CTO Guidebook on Measuring Engineering ROI; How Modern Startups Mature Their Teams; Use AI Agents Productively with Tested Engineering Principles

Issue #73 Bytes

🌱 Dive into Learning-Rich Sundays with groCTO ⤵️

Article of the Week ⭐

“Attempts to attribute a fair portion of the company’s business outcomes to the engineering department are seriously challenged by the tension between engineering’s key role, on one side, and the opaqueness of the chain of causation between engineering work and business outcomes, on the other.“ —Itzy Sabo

How to Measure Engineering ROI: A Practical Guide for SaaS CTOs

When Marketing says “Every dollar brings back three,” and Sales points to closed revenue, engineering is left holding velocity charts and uptime stats. Impressive perhaps, but they don’t answer the question “What do we get for every engineering dollar we spend?”

The shift that unlocks ROI

Engineering ROI becomes clear only when you start optimizing for impact and stop optimizing for activity.

• Shipping features does not mean creating value.

• Velocity does not mean effectiveness.

• Delivering what was asked does not mean moving the business forward.

Your work must map directly to business outcomes like churn reduction, expansion, activation, onboarding speed, and revenue per account. That shift gives engineering permission to say no to low-value work and yes to the initiatives that actually move the company.

Work measurable by default

Revenue is lagging. By the time you see it move, three other teams have touched the customer journey. To establish clear ROI, you attach leading indicators to every major engineering initiative.

• Feature usage

• Conversion to “aha” moments

• Drop in support tickets

• Faster time-to-value

• Improved activation rates

The only rule that matters: A feature isn’t done until we can see what it’s doing.

This creates early prediction, clean attribution, and a way to kill failing ideas before they burn quarters of engineering capacity.

Engineering initiatives to demonstrate

Instead of pretending you can isolate exact revenue shares, you build direct causal stories between engineering work and business results.

A simple pattern to follow:

• Identify the business goals.

• Commit engineering to specific, outcome-driven initiatives.

• Deliver and report the impact in business language.

Example:

• Increase ARR by 60%

• Reduce churn from 20% to 10%

• Fix the top churn triggers

• Projected revenue lift: +$10M over 12 months

• Engineering cost: low–mid six figures

Two numbers for a ROI-concrete story

You don’t collapse the whole org into one “ROI number.” Split engineering into two economic functions that every business leader already understands:

• Supporting the base.

• Creating new growth.

You reflect those with two ratios.

Engineering Operating Efficiency
How much existing revenue do you support per KTLO dollar?

You estimate the true cost of keeping the service stable: on-call, infra, debugging, support, a slice of salaries, staging environments. You compare that to revenue from customers who were already paying before the period. This becomes your measure of operational leverage.

Engineering Growth Efficiency
How much new revenue do you enable per new-capability dollar?

You take revenue from newly activated paying customers. You amortize your feature development and product investment over a few years so you don’t distort the current period. This becomes your measure of innovation leverage.

These numbers don’t need perfect precision, merely consistency. They show trends, create accountability and help frame engineering as an economic engine, not a cost center.

The traps you avoid

Full attribution credits everything to Sales or Marketing. Fractional attribution turns into political warfare. Causal attribution collapses under noise and overlapping initiatives.

These models hurt collaboration and distort incentives. Your ROI model must be useful and actionable, especially if they seem unfair due to sales departments individual bonuses.

Useful means it guides decisions, aligns incentives, and helps you win resources without creating cross-functional hostility.

Other highlights 👇

Startup Engineering Team Organisation

Startups tend to cycle through the same team structures as they scale, not because leaders lack imagination, but because each model collapses under its own constraints. Marc Gauthier walks through that progression using an 18-engineer, post-Series A B2B SaaS as the running example. Every organisational model solves one bottleneck while creating a new one. The real skill is recognising when to pivot.

1. Technical Teams → Fast craft growth, constant cross-team pain

Early on, grouping by stack feels natural: frontend with frontend, backend with backend, mobile with mobile. It works until every project needs all three skills.

Works:

• Strong peer support

• Fast craft growth

Breaks:

• Every feature is cross-team

• PMs can’t get clear ownership

• No deep product context

Which leads us to the obvious next step:

2. Squads → Clear owners, faster product work, rising tech debt

So the company rotates to domain-focused squads. Engineers love solving real business problems with a cross-functional team. PMs finally have a clear partner.

Then the bill arrives:

• “Core” engineering work has no natural home

• Framework upgrades, test flakiness, infra improvements get perpetually deferred

• Engineers feel isolated in their stack, overloaded with feature pressure

You get product velocity today at the cost of delivery stability and unowned infra problems.

