Easy to Approve, Hard to Kill

Product leadership is now measured by a new scoreboard: growth, margin, and executive confidence, not feature throughput. Yet many product organizations still operate with a delivery-first decision system. The consequences are familiar: roadmaps that read like promise lists, prioritization debates that turn political, decisions that reopen under pressure, and escalation cycles that slow outcomes and erode trust. Most teams don’t struggle because they lack smart people; they struggle because they lack a scalable decision discipline.

What’s changed is not just the scoreboard, it’s the standard of proof. Boards and CEOs now expect product leaders to quantify impact in hard terms: revenue, margin, and capacity, on a credible timeline. That pressure reframes product leadership as a fiduciary role: accountable for capital allocation, not feature management.

In this environment, business fluency is not finance training, and not just a vocabulary upgrade. It is the ability of product leaders to operate as executives — to frame strategy as investable bets, size and stress-test assumptions, articulate tradeoffs, define decision rights, and pursue outcomes with discipline. Business fluency connects product choices to business outcomes through unit economics, pricing logic, ROI pathways, and leading indicators that signal whether an investment is working.

What’s changed is not just executive expectations, but the environment in which product decisions are made. Execution has become faster, iteration cheaper, and the cost of producing plans, analyses, and alternatives significantly lower. In this environment, organizations can commit to more initiatives with less visible friction, even when underlying assumptions, tradeoffs, and stopping conditions remain unclear. The result is not better decision-making, but faster accumulation of reversible commitments.

When business fluency is missing, product decisions create organizational friction. Strategic bets lack a clear business claim and an accountable owner. Executives are asked to fund initiatives without credible evidence of impact on growth, retention, or margin. Confidence erodes. And without formal governance, especially Kill/Adjust Checkpoints, teams relitigate priorities, escalations increase, and accountability blurs.

AI is a major accelerant of this shift, but not the root cause. It compresses effort and lowers the cost of addition, while leaving the hard work of judgment, prioritization, and subtraction unchanged. In organizations without business fluency, this acceleration compounds existing friction rather than resolving it.

Level 1: Feature-First. Product is managed as a delivery function. Roadmaps are built from requests and capacity, and success is measured by output. Teams can explain what they are shipping but struggle to defend why it matters economically or strategically. Decisions are reactive and escalations are common because tradeoffs are not framed in a shared business language.

Level 2: Product Metrics. Teams begin instrumenting outcomes and reporting on performance. They can tell a story about adoption and usage but cannot tie initiatives to economic or strategic value. Business cases require translation across Finance and GTM, and decisions still reopen because investment logic is implicit.

Level 3: Product Contribution. Teams use available data to assess the financial contribution by product. Initiatives are framed by product and by segment but lack formal risk assessment, and leading indicators. Executives have visibility to opportunity costs and tradeoffs.

Level 4: Portfolio Operator. Product leadership runs a true operating system. Decision rights and exception lanes are clear. Governance cadence is established. Outcomes are managed through a scorecard and Kill/Adjust Checkpoints prevent slow failure. Resources are allocated based on performance and strategic value, not advocacy. Executive confidence rises because decisions are durable and measurable.     

Product teams leverage a concrete set of artifacts that leadership can immediately use:

Business Fluency provides the decision discipline product leaders need. But when the investment itself is AI, the rules shift in ways that catch even fluent leaders off guard.

The core principles transfer, perhaps 75-80% of the framework applies directly. Frame investments as bets. Define decision rights. Establish kill conditions. All essential. But the remaining 20% isn't a rounding error. It's where AI investments go sideways.

Outcomes become probabilistic, not deterministic. Traditional software investments follow predictable logic: build X, ship Y, measure Z. AI investments deliver confidence intervals. Your model achieves 87% accuracy. Is that good? Depends entirely on the cost of the 13% it gets wrong. When evaluating AI investments, leaders must quantify error costs, not just success metrics. A claims fraud model that's 87% accurate but flags legitimate claims for denial creates legal exposure that dwarfs the savings from caught fraud.

