Is AI coming for software development?

Software development will proceed much more quickly thanks to artificial intelligence, and continuous delivery will become regular. Processes and roles, particularly testing, will need to change.

Software development is changing in both significant and subtle ways due to artificial intelligence. Although many businesses are rushing to introduce features that can use AI, this technology has much more potential. Instead, AI will serve as the cornerstone for the majority, if not all, SaaS products. SaaS technologies will be able to continuously create new efficiencies across a number of corporate processes thanks to machine learning and AI models. The cornerstone for a new method of development should be considered as AI.

Delivery of software will develop into a utility. The backlog of high-value additions and innovation will suddenly burst into production, while the grunt tasks that exacted a high tax for additional value will simply happen. The human race won't be replaced. Instead, you will witness software engineers' higher potential being realised.

SaaS explained

Over the past ten years, consuming software as a service has dominated the market for enterprise applications. Along with platform as a service (PaaS) and infrastructure as a service (IaaS), it is one of the three main types of cloud computing. Since the concept's introduction in the early 2000s, SaaS has emerged as the preeminent method of software delivery.

Software as a Service, or SaaS, refers to the hosting of software by a third party provider and its online service delivery to customers. Although the majority of SaaS applications are geared towards business customers, some have found success with individual consumers.

In professional contexts, users obtain enterprise software or productivity tools from a service provider rather than from their company's private data centre. Common examples of SaaS applications that were once hosted and distributed by companies' own data centres include Microsoft 365 and Salesforce.

The old method of buying software once and having to host, implement, and maintain it on your own is very different from SaaS. A multitenant architecture, which allows a service provider to distribute many copies of the same software from a single physical server, enables the SaaS delivery model. Although there is a common code base that can be patched, updated, and maintained centrally, each user or company has its own version of the programme with the corresponding modifications, data, and access controls.

Consequently, software can be purchased by individuals or for a small group of users and paid for on a monthly or annual subscription basis per "seat," as opposed to making a sizable upfront investment in a perpetual (permanent) licence, starting a time-consuming implementation, and committing to years of maintenance, upgrades, and support contracts. The three most common forms of cloud consumption are platform as a service (PaaS), infrastructure as a service (IaaS), and software as a service (SaaS). Simply put, IaaS offers the fundamental components for using cloud services, such as computing, storage, networking, and monitoring, while PaaS combines these fundamental components into a platform for developing applications that is simpler to use.

SaaS is distinctive since it doesn't cater largely to software developers. In contrast, common business tools like email, customer relationship management (CRM), and financial management software are accessible on-demand from any location via a web browser, desktop computer, or mobile device. SaaS enables authorised users to access an application from anywhere on any authorised device without needing to be within the company firewall as employees have become increasingly dispersed and remote. SaaS facilitates concurrent use of the same tool or document, which increases real-time collaboration. SaaS also allows businesses more flexibility as they expand since, if usage is properly recorded and maintained, they only have to pay for the users of the programme.

Because the SaaS provider takes care of everything, this model significantly lessens the maintenance burden on IT teams to stay current with the latest releases or applying patches like security upgrades. SaaS introduces a new set of dangers as well, the majority of which stem from a dependency on a third-party provider to uphold the security and availability of their services for consumers.

Customers depend on the supplier to develop new features and fix faults quickly, unlike the highly configurable business apps of the past. They also call for the provider to keep the software accessible. As we've seen multiple times in the cloud era, service provider failures can simultaneously impact thousands or even millions of clients. Along with an increased reliance on SaaS come portability limitations. It would take considerable effort and a compelling argument to move all of a company's CRM data across the internet to another SaaS provider (or back to a private data centre). Other concerns include security and privacy, particularly if a reputable service provider suffers a data breach. Although a SaaS provider compromise may effect far more customers than a breach at a single private data centre, the industry opinion is that SaaS security is significantly stronger than the protection in most enterprise data centres.

Platform thinking follows design.

With AI at the centre of platform (and SaaS) development, "design thinking" will start to change into "platform thinking." In a future powered by AI, exploration and learning will be crucial. Software design will change from being outcome-focused to goal-focused. AI will enable development teams to:

Instead than focusing only on design prototypes, quickly construct and deploy functional proofs of concept (POCs).

Conduct A/B and multivariate tests on actual end users.

Identify and roll out applications that have been thoroughly tested based on data from live users.

Platform thinking will permeate all enterprises because AI empowers experts of all skill levels to develop, deliver, and enhance both processes and technology. The final result will be the ability for every individual in the company to quickly turn ideas into reality.

Team composition and skill sets will need to change if AI becomes an essential component of software development (and ultimately business processes). The AI engine will actively participate in the software delivery team and take on a variety of forms, including platform ideas, companion bots, analytics, and reporting.

Using AI to enhance software delivery

Despite accolades for agile methodology's widespread adoption, only a small number of companies have actually achieved continuous delivery. True agile will be made achievable by AI acting as an extension of your software delivery teams. Teams will be able to deliver updates in a continuous flow thanks to intelligent automation.

As bots construct the underlying code, design systems will be dynamically built and implemented. Self-built POCs will make it possible to test all features right away. Integral and developing test automation will guarantee quality and significantly boost velocity.

What impact AI will have on software development jobs

Businesses will need to plan ahead and take into account the part AI will play in platform engineering. There will be more job opportunities when this new method of growth takes hold.

The business analyst's position will be expanded to include driving company strategy. Most likely, individual user stories, requirements, and approval criteria will be written by AI. corporate analysts will evaluate AI-generated concepts and promote corporate alignment to platform thinking rather than capturing criteria. Business analysts will represent this component of the plan, which will be driven by AI and technology.UI design roles will be outpaced by roles in interaction design. Demand for UI design to individually layout pages and business process flows will decline as visual AI quickly develops. Through JavaScript design frameworks, graphical guidelines, and ongoing user testing, interaction designers will direct AI in the creation of UI and UX.

The field of test architecture will grow to be highly lucrative and in demand. Continuous testing will be essential for software that is produced autonomously. There will be a greater requirement for testing than ever before as the delivery lifecycle shortens. It won't be sufficient to automate user tests based on acceptance criteria. Complex test infrastructures will be created, implemented, and maintained by test architects who will also run ever-evolving regression suites, continuously conduct exploratory testing, and test new functionality from beginning to finish.SaaS will ultimately be built on AI, which will profoundly alter the day-to-day work of software developers. In a world of AI-driven software development, continuous testing will be the deciding factor, determining which organisations succeed and which lag behind in this new speed of work.

Conclusion

AI is already showing to have an impact on numerous industries and will probably do so in the future, unlike the revolving door of much-hyped technologies over the previous five years. By enabling automation, enhancing decision-making, and releasing fresh insights from data, artificial intelligence (AI) is a revolutionary technology that is already revolutionising enterprises and sectors.

AI is a potent tool that can be utilised to improve the effectiveness of a wide range of technical and non-technical jobs. It's crucial to keep in mind, though, that it cannot take the place of human intelligence and innovation. Thoughts can be sparked by AI, but it is ultimately up to us how to put them to use.