It begins with a clear, compelling product vision. Ideally, this vision is a narrative rather than bullet points. It should project 5–10 years into the future from the customer’s point of view—bold enough to inspire your team and aspirational enough to excite your customers.
To move from vision to execution, product strategy must prioritize 1-3 problems that promise the greatest impact.
In enterprise software, innovation often stalls due to a lack of focus. AI amplifies this challenge by multiplying options and raising expectations across stakeholders. With so many potential problems to solve, it’s easy to lose sight of what matters most.
You can't create focus by building consensus. When you try to solve everything, you solve nothing. Instead, product teams must assess threats and opportunities in depth and place informed, measurable bets.
Ultimately, it falls to startup CEOs or senior enterprise leaders to make those strategic choices—and to communicate them with clarity and conviction across teams and stakeholders.
Principle: Driven by Value Hypothesis
Whatever you choose to focus on must be anchored in a value creation hypothesis. It’s not enough to build solutions your customers love—they must also deliver measurable value to your business. Every product must strike a balance: valuable and usable for the customer, feasible and viable for the team to build.
As Roger Martin explains, Where to Play (your chosen market focus) is inseparable from How to Win (your mechanism for creating value). A product vision or strategy is incomplete without a working hypothesis for how your solution will deliver value to customers and capture value for your business.
One side of that hypothesis is your customer value proposition. The other, less often articulated, is how your business benefits—how value is captured.
For startups, this means making deliberate choices around differentiation, go-to-market channels, and monetization strategies. For enterprise product teams, it may involve defining and measuring gains in employee productivity, operational efficiency, or business capability.
Principle: Insights Based
Richard Rumelt emphasizes that good strategy begins with a “diagnosis”—a clear, accurate understanding of the core challenge to overcome. In Innowaring, this means developing deep insights about the customer problem before jumping to solutions.
Customer problems often aren’t what they appear to be. A pervasive solutionist bias in modern tech culture pushes teams to build quickly—sometimes before they truly understand what needs solving. Insight requires patient investigation.
Effective diagnosis means exploring a range of insights:
Customer Interactions – Direct observations, user interviews, support conversations
Data Analysis – Usage trends, win/loss analysis, unexpected or edge-case behaviors
New Technology Possibilities – A step change in what’s possible, such as LLMs enabling novel interface patterns
Market or Industry Sensing – Seeing what others miss: unmet needs, shifting expectations, or social/economic dynamics shaping user behavior
A true insight often challenges conventional wisdom. It may contradict what “everyone knows” in your market.
And insight isn’t just about the problem—it includes the constraints. Technical limitations, organizational barriers, and market realities are essential inputs. This is especially true for AI-native products, where the technology is advancing rapidly—but not evenly. Good strategy, here, is grounded not just in inspiration but in deep, practical understanding.
Principle: Making Choices
Strategy isn’t a plan or a vision—it’s about making choices. As Roger Martin emphasizes, effective strategy requires explicit and integrated decisions about where to play and how to win—especially when resources are limited.
For AI-native products, these choices are particularly consequential. The sheer breadth of possibility can be overwhelming. That’s why focus is not just a luxury—it’s a competitive advantage.
In Innowaring, these strategic choices often include:
Market Segments – Which workflows carry the most pain? Who experiences them? What are the 2–3 most critical problems worth solving?
Value Propositions by Segment – Which personae, jobs to be done, outcomes, and alternatives define each segment?
Source of Differentiation – What makes us unique or meaningfully better?
Trade-Offs – What won’t we do, even if it’s tempting?
Key Metrics – How will we test assumptions and reduce risk?
Growth Mechanisms – Will we grow through product-led or sales-led approaches? What channels will we prioritize?
Required Capabilities – What must we build or acquire to deliver on our strategy?
Making these choices is rarely comfortable. In enterprise environments, stakeholders often resist decisions that deprioritize their needs. In startups, saying no to promising opportunities can trigger real anxiety. And in AI, where the potential feels infinite, the pressure to chase everything is intense.
But strategy isn’t about exploring every path. It’s about committing to the few that matter most—and having the discipline to let the rest go.
Principle: Transparent and Measurable
Strategy only works when it’s understood and tracked. To drive meaningful progress, strategic choices must be transparent and measurable, and they must cascade through the organization.
When product teams understand which problems matter most and why, they can make smarter day-to-day decisions about features, design, and technical trade-offs. Transparency begins with language: strategy should be articulated clearly and be accessible. Strip away buzzwords and jargon—clarity builds alignment. Everyone, from engineers to sales reps, should be able to explain the core strategic choices and why they matter.
Measurement is equally essential. The most effective metrics link directly to customer value and business outcomes—not just internal implementation milestones. Define specific, quantifiable indicators of success. Establish baselines before execution and create regular review cadences to track progress.
OKRs (Objectives and Key Results) are one powerful technique for cascading strategy into actionable focus areas. Once you've made your strategic choices—about where to play, how to win, and what to prioritize—OKRs can help express those choices through clear objectives and measurable results across teams. They’re not a substitute for strategy but a way to operationalize it: to align efforts, drive accountability, and track progress over time.
A culture of transparency must also extend to outcomes. Celebrate wins, but also acknowledge when metrics reveal a misstep. When a strategic choice isn’t delivering, call it out and pivot. In AI-native product development—where uncertainty is the norm—this kind of disciplined learning is not just helpful; it’s essential.
The Innowaring Concept of Strategy is built for the realities of modern innovation—where technology evolves at breakneck speed, uncertainty is constant, and the pressure to deliver is relentless. In this environment, strategy must inspire and guide. It begins with focus, is driven by value and insight, and takes shape through bold, explicit choices. It must be transparent, measurable, and shared across the organization—not as a static plan but as a dynamic system for alignment and learning.
Innowaring’s principles-oriented approach offers a way to turn strategic thinking into deliberate action, helping teams move with clarity, confidence, and resilience in the face of rapid change. In a world where everything feels possible, only the teams that choose with purpose will truly innovate.
This post is part of a series outlining my principles-first approach to AI-Native Product Innovation. Links to the previous posts (Part 1, Part 2, Part 3)
The next post describes the Flow dimension of Innowaring.