The most popular use cases of AI for software outsourcing and development

As AI continues to proliferate the world we live in, applying it and its various subset technologies such as machine learning, deep learning, generative AI and conversational intelligence (among numerous others) has become more the norm than an exception. Likewise, software outsourcing companies and most (if not all) software development teams have embraced AI and other intelligent technologies to assist them with myriad efforts across the workplace, for both daily duties as well as for proactive initiatives.

In this article, we discuss some of the top use cases for AI, both for core software development as well as for related fields such as data analytics and customer experience. Additionally, we have included a quick guide to help organisations decide which AI tools are suitable for their unique needs and constraints. Read on to know more!

Interested in exploring AI-powered software development? At EFutures, our teams consist of highly skilled, dynamic experts who can adapt and scale to deliver on the most niche of requirements. Contact us today to know how we can lift your software development project off the ground, with the right set of intelligent tools and technologies.

AI for core software development

The typical software development team is often inundated – and this is even the case with projects that are smaller than usual. AI becomes valuable in such environments, as it can be utilised in many ways to help streamline duties for team members. With software development teams usually having the expertise to build custom code, the same skills can be repurposed to build custom algorithms that can self-learn thanks to machine learning. On the other hand, numerous AI-powered SaaS tools are also readily available, which can be implemented with a quick subscription and login.

Irrespective of which pathway (custom-built or SaaS) is chosen, some of the key areas that intelligent technologies can assist software development teams include:

• Smart code generation, formatting and debugging,
• Test case automation for quality assurance purposes,
• Automated regression testing,
• Anomaly detection during runtime,
• Automated task creation, assignment and reminders.

AI for data analytics

Data analytics is a broad use case that doesn’t only apply to software development. However, it can be tailored to meet KPIs specific to any use case. In the case of software outsourcing, working on projects for multiple companies will require the simultaneous monitoring of all projects, while in-house software development teams will need to monitor workability of applications and other connected business systems – usually on a 24/7 basis. Additionally, AI-powered data analytics can also be beneficial for each client, depending on the industry they operate in, and the goods/services they specialise in.

As a result, key data analytics use cases for software outsourcing and software development purposes include:

• Predictive analytics, for forecasting issues relating to performance, as well as for forecasting any future needs in terms of hosted resources/infrastructure,
• Application behaviour analytics, which can help indicate if any anomalies exist,
• Big data analytics, for determining patterns within data through data modelling and model training,
• Summarising documentation, with generative AI

AI for Customer Experience (CX)

As software outsourcing companies work on a long-term basis with their clientele, it’s imperative to grow as their clients’ needs grow, as well as when priorities and objectives shift. Staying in tune with their customers’ varying needs is one of the most significant ways to progress, thereby making CX a prime point of consideration for software outsourcing/software development teams as well.

AI can be applied to CX in the following ways:

• Building customer profiles that offer a 360-degree view of each customer,
• Analysing engagement and conversions, and assigning scores to determine customer interest and viability,
• Analysing customer churn and buyer intent, to orchestrate campaigns that are targeted towards the right calibre of leads,
• Using Natural Language Processing (NLP) and sentiment analysis tools to analyse customer conversations and emotions behind a product/service and the overall brand, for promptly addressing issues with relevant context,
• Tailoring customer journeys with Digital Experience Management (DXM) solutions, based on varying factors such as customer engagement, as well as whether a customer is brand new or returning.

AI for cyber security

With software and IT outsourcing companies now also being cyber security partners for their clientele, teams need to maintain a suitable cyber security toolkit in order to monitor and remediate threats. Software/IT outsourcing companies usually offer cyber security services either by teaming up with a dedicated cyber security vendor, or through maintaining an in-house cyber security team by hiring relevant security experts.

In either case, comprehensive analytics technologies are still required to ensure round-the-clock monitoring and the timely remediation of any threats. Some ways that AI can help elevate cyber security efforts include:

• Automatically detecting threats, as well as triaging them depending on severity. This way, AI can power SIEM (Security Information and Event Management) and SOAR (Security Orchestration and Automation Response) systems,
• Alleviating false positives, especially as security teams commonly experience alert fatigue,
• Intelligently detecting anomalies and gaps within the security framework,
• Conducting AI-powered vulnerability, risk, perimeter and policy assessments,
• Leveraging threat intelligence by identifying both known and zero-day threats.

