Advancing to Digital Optimization After Transformation

Digital optimization focuses on identifying opportunities to tune an existing digital business to more effectively serve the specific needs of that organization—moving beyond simply ensuring that your organization offers a digital experience for your customers to aligning investments and updates to your digital infrastructure with your business goals. While this is similar in some ways to continuous digital transformation, the business focus remains the same during the optimization process, rather than driving a change in the digital experience offered to customers.

Following this definition, digital optimization will, by necessity, be unique to the business needs of each organization. Still, data on existing digital transformation trends suggests that digital optimization will share features across organizations and industries. For example, 88% of respondents to the F5 2024 State of Application Strategy survey report using a hybrid or multicloud architecture, but many organizations continue to encounter challenges related to their multicloud implementations. Per the ESG report on application modernization trends across distributed clouds, 30% of organizations struggle with consistent application of security policies, 30% have integration and interoperability challenges, and 28% encounter difficulties with managing heterogenous infrastructure. Clearly, complexity remains a significant challenge for modern digital businesses.

While organizations must take steps to identify and address complexity, three steps emerge consistently as optimization opportunities for businesses with hybrid and multicloud architectures.

1. Ensuring effective connectivity across your infrastructure: 90% of IT and networking professionals cited multicloud networking (MCN) connectivity as a “critical” or “very important” need in the Futuriom Multicloud Networking and NaaS Survey Report…and it’s easy to understand why. In the absence of effective connectivity, application performance suffers, visibility is lost, and the fragmentation of information makes cost control substantially more difficult.
2. Adopting a consistent security posture: Security poses a challenge for organizations trying to integrate cloud-native applications in existing application environments, with 44% identifying security as an app integration challenge.
3. Improving monitoring and management: End-to-end visibility is crucial to ensuring compliance with data security protocols and monitoring application performance. It also makes it easier to identify opportunities for consolidation and cost reduction.

In all cases, starting with a clear multicloud disposition and strategy that is suited to your business will make it easier to identify solutions that will help you tame growing complexities. Multicloud networking solutions are a pivotal investment that can help with optimizing your digital transformation efforts. Both GigaOm and ESG have identified factors to consider when choosing the best strategy for your organization and benefits that may come with the implementation of MCN software, including improved network security (49%), improved network performance (44%), and improved application performance (36%) topping the list.

What is AI in networking?
Within the context of networking, AI generally falls into one of use cases. First, AI can be used to identify opportunities for network optimization by finding patterns in performance data. Second, AI applications are built to make use of networking infrastructure, with certain aspects of the AI model and usage informing how an organization must connect and protect elements of its networking infrastructure. The remainder of this post will focus on the latter use case.

What can we learn from AI in networking?
Optimizing your digital infrastructure to efficiently and effectively serve your business goals can have the benefit of freeing both capital and human resources to focus on innovation. Generative AI is a great example of an emerging technology that benefits from a flexible hybrid or multicloud architecture. IDC predicts that AI implementation will be a key driver of the adoption of multicloud networking solutions, with AI expenditures exceeding $500 billion by 2027. Enterprise organizations are particularly likely to require multicloud networking solutions for their AI implementations, as they are most likely to stand up a combination of public and private AI, with services provided.

Even within a single AI model implementation, multicloud networking matters. The output quality of AI models depends on immaculate data hygiene during ingest and training, and peak performance relies on effective inference workload management at the edge. The integration of AI into an existing application landscape adds complexity and expands the threat surface, making consistent security even more important. While this seems daunting, the right multicloud networking solutions, factoring in connectivity (including container networking solutions), security, and visibility can set you up for success with AI. Organizations that have already implemented robust multicloud networking solutions have an advantage when dealing with potential challenges related to AI in networking. This trend also highlights how ensuring secure, effective connectivity and implementing a consistent security posture helps ensure that organizations are ready to take advantage of the latest tech innovations by creating a flexible architecture that can protect and process data securely and effectively, then deliver outputs wherever needed.