AI can help developers complete some software tasks faster, but that does not automatically make software cheaper to deliver, more reliable, or safer. The evidence so far points to a more useful conclusion for leaders: AI can amplify the way an organization already works. To benefit, your organization needs clear use cases, prepared teams, effective safeguards, and measures that track the whole delivery system—not just code written or time saved.
Does AI actually make software cheaper to build?
Not necessarily. AI assistance may reduce the effort involved in specific tasks, but a task-level gain is not the same as a reduction in the total cost of building and operating software. Review, testing, rework, security, maintenance, and delivery delays all affect the full cost. The sources below measure different outcomes in different settings; none establishes a universal decline in software lifecycle costs.
| Evidence | What was measured or reported | What leaders can reasonably infer |
|---|---|---|
| Microsoft Research, 2025 | A pooled analysis of three randomized field experiments at Microsoft, Accenture, and an unnamed Fortune 100 company estimated a 26.08% increase in completed tasks among 4,867 developers using a coding assistant. The standard error was 10.3%, and the authors note that individual experiments were noisy. | This is encouraging task-completion evidence from those settings—not a forecast of equivalent savings in budget, headcount, or delivery time elsewhere. |
| DORA, 2025 | A 25% increase in AI adoption was associated with 1.5% lower delivery throughput and 7.2% lower delivery stability. | The reported association is not proof that AI adoption caused either result. It does show why leaders should check delivery outcomes alongside individual productivity. |
| Capgemini Research Institute, 2024 | In its survey of software professionals, 27% of organizations reported having platform and tool prerequisites in place, while 32% reported having talent prerequisites in place. | These are dated survey findings, not current prevalence estimates. They highlight that readiness involves more than buying or enabling a tool. |
| OpenAI, 2025 | OpenAI reported that 75% of surveyed workers said AI improved the speed or quality of their output. ChatGPT Enterprise users attributed 40–60 minutes saved per active day to AI use. | These are vendor-published reports about surveyed workers and users of OpenAI’s product, not an independent engineering benchmark or a measure of software lifecycle cost. |
The differences matter: completed tasks, self-reported time savings, and delivery stability describe separate parts of the work. A leader who treats one as a stand-in for all the others risks scaling activity without improving the software customers receive.
Why do results vary between organizations?
DORA’s 2025 State of AI-assisted Software Development report frames AI as an amplifier of organizational strengths and weaknesses. Its analysis draws on more than 100 hours of qualitative research and survey responses from nearly 5,000 technology professionals around the world. That broad evidence base does not mean every organization will see the same effect. DORA’s report emphasizes the conditions into which AI is introduced.
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A team with clear ownership, reliable tests, manageable work batches, and useful review practices may be better positioned to turn assistance into completed, maintainable software. Where requirements are unclear or review capacity is already strained, more generated code can add work downstream. DORA’s report describes a possible mechanism: faster code generation may encourage larger batches that take longer to review and can contribute to instability. The report’s adoption figures are associations, not proof of that causal chain.
Evidence from generative AI in real workplaces points to similar variation. A July 2024 Microsoft Research synthesis of more than a dozen studies says effects differ by role, function, and organization, and depend in part on adoption and utilization. In practice, access to a tool does not tell you whether employees use it, use it well, or improve the outcomes that matter.
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What does organizational readiness require?
Readiness is the capacity to use AI in a specific workflow without losing control of quality, security, or delivery. Before expanding use, check these six areas:
- A valuable, bounded use case: Identify a concrete task and the outcome you want to improve. Avoid treating “more AI use” as the goal.
- Workflow and platform fit: Confirm the tool works with the repositories, build systems, review processes, and tests teams actually use.
- People and training: Give developers guidance on effective use, limitations, and verification. Check whether teams have enough time and skill to review suggestions.
- Governance and security: Make approved-use expectations clear. Assess exposure of source code and sensitive data, code provenance, intellectual-property concerns, and human-review requirements.
- Delivery capacity: Preserve time for review, testing, and feedback. Ensure teams can manage the work that faster generation may send downstream.
- Measurement and learning: Establish a baseline, monitor outcomes, gather developer feedback, and adjust the workflow when results or risks change.
Capgemini Research Institute’s 2024 survey illustrates why these checks matter: it reported that over 60% of organizations lacked governance and upskilling programs. The figures are historical survey results, not a claim about the proportion today. In the same report, 63% of software professionals who used generative AI said they used unauthorized tools. That finding makes unofficial use a governance signal: employees may be seeking capabilities the organization has not made available or approved. Capgemini identifies risks including hallucinated code, code leakage, and intellectual-property issues.
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How should you introduce AI into software work?
- Select one bounded workflow. Choose a task where assistance could matter, name the outcome to improve, and define what is out of scope. A small, meaningful pilot is easier to evaluate than a broad mandate.
- Set rules before access expands. Tell employees which tools and data uses are approved, what information must not be shared, and what review is required before AI-assisted work is merged or shipped.
- Prepare the people and process. Provide practical training and connect the tool to existing development workflows. Make sure the team has capacity for verification, review, testing, and rework rather than assuming generated output is ready to use.
- Compare results with a baseline. Track task completion and developer experience alongside quality, review burden, delivery throughput, stability, and security. Include the cost of review and rework when assessing total lifecycle cost.
- Expand only when the evidence supports it. Use pilot results and team feedback to improve the workflow. If task speed improves but quality, stability, or review load worsens, address the delivery bottleneck before increasing rollout.
This approach also reflects the distinction between work output and employee experience. In Capgemini’s 2024 report, Enel Grids’ Head of ICT Industrial Delivery Fabio Veronese said: “We are more ambitious. For us, improving development productivity with generative AI is not just about lines of code. It is also about developer experience.” That is a practitioner’s perspective, not a measured conclusion, but it points to a useful design question: does the change make work better as well as faster?
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you measure beyond code produced?
Use measures that show whether assistance improves the full path from a developer’s task to reliable software in use. Choose a small set suited to the pilot, and interpret them together rather than declaring success from a single productivity figure.
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- Task and developer outcomes: Monitor task completion and developer feedback, including whether the tool is useful in the actual workflow.
- Quality and rework: Check the work that reaches review and testing, how much review effort it requires, and whether additional corrections are needed.
- Delivery performance: Follow throughput and stability, not only the speed at which code is drafted.
- Risk: Track whether approved-use rules are followed and whether security, provenance, or intellectual-property concerns arise.
- Total cost: Include tool and enablement costs as well as time spent reviewing, testing, correcting, and maintaining the resulting software.
The reason to pair these measures is visible in the evidence: one set of field experiments found a task-completion gain in specific settings, while DORA reported an association between greater AI adoption and lower delivery throughput and stability. Neither finding alone answers whether AI is creating net value for your organization.
How should you evaluate AI options?
The available evidence does not establish one best tool or deployment model for every organization. Compare options against the work, constraints, and outcomes of your own teams.
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| Evaluation area | Questions to answer |
|---|---|
| Task completion and quality | Does assistance help with the selected task, and is the resulting work acceptable after verification? |
| Delivery throughput and stability | Do teams complete and deliver work effectively without worsening delivery reliability? |
| Workflow fit | Does the option fit the organization’s repositories, build systems, code review, and testing practices? |
| Code and data handling | How are source code and sensitive data treated, and are the rules compatible with organizational requirements? |
| Security, intellectual property, and governance | Can the organization set and enforce approved-use expectations, review code provenance, and handle relevant risks? |
| Training and adoption | Can developers learn to use the option effectively, and can the organization see whether it is actually used? |
| Total cost | What are the costs of the tool, rollout, training, review, testing, rework, and maintenance? |
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