Machine Learning is on the Edge; Getting Ready for the Leap

September 1, 2023

The world of artificial intelligence (AI) and machine learning (ML) is constantly evolving. As we approach 2025, a significant transformation is underway. More than 50% of enterprise-critical data will be created and processed outside traditional data centers or the cloud. This paradigm shift presents both opportunities and challenges for businesses looking to leverage AI and ML effectively.

In this blog post, we will explore the growing importance of edge computing in the context of machine learning and how organizations can prepare themselves for this leap into the future. We will discuss key challenges, assumptions, perceptions, and best practices associated with this evolving landscape.

The State of AI in 2025

As we gaze into the AI landscape of 2025, two stark statistics demand our attention:

Data Beyond the Cloud: More than 50% of enterprise-critical data will originate outside the conventional boundaries of data centers and cloud infrastructure.
Operational Realities: However, a mere 50% of AI initiatives are projected to reach operational status within enterprises, with a paltry 10% delivering meaningful return on investment.

These figures underscore the importance of understanding and adapting to the evolving AI landscape, with a particular focus on the edge.

AI on the edge- medical — AI is used in medical devices

Challenge 1: Managing Data Generated Outside the Cloud

One of the foremost challenges in this new AI era is managing data generated at the edge. Currently, many organizations view data originating outside the cloud as merely an additional resource to fine-tune their machine learning models within the familiar cloud environment. This approach often results in one of two scenarios:

Treating edge deployments as a mere extension of the cloud, where models are deployed and inferences made without a deeper understanding of the unique capabilities and challenges of the edge
Dismissing the potential of edge computing altogether, driven by the assumption that its utility is confined to specific industries.

However, beneath the surface lies a goldmine of untapped potential. Edge-generated data, when harnessed strategically, has the potential to deliver a significant competitive advantage.

Best Practices

Data at the edge necessitates the development of advanced real-time automated decision-making capabilities. Yet, traditional data pathways, from edge to cloud to insights and, finally, to value realization, can be time-consuming and inefficient. To remain competitive, we must expedite the transition from insight creation to value realization through the implementation of shorter feedback loops. This approach enables organizations to swiftly translate insights into actionable outcomes, reducing latency and delivering more immediate business value.
Rather than regarding edge deployments as a passive resource, we must proactively harness and leverage this data source. By adopting this proactive stance, organizations position themselves to gain a competitive edge in a landscape where others may underestimate or overlook the substantial value concealed within edge data.

Challenge 2: Going from Data to Value

Most organizations today recognize that accurate insights and models hold the promise of business gains They understand the fundamental equation: the swifter the generation of insights (Insight velocity), the shorter the path to making decisions (Decision velocity), and, consequently, the faster the Time to Value.

But beyond merely understanding the potential of insights, we must focus on making them measurable and operational. It’s the measurable insights that guarantee sustainable business gains. To do that, organizations must have the capability to quantify and track the impact of these insights on their operations.

Best practices

The key to accelerating decision velocity and, subsequently, driving business value is the operationalization of insights. This means translating insights into practical, actionable measures that directly influence the day-to-day functioning of the organization.
The transformation we seek goes beyond just data; it involves the way we organize people, processes, and tools. To maximize value, organizations should orient themselves around outcomes rather than outputs. It’s about aligning every facet of the organization towards achieving tangible goals.

Challenge 3: Strategizing for AI Tools and Risk Management

Many organizations find themselves in a situation where the existing tools do not align with their specific compliance requirements. This challenge is usually tackled in two ways:

Organizations embark on building custom solutions
Embrace the AI tools readily available in the market.

Some organizations remain unprepared to fully leverage the tools available in the market. Furthermore, there there is a tendency to prioritize initiatives that pose minimal risk, often resulting in modest or uncertain rewards.

Best Practices

Building custom tools should be driven by the potential for transformative differentiation. Rather than solely focusing on immediate needs, we must question whether building our own tools affords us a “first mover” advantage or, more importantly, the “best mover” advantage against competitors.
A vital shift in perspective involves embracing a partnership and joint collaboration mindset with AI tool vendors. Recognizing their expertise and resources can lead to mutually beneficial outcomes. Collaboration fosters innovation and accelerates progress.
To seize high-reward opportunities, it’s imperative to de-risk them through small-scale experiments and incremental learning. This approach allows organizations to validate the feasibility and potential rewards of novel initiatives while minimizing the associated risks.

The Bottom Line

The challenge of managing data generated on the edge extends beyond efficient data handling; it represents a paradigm shift in perspective. To thrive in this new era, we must embrace edge-generated data as a valuable and actionable resource. Through the development of real-time capabilities, shortening feedback loops, and the active exploitation of the best ways to leverage edge data, organizations can seize the leadership position in this transformative age of AI and edge computing.