Before purchasing an AI coding tool, define the improvements you expect from its adoption. Your evaluation should reflect your goal: reducing the time needed to prepare changes, improving testing or making an existing system easier to work with. In every case, account for the time needed to review and refine the output so that it meets the team’s requirements.
Below, we explain how to follow these steps as you prepare your team for 2027.
Which AI coding tools should you consider for 2027?
We have compared GitHub Copilot, Cursor, Claude Code, OpenAI Codex, JetBrains Junie and Gemini CLI. We selected them to illustrate different ways of working: assistance in the editor, task execution within a repository and interaction through the terminal. A repository stores code and its change history, while a terminal lets developers run tools using commands. Our comparison draws on information in the vendors’ documentation. For each tool, we identify a use case and a task that will help you assess its suitability for your project.
| Tool | When to include it in your comparison | What to check in your project |
|---|---|---|
| GitHub Copilot | When code suggestions, issue management and change reviews within GitHub are important. | Follow a small change from issue analysis to preparing a pull request. Check the quality of the output and how organisational policies apply in the selected mode. |
| Cursor | When the team wants an agent that searches the project, edits files and runs commands. | Assign a change involving several related files. Assess adherence to project conventions and the time needed for review. |
| Claude Code | When developers want to carry out multi-step tasks using code, the terminal and the team’s existing tools. | Assign a bug fix that requires analysing several modules and running tests. Record how often a developer needs to intervene. |
| OpenAI Codex | When repository work involves analysis, planning changes, editing files and running development tools. | Check how the task is completed in the configured environment. Assess the correctness of the change, test results and control over permitted actions. |
| JetBrains Junie | When the team uses a JetBrains IDE supported by Junie and wants to plan and carry out tasks with an agent’s assistance. | Assign a change that includes tests and check which project information the agent receives. Verify support for the IDE version your team uses. |
| Gemini CLI | When the team prefers an agent powered by Gemini models, running in a terminal on the developer’s computer or in Google Cloud Shell. | Complete a development task using your chosen authentication method. Check the applicable usage limits, billing terms and data processing policies. |
Many products support several ways of working. During the pilot, record the tool name, model, operating mode and configuration. An agent running in the cloud may have different access to libraries, documentation and services from one running on a developer’s computer. Account for these differences when comparing results.
How do you match AI tools to your development team’s tasks?
Start by identifying a specific activity you want to improve. Translate the broad goal of adopting AI in IT into a task with measurable results: preparing tests for an existing module, updating an integration or reducing the time needed to investigate a reported bug.
- Working with an existing system: check whether the tool can locate the relevant code and explain dependencies. Ask for references to files so that you can verify its answer.
- Implementing repetitive changes: assess adherence to existing project patterns, error handling and the scope of modifications. A useful task might involve adding a field to an existing workflow, including validation.
- Creating tests: check whether the generated test detects a known bug and reflects business requirements. Also verify that it covers the relevant scenarios and rests on sound assumptions.
- Modernising code: compare the system’s behaviour before and after the change. Account for the library versions in use and compatibility requirements for other components.
Start with tasks that have clearly defined expected outcomes and verification methods. Where documentation is incomplete, use the pilot to establish how much time the team spends supplying AI with the missing context.
How do you assess code protection and AI agent permissions?
Before giving a tool access to your company’s repository, define the requirements its configuration must meet. These include the scope of shared data, where it is processed, how long it is retained and the available controls. For client projects, also check the agreed rules for using external services.
Check data flows for the feature you use
Cursor’s documentation distinguishes between requests sent to models and cloud agent operations, which require storing a copy of the repository. This shows why the assessment should cover the specific feature and selected model. Check separately whether data is used to train models, how long it is retained and where it is processed. Even when an application runs on an employee’s computer, check where the information supplied to the model is sent. The location of the interface and the location of data processing are two separate aspects of the assessment.
