The pitch you've already heard
A vendor or partner walks into your office and tells you AI development services will speed your team up 10x. They show a slide of GitHub Copilot statistics. They quote a productivity study. They mention specific Fortune 100 customers. They quote you a number.
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Most of those slides are real. Some of the numbers are even accurate. The math the vendor isn't doing is what those numbers mean for your specific engineering team, your specific tech stack, your specific quarter.
This piece is the math the vendor isn't doing.
The numbers that are actually true
Let's start with what's verified. GitHub Copilot has 20 million cumulative users as of July 2025, 90% Fortune 100 adoption, and enterprise customer growth at 75% quarter-over-quarter. Microsoft and Accenture's joint study of 4,800 developers found 55% faster completion on controlled JavaScript HTTP server tasks. Copilot generates an average of 46% of code written by users, with Java developers at 61%.
Those numbers are real. They're also the marketing version. Here's the version your CTO needs.
The numbers that get omitted
That's a quarter of degraded output before the gains arrive. The 55% faster number applies to specific narrow tasks (JavaScript HTTP servers), not to general engineering work. The 46% code generation rate measures how much code gets accepted, not how much of that code survives review or how much was high-quality to begin with.
Zoominfo's enterprise case study with 400+ developers shows an average suggestion acceptance rate of 33%, with 20% of suggestions actually accepted as lines of code. The 80% that aren't accepted are still time the developer spent reading and rejecting suggestions. That's not free time.
GitClear's 2025 research found 4x growth in code clones, indicating AI-assisted developers are increasingly reusing similar code patterns rather than refactoring them. That's a long-term tech debt accumulation pattern that won't show up in the first-month productivity report.
29.1% of Python code generated by AI assistants contains potential security weaknesses. That doesn't make AI development services bad. It means the savings on engineering hours need to be net of the security review costs they generate.
The actual CTO question
The vendor's pitch is "do AI development services work." That's the wrong frame. AI development services work, in the sense that they do something measurable. The right CTO frame is whether the specific productivity gains, net of ramp time and tech debt, net of security review overhead, net of license cost, justify the investment for your specific workload.
For some workloads, the answer is clearly yes. High-volume boilerplate-heavy code (test scaffolding, CRUD endpoints, configuration management) sees large net gains. For others, the answer is more nuanced. Architectural work, novel algorithmic engineering, security-critical systems are categories where AI assistance provides modest help and may add review overhead.
The CTOs winning this conversation have done the workload-by-workload analysis instead of accepting the marketing claim. They route AI assistance toward boilerplate-heavy work, keep their senior engineers focused on architecture and security review, and measure productivity gains against acceptance rate and review burden rather than against generated code volume.
The four workload categories
A useful taxonomy for the CTO conversation. Most engineering work falls into one of these.
Category A: High-volume boilerplate. Test scaffolding, REST endpoint stubs, basic data transformations, repetitive refactors. AI assistance shines here. Net productivity gains are real and durable.
Category B: Greenfield feature work. Building new functionality with established patterns. AI assistance helps moderately on the parts that look like Category A (the boilerplate inside the feature) and provides limited gains on the genuinely new parts. Net positive, with caveats.
Category C: Brownfield work in legacy code. Modifying existing systems where the model lacks deep context about the codebase's history. AI assistance often produces suggestions that compile but miss the architectural intent. Net effect is mixed; gains depend heavily on developer experience.
Category D: Security-critical, novel, or architecturally-significant work. AI assistance is a starting point at best. The engineering work is in evaluation, threat modeling, architectural design. AI generates code; the engineer does the harder work.
A CTO deploying AI development services should know what fraction of their team's work falls into each category. The team doing 70% Category A and 30% Category D will see different ROI than the team doing 20% Category A and 80% Category D. The pitch decks assume the first team.
What the vendor pitch leaves out about ramp time
The 11-week ramp Microsoft documented deserves separate discussion. Most pitches treat it as a minor footnote. For a CTO, it's a strategic question.
Eleven weeks is roughly one quarter. That's one full sprint cycle where output is degraded while the team learns. If your engineering org has any flat-or-declining-productivity quarters in the trailing twelve months, the ramp gets blamed for things it didn't cause. If the team is ramping during a critical product launch, the timing produces predictable misery.
The CTOs handling this well sequence deployments. Roll out to one team first, ramp, measure, then expand to the next team. Don't deploy across the engineering org simultaneously. Don't deploy during high-stakes launch windows.
The security review cost
29% of generated Python code has security weaknesses. That's a real number, and it has a cost shape. Senior engineers reviewing AI-generated code for security issues are doing review work at senior engineer rates. The productivity gain at the junior level is partially offset by the review burden at the senior level.
The net is still usually positive. But the math is closer than the pitch suggests, and the math depends heavily on what code review discipline already existed. Teams with mature review practices absorb AI security review naturally. Teams without it are now paying review costs they weren't paying before.
How Logiciel fits this conversation
Most engineering leaders who reach out to us about AI development services have already deployed Copilot or a similar tool and are trying to understand whether the actual productivity gains justify the actual cost, including the hidden ones we've described above.
The work we do isn't selling AI dev services. It's helping you measure them properly: workload categorization, ramp time tracking, acceptance rate by code type, security review burden, tech debt accumulation. The measurement framework is the deliverable. The decision to expand or contract the AI tooling stays with you, informed by real numbers from your environment instead of the vendor's marketing version.
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Call to Action
The 30-minute move
Book a working session with a senior Logiciel engineer. Bring your AI dev services adoption data if you have it, or the pitch you're currently evaluating. We'll walk through the workload taxonomy and tell you what numbers to track over the next quarter to make the case defensible.
Book the 30-minute AI dev services session →
Frequently Asked Questions
Should we be using AI coding assistants at all?
Almost certainly yes, on the right workloads. The question is which workloads, with what guardrails, and at what measured cadence. The default-on, default-everyone approach is what produces disappointing results.
How do we measure actual productivity gain?
Not by code volume generated. By feature throughput net of bugs, security findings, and ramp cost. The honest measurement is harder than the vendor's metric and worth doing.
We have a small team. Does this still apply?
More than for large teams. Small teams feel the ramp dip harder. Smaller teams also have less buffer for security review cost. Be more careful, not less.
What about Cursor, Claude Code, and the newer agentic IDE tools?
Same framework, different tool. Agentic IDE tools shift the workload distribution (more Category A and B handled autonomously, less manual coding required) but the same measurement discipline applies. Don't assume the pitch.
What's the one number our team should track this quarter?
Acceptance rate of AI suggestions, broken out by code category. If you can't tell at the end of Q1 which category of work the assistant is helping with most, you don't have the measurement to make the next decision intelligently. --- Sources cited: - GitHub Copilot statistics 2025: 20M users, 90% Fortune 100, 75% QoQ enterprise growth - Microsoft/Accenture: 55% faster on controlled tasks; 11-week ramp - GitClear 2025: 4x growth in code clones with AI assistance - 46% of code AI-generated; 61% for Java
