Over the years, the software development lifecycle has evolved significantly to support new and emerging innovations. Up until the early 2000’s standalone and siloed development and quality assurance models thrived in the development lifecycle. But in those days, software was considered merely a support service for core business functions. As markets experienced a paradigm shift to a digital-first ecosystem, enterprises began reimagining the role played by software. Consequently, the foundations of agile methodologies were implanted in the development journey, and very soon, it created a major revolution in the way enterprises built their IT assets.
Cloud became the de facto choice of applications, and businesses embraced a productized path for developing all IT assets since it was agile-friendly, resilient, and better manageable.
The existing agile framework for cloud-friendly enterprise application ecosystems
The agile methodology driving successful transformation with cloud-first development philosophies focused primarily on
· Intense collaboration between developers and customers to ensure that feedback is rightly channelled into every development initiative.
· Frequent release cycles ensuring that more value is delivered quickly and sets the tone for iterative development benefits.
· Simplifying the entire development process by delivering the most critical features first and ensuring that enough resources work on complex parts for better results.
· Promoting a culture of transparency and open communication with a mix of collaborative approaches supplemented by tools and processes.
· Enabling continuous and iterative improvement in the development process that involves all stages from the most granular components to full-scale features.
Standalone QA teams as well as isolated deployment practices disappeared from the digital landscape. The cloud migration was seen as the next big thing in the technology space, but the new paradigms of agile possibilities soon faced a new revolutionary contender.
Disrupting the tech world like never before – The rise of Generative AI (GenAI)
While the existing agile methodologies worked well for cloud-driven innovations, the shift in focus for businesses from cloud to AI meant that the traditional rules of agile had to be rewritten to match the AI age. The need for rapid changes in agile development approaches became evident when GenAI began to go mainstream in late 2022.
Within a short period, it brought in a transformational change. Sectors ranging from finance to healthcare and creative arts began to see widespread impact. In 2024 alone, Generative AI projects attracted nearly USD 33.9 billion in private investment worldwide. What began as an information discovery approach, GenAI soon diversified into several domains, and software development was a key impact point.
The era where code writes code
AI has penetrated so deeply into software development practices that major companies are confidently relying on GenAI to build their next-generation codebase autonomously. For perspective, Google says that 25% of the new code used across its products is generated by AI. In other words, the code created by engineers for AI capabilities is now being used to write the code for the next dimension of digital experiences..
Building new GenAI capabilities requires organizations to have unique insight-driven LLMs, development approaches, and best practices that assure better adaptability to market trends. This is where a platform-centric approach for software development makes better sense for enterprises in the era of GenAI.
Why is a platform approach better for new enterprise GenAI capabilities?
A platform approach comes with the inherent advantage of unifying the underlying digital architecture for different digital products or services that an enterprise builds with respect to data storage, resource management, and operations control. This provides better control and lower friction when different service modules or components need to collaborate to realize a capability like GenAI. It also improves security coverage with uniform best practices implementation.
Building platforms for GenAI needs a product engineering mindset - Solving the LLM puzzle
When it comes to nurturing the next generation of AI capabilities with a platform approach, it is important to understand the criticality of making LLMs the center of attention in that approach.
LLMs drive the fundamental intelligence that powers GenAI services or capabilities that modern digital experiences demand. However, despite the degree of contextual understanding, they may interpret scenarios incorrectly and generate unexpected outputs. Also, LLMs are constantly evolving to adapt to new and emerging contextual scenarios in different sectors. Furthermore, LLMs are as accurate and powerful as the data used to train them. There is a need for strategic data accuracy assurance if the LLMs are to be trusted for complex and critical AI use cases.
The unpredictability quotient, accuracy concerns, and evolving reasoning challenges with LLM make it nearly impossible for engineers to execute traditional application development workflows in their work streams. What they need is a product engineering mindset that helps in adapting enterprise solutions initiatives to the ground realities of LLMs and follows a platform-driven development approach.
The benefits of a product engineering mindset
Assures LLM viability
Product engineering brings user-centricity to LLM development, which ensures that the intended customer objectives are met when LLM-based digital services are created.
Continuous improvement approach
With a product engineering mindset, there will be a dedicated focus on constantly monitoring the LLM’s performance. Baseline performance and effectiveness can be compared with internationally accepted standards to determine adjustments needed periodically in the development stream.
Holistic use-case coverage
A product engineering mindset enables teams to collaborate to achieve the overall intended LLM outcomes when used in a service. This is achieved by bringing domain experts, quality professionals, product managers, and engineers onto the same page for unified operations. This will ensure better accuracy for LLMs in all aspects of their applications.
Embracing the product engineering mindset for an AI-driven future
CIOs need a new playbook to build the tech that powers the AI infrastructure of tomorrow for their enterprises. Traditional agile practices will not be sufficient to support the scale of dynamics involved in avenues like GenAI. Moving to a platform-centric approach with a product engineering mindset is the sure-shot formula for success. However, this journey requires strategic guidance, tailored domain expertise with LLM modeling, and end-to-end knowledge about resources needed for building AI capabilities.
With a DNA driven by a legacy of product engineering excellence, Wissen is positioned as the best option for enterprises to partner for building their AI capabilities. Get in touch with us to learn more.
FAQ
What is the platform approach for application development?
It is an approach that focuses on leveraging different digital capabilities as a unified platform-based solution, which is easy to maintain and operate.
How is AI development different?
The complexities involved with LLM training and modelling make AI development more complex than any average software development experience.
How is GenAI impacting software development?
GenAI services are helping enterprises generate code that powers their key IT assets and helps in building and integrating new features into their tech stack faster than ever before.
