In recent years, software development has witnessed a tremendous transformation. In an effort to maintain a competitive advantage, enterprises have rapidly adopted and incorporated emerging technologies, and Artificial Intelligence (AI), including Machine Learning (ML), is no exception.
With AI and ML heavily involved in software development cycles, enterprise software is well-placed to be smarter and more robust.
According to IDC, by 2025, 90% of new enterprise apps will be AI-powered.
This approach promises to enhance their ability to build new products and services, deliver enhanced customer experiences, and improve the way they operate.
We have already seen the impact of AI on application development with Agile test automation, and Robotic Process Automation (RPA). In this scenario, smart algorithms strive to make each iteration easier, faster, and more reliable.
This approach is expected to help companies gain a competitive advantage from shorter reaction times, improved customer satisfaction, and greater success with product innovation.
Why Is Legacy Enterprise Software Ineffective?
Bespoke enterprise software is built to solve business-specific problems. This highly customised software is centralised and supports internal functions like accounting, human resources, enterprise resource planning, and more.
This bespoke software development model was successful until now. Today, legacy enterprise software falls short when it comes to speed, scalability, and overall efficiency. This is attributed to its dependence on human intervention, which comes with errors like personal biases and incorrect data input.
If human error isn’t detected early, it can have a domino effect across the organisation with dire consequences. Furthermore, when custom software relies on human effort, it’s challenging to quickly and reliably generate accurate insights.
Traditional enterprise software isn’t adaptable, and businesses often change how they work to accommodate the software. While the bespoke software development model has helped eliminate this problem, adjusting quickly to keep up with a rapidly evolving marketplace still proves to be a challenge.
How AI and ML Change and Improve Enterprise Software
Smart algorithms are now heavily involved in every phase of enterprise software development. From discussion to deployment and beyond, AI, ML, and Deep Learning (DL) algorithms improve and optimise the process through each iteration.
With the help of AI and automation, companies can also accelerate development while reducing technical debt. This approach helps cut costs while mitigating risk through each iteration.
Planning and Analysis
The conceptual phase of the development cycle demands the most human intervention. However, when you add AI and Natural Language Processing (NLP) tools, you can gather relevant data to identify errors, loopholes, inconsistencies, and shortcomings in the system quickly.
NLP tools are programmed to understand business and user requirements and make accurate predictions through analytics. While it would take teams of human software engineers several hours to collect, analyse, detect, record, and segregate, AI can do it in a flash.
Software design requires close collaboration between different departments, like research and development, software developers, and designers. It’s a highly complex and demanding phase in the software development cycle that requires clear and transparent communication.
When you add smart algorithms into the mix, it facilitates a cognitive approach to software design. For example, AI and ML collect relevant data (user requirements, business vertical, geolocation, etc.) and intuitively recommends the most viable design approach (which demands the least manual or human intervention).
Through extensive training and experience, these smart algorithms become smarter to engage in software design more efficiently and effortlessly. So bespoke development teams can propose a definitive solution and settle on the right design without the risk of errors.
Software Programming (with Some Automation)
Robust enterprise software is brought to life with the help of millions of lines of code. Until now, this was churned out manually by multiple software developers, line by line.
In this scenario, ML and NLP understand the idea and project requirements and convert it into executable code (and reduce the workload to some extent). While AI can’t build the whole program for you, it does go a long way to take some of the burden off developers.
Testing is a critical part of enterprise application development. It’s the only way to ensure that the product works as it was intended. Arguably, AI will probably take over the whole process of software and Quality Assurance (QA) testing shortly.
While AI hasn’t achieved full autonomy just yet, it does play a significant role in labour and time-intensive, repetitive tests, accelerating time to market, cost-effectively. The best part of this is AI-based testing tools rapidly engaging in multiple and complex testing exercises with precision.
When it comes to deployment, again, AI and ML have a role to play. In this scenario, smart algorithms help the development team make tactical decisions, ascertain viability challenges, and deploy AI and ML models.
Software development teams are also enabled to analyse past projects and make decisions that reduce the risk of failure and potentially deliver a higher return on investment.
What Are the Key Benefits of AI and ML in Enterprise Software?
When organisations make enterprise software intelligent, ML takes over the critical process of deriving valuable insights. This is because ML frameworks ensure that relevant data is collected, cleaned, and analysed to boost overall efficiency and productivity.
For example, if you’re a retailer leveraging bespoke ML-based software, it can disrupt traditional processes, improve their understanding, and better forecast customer behaviour.
Other benefits of developing smart enterprise software are as follows:
- Achieve enhanced data security
- Enable and enhance employee intelligence
- Enable seamless bugs and error identification
- Facilitate smart business decisions
- Generate accurate insights and analysis
While AI in an enterprise setting is still in its infancy, the future is bright. However, to accelerate smart enterprise software development, we have to first attract top tech talent.
Should Businesses Outsource the Development of Enterprise AI/ML-Driven Software or Build It in In-House?
Whether you choose to build smart bespoke software in-house or outsource it nearshore, the decision often comes down to available resources. If you have the budget, you can compete with multinationals and attract leading AI and ML engineers.
According to Indeed, an AI/ML engineer commands a base salary between £42,964 to £62,825 (plus benefits) a year in the UK (depending on their specialized skills). According to Glassdoor, an AI/ML engineer with similar background commands on average about £30,000 annually in Ukraine.
When you compare the numbers, the cost-savings are apparent, making outsourcing a highly attractive option. However, it doesn’t have to be a completely outsourced project. You also have the choice of augmenting your in-house team with an extended team set up overseas.
As smart algorithms rise to prominence across industries, the demand for top tech talent will continue to outstrip supply. So for some, outsourcing might be the only option to maintain a competitive advantage.
Are you looking for professional help building a cutting-edge bespoke enterprise solution from scratch or replacing and modernising your legacy system while harnessing the power of AI/ML? Schedule a commitment-free consultation now to discuss how we can help!