Technology doesn’t stay in one place — and neither should businesses. Recently, GenAI entered the scene with a flourish, and now the hype is already subsiding, replaced by logical concerns about ROI and the most productive ways to apply the technology.
According to the Gartner Hype Cycle for Software Engineering, 2023, this trend will keep accelerating, with expectations that by 2027, 50% of enterprise software engineering will be executed with AI-powered coding tools. As 2025 approaches, questions keep piling up. Is the era of AI coding assistants approaching? Will software development be changed forever? The only way to find out is to closely examine the upcoming trends and evaluate their impact on the most common software engineering challenges.
The evolution of AI-augmented software engineering tools: how it started and how it’s going
AI’s transformative potential was recognized in 2017 when NVIDIA CEO Jensen Huang stated that AI was “eating software.” The process started with a seemingly small step: using AI to enrich TODO comments with more insights and make them clearer for developers.
The introduction of AI-powered TODO comments made a lot of difference for software engineers. They allowed for embedding more context and proposed solution details, complete with links and necessary documentation for successful project development and onboarding new team members as quickly as possible.
Soon, that step turned into a leap. Following successful implementation in software engineering, AI quickly progressed from a tool for development teams to an ever-present assistant that enabled rapid knowledge sharing and problem-solving across all enterprise teams. Platforms like Copilot and ChatGPT are the products of this transition, and their potential has yet to be explored from the angle of enterprise data management and task monitoring.
Logically, the question is: what’s next?
AI-augmented software engineering: exploring the road ahead
Following the 2023 Gartner Hype Cycle Analysis, there are several major developments to expect by 2027:
AI coding assistants
Gartner estimates at least half of enterprise software engineers will utilize machine learning at the new level via ML-enhanced code generation tools.
Several factors can explain the growing need for AI coding tools:
- Growing software demand
As 60% of businesses plan to increase their software spending, executives become more proactive in embracing digital transformation. Therefore, the technological vector is becoming more aligned with business strategy, involving the financial department and strategic analysts. Such a development puts even more responsibility on software developments and exposes the necessity for clear communication, transparency, and information sharing. - Limited developer capacity
Intense digitization means more projects — and more projects mean more work for developers. However, such demand puts immense pressure on teams and stretches human resources thin. As the software engineer turnover rate reached 57.3% in 2024, enterprises need to utilize any available measures to secure workplace productivity and high employee retention. - Stressful work environment
When planning their digital transformation strategy, executives have two alarming numbers to consider. At least 79% of developers admitted experiencing burnout at some point in their work, while 58% of software engineering leaders said that positive workplace experience and optimizing developers' workload should be one of enterprise leaders' top priorities.
An AI coding tool can point out issues with the code, analyze it, and provide instant feedback. From a professional perspective, this assistance is highly valuable and greatly impacts the development flow.
With their game-changing feature, AI-augmented coding assistants are expected to address the challenges mentioned above and introduce multiple benefits to software engineering, from organizing the work between team members to accelerating the development process.
Code prediction
Analyzing the context of the code and automatically suggesting the next blocks of code.
Code optimization suggestions
Detecting areas for improvement and offering informative optimization suggestions.
Error identification
Finding errors in the code and providing recommendations on fixing them.
Code commenting and documentation
Generating code comments for each code section, creating documentation, and writing detailed instructions.
AI-augmented software engineering
Essentially, AI coding assistants are the first messengers of a greater change—AI-powered software engineering (AIASE). However, what will this change bring?
First of all, AI systems won't handle software development processes from start to finish without human input. Such capabilities are far beyond what existing AI can do, and this will not change in the near future.
AI has its limits and weaknesses. It can be trained to learn and analyze data, but it doesn’t have the critical thinking necessary for a 360 perspective on the project. It’s a tool, first and foremost. So, when you add AI into software engineering, you equip your teams with everything they need for faster and more productive work.
AI-augmented software engineering (AIASE) aims to provide consistent support across multiple software development areas.
Coding
Facilitating code writing with context-aware code snippet generation and informative suggestions.
Research
Injecting knowledge into the integrated development environment to enable steady and results-rich workflow.
Version control
Simplified management and tracking of changes to software code, complete with automated metric monitoring, algorithm health checks, and alarm thresholds.
Collaboration
Ensuring code uniformity by consistently updating teams on software engineering progress and notifying them about coding comments, changes, and suggestions.
Testing
Elevating test planning, test creation, test data generation, and defect analysis with the help of intelligent algorithms that identify issues on the spot and assist with building testing strategies.
It's worth noting that the current range of available AIASE tools remains quite limited. Many tools are still in their early stages of development and need to be tested in different environments to prove their value and ability to deliver the following benefits:
- Evolved testing
Software engineering teams already use test automation to manage their development cycle efficiently and run test scenarios faster. With AI-powered test automation, teams gain even more opportunities to enhance their testing routines with greater accuracy, securing error-free outcomes. In addition to enriching automated software testing types, AI-enhanced testing allows development teams to create enhanced self-healing tests that accelerate troubleshooting and minimize the probability of bugs.
