By Sahil Singh
Most CTOs operate with incomplete visibility into their own product. They know the roadmap. They know the team structure. But they struggle to answer basic questions from the board: How much of our investment goes to maintenance vs. new capability? Where are the risks in our system? What is the actual state of our technical infrastructure? Technical debt statistics tell part of the story, but true product visibility requires a broader practice that connects code reality to business narrative.
This gap is not a personal failing. It is structural. Engineering organizations generate enormous amounts of data, commits, deploys, incidents, velocity metrics, but that data rarely synthesizes into a coherent picture that a CTO can present with confidence to non-technical stakeholders.
The Visibility Problem
CTOs face a paradox: they sit at the intersection of technology and business, yet the information systems that serve each side rarely connect.
On the business side, dashboards show revenue, churn, NPS, and pipeline metrics. These are clean, well-understood, and updated in real time.
On the technology side, dashboards show uptime, deploy frequency, and maybe some velocity charts. These metrics describe operational health but say little about the product itself. They tell you the engine is running but not where the car is going.
The gap between these two views is where risk hides. A product with excellent uptime and steady velocity might still be accumulating debt that will slow delivery next quarter. A team shipping features at a healthy clip might be building on a foundation that is quietly degrading. Without visibility into the product itself, the code, the architecture, the dependency graph, the health of each module, a CTO is flying partially blind.
The cost of this blindness surfaces in board meetings. When a board member asks "why did this feature take six months?" or "what is our technical risk exposure?" the CTO either gives a vague answer or scrambles to assemble data from multiple sources. Neither outcome builds confidence. Understanding the cost of nobody knowing the system is the first step toward fixing it.
What Boards Want to Know
Board members and executive teams ask a predictable set of questions. Being prepared for them is a core CTO responsibility.
Where does engineering investment go? Boards want to see a breakdown: how much effort goes to new features, how much to maintenance, how much to infrastructure, and how much to debt remediation. They want to see this ratio over time. If maintenance is consuming an increasing share, they want to understand why and what the plan is.
What is our technical risk exposure? This includes dependency vulnerabilities, single points of failure, key-person dependencies, and architectural limitations. Boards think about risk in portfolio terms. They want to know the probability and severity of technical problems, not the specific technical details.
How does our technology compare? Boards benchmark everything. They want to know whether your tech stack, your architecture, and your team's practices are in line with industry standards. They do not need to understand microservices, but they need to know whether your architecture is an asset or a liability.
Can we scale? Whether the plan involves doubling users, entering a new market, or supporting an acquisition, boards want to know whether the technology can handle growth. This requires visibility into performance bottlenecks, architectural limits, and capacity planning.
What is the team's health? Beyond code, boards care about engineering retention, onboarding effectiveness, and developer satisfaction. These human metrics are leading indicators of future technical outcomes.
Metrics That Matter
Build your visibility practice around metrics that connect technical reality to business outcomes.
Investment allocation ratio. Track the percentage of engineering effort across four categories: new features, enhancements to existing features, maintenance and bug fixes, and infrastructure and debt. Present this as a quarterly trend. A healthy ratio shifts based on business phase, but sudden or unexplained changes should trigger investigation.
Velocity trend by domain. Rather than a single velocity number, track velocity by product area. This reveals where the codebase enables fast delivery and where it slows teams down. Declining velocity in a specific area often signals accumulated debt.
Incident frequency and blast radius. Track not just how many incidents occur, but how many customers each one affects and which code modules are responsible. This connects operational metrics to product health.
Dependency risk score. Aggregate the age, vulnerability count, and maintenance status of your dependencies into a single score. Present it as a trend line alongside the specific count of critical vulnerabilities.
Architecture fitness. Measure how well your actual architecture matches your intended architecture. This includes coupling between services, API contract compliance, and data flow consistency. Deviation from intended architecture is a leading indicator of future technical problems.
Developer experience metrics. Build time, test execution time, time to first deployment for new engineers, and developer satisfaction surveys all reflect the health of your technical environment. These metrics are leading indicators: when developer experience degrades, delivery velocity follows within one or two quarters.
For specific benchmarks and industry comparisons, see our analysis on technical debt statistics.
Building a Visibility Practice
Visibility is not a tool you install. It is a practice you build.
Step 1: Connect your data sources. Pull data from your code repositories, CI/CD pipeline, incident management system, project management tool, and developer surveys into a unified view. Tools like Glue automate the codebase analysis portion. Other data sources may require custom integrations or manual collection.
Step 2: Establish a reporting cadence. Monthly internal reviews and quarterly board-ready reports create accountability. The monthly review is where you identify emerging issues. The quarterly report is where you tell the story to stakeholders.
Step 3: Assign ownership. Someone needs to own the visibility practice. In smaller organizations, this might be the CTO directly. In larger ones, a technical program manager or engineering operations lead is appropriate. The owner ensures data is current, reports are generated, and trends are investigated.
Step 4: Calibrate with engineering leads. Raw metrics without context are dangerous. Before presenting data externally, review it with your engineering managers. They can explain anomalies, provide context for trends, and flag metrics that may be misleading.
Step 5: Tell stories, not just numbers. Boards respond to narratives. "Our dependency risk score increased by 15 percent this quarter because we deferred a planned migration to prioritize the enterprise launch. We have scheduled the migration for Q2 and expect the score to return to baseline by Q2 end." That narrative is infinitely more useful than a chart showing a line going up.
Step 6: Make it bidirectional. Visibility should not only flow upward to the board. It should flow downward to engineering teams. When engineers see how their work connects to the metrics leadership cares about, they make better local decisions. When they understand that reducing complexity in a specific module directly affects the CTO's quarterly risk score, the motivation for health work becomes tangible.
Step 7: Iterate the metrics. Your first set of metrics will be imperfect. Some will not measure what you thought they did. Others will prove unreliable. Review and refine your metrics quarterly. Drop the ones that do not drive decisions. Add new ones as your understanding matures.
Product visibility is ultimately about closing the gap between what your organization builds and what your organization understands about what it builds. For CTOs, this practice is the difference between presenting to the board with confidence and presenting with hope.
FAQ
How can CTOs get visibility into their product? CTOs build visibility by connecting data from code repositories, CI/CD pipelines, incident systems, and project management tools into a unified view. AI-powered tools like Glue automate codebase analysis. The key is establishing a regular reporting cadence and assigning ownership of the visibility practice.
What product metrics should CTOs track? Focus on investment allocation ratio (features vs. maintenance vs. debt), velocity trends by product area, incident frequency and blast radius, dependency risk scores, architecture fitness, and developer experience metrics. These connect technical reality to the business outcomes that boards and executives care about.
How do you communicate tech health to the board? Frame technical metrics in business terms. Show investment allocation trends, connect code health to delivery velocity, and translate risk scores into probability and severity language. Use narratives that explain trends and present plans, not just charts. Boards respond to stories with clear cause, effect, and action plan.