Artificial intelligence is already changing the way software is built.
Today it can help generate code, create tests, understand a codebase, document components, detect errors, build prototypes, and carry out tasks that used to require far more manual work.
But there is an important difference between speeding up the writing of software and replacing the process required to build a system that supports a real business.
For relatively simple applications, prototypes, or tools with clearly defined requirements, AI can significantly reduce the effort required to reach a first version.
The picture changes when we talk about enterprise software with business rules, different users and permissions, integrations, migrations, high data volumes, security, infrastructure, and processes that are critical to an organization.
In these projects, writing code is only part of the work.
Artificial intelligence can speed up that part and many others, but it is still necessary to discover what should be built, make technical decisions, validate results, and take responsibility for how the system ultimately performs.
AI is already part of the software development process
Adoption of AI tools among developers is high.
The 2025 Stack Overflow Developer Survey found that 84% of respondents were using or planning to use AI tools in their development process, while 51% of professional developers used them daily.
This is no longer just an experimental trend.
Code assistance tools, development agents, and language models are being incorporated into IDEs, repositories, QA tools, documentation, and DevOps processes.
However, adopting AI does not automatically mean the entire process becomes faster.
DORA's State of AI-assisted Software Development report makes a particularly relevant point: AI works mainly as an amplifier. It can amplify the strengths of an organization that already has good processes, but it can also amplify its existing problems.
In other words:
AI can generate more software quickly, but that does not guarantee the right software is being built.
What parts of software development can AI speed up?
There are activities where AI can add productivity quite directly.
Code generation
A developer can describe a feature and quickly obtain:
- initial structures;
- functions;
- components;
- queries;
- endpoints;
- validations;
- data transformations;
- or implementation examples.
This reduces the time spent writing repetitive code or solving relatively well-known technical problems.
Prototyping
AI makes it possible to turn an idea into something visible much faster.
A proof of concept that previously could require several days of work can begin to take shape in hours.
This is especially useful for validating:
- an interface;
- a flow;
- an integration;
- an initial architecture;
- or the technical feasibility of an idea.
Understanding existing code
In large systems, a significant part of development time consists of understanding code that already exists.
AI tools can help:
- explain functions;
- trace dependencies;
- identify where a given piece of logic is implemented;
- summarize modules;
- analyze errors;
- and find likely points of modification.
Documentation
They can also generate a first version of:
- technical documentation;
- API descriptions;
- comments;
- manuals;
- use cases;
- and explanations of existing components.
Testing
AI can help propose:
- unit tests;
- test cases;
- test data;
- edge-case scenarios;
- and validations.
This can increase the speed at which a team builds initial coverage.
Refactoring and repetitive tasks
Changing patterns, updating repetitive code, migrating certain structures, or making similar modifications across multiple files are scenarios where development agents can generate significant time savings.
The result is that a developer equipped with good AI tools can perform certain activities much faster than before.
But that does not necessarily mean the whole project speeds up in the same proportion.
Writing code is not the same as developing software
This distinction is fundamental.
When a company hires the development of a platform, it is not just buying lines of code.
It is trying to turn a business need into a system capable of operating reliably.
Before development, it is usually necessary to answer questions such as:
- What is the real problem?
- How does the process work today?
- What should be automated?
- What exceptions exist?
- Who uses the system?
- What permissions does each person have?
- What happens when an operation fails?
- Which other systems must it communicate with?
- What information must be migrated?
- What volume of operations must it support?
- What level of security does it need?
- Which decisions should be automatic and which require human intervention?
AI can help analyze the answers.
What it cannot do autonomously is know a business that has not yet been properly explained.
The bottleneck is often not in programming
In enterprise projects, a considerable part of the difficulty lies in turning scattered knowledge into precise rules.
Imagine a seemingly simple process:
approving a request.
At first it might seem there are only two states:
Approved or Rejected.
When the operation is analyzed, questions emerge:
- Who can approve it?
- Can there be more than one level of approval?
- Does the flow depend on the value of the operation?
- What happens if the approver is absent?
- Can it be sent back for corrections?
- Can it be modified after approval?
- What must be recorded?
- Should another person be notified?
- Which system should receive the result?
- Are there exceptions?
- What happens with old requests?
AI can quickly write the code for a workflow.
But first someone must define what the correct workflow is.
That knowledge is usually distributed among managers, users, previous systems, documents, spreadsheets, and processes that may even work differently in different areas of the company.
That is a significant part of software engineering work.
Business rules remain one of the biggest challenges
At Kubo we have found that business rules are one of the factors that can most increase a project's complexity.
Not because they are necessarily hard to program.
The problem is identifying them, understanding them, and turning them into consistent behavior.
An enterprise platform can contain hundreds of decisions that depend on one another.
AI can generate a function correctly from a prompt.
