New tech, new problems: Why application development needs a big-picture view

Technology continues to rapidly advance, particularly with the ongoing evolution of generative AI, the growing emergence of innovative methods for leveraging data, and new platforms that enable companies to rapidly develop SaaS offerings. 

However, many organizations have approached innovation without a comprehensive strategy or holistic view of their applications, simply focusing on adding the latest features or trendy tools. As a result, they are facing challenges related to application performance, scalability, efficiency, and security.

To ensure the success of application innovation, enterprises must maintain a big-picture view of their applications. They should understand how integrating new technologies will require them to scale their compute and storage resources, the impact these technologies will have on end users, the architectures required, and the maintenance support that will be necessary. As part of this, enterprises also need to set attainable interim goals that generate rapid ROI and support their long-term goals. 

The Challenges Enterprises Face In Application Innovation

Today, enterprises face many challenges in innovating their applications, but many have a solvable path. When approached strategically, organizations are in a prime position to capitalize on current technologies to truly innovate. 

Legacy Systems: Legacy systems are one of the first hurdles an organization has to overcome when innovating their applications. Depending on how outdated and robust the systems are, this can introduce complexities, including the sophistication of the engineers needing to migrate the systems and the strategies needed to innovate, leading to costs that may not be incurred in newer infrastructures. Legacy systems can also have a profound impact on how organizations plan to scale. For instance, an organization that is moving from a pilot phase to full-scale deployment while maintaining performance and reliability can be difficult if engineers are working in outdated systems.

Data Security and Compliance: When transforming their systems, enterprises must take a close look at their data and security compliance efforts. During any migration or new application development, it’s critical that the technology is secure and compliant, especially in regulated industries. For example, if a healthcare provider wants to create an app that allows them to better track appointments and records of patients coming into a facility, they must comply with HIPAA, GDPR, and other compliance standards depending on how and where the application is being implemented. 

Talent Gap: Talent is an area that should never be overlooked. According to the IBM Institute for Business Value, executives estimate about 40% of their workforce needs to reskill over the next three years due to AI and automation. This, coupled with the fact that there is a shortage of skilled professionals to drive innovation and manage advanced technologies, can make it difficult for organizations to harness the right talent to take their applications to the next level. Today, many organizations are investing in how generative AI can bridge some of these skill gaps. Still, when it comes to devoting time to strategically build the robust applications customers seek, AI isn’t going to be able to do it alone. 

Stakeholder Alignment, Change Management, and Budgeting: Aligning IT and business teams to drive innovation initiatives collaboratively is extremely important, and is directly tied to the investments that organizations will spend on these projects. Business leaders must balance the costs of innovation with measurable ROI, while also ensuring seamless adoption and minimizing resistance within the organization.

Bringing A Comprehensive Approach to Application Innovation 

A well-rounded approach to application innovation can deliver significant value across areas such as application performance and end-user satisfaction, and ultimately, help organizations prepare for future technologies. 

When enterprises think about how to enhance their application performance, modern architectures, such as microservices or serverless infrastructures, can help with scalability and resilience. For example, when there is a hurricane, insurance companies may see an increase in claims. With modern architectures, these companies can scale their processing services to handle the inbound claims that they aren’t typically used to. Additionally, the implementation of AI-driven monitoring can help organizations predict and resolve issues proactively, allowing humans to use the time to strategize and prepare for how the company will continue to innovate in the future. Lastly, agile pipelines, DevSecOps, and site reliability engineering (SRE) tools can enable secure, rapid deployments, and observability.

The end-user should always be top of mind when organizations plan their approach to new applications. What can be done now that hasn’t been done before? How can we provide the best, frictionless experience? With AI tools, organizations can deliver personalized features customized to every user. For example, if a consumer is using a retailer’s new app, browsing and purchase history from previous website visits should be translated into the app for a more comprehensive experience. Additionally, innovative, intuitive design and consistent app performance are essential. Application developers that think about how a consumer browses or purchases, while also ensuring low downtime or fast responses, will set themselves apart. Services should not only improve engagement, but solidify trust. 

Ultimately, enterprises should always consider how to best prepare their infrastructures for future technologies. There is not a one-size-fits-all approach to how applications are developed, and as seen with some of the challenges of working with legacy systems, organizations should always be open to modernizing. 

Organizations that think about how to implement modular frameworks to simplify the integration of new tools and technologies will put themselves ahead. Additionally, ensuring that engineers and other technical staff are continuously upleveling their skills with AI, automation, and analytics training ensures teams stay ahead and are able to use these tools to their advantage. Lastly, enterprises should leverage data to guide them to smarter decisions that better align their technology with business goals. 

At the end of the day, enterprises that adopt a big-picture view of how they go about their application development will not only meet today’s demands but also build a solid foundation for long-term innovation and adaptability.

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