- Improved productivity
Software development cycles put engineers in very intense and dynamic environments. They work on a tight schedule while minimizing the risk of errors at every stage. Due to this, teams often find themselves stuck between pressing deadlines and large amounts of tasks that require their immediate attention. This issue becomes particularly challenging when there is a shortage of talent and numerous projects planned. In this situation, AI-powered engineering becomes a time-saving solution, relieving developers of the bulk of routine tasks and letting them concentrate on the most complicated parts.
Software engineering is full of repetitive steps that, while necessary, take too much time and focus from more complex and high-value tasks. You can’t skip the monotony if you don’t want to end up with a buggy product. However, you also must make it to the deadline, so you don’t have enough time to polish the features like they deserve to be polished. The introduction of AI addresses this dilemma, letting developers operate more comfortably.
- Optimized product delivery
Developing a high-quality digital product will always take considerable time and effort. However, with AIASE taking care of the most time-consuming parts of project development and enabling early error detection, software engineers become able to manage their schedule more efficiently, reducing the risk of missing deadlines and closing each stage without setbacks or complications.
Platform engineering
Since developer experience (DevEx) plays a large role in successful project development, it's logical that improving that experience should be a top priority for technology executives.
As technology evolves, ecosystems are growing more complicated, and security concerns multiply. Due to this, enterprises need to think about new ways of empowering their development teams and improving risk management. Platform engineering allows them to close these goals — and accomplish even more in the long run.
Platform engineering is a practice that gained momentum in 2022 following the professionals’ intent to address the most relevant needs:
- Greater developer experience
Reducing burnout and cognitive toil on developers has become crucial for enterprises that want to reduce their turnover rate while increasing talent retention. However, there is a trick: good DevEx isn't created through best practices. It's enabled by creating optimal conditions and healthy guidelines.
Within traditional frameworks, developers often have the right idea of what to do—but they lack the means of how to do it. There are too many communication gaps and too few useful resources. As a result, developers end up being overwhelmed by the issues they can't solve.
- Self-service options
Since enabling DevEx is the key to happier teams and healthier environments, it’s important to let developers follow their intuition and give them more control over configuration, deployment, and infrastructure interactions. These levels of creativity and freedom considerably change the relationship between the teams and stakeholders while ensuring long-term efficiency.
This practice is commonly called "You build it, you run it," but in my opinion, the more appropriate name would be "You build it, you support it" because it's mostly about maintaining the code throughout the entire lifecycle.
- Simplified workflow navigation
The larger the organization, the longer it will take developers to navigate its infrastructure and select and organize the tools they need for work. When the implemented toolset can't add necessary functionalities or doesn't fit the intended goals, developers' work is further complicated, leading to a considerable time loss and a drop in productivity.
Shaped from DevOps practices, platform engineering made closing all of these objectives possible by providing developers with an internal developer platform—a secure framework that enabled much-needed self-service.
Such platforms are built on an enterprise's internal development practices, providing teams with "golden paths," i.e., customized guided workflows that walk developers through the journey of building, testing, and deploying an application. Within golden paths, developers can stay compliant with critical requirements without slowing down their performance.
In short, an internal developer platform is a product where developers are owners and end users. Therefore it converts their knowledge, needs, and objectives into features, considerably reducing the cognitive load and breaking down complex enterprise infrastructure into abstract layers.
To develop an internal developer platform, an organization needs a platform team, which usually consists of DevOps engineers, product managers, and site reliability engineers. Together, they address common organizational challenges, gather feedback from developers, and incorporate it into platform features, identify crucial workflows, and transform them into golden paths. According to Gartner, around 80% of software engineering enterprises will be reinforced by such teams by 2026 to accelerate product delivery and gain more predictable business value.
Products are becoming more tailored to users' needs — developers and engineers included. The growing popularity of platform engineering and growing demand for platform teams are natural responses to growing ecosystem complexity and the increasing mental and cognitive strain development teams are currently experiencing.
AI-augmented software: separating expectations from reality
Although the trends predicted by Gartner paint a very promising picture for software engineering, the reality shows that there is more to AIASE than positive expectations. After exploring the effect of GenAI tools on the work of over 800 developers, a study by Uplevel revealed the following issues:
- No impact on efficiency
Contrary to popular expectations, the use of AI tools didn’t accelerate the development cycle. In some cases, the development cycle saw a decrease of 1.7 minutes, which is clearly not enough for a significant change. - Increase in bug rate
Although the introduction of AI tools was supposed to reduce the risk of bugs, software engineers utilizing Copilot and other GenAI tools reported a 41% increase in the bug rate. Meanwhile, their issue throughput remained unchanged. - Persisting burnout rates
Following the issue mentioned above, the results for minimizing employee burnout turned out to be different from the desired outcomes. While there was a 17% drop in extended working hours (the main cause of employee burnout) for engineers with access to Copilot, professionals who worked without Copilot had their extended working time reduced by 28%.