3. Chapters → Technical quality rebounds, but planning collapses

Chapters (communities of practice) are introduced to restore engineering standards. And they do:

• Tech debt starts shrinking

• Knowledge spreads

• Consistency increases

But product planning takes a hit:

• PMs can’t rely on engineer availability

• Quarterly commitments become fiction

• Squads fight chapters for headcount slices

The pendulum swings too far: meetings loom as resource-management and trying to split an engineer’s time to allocate them partially introduces friction and visibility concerns. Next!

4. Dedicated Core Team → Clarity returns at the cost of silos

To calm the chaos, the company builds a Platform/Core squad and restores predictable product bandwidth.

Reality check:

• Product squads lose key seniors

• Core engineers get stuck doing unglamorous maintenance

• Product engineers disengage from shared technical responsibility

• Two cultures form: “the builders” vs “the fixers”

This allows a growing company to continue delivering. However, the systems and ownership of code becomes brittle and political.

5. One-Shot Projects → Clean on paper, messy in practice

At this point senior leaders become tired and concerned of constant reorgs. The org tries temporary project teams for big initiatives.

Predictable outcomes:

• Timelines slip

• People rotate in/out mid-project

• Burnout spikes

• Work stranded after the team disbands

Shiny simplicity, painful execution.

6. Staff Engineers + Light Chapter Work → The most stable model so far

The company settles on a hybrid:
• Staff engineers roam to unblock the hardest problems
• All engineers spend ~20% on chapter work (without micromanaging allocation)

This works better:

• Staff engineers amplify squads where it matters

• Big upgrades finally ship

• Chapters regain value without dominating planning

• Shared ownership returns

It’s also fragile staff carry enormous load, and hiring strong ones is brutal. This setup works as long as there is meaningful work, until the lack of energy or direction collapses into one of the previous setups, repeating the cycle.

What cycle are you in right now?

---

The Engineers Who Can’t Use AI Agents Don’t Have a Tools Problem

Adoption gaps with AI coding agents rarely come from tooling, training, or experience level. The real dividing line is that some engineers can externalize context very well, making it easier to “explain” to the machine agents what it is exactly that they care about and work on.

Why some engineers excel with agents

Engineers who do well with AI tools naturally explain their thinking with clarity, they:

• Provide full context beyond shorthands or shortcuts.

• Describe constraints, past decisions, and edge cases.

• Frame prompts the way they’d onboard a new teammate.

They’ve built the habit of making their mental model explicit, so the agent has everything it needs to produce useful work.

Two patterns show up in contrast, in teams that either cannot apply these techniques at all or get stuck past a few prompts:

They understand their own system but can’t articulate it. Years of pattern-matching and intuition mean they rarely had to explain their reasoning, and that muscle atrophied.

They never built a deep model of the system in the first place. Traditional development allowed people to ship code by copying patterns, following templates, and relying on tribal knowledge. With AI, that falls apart because the agent requires clear, accurate explanations.

In both cases, prompting fails because the underlying understanding or communication isn’t strong enough, practiced enough or precise enough.

Make your context visible

Effective practices include:

• Reading the codebase deliberately to build real understanding

• Pair programming to practice explaining decisions

• Architecture reviews that surface gaps in reasoning

• Coding katas focused on narrating intent, not just producing code

• Book clubs and shared learning to rebuild fundamentals

All these skills are learnable, by the way, the wider software engineering community captured these decades ago in Extreme Programming. And not only that, even if your team does not rely on AI agents, these principles and practices have become essential for modern software development.

---

Engineering Management vs. Project Management

Many confuse engineering management with project management. The overlap makes it easy to see why.

Both involve leadership, planning, and execution. Both drive projects to completion. But their goals, focus areas, and responsibilities differ significantly.

This confusion can lead to hiring mistakes and inefficient workflows.

A project manager ensures a project is delivered on time and within scope. Project management generally refers to managing a singular project. An engineering manager looks beyond a single project, focusing on team growth, technical strategy, and long-term impact.

Strong communication skills and soft skills are essential for both roles, as they help coordinate tasks, clarify priorities, and ensure team understanding—key factors for project success and effective collaboration. Both engineering and project management roles require excellent communication skills.

Understanding these differences is crucial for businesses and employees alike.

---

Find Yourself 🌻

That’s it for Today!

Whether you’re innovating on new projects, staying ahead of tech trends, or taking a strategic pause to recharge, may your day be as impactful and inspiring as your leadership.

See you next week(end), Ciao 👋

---

Credits 🙏

Curators - Diligently curated by our community members Denis & Varun

Featured Authors - Itzy Sabo, Marc G Gauthier, Norman (for ADD)

Sponsors - This newsletter is sponsored by Typo AI - Engineering Intelligence Platform for the AI Era.

---

1) Subscribe — If you aren’t already, consider becoming a groCTO subscriber.

2) Share — Spread the word amongst fellow Engineering Leaders and CTOs! Your referral empowers & builds our groCTO community.