Technology timelines compress dramatically. Traditional investments assume 3-5 year platform stability. You make architectural decisions expecting them to hold. AI investments operate on 6-12 month horizons at best. The model you're building today may be obsolete before it reaches production, not because you built it wrong, but because foundation models are constantly evolving. This demands build-versus-buy checkpoints at quarterly intervals, not annual planning cycles.

The asset isn't the code. It's the data. In traditional software, intellectual property lives in the codebase. In AI, your training data is often more valuable than the model architecture. And that data has costs traditional business cases miss entirely: acquisition, cleaning, labeling, ongoing curation. Organizations routinely discover that 60-80% of their AI project costs sit in data preparation, not model development. Investment frameworks must treat data as both asset and liability.

Operational costs scale with success. Traditional software follows a comforting pattern: build once, run indefinitely with marginal maintenance. AI breaks this model. Inference costs scale with usage, meaning success makes you more expensive to operate. Models drift as the world changes, requiring retraining cycles that never appeared in your original business case. Unit economics must account for ongoing operational load, not just development investment.

Value realization follows unfamiliar curves. Ship a traditional feature and adoption begins immediately. Users either engage or they don't, and you know quickly. AI value realization depends on trust-building that unfolds over months. Users need to develop confidence in recommendations before they act on them. Override rates start high and (hopefully) decline. Leaders need leading indicators like accuracy trends, override patterns, and confidence calibration long before lagging indicators like revenue impact become visible.

Risk profiles evolve in real-time and fragment across jurisdictions. Traditional regulatory landscapes are known quantities. Compliance frameworks exist, and they're largely consistent across markets. AI operates in rapidly shifting terrain where yesterday's acceptable practice becomes tomorrow's legal exposure, and what's permissible in one geography may be prohibited in another. The EU's AI Act imposes requirements that don't exist in the US. China's algorithm regulations differ from both. Bias patterns that seemed benign create discrimination claims. Explainability requirements emerge mid-project. Kill conditions must now include fairness metrics and regulatory triggers that vary by market and evolve between quarterly reviews.

The investment model itself inverts. Traditional projects follow Plan → Build → Launch. You analyze, you decide, you commit, and then you learn whether you were right. AI projects cannot work this way because the variables are too uncertain and the landscape shifts too quickly. Instead, they demand Experiment → Learn → Scale (or Kill). You commit resources not to a predetermined outcome, but to a learning process with explicit checkpoints. The investment thesis becomes "we'll learn whether this works" rather than "we know this will work." This requires organizational comfort with funded uncertainty, which is a significant cultural shift for companies built on traditional project governance.

None of this invalidates Business Fluency. Rather, it intensifies the need for it.

The reality is that most organizations find it far easier to start projects than to stop them. Ideas get approved with minimal friction: a compelling pitch, a willing sponsor, a small initial budget. But killing an initiative? That requires someone to declare failure, teams to be reassigned, sunk costs to be acknowledged. So projects that should have died months ago keep shambling forward, consuming resources while producing nothing. Call them zombie projects: undead initiatives that crowd out the living.

And AI makes this pathology worse, not better. When execution friction drops but judgment requirements stay constant, organizations don't make better decisions. They make more decisions, faster, with less scrutiny. Easy to approve, hard to kill becomes easier to approve, still hard to kill. The zombie population grows.

Business Fluency provides the operating system for decision discipline. These seven modifications ensure it can handle the AI workloads now arriving on every product leader's desk, and give leaders the language and frameworks to kill what needs killing before the zombies take over.

Product leadership can no longer rely on intuition, persuasion, or momentum alone. As product organizations take on a portfolio role, the quality of decisions, not the volume of output, determines whether capital compounds or leaks. In an environment where execution is faster and commitments are easier to make, the cost of weak decision discipline rises. Business Fluency, however, provides the operating standard that makes product decisions governable, durable, and repeatable. And by doing so, confidence lives in the system, not in individuals, and strategic intent survives pressure, speed, and change.

Break a Pencil,

Michael

www.breakapencil.com

PS: This piece was co-authored with Mike Smart. If you want to discuss how business fluency applies to your product organization, connect with Mike or me on LinkedIn.