Additionally, recent findings by Purdue University suggest that AI tools aren’t yet capable of fully assisting with coding. According to research, around 52% of programming answers provided by ChatGPT were incorrect.
What was even more alarming was the discovery that 35% of programmers questioned in the research chose AI-generated answers due to their convincing tone and comprehensive style. Although only 12 programmers took part in the study, the results still illustrated how AI can lower engineers’ guard and trick them into overlooking mistakes and errors.
AI is often marketing as infallible, so people expect to get a tool that makes no mistakes and delivers correct data only. However, this is not what they get: they still need to review replies and suggestions, checking them for errors. As a result, development cycles remain unchanged, and so does the burnout rate.
Another issue demonstrated by AIASE was vulnerability replication. For example, Snyk research revealed that GitHub Copilot was suggesting insecure code to projects with insecure codebases. In other words, after analyzing the context, AI didn’t point out the vulnerabilities but rather incorporated them into its code generation. Therefore, instead of reducing issues, Copilot was actually accumulating them.
So, what do these findings imply? Were the optimistic forecasts false, and are AI-augmented tools inefficient?
In truth, the studies indicate that AI-augmented tools aren’t efficient yet, and optimistic predictions are premature. Integrating AI into every stage of software development has potential, but achieving this will take much longer—and fully efficient AIASE solutions aren’t likely to be ready by 2027.
However, this doesn’t mean that artificial intelligence can’t help software development engineers at all.
Currently, several areas in software development engineering can be improved with the help of GenAI:
Collaboration
Keeping all project teams on the same page by updating them on the development progress and results, exchanging suggestions, and monitoring deadlines.
Education
Onboarding new team members on the ongoing project by automated documentation management and updated knowledge database.
Testing
Testing non-complex functions for faster evaluation of outcomes and results, exploring the performance of features from the end user POV.
If there is a golden rule for leveraging AI-augmented tools in software engineering, it’s this: don’t expect them to do your team’s job. Software development is complex and requires critical thinking, attention to detail, and the ability to effectively utilize previous project experience. While artificial intelligence can’t judge or think critically, it excels at handling basic tasks and clearing informational clutter—something you should capitalize on.
AI-augmented software engineering: how to prepare for the future?
So, the era of AI-managed coding and development may not be coming as soon as expected. Does this mean that enterprises shouldn’t prepare for new disruptions?
The answer is no.
Not only does GenAI need to undergo further changes before it can augment software engineering effectively, but businesses also need to change their approach to AI.
Therefore, leaders and executives should address existing AI biases and adopt a more realistic view of the technology.
- Establishing clear goals
The time of buzzwords is over. It’s essential to start identifying specific areas for improvement where AI can be applied and outline the desired outcomes. Doing so will make it much easier to measure AI ROI correctly and enable enterprises to discover their personalized KPIs. - Communicating with employees
The best way to determine the value of GenAI is to see how departments can utilize it. Leaders should invest in surveying and training employees to identify the most enthusiastic units and explore how they leverage the technology
There are general ideas of how GenAI can improve employee productivity—and then there is applying GenAI in practice. The latter is much more important. You want to know feedback from your teams: how they use the technology and where they use it. The ultimate goal is to tailor GenAI to their needs, not to make them adapt to GenAI.
- Exploring various GenAI use cases
Staying realistic shouldn’t mean being inactive. Executives should analyze innovation from every potential angle to get a full understanding of its value and potential impact. If any stakeholders have an idea for implementing GenAI, there should be time and resources to test this idea. - Enabling collaboration
Even before GenAI adoption, back-and-forth communication between developers and model-building teams should be the norm. Executives need to work on establishing frameworks for keeping teams informed about each other’s tasks, progress, and roles. Better visibility of team structures will also be useful during A/B testing, which is mandatory for getting an objective evaluation of AI efficiency. - Managing risks, trust, and security
There can’t be GenAI use without safeguards. Business leaders need to take care of these in advance by consulting with their enterprise governance department and data analysts, laying down the groundwork for transparent, secure, and risk-free use of AI (AI TRiSM).
Although AIASE tools are yet to make their grand entrance, software engineering is on its way toward faster development cycles, streamlined routines, and healthier work processes. Every large change expected by Gartner is united by the same need—making developers’ work easier, faster, and more collaboration-friendly. So, talents, leaders, and visionaries should contribute to fulfilling that need through
It’s very important to remember the strongest point of GenAI—its ability to dive into an immense sea of data, organize it, and deliver the right information at the right moment. It’s the cooperation between human talent and machine precision we should be working towards—and the journey looks promising.
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