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gettyAI and automation are helping software teams write code, test applications and deploy updates faster than ever. But greater speed doesn’t necessarily mean greater effectiveness—especially if rapid output comes at the expense of quality, security, maintainability or meaningful business results.

As these tools become more deeply embedded in software development, CTOs need ways to distinguish genuine performance gains from simple increases in activity. Below, members of Forbes Technology Council share the metrics and signals leaders can use to assess whether their tech teams are delivering stronger outcomes, not just moving faster.

Post-Deployment StabilityI would focus on the stability of production post-deployment. While using AI will help teams deploy faster, if the number of incidents, rollbacks or performance degradation increases, then quality is being traded for speed. Teams that deploy effectively are those that deploy with stability, regardless of load. – Ajay Pandey

Breadth Of Team ContributionThe signal worth watching: Is AI expanding who contributes to your build or just making the developers faster? Propel’s senior engineering leaders who’d moved into management are writing prototypes again. QA and product managers are active contributors in AI-enabled build tools. When the barrier to contribution drops across the team, that’s effectiveness. Faster solo output is just efficiency. – Ross Meyercord, Propel Software

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Senior Staff Time AllocationWatch where your senior people’s hours actually go. Everyone’s tracking rework and failure rates, but the real test is whether AI bought your best engineers time for the hard, human work you hired them for or just buried them in cleanup of machine output. Effectiveness shows up as attention redirected toward architecture, customers and judgment calls. If you’re faster but their calendar looks identical, you automated the easy part and left the ceiling intact. – Lindsey Witmer Collins, WLCM “Welcome” AI Studio

Change Failure RateCTOs should watch the change failure rate, not just deployment velocity. AI can accelerate code generation, but if releases lead to more vulnerabilities, outages or rollbacks, productivity is an illusion. The goal is to improve delivery speed while simultaneously reducing operational and security risk. – Margaret Fairchild Morton, Waterfall Asset Management

Code Explanation RatePick random lines from AI-written code and ask the engineer who shipped it to explain them. This is your metric. At Google, code approval was gated on readability, because code is how knowledge moves between engineers. AI broke that link: Teams now ship code nobody absorbed. Output tells you how fast you are going. The explanation rate tells you who is actually driving: your engineers or AI. – Prashant Jalan, Guickly

Production Defect RateSpeed is easy to measure. Trust is what matters. Watch the production defect rate—defects escaping into production relative to code shipped. AI tools generate plausible code fast but struggle with edge cases, security boundaries and real-world conditions. If the defect rate climbs as velocity rises, you have a faster way of creating technical debt and eroding customer trust, not a more effective team. – Rhett Alden, Elsevier

High-Value Token UseStart gauging token burn against high-quality data, not just ingestion and processing things that have zero value. So many leaders are focused on token consumption, but against what? Ungoverned data? – Jacqueline DeStefano-Tangorra, DataOps

User And Business ImpactCTOs should track how often delivered work leads to real gains in user value or business results. Measure feature adoption, fewer customer issues and better key metrics linked to engineering. Focus on outcomes over speed so teams don’t just build faster but build what matters. – Manish Gupta, TestingXperts

Idea-To-Safe-Production Cycle TimeCTOs and transformation leaders are often the ones managing the AI budget, which means they have to help connect the teams competing for it. Security, risk, compliance and engineering will each have valid priorities, but they need a shared outcome. I would watch cycle time from idea to safe production. If teams ship faster but create more rework, risk or review burden, they are not becoming more effective. They are just moving the bottleneck. – Shashwat Sehgal, P0 Security

Rework RateCTOs should track rework rate, not just velocity. A team that ships fast but keeps rebuilding the same feature is not truly moving faster. The real signal is whether work reaches production cleanly the first time, without a second wave of fixes later. Leaders should ask whether AI and automation are removing friction and improving reliability or simply creating more noise than signal. – Siri Swahari, CGI

Rollback-Free Change RateCTOs should track the percentage of code changes that do not need a rollback, hotfix or major rewrite within the next 30 days. AI can help teams ship more code, but more output does not always mean more progress. If the same work keeps coming back as bugs or rework, the team is only moving faster in circles. – Jeetendra Gangele, BluePill

Delivery Value Without Added RiskIn my experience, CTOs should track how often faster delivery produces measurable business value without increasing defects, rework or operational incidents. Personally, I look beyond output volume to adoption, quality and outcomes. If teams ship more but customers benefit less or support work rises, they are faster but not more effective. – Laxmi Vanam

Customer Feedback Loop SpeedCTOs must look beyond deployment speed and monitor the velocity of customer feedback loops. Rushing to ship AI tools just to meet deadlines risks eroding customer trust. True effectiveness is signaled by a team’s ability to continuously measure, learn and iterate based on production data. Speed means nothing if you aren’t building a tighter loop between the product and the user. – Anuradha Choudhury, Reputation

Time Spent Solving ProblemsTrack how much time your engineers spend on actual problem-solving versus process overhead. AI tools can make teams faster, but faster isn’t the same as more effective. If your people are spending more of their week on real engineering judgment and less on boilerplate and configuration, the tools are working. If they’re just shipping more of the same, you’re only accelerating, not improving. – Anthony Spadaro, Smart Founder Lab

Customer-Impacting Defects Per ReleaseWatch customer-impacting defects per release. AI may increase code output and deployment speed, but effectiveness means delivering reliable value. If releases accelerate while defects, rollbacks or support incidents rise, the team is producing more, not improving. Pair velocity with quality, adoption and recovery metrics. – Swati Deepak Kumar (Nema), Citigroup

Recurring Corrections RateWatch how often the same problem gets solved twice by your team or by AI tools. Velocity metrics hide this. If the same corrections keep recurring, nothing is being learned, and cost scales with usage instead of falling with experience. The next edge is AI that truly learns from each interaction rather than just recomputing every answer. This development is going to be the key to the future of work. – Charles Yeomans, Atombeam

Engineering LeverageMeasure engineering leverage: the sustained business value delivered per engineer, not lines of code or deployment speed. AI can accelerate output, but only higher-quality, resilient systems that reduce future effort represent real productivity gains. – Gopichand Mannava, State of Connecticut

Problem Alignment And ImpactCTOs must track problem alignment and impact. Speed is not the only metric; the ultimate value will be about picking up the right problem to solve. Take Starbucks: They want to stop using SaaS products and build their own tools. Is that the best and most effective solution? Time will tell. The ultimate signal isn’t how fast your team can build, but whether they should be building it at all. CTOs will need to work with business teams more than ever before! – Neda Nia, Stibo Systems

AI Job Failure RateWatch your AI job failure rate, not your deployment count. Enterprises are shipping faster but still hitting double-digit failure rates because no one connected infrastructure signals to outcomes. A team that moves fast but can’t trace why work fails isn’t more effective; it’s just failing faster. Effectiveness shows up in root cause time, not release velocity. – Paul Appleby, Virtana Corp.