Thanks to their immense popularity and versatility, generative AI companies have taken centre stage for businesses both big and small. Offering diverse capabilities that range from content generation to personalised recommendations, the options for customising a genAI model to suit any application are practically endless.
Additionally, software outsourcing companies are also interested in hiring AI developers that have prior experience building and training genAI models, so increasing client demand can be met. Software development and cloud management are two such key areas for these companies – and we discuss precisely these, in this article.
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Owing to its core generative capabilities, genAI, of course, can significantly contribute towards building quality and error-free code. Although genAI cannot completely replace the skills of an AI developer, it can be particularly useful in scenarios where developers need some support to reconstruct new code in a structure that is unfamiliar to them, to especially support any older code that may have been written using that same structure.
Code reviews are another big use case for genAI in software development; instead of manually reviewing code that has been written, genAI can do the task of identifying any errors or inconsistencies with greater accuracy, well before the code reaches the testing and QA stage.
Software testing is an area that requires immense focus and observation, even for the most minimalistic of applications. Many tests are also repetitive, possibly causing software testers to miss errors owing to excessive familiarity and decision fatigue. Additionally, software testers may be assigned to multiple projects, especially if they are part of web development outsourcing companies.
GenAI can support software testers by automating common tests, thereby significantly reducing error rates while freeing them to inspect applications for other, more niche issues. GenAI can also be a boon for regression testing. As applications perpetually receive upgrades and patches, it can be hard to manually keep track of regression tests that need to be done. Missing a regression test can prove to be damaging and even costly, as system downtimes or customer dissatisfaction can cause churn.
GenAI can therefore assist by keeping track of, and executing all regression tests that need to be done prior to every update, so smooth functionality is always maintained – and older features don’t break when new ones are released, most importantly.
In this day and age where times to market are constantly being slashed (while security and overall app performance continue to remain top concerns), teams are pressed for time to notice any anomalies within their code, UI and customer journeys. This also applies to cloud infrastructure and network management, especially with zero-day vulnerabilities always posing major risks. GenAI-powered anomaly detection can assist both software development and IT teams, by identifying gaps in everything – from application code to security frameworks.
Anomaly detection tools have commonly been known to cause alert fatigue, with seemingly minor alerts being flagged as severe – or flagging items that aren’t even an issue in the first place. GenAI, and AI overall, can eliminate these occurrences, thanks to highly trained models that flag as well as triage threats, so IT and security teams aren’t inundated and know precisely what requires their attention.
When developers need to work with older code that was written by someone else, it can demand painstaking effort to understand this code – especially if no documentation is present, and the code in question is being looked at after a long period of time. GenAI can help bridge the gap by analysing any kind of code (including legacy code written in a programming language that is now obsolete), and deliver key summaries that are easy to digest, along with relevant context.
This can help developers reduce turnaround times as well as effort when working with legacy or undocumented code, while also receiving assistance from genAI to reconstruct any code in a structure that may be more familiar, or conducive to modern code writing practices.
When conducting any migration procedure, things could go wrong – even after factoring all possible scenarios and subsequent strategies for mitigation. The stakes become even higher when older, legacy systems are involved. Failed migration processes can render entire systems unusable, while also corrupting data. Therefore, ensuring a smooth and accurate migration of data and other resources is of paramount importance.
GenAI can significantly streamline migration efforts – be they for migrating code over to a more modern hosted infrastructure, or facilitating the migration of data from a legacy on-site suite, and over to a hosted or hybrid setting. Provided sufficient big data is available and models have been comprehensively trained, genAI can help predict possible outcomes, both positive and negative.
Additionally, for models that have been trained even more extensively, genAI tools can recommend possible solutions, thereby bolstering IT teams with knowledge on what to expect, and what to prepare for – while reducing the trial and error that could possibly cause damage.
Before your business and software development teams consider any genAI tools for your project, it is first advisable to ascertain what problems exist, and how they can best be addressed. If your teams are considering genAI, it is possible that a lot of manual effort is currently being expended, and there is an expectation to reduce the same. Reducing error rates could also be another objective which once again, could be borne out of excessively manual and/or monotonous work.
In order to determine where your organisation stands in its existing technology stack, gather relevant members from your team and pose the following questions, to get a discussion rolling:
Depending on the feedback received, these insights can all be collated into a brief. In turn, this brief can be used as a primary point of reference for your software outsourcing vendor or another third-party AI vendor, so they know what they need to establish in order to help your organisation meet its objectives.
Once a strategy has been finalized, it is also advisable to start small – ideally with a preliminary version. This can serve as a pilot to help determine what is working and what needs to be improved, so that future versions of models that are trained are done so based on your organisation’s unique expectations.
GenAI has almost become a household term, especially with the release of platforms such as ChatGPT and Claude. However, genAI can go much further through custom model building and tuning, thereby offering organisations, their software development teams as well as their IT teams a multitude of uses.
Some of the ways genAI can assist software development and IT teams include:
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