If the prompt contains an incomplete rule, it will likely produce an equally incomplete implementation.
So the problem is not the ability to generate code.
It is the quality of the context used to generate it.
Integrations do not disappear with AI either
Integrations are another example.
A system may need to communicate with:
- an ERP;
- a CRM;
- a payment gateway;
- a logistics provider;
- an accounting system;
- a government API;
- a legacy system;
- or a platform built by another vendor.
AI can considerably help read documentation, generate API clients, or interpret responses.
But when the documentation does not match the system's actual behavior, other problems appear.
There may be:
- incomplete data;
- inconsistencies;
- timeouts;
- asynchronous processes;
- old APIs;
- usage limits;
- authentication problems;
- duplicated information;
- states that do not match;
- and scenarios that are simply not documented.
Solving these problems requires testing, observing, and making decisions.
Something similar happens with migrations
Moving information between systems can seem like an ideal activity to automate.
And to a large extent it is.
AI can help write scripts, find patterns, and transform structures.
But first, questions such as these must be answered:
- What information must be migrated?
- What is the source of truth?
- What do we do with inconsistent records?
- How do data coming from several sources relate?
- What information must be kept for legal reasons?
- How do we validate that the migration completed correctly?
If you work with legacy systems or software that needs modernization, this context and validation work is even more important.
Automating a wrong decision only makes it possible to make the mistake faster.
AI does not eliminate architecture
Architecture is another important aspect.
When building a prototype, many technical decisions have limited consequences.
In an enterprise platform they can affect years of operation.
Someone must decide:
- how to divide the system;
- where to store information;
- how to handle authentication and permissions;
- how to respond to failures;
- which components must scale;
- what information must be audited;
- when to use synchronous or asynchronous processing;
- which external services to use;
- and how much complexity the solution really needs.
AI can propose architectures.
It can even generate different alternatives.
But selecting an architecture is still a matter of trade-offs.
There is no universally correct architecture.
A technically sophisticated solution can also be the wrong solution if it introduces complexity the business does not need.
More code does not necessarily mean greater productivity
This is one of the interesting paradoxes of AI applied to development.
Generating code has become much easier.
Reviewing it, understanding it, and maintaining it still has a cost.
The 2025 Stack Overflow survey shows this tension clearly: although AI adoption is high, 46% of surveyed developers said they distrust the accuracy of its results compared with 33% who said they trust them. In addition, 66% cited frustration with receiving solutions that are “almost correct” and 45% said debugging AI-generated code can take more time.
This introduces a new problem:
generation speed can outpace validation capacity.
An agent can modify dozens of files in seconds.
That does not eliminate the need to verify whether those changes:
- correctly solve the problem;
- introduce regressions;
- preserve the architecture;
- meet security standards;
- preserve compatibility;
- and can be maintained in the future.
AI productivity depends heavily on context
There is still no single figure that can tell us how much AI speeds up software development.
Results vary according to the type of task, the team's experience, the project, and the tools.
For example, GitHub research has shown significant gains in well-defined programming tasks using code assistants.
However, a controlled study published by METR in 2025 found a very different result: 16 experienced developers working on large, mature open source repositories took, on average, 19% longer when they were allowed to use the AI tools available during the study. The researchers themselves warn that the result represents a specific scenario and a snapshot of the tools available in early 2025, not a universal conclusion about AI and programming.
That difference matters.
AI can be extremely efficient building something new with few constraints, and much less efficient when it needs to work inside a large system whose history, architecture, and accumulated decisions do not easily fit into a prompt.
Can AI build a complete application?
Yes.
In certain contexts, it already can.
Today it is perfectly possible to generate functional applications from relatively short prompts.
This works especially well when:
- the problem is clearly defined;
- known patterns exist;
- the application has few users;
- there are few business rules;
- integrations are simple;
- operational risk is low;
- and a mistake has limited consequences.
For example:
- a small administrative tool;
- a prototype;
- an interactive landing page;
- a dashboard;
- an internal automation;
- an application to validate an idea.
The result can be surprisingly good.
But there is a considerable distance between an application that works and an enterprise platform that must keep working for years.
Where does it start to get complicated?
Difficulty grows when the system simultaneously includes:
- multiple types of users;
- complex permissions;
- transactional processes;
- business rules;
- mobile and web applications;
- integrations;
- migrations;
- historical data;
- concurrency;
- availability;
- auditing;
- security;
- critical operations;
- and continuous evolution.
Each component increases the relationships between the others.
Complexity does not grow only because there are more features.
It grows because features begin to depend on one another.
How the software production chain changes with AI
AI does not necessarily eliminate the software production chain.
