AI is so pervasive in the business world today that one can barely turn a corner – literally or figuratively – without seeing a reference to it. It is omnipresent at events, top of the agenda in meetings, and, of course, top of mind in business relationships.
While AI has been around for decades, the emergence of GenAI has propelled AI to the forefront of technology, IT, and IT services. It is the advent of a new normal in the business world, one that includes AI and GenAI, and there’s no way back.
The question is no longer “Do you use AI?” It’s “how do you use AI?” And, given its capability to create or produce new content, such as text, images, music, or even lines of computer code – GenAI can be one of the biggest growth drivers.
The applications on which your business is reliant increasingly may incorporate AI. Perhaps more important is, to keep them up-to-date and fit-for-purpose in a rapidly changing landscape – one in which we’re moving to the cloud or multi-cloud, responding to heightened regulations, and countering cyber-security threats with ever-greater sophistication and frequency like never before – the use of AI in any application modernization project you undertake is both inevitable and sensible.
After all, the use of antiquated application systems prevents the agility needed to deal with a rapidly evolving marketplace, not to mention the unnecessary negative impact on expenses. So, businesses are rightly questioning how to build and maintain modern enterprises that fulfill their internal operations and, more importantly, meet a cadence that satisfies their customers.
Adding numbers to the picture
According to Gartner, the current $428 billion application services market is forecast to grow at a five-year compound annual growth rate (CAGR) of 8.4%, reaching $631 billion in 2027. Growth in this global market across all industry sectors will be driven by the continuous demand for application modernization, cloud adoption, and digitization.
Importantly, though, the technological research and consulting firm predicts that by 2027, 90% of service providers will use GenAI for software development and engineering, including code completion and optimization, automated debugging, and automated quality control (QA). This is up from 26% in 2023, meaning adoption is likely to have more than trebled in four short years.
So, as organizations look to adopt GenAI for virtually every business process, leaders are increasingly searching for unique, customized application solutions. However building, deploying, and analyzing comprehensive AI tools can be complex, with the modernization of applications using AI and GenAI being one of the toughest challenges.
You don’t have to do it alone:
If it’s your job to ensure that your applications don’t get left behind in today’s—and tomorrow’s—competitive market, just getting started can be a huge hurdle. What strategy is best, most cost-effective, least risky, and manageable—from a governance perspective—is best?
Let’s break things down by considering the two major routes that application modernization projects take, both when reviewing systems, and bringing updated versions into production and maintaining them: reverse engineering and forward engineering. The two routes are not mutually exclusive; in complex hybrid projects involving multiple applications feeding into numerous systems, you’ll deploy both. But we can reflect on them separately.
Through deductive reasoning, reverse engineering involves establishing how legacy devices, processes, systems, or software work so that you can repurpose obsolete objects or perform security analysis. The approach addresses cost-effectiveness and risk.
There are three basic steps common to all reverse engineering: extracting the relevant information needed in operations; modeling combined sets of information into abstract models to guide the design of new systems; and reviewing and testing.
And, businesses require the ability to sustain application development history. Reverse engineering delivers on this requirement. It helps to rapidly and effectively unpick and analyze their work, before helping to reconfigure it and bring it up to date.
In contrast, forward engineering is all about establishing your projects in or enrolling them into, an appropriate and well-managed software development lifecycle (SDLC). Many of the underlying methodologies will be the same as those used in reverse engineering.
GenAI can play an important role in both of these processes. If you want to succeed, it is essential to application modernization. And the more moving parts you have, the less you can rely on traditional methods that most often lead to delay, human error, and even more complexity.
It’s also worth emphasizing some of the benefits associated with a GenAI-fueled methodology, one that tends to also involve moving from waterfall-based project management to agile or DevOps practices: Our internal delivery use cases show that with GenAI, modernization-effort productivity will rise between 15 and 30%; the resultant cost reductions are significant and the mean time to respond (MTTR) and time to market (TTM) will both go down significantly. All of this eventually improves client and user experiences markedly.