Verify how security controls work in practice
GitHub documents limitations to Copilot’s content exclusion feature, including the lack of support in the Edit and Agent modes specified in its documentation. Any assurance that AI cannot access a particular directory therefore needs to be verified in the exact client and mode your team will use. For tools that run commands, also check environment isolation, network access and approval requirements. These controls are described in the documentation for Codex and Claude Code. During the pilot, use files containing sample data to test restrictions without exposing real passwords, access keys or other confidential information. Treat mandatory requirements as conditions for proceeding with the evaluation. If a particular setup does not comply with project policies, consider another configuration or product before comparing productivity.
How do you compare AI tools using pilot results?
Once you have checked data and access requirements, you can compare the shortlisted tools’ effectiveness. In the matrix below, we assign the greatest weight to code correctness and the effort required to review and approve it. Adjust the weights to your project’s priorities before starting the pilot so that every tool is assessed on the same basis.
| Criterion | Weight | Data needed for the assessment |
|---|---|---|
| Correctness and quality of changes | 35% | Compliance with acceptance criteria, defects identified and the extent of required corrections. |
| Total team effort | 25% | Time spent preparing the task, implementing the change, reviewing, verifying and making corrections. |
| Project fit | 20% | Ability to work with the project’s languages, dependencies, documentation and testing tools. |
| Cost predictability | 10% | Actual usage, the cost of unsuccessful attempts, usage limits and spending controls. |
| Ease of use and learning time | 10% | Time needed to use the tool independently, difficulties encountered and support required. |
Score each criterion on a scale of 0–5, defining what each score means in advance. To calculate the overall result, divide each criterion’s score by 5, multiply it by its weight expressed in points and add the results. The maximum overall score is 100 points. For example, a score of 4/5 for a criterion weighted at 35% contributes 28 points. If data is missing, mark the criterion as unassessed and gather the necessary information before calculating the result. Include a brief justification and examples of changes with your assessment. A common scale makes discussions between developers, the technical lead and the budget owner easier. Also set minimum quality requirements. If critical errors recur in a tool’s output, investigate their causes before adoption, regardless of its overall score.
How do you measure costs and productivity when using AI for software development?
Include licences, model usage fees, configuration, training and team time in your budget. Particularly with agents that perform multiple steps, measure the cost of the entire task, including attempts that do not produce an accepted change.
Cost per accepted task = total human labour and tool costs allocated to a group of tasks Ă· number of accepted tasks in that group. Compare tasks of similar complexity and with comparable quality requirements. Show adoption costs separately. When assessing cost-effectiveness over a longer period, specify how many months you spread these costs across.
Example: what does faster code preparation deliver?
Suppose preparing and implementing a change takes 100 minutes, or 60 minutes with AI assistance. Add the time spent on review, verification and corrections to the comparison. This allows you to calculate the time saved across the entire task.
| Stage | Without an AI tool | With an AI tool |
|---|---|---|
| Preparation and implementation | 100 minutes | 60 minutes |
| Review, verification and corrections | 40 minutes | 65 minutes |
| Total effort | 140 minutes | 125 minutes |
In this example, preparation and implementation take 40% less time. Total effort falls by 15 minutes, or approximately 10.7%. A financial calculation should also account for the cost of the tool and the labour rates of those involved. Measure the elapsed time from starting work to acceptance separately, as this also includes waiting for a review or for the environment to become available.
Research helps explain why measuring results in your own team matters. The 2025 DORA report highlights the role of work organisation in getting value from AI. In METR’s early-2025 experiment, experienced open-source developers took 19% longer to complete the tasks studied when using AI. The finding applied to a particular group and the tools available at the time. In its February 2026 update, METR described participant and task selection issues that made it difficult to reliably estimate the impact of newer tools. These findings should not be translated directly into projected savings for a specific team in 2027.
How do you run a pilot of AI tools within your team?
Divide the pilot into four stages: preparing the comparison, configuring the tools, completing tasks and evaluating results. The schedule below spreads these stages across four weeks. Adjust the time allocated to each stage to suit the number of tasks, release cycle and project quality requirements.
