For the report, “Leading with edge computing: How to reinvent with data and AI”, Accenture surveyed 2,100 C-suite executives across 18 industries in 16 countries. The research surveyed edge adopters across industries and geographies with distinctive, industry-specific use cases to identify future edge developments. The study revealed that edge computing will be essential to remaining competitive in the future, according to 83% of the respondents. Meanwhile, 81% believe failure to act quickly can lock them out from the full benefits of the technology.
Accenture on Edge and Gen AI
The widespread adoption of artificial intelligence, including generative AI, has underscored the pivotal role of edge computing in the era of digital transformation. As data assumes its position as the primary driver of innovation, edge computing emerges as a critical enabler for enterprise usecases. Gen AI then facilitates real-time or near-real-time data analysis and helps provide business insights. As artificial intelligence, including generative AI, becomes pervasive and data becomes the fundamental fuel of digital transformation, edge is playing a critical role by enabling data analysis in real- or near-real-time and delivering key business insights. It serves as a critical input to rapidly evolving AI algorithms, further adding to its importance going forward. According to Accenture, global spending on edge is expected to be $208 billion in 2023, a 13.1% jump from 2022. Enterprise and service provider spending on hardware, software, and services for edge is forecast to sustain this pace of growth through 2026 when spending will reach nearly $317 billion.
A Maturity Model for Edge Computing, in four approaches
Accenture’s research has identified four principal enterprise approaches to edge computing (below). These approaches can be contextualized in terms of their integration into the digital core, which harnesses the synergies of cloud computing, data analytics, and AI through an ecosystem of interoperable systems designed for the agile development of capabilities.
These four distinct edge approaches—Ad Hoc, Tactical, Integrated, and Super Integrated—largely pivot on factors such as strategic alignment with business imperatives, scalability across organizational dimensions, and the maturation level of the underlying technology. One way to assess an organization’s edge computing maturity is to use a maturity model. A maturity model is a framework that can help you to identify your current state of maturity and to develop a roadmap for improvement.
There are a number of different edge computing maturity models available. Accenture discusses its model in the report. The model is based on three overall types of approaches.
Type 1: Ad Hoc
Organizations at the Ad Hoc level of maturity are typically in the early stages of edge computing adoption. They may have a few one-off edge deployments, but these deployments are not integrated with the rest of the enterprise’s systems. This can make it difficult to scale edge computing or to integrate it with other technologies for maximum return.
Type 2: Tactical
Organizations at the Tactical level of maturity have a more strategic approach to edge computing. They have identified a specific set of use cases for edge computing and have deployed edge infrastructure to support those use cases. However, their edge deployments are still relatively siloed and are not fully integrated with the rest of the enterprise’s systems.
Type 3: Integrated
Organizations at the Integrated level of maturity have a fully integrated edge computing strategy. Their edge deployments are integrated with the rest of their IT infrastructure, including their cloud environment. This allows them to scale edge computing more easily and to integrate it with other technologies for maximum return.
Type 4: Super Integrated
Organizations at the Super Integrated level of maturity are using edge computing to transform their business. They are working with innovation partners to develop new edge-first business models and applications. These organizations are seeing the best outcomes from edge computing and are able to accelerate innovation more quickly.
How Integrated Is Your Edge With Your Enterprise?
- Type 1 (Ad Hoc) and Type 2 (Tactical) are the least successful adopters. Their edge deployments are one-off and strictly tactical, or otherwise not integrated with the enterprise’s systems. This hobbles their efforts to scale the technology or integrate it with other technologies for maximum return.
- Type 3 (Integrated) scales edge and integrates it deeply with the cloud and the greater IT strategy.
- Type 4 (Super Integrated) is the most transformational adopter, working with innovation partners to develop an edge-first business. These types demonstrate better outcomes and the ability to accelerate innovation.
Super Integrated organizations are seeing the most success, in part because they build edge on their digital core, integrating it with cloud, data, AI, and interoperable applications and platforms.
Conclusion
As AI becomes more pervasive in the enterprise, edge computing will play an increasingly important role. AI models are often data-intensive and require real-time processing to be effective. Edge computing provides the ideal platform for running AI models at the edge of the network, where the data is generated. The most advanced edge adopters are already reaping the benefits of this technology. According to Accenture, they are 4x more innovative, 9x more efficient, and nearly 7x cost-effective than their peers.