Understanding these “new-normal,” application-modernization fundamentals will allow you to successfully navigate towards future-proofed solutions, those that are likely to involve a mixture of in-house deployment and help from external experts and vendors.
1. Still attached to legacy applications?
Take a step back for a moment and ask yourself: how many queries relating to our legacy applications have my team or I had to field in the last month alone? Maybe you have a support desk ticketing system or similar from which you could derive a number. The chances are, though, that the number is indicative of resource consumption that would better be applied more effectively—with a more effective solution.
NTT DATA’s Global Infrastructure lifecycle management report indicates that 80% of organizations agree that inadequate or outdated technology is holding back innovation. The bitter truth is that, by clinging on to legacy applications, you’re wasting time, generating massive lost-opportunity costs, building up problems for the future, and taking on risks that you could do without. And as time marches on, these problems are compounded.
Of course, it’s not always that your applications don’t work; it’s as much that, the older they are, the less effectively they work. The sense of not knowing if they will work and forever scrabbling around for hard-to-obtain answers to every failure induces real anxiety and overruns, which is terrible for productivity and bottom lines.
Legacy applications that generate more questions than answers and increase what we call a company’s ‘technical debt’ are unsustainable. You must adapt to survive, and GenAI can play a part in modernizing these apps.
2. The cloud and improved scalability
Are you in the cloud? In all probability, you’re unable to answer this question with a simple ‘yes’ or ‘no’; like most businesses, some of your apps are likely in the cloud and some aren’t.
But what you probably can answer with a simple ‘yes’ is: do you feel that the apps that are in the cloud provide you with more peace of mind than their on-prem counterparts when it comes to security, efficiency, and knowing the basics of how they work?
So, to anyone working in this space, it should be obvious how migrating legacy systems to the cloud – as well as just hosting new apps there – can improve scalability, flexibility, and cost-efficiency as part of your wider activities, and how this in turn drives measurable business growth.
In most cases, the question isn’t whether to migrate; it’s how to do it with as little fuss and pain as possible, given the complexity of the task and how you can’t move everything at once. Again, GenAI offers a big boost here.
This brings us neatly to my third and final key area.
3. Streamlining and enhancing user experiences
GenAI can accelerate modernization efforts by streamlining development and enhancing user experiences – in all the areas we’ve discussed and more.
GenAI uses advanced large language models (LLMs) to generate new and original content, a subject for a whole new article. Look for services that will help you leverage the power of this flavor of AI to drive innovation and growth at your company.
Be aware that not all LLMs are alike. Some models out in the market may not be the best match for your business and your business process needs. For most models, the energy required for learning is said to be equivalent to one nuclear power plant for one hour (in the case of GPT-3), and operating them requires massive GPU clusters. Tuning for specializing in various industries and inference costs are enormous, posing issues for sustainability and the economic burden on companies preparing learning environments.
To serve as an integration base for countless LLMs, a safe, low-latency environment on par with local environments is necessary. For instance, NTT constructed an environment utilizing the IOWN All-Photonics Network spanning data centers hundreds of kilometers apart, enabling secure LLM learning with minimal performance degradation by connecting GPUs and storage between data centers.
Learn about more easily adaptable LLMs, able to be customized by company and industry and fine-tuned with less costly and more sustainable methodologies.
From using custom AI models to integrating models into existing business processes, with the right support and solutions, you can unlock the full potential of this exciting new technology – and stay ahead of the competition.
Six of the best
I’ll finish with some key pointers to keep front of mind in all application modernization activities:
● Craft a holistic, measurable modernization strategy locked onto business objectives
● Take an iterative approach, using pilots to continuously refine processes
● Bake in security from the start through DevSecOps best practices
● Embed user-centric design thinking into every stage of development
● Foster a culture of innovation through training, communities of practice, and leadership
● Create spending plans that could potentially open up funding by demonstrating incremental return on investments and outcome-based progress
Weave GenAI into this thinking and your chances of guaranteeing a better future for your enterprise.