Week 1: select tasks and establish a baseline
Gather data from recently completed, similar work. Select several task types: fixing a bug, adding a small feature, preparing a regression test and modifying an existing integration. Prepare acceptance criteria and tests to verify the results. Record the effort previously required and how defects are counted.
Week 2: configure the tools and prepare users
Narrow the shortlist to two or three tools. Define permitted repositories, permissions, models and spending limits. Provide users with training of a similar scope and access to the same project information. Include learning time in adoption costs and begin measuring routine work once training is complete.
Week 3: carry out comparable tasks
Assign similar tasks to different working approaches, using random allocation where possible or a predetermined rotation. Account for developers’ experience and familiarity with the project. Having the same person repeat an identical task may give them an advantage because they remember the solution. Also record unsuccessful attempts and instances where a developer takes over from the agent.
Week 4: compare quality, time and costs
Analyse results separately for each task type. Report the median alongside the mean and the most costly cases. Check whether reviews and testing can keep pace with the number of proposed changes. Base the decision to expand adoption on results that recur across multiple tasks. After the first releases, add defects discovered during use to the assessment.
Conclude the pilot with a clearly scoped decision: adopt the tool for selected tasks, extend the trial or choose another solution. Assign responsibility for configuration, set a reassessment date and define the conditions for increasing the number of users.
How do you connect AI tool selection with software delivery quality?
Your plan for 2027 should also account for the work of those who review and accept AI-generated changes. If the pilot shows that preparing regression tests, analysing requirements or organising testing accounts for a substantial share of the effort, include these areas in the next stage of improvements. We explore the broader context in our article on how AI is changing the work of developers, testers and analysts.
At TTMS, we also develop QATANA, an AI-assisted test management tool. It supports drafting test cases, selecting regression test suites, organising manual testing and analysing automated test results. Consider this support when your process analysis identifies a need to improve the testing team’s workflow.
If you are planning to use AI in software development, talk to us about your team’s use cases and requirements. We can start with a single process and work together to define the expected outcome and how to measure it.
FAQ: selecting and adopting AI tools for a development team
Should a company choose one AI tool for all developers?
A common standard can simplify administration, support and cost control. However, it is worth checking whether teams use similar technologies and have comparable needs. A company maintaining different systems can establish a shortlist of approved tools and define their intended uses. Each exception to this list should have a designated person responsible, a justification and a review date. This approach helps the company retain control over tool selection while accommodating the requirements of individual projects.
How should you introduce junior developers to AI coding tools?
Access to the tool should be combined with guidance from a more experienced colleague. Junior developers should be able to explain their chosen solution, identify its limitations and independently verify that it works correctly. Reviewing an AI-generated change before running tests is a useful exercise. A mentor can ask them to identify assumptions that the AI has not verified and describe how the solution handles invalid input. When evaluating adoption, account for mentoring time and the developer’s growing ability to work independently.
How should you agree on AI use with an external development team?
Agree on the permitted tools, models and working methods, along with the materials that may be shared with them. Specify who administers accounts, manages usage and billing, and reviews changes. The external team should follow the same acceptance criteria as your internal developers. Also define the procedures for switching tool providers or ending the engagement. Ensure that the agreed scope of AI use complies with the contract and project policies, particularly where tools will process client-owned code.
How can you reduce your team’s dependence on a single AI provider?
Keep code, tests, architecture documentation and acceptance criteria in systems controlled by your organisation. Agent instructions should be version-controlled alongside the project, with general principles kept separate from product-specific settings. Before purchasing a tool, check how to export the data you need and revoke its access to company resources. Retain the task set used during the pilot, as it will make evaluating an alternative easier. Also account for the cost of retraining the team and migrating the configuration.
When should you reassess your chosen AI coding tool?
Reassess the tool after a significant change to the model, operating mode, data processing policies or billing structure. Changes to the project’s technology stack or recurring problems with output quality are further reasons for a review. The retained task set allows you to run a focused comparison using the new configuration. Match the scope of the review to the extent of the change and the criticality of the system. The person responsible for the tool should document the findings and the decision to continue using it or revert to the previous settings, where that option is available.
