Development teams are facing new challenges and pressures that focus on business when developing large enterprise projects in the corporate world. Their focus is not only on delivering high-quality applications but also on doing so with limited resources, all while minimizing disruption to daily business operations. The shortage of subject matter experts adds to the complexity, leaving teams with few answers when questions about requirements or expected outcomes arise.
As a result, development teams often find themselves working within a silo, making design decisions based solely on their own experiences and insights. This lack of collaboration can lead to missed opportunities and gaps that only become clear at the time of delivery. The question then becomes: how can development teams prove that what they’ve designed and built meets the business’s needs and delivers value to the end users consuming the application?
With the help of generative AI, the divide between business expertise and development teams can be bridged. This partnership enables the creation of high-quality solutions in a fraction of the time and budget, all while avoiding the disruptions caused by unproductive meetings and uncovering unforeseen challenges.
Realities of enterprise projects
When building quality enterprise applications, the root of most project issues lies in translating business requirements into clear technical specifications. Stakeholders and subject matter experts (SMEs) often express business value and objectives in shorthand, oversimplifying complex topics. This leads to ambiguity within the development team, as they are left to interpret and justify their implementation at delivery based on what they understood from these condensed statements. Without a full understanding of the underlying details, the development team faces challenges in aligning the final product with the original business goals.
As a result:
• Misalignment of expectations between executive management, stakeholders, and SMEs creates confusion and inefficiency.
• Costly rework efforts often surface during the user acceptance process, leading to delays and increased costs.
• A lack of clarity around objectives during the quality assurance phase hampers progress and increases the risk of errors.
• Missed time-to-market deadlines pressures already stressed teams, prolonging delivery and escalating stress levels.
• Disagreements arise among internal and external contributors over the clarity and understanding of requirements, design, and implementation, further complicating the process.
An AI-Powered Solution Case Study
If a technical delivery team were tasked with stepping into a client’s project blind and delivering a high-quality application that maximizes business value in six months or less, without disrupting business operations, it would seem like an unattainable goal. In such a scenario, one might conclude that the task is unrealistic or propose a reduced scope that falls short of expectations. Traditionally, teams would spend weeks in lengthy meetings to clarify requirements and approve designs, leaving little time to build, develop, test, deploy, and deliver the solution. Adding to the complexity is the need for resources across multiple time zones to maintain a 24/7 operation schedule.
By arming the team with a generative AI tool, the team could improve the entire process from start to finish:
• Stakeholders would set deliverable expectations and objectives to achieve business value.• The business analyst would provide prompts to generate boilerplate, detailed requirements that can be refined in minutes rather than starting with a blank canvas.
• Architects can input those results into technical specifications, make any critical adjustments or, in some cases, take them as is and turn them into feature/function design documentation for the development resources.
• Architects can also take advantage of generative AI to produce mockups, diagrams, and mock data for the team to use to maximize parallel development dependencies.
• Developers can then use these artifacts to generate boilerplate code and start fine-tuning it to meet requirements instead of creating new code from scratch.
• Quality assurance testers can take all the information and use generative AI to create test scripts to execute expected results in multi-platform, multi-conditional, and multi-device environments.
• DevOps teams can produce quality CI and CD pipelines and workflows that can rapidly adapt as fast as design changes are made and merged into repositories.
The result is achieving a delivery schedule that meets expectations, is under budget, does not impact the health and welfare of the human factor, and is achievable with measurable results that are not disruptive to day-to-day business operations.
Studies have shown that teams using generative AI to automate and augment boilerplate and traditionally disruptive project delivery activities have seen 40% or more cost reductions compared to typical budgeted and time estimations. By partnering with a team using generative AI, an estimated six-month project could be fully delivered in less than ten weeks and meet business value expectations.
Getting Better Everyday
As the future of generative AI continues to evolve and the pressures on development teams grow to deliver more with fewer resources and less time, delivery teams can leverage AI to bridge the gaps often caused by incomplete business understanding. Gone are the days of endless meetings and time-consuming activities to grasp complex business processes is over. AI now has the capability to comprehend these intricacies in real-time, streamlining the process and enabling teams to focus on what truly matters: delivering high-quality results efficiently.
Using generative AI to bridge the gap of understanding complex business requirements will strengthen the services and ROI that product organizations invest in to achieve technical excellence!
At Verinext, we understand every business and development team will have unique AI needs. By taking a tailored approach to developing your AI strategy, we can significantly enhance operational efficiency and the decision-making processes. This customization allows for optimizing workflows, predictive analytics specific to industry needs, and creating intelligent systems that will evolve with your business. To learn more about our AI solutions, visit our website or contact an expert today.
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