Opinions
Sharp takes on strategy, execution, ideas, projects, products, and the messy work of making businesses smarter.
Too Many Subscriptions. Not Enough Business.
Most organizations are paying for too many SaaS tools that each solve one slice of the business. Strategy over here. KPIs over there. Projects somewhere else. Ideas in another system. Adoption barely tracked at all. Then traditional pricing plans make it worse by cutting off useful features just when teams start to scale. The future is not more subscriptions. It is fewer, more connected systems that help the business actually work as one.
Stop Chatting With Apps. Start Chatting With Your Business.
Every SaaS product is adding an AI assistant. Most can answer questions about the data inside that one product. Useful, yes. Transformational, not really. The bigger opportunity is a conversational layer across strategy, business models, metrics, ideas, projects, adoption, evidence, and feedback. That is where Clever Duck and INaROW are heading: not chat with your app, but chat with your business.
Not Every Metric Needs to Bow Down to Revenue
Organizations love revenue metrics because they feel serious. But most ideas do not create revenue directly, at least not at first. They change behavior, reduce friction, improve confidence, increase adoption, shorten cycle times, improve quality, or create learning. If we only measure ideas through revenue, we flatten the work and miss the real mechanism of impact. Better metric categories make ideas sharper, easier to test, and much easier to explain.
You Can Never Get Around What You Gotta Go Through
A song lyric stuck with me: “You can never get around what you gotta go through.” It is a pretty good warning for the current AI rush. Many organizations want generative and agentic AI to magically solve work that was never properly understood, mapped, structured, or connected in the first place. But AI is not a shortcut around context. It is highly dependent on it. If the inputs are thin, fragmented, stale, or politically sanitized, the outputs will be too. The work required to get this context layer achieved is way harder than applying AI .
AI Gets It Wrong. So Do We.
People are quick to declare AI useless the moment it gets something wrong. But humans misread briefs, make bad assumptions, and produce weak first drafts all the time. The difference is that we know how to review, challenge, and improve human work. The same discipline should apply to AI. Failure is not proof the tool has no value; often, it is the start of the useful feedback loop.
Personal AI Mastery Is Not the Endgame
People inside businesses are already using AI to work faster, automate tasks, and sharpen their own thinking. That is progress. But personal productivity is only the first phase. The bigger question is whether organizations can turn scattered AI-assisted work into collective intelligence. When observations, ideas, hypotheses, experiments, evidence, decisions, projects, and metrics start to connect, AI stops being a private productivity trick and starts becoming part of a true organizational supermind.
Strategies Do Not Have to Be Boring
Strategies often fail to travel because they are expressed in only one format: the formal executive version. But people process meaning differently. Some need structure, some need story, some need visuals, some need customer language, and some need plain English. Alternative expressions of strategic intent can make strategy sharper, more memorable, and easier for people across the organization to actually use.
Stop Pretending Change Champions Are Magic Fairies
Change champions can help adoption, but they cannot rescue a change the rest of the organization has not properly owned. Too often, businesses nominate a few enthusiastic people and expect them to cheerlead everyone through weak design, poor leadership, late involvement, and missing manager support. Real adoption is an all-hands-on-deck job.
Your Business Model Canvas Is Not a Workshop Exercise
A Business Model Canvas should not be a workshop souvenir. Used properly, it gives an organization a shared language for how the business creates, delivers, and captures value. That common context helps teams connect ideas, projects, metrics, strategy, and day-to-day decisions instead of optimizing their own corners from different maps.
Your Ideas Are Not Ideas Yet
Most organizations are not short of ideas. They are short of well-constructed ones. Too often, vague assumptions get promoted into projects before anyone has named the hypothesis, tested the bet, or gathered evidence. This article argues for a sharper way to work: turn ideas into structured strategic bets before execution makes the wrong thing expensive.
Stop Measuring Product Managers Like They Control Reality
Product managers should care about outcomes, but judging them only by whether a metric moved creates bad incentives and fake accountability. A better scorecard looks at what PMs can actually improve: the quality and breadth of feedback, the clarity of hypotheses, the strength of the design process, the evidence behind decisions, and whether the business is placing better product bets.
Ideas Come From People. So Why Do We Make Them So Hard to Share?
Most businesses say they want ideas from their people but then make those ideas surprisingly hard and intimidating to share, articulate, and connect to strategy. Good ideas often begin as rough observations from people close to the work. The real opportunity is to give those people a common language for turning what they notice into hypotheses, experiments, evidence, and better decisions.
Project Management Became a Chore Because We Stripped Out the Why
Project management becomes a bore when the work is stripped of its context. When contributors only see tasks, deadlines, and status updates, they become task-completers instead of owners of the outcome. Better execution happens when projects stay connected to the ideas, hypotheses, features, metrics, and strategic choices that made them worth doing in the first place.
Product Managers and Project Managers Need to Stop Pretending They Live in Different Worlds
Product managers and project managers are not the same role, but modern work needs them to share more than a project board. When one owns the why and the other only chases the when, teams end up with feature theater, checkbox delivery, and status updates that hide weak assumptions. Better execution happens when both roles understand the chain from idea to hypothesis, evidence, project, metric, and outcome.
Stop Pretending You Did Not Use AI
People are using AI to draft, summarize, brainstorm, edit, research, and sharpen their work — then acting like none of it happened. That is the weird part. There is no shame in using AI well. The real issue is pretending the assistance did not happen, especially when human judgment, verification, and ownership are still what make the work valuable. Lean in. Own the tool. Own the work.
Product Managers Do Not Need to Be the Smartest Expert in the Room
Product managers do not need to be domain experts, technical architects, and scrum masters all rolled into one. Domain knowledge and technical fluency help, but the real job is better decision-making: understanding the problem, exposing assumptions, connecting evidence to outcomes, and helping the team learn what is worth building.
The 'Killer Feature' Honeymoon Period Always Ends
Startup founders often obsess over the killer feature, and that focus can be useful early on. But a feature is not a business model. The harder question is whether the company can create, deliver, and capture value at scale. Business Model Canvases help founders get the blinkers off and test the whole model, not just the shiny product moment.
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