It changes it.
| Stage | How AI can help | What still requires judgment |
|---|---|---|
| Discovery | Summarize information, organize requirements, analyze documents | Understand the business and discover real needs |
| UX/UI | Generate proposals and prototypes | Validate users, processes, and experience |
| Architecture | Propose patterns and alternatives | Choose trade-offs and own decisions |
| Development | Generate and modify code | Validate implementation and maintainability |
| Integrations | Read documentation and create connectors | Resolve real behavior and exceptions |
| QA | Generate tests and scenarios | Define what it means for the system to work correctly |
| Security | Detect patterns and assist reviews | Assess risk and accept responsibility |
| DevOps | Generate configurations and automations | Operate real infrastructure and respond to failures |
| Evolution | Analyze changes and generate implementations | Prioritize according to the business |
The consequence may be a faster production chain, but not necessarily a chain without people.
The developer is changing too
The developer's role will probably dedicate progressively less time to some mechanical tasks.
But other skills will grow in importance:
- understanding complete systems;
- defining problems correctly;
- reviewing generated code;
- detecting conceptual errors;
- designing architectures;
- understanding security;
- managing context;
- working with users;
- and making technical decisions.
Knowing how to produce code will remain important.
But knowing what code should exist and how it should behave within a larger system may become even more important.
Will AI make software development cheaper?
In some parts of the process, probably yes.
If an activity used to require several hours of manual writing and can now be solved with AI assistance in much less time, there is a real productivity gain.
But we should not assume that a reduction in the time needed to program automatically becomes an equivalent reduction in the total cost of the project.
A project also includes:
- analysis;
- design;
- meetings;
- decisions;
- architecture;
- integrations;
- migrations;
- QA;
- infrastructure;
- management;
- deployments;
- fixes;
- and evolution.
In addition, there is another effect.
When producing software becomes faster, it also becomes economically viable to build things that previously fell outside the scope.
The productivity gain can end up becoming both a reduction in effort and greater capacity to develop features.
If you want a reference on cost ranges and factors, you may be interested in our article How much does custom software development cost in Colombia?
So what changes for a company that needs to develop software?
The question should no longer be:
“Does the development company use AI?”
Probably most modern teams will use it in some form.
The more relevant question is:
“How does it use AI within its process and how does it control what AI produces?”
A company should understand:
- who validates the generated code;
- how testing is performed;
- how information is protected;
- how changes are controlled;
- how architecture decisions are made;
- how security is managed;
- and who is accountable for the final result.
The tool used to generate part of the code matters less than the team's ability to turn it into reliable software.
AI as an accelerator, not a substitute for the process
At Kubo we see artificial intelligence as a tool to increase the capacity of development teams.
It can speed up tasks.
It can eliminate repetitive work.
It can reduce the time needed to explore solutions.
It can help understand large systems.
And its capacity will probably keep growing rapidly.
But an enterprise software production chain includes more than producing code.
It includes discovering problems, modeling processes, making decisions, integrating systems, validating results, and being accountable for how the product works.
As long as software represents real business processes, the need to understand those processes will remain.
AI can help build the solution.
Understanding which solution should be built remains the most important problem.
Frequently asked questions
Can artificial intelligence replace software development?
It can automate or speed up an ever-growing share of development work, especially code generation, documentation, testing, and repetitive tasks.
However, developing enterprise software also requires understanding processes, defining requirements, making architecture decisions, integrating systems, validating results, and operating the product.
Can AI create a complete application?
Yes. For simple applications, prototypes, and clearly defined problems, it is currently possible to generate a very significant part of an application using AI tools.
The difficulty increases considerably when there are complex business rules, integrations, migrations, security, multiple users, and operational needs.
Does using AI reduce the cost of developing software?
It can reduce the effort required for certain activities, but there is no universal reduction that applies to every project.
The impact depends on how much of the work really corresponds to programming and how much depends on analysis, integration, validation, architecture, and operation.
Which tasks can AI speed up the most in software development?
Among others:
- code generation;
- prototyping;
- documentation;
- initial test creation;
- code explanation;
- refactoring;
- assisted debugging;
- and automation of repetitive tasks.
Why is it necessary to review AI-generated code?
Because a result can be syntactically correct and still contain functional errors, security issues, incorrect architectural decisions, or behaviors that do not match the business rules.
What changes for development teams?
The ability to write code will remain important, but the value of skills such as architecture, systems analysis, review, integration, security, and business understanding increases.
The opportunity is not in replacing development: it is in improving it
Artificial intelligence is driving one of the most important changes the software industry has experienced in years.
Ignoring it would make little sense.
But there is also no need to assume that generating code means replacing the entire engineering process.
The opportunity lies in combining both capabilities.
Using AI to speed up what can be automated, and using experience, engineering, and business knowledge to solve what requires context and accountability.
At Kubo S.A.S. we have been developing custom software since 2008 and have participated in more than 180 projects for organizations in Colombia and other markets.
The technology used to build software will keep changing.
The goal remains the same:
building systems that correctly solve real business problems.