AI for recruitment

Software outsourcing companies are always hiring, in order to meet continuous client demands for maintaining existing teams as well as for augmenting existing skill sets. Whether it is to hire developers for startup clientele or for established organisations, AI can significantly assist software outsourcing companies with recruitment, end-to-end. This means that finding the right candidates, shortlisting them, as well as subsequently screening, hiring and evaluating their performance post-onboarding is all possible, thanks to intelligent recruitment solutions which are powered by AI.

In addition to end-to-end recruitment management, some niche ways that AI can assist with hiring the right candidates for software outsourcing and software development teams include:

• Filtering CVs to narrow down a candidate list that meets targeted job criteria,
• Automatically conducting background checks to determine suitability,
• Generating offer letters and onboarding resources for accepted candidates,
• Conducting post-hire performance evaluations to determine long-term suitability and viability for each candidate.

AI for compliance

Adhering to local and international regulations are an important factor of consideration, as every software or application that is built will experience an inflow and outflow of data. As a result, regulations are bound to apply to your software product, depending on where your business is based, as well as the geographical regions and industry you operate in, among other factors.

With compliance being an area that could cause negative impacts if not adhered to correctly, maintaining accuracy and punctuality when conducting audits becomes all the more crucial. Add to this the fact that software outsourcing and software development teams need to fulfil compliance requirements, as they are directly involved in developing and maintaining applications that process large amounts of data with every visit, click and transaction that is made.

Some ways that AI can contribute towards compliance management within software development teams include:

• Automated audits that collate all items (even on a real-time basis, if needed),
• Automatically detect regulatory changes as they happen, and either alert software development teams to make adjustments, or execute said adjustments via automated workflows,
• Detect anomalies and fraud, and subsequently block or quarantine any transactions that follow,
• Scan documentation for non-compliant information.

Which set of AI tools are suitable for your business’s software development needs?

Assess what your requirements and objectives are

With a limitless range of AI tools available in the market today, it is necessary to know which tools are best suited to the unique needs of your business. No two businesses have the same requirements, no matter how similar they may be in terms of what they specialise in, and who their customers are. For this reason, it is always advisable to conduct a thorough needs assessment prior to searching for any AI and ML-powered tools.

In order to assess your business’s needs, gather relevant team members, including those from your software development team to have a discussion that revolves around:

• Objectives that aren’t being met via your software/application (or instead, are being met partially),
• Tasks that require a significant amount of time and manual effort,
• Tasks that are highly prone to errors due to manual intervention,
• Any bottlenecks,
• A lack of data flow, especially from one unit to another and vice versa,
• A lack of segmentation and personalised recommendations,
• Loopholes in security posture and access policies.

Discussions surrounding these and any related topics can not only offer clarity on what’s lacking and what needs to be fixed, but can also serve as a primary point of reference when searching for relevant AI tools for your software development project.

Start small

Whether your business and software development teams decide on a proprietary data model or something that’s readily usable, starting on a smaller scale is always wise. This is so that teams minimise any learning curves that may be involved, and are able to gauge the overall effectiveness of the tool that has been deployed. In turn, this helps to decide whether tools should be scaled up, down, or fixed before investing any further.

Monitor progress and performance – then improve as required

Once your AI tool of choice has been deployed, in addition to gauging user friendliness, gauging whether it is fulfilling the objective(s) it was originally meant to fulfil is essential. For example, are the workflow automation recommendations provided by your newly deployed Robotic Process Automation (RPA) solution useful for your team? Or, have turnaround times of certain processes that otherwise consumed excessive amounts of time, improved?

Answering questions such as these, and those that are centred around your software’s KPIs can help teams further determine whether the AI tools deployed are ultimately worthwhile.

Summing it all up…

The modern IT outsourcing company is now a hub for most things pertaining to digital innovation, as software developers possess the skills needed to build and train custom AI and ML-powered data models. Whether it’s to streamline workflows within the software development lifecycle, or to implement intelligent automation and personalised recommendations in the applications they build for clientele, today’s software and IT outsourcing companies are at the forefront of building, testing and delivering automated technologies that can help meet customer and employee preferences, while contributing to bottom line growth.

Being a macro category, AI presents an endless range of use cases. When it comes to software development, though, applications pertaining to test case automation, data analytics, CX, cyber security, compliance and recruitment are some of the most common. However, choosing the right set of AI tools is always best carried out following a comprehensive business assessment with your team. Thereafter, shortlisted tools can be deployed on a small scale, then tested, and scaled up or down depending on user and customer preferences.