The use of AI in Edge Computing opens up exciting opportunities across industries, offering benefits like real-time decision-making, low latency inferencing, and enhanced data security. However, quantifying these benefits and demonstrating tangible returns on investment remains a challenge for many companies.
Introducing any new technology stack takes time to unleash its full potential and effectively showcase its value to stakeholders and decision-makers. This applies not only to Edge-AI but to any emerging technology.
The term "business value" holds different interpretations among stakeholders in an Edge-AI project. However, it can generally be categorised into two overarching cases.
1) Cost reduction and increased profit margins by optimizing infrastructure, communication, and human resources.
2) Minimization of business continuity risks, such as cyberattacks, data compliance, and sustainability challenges.
Although many Edge-AI use cases are still in the Proof of Concept or initial deployment stages, it's important to note that these projects are driven by business leaders who have well-defined strategies for creating value.
Organizations invested in cloud computing are now exploring cost-saving opportunities by partially shifting applications to edge computing. This shift can help reduce recurrent expenses related to data bandwidth and cloud storage, offsetting the upfront investment in edge technology.
In extreme cases like high definition 1080 or 4K computer vision, where large data streams are involved, moving the processing to the Edge can end up saving over 90% of the associated infrastructure costs.
We don't just consider cloud-to-edge migrations. Even in specific Edge use cases, significant savings can be achieved compared to the status quo. Take manufacturing line inspection as an example. This task in factories is time-consuming and requires skilled workers.
Implementing Edge AI for inspection processes can significantly reduce costs. According to Nvidia, one specific customer experienced a total operating cost reduction equivalent to 30% of total manufacturing expenses. This can have a substantial impact on companies' annual accounts
At Barbara, we are privileged to collaborate with business-oriented companies like Acciona. By implementing AI in water network monitoring and operation processes, they have achieved faster and more accurate analysis. Leveraging historical data has led to improved results and forecasts, particularly in the real-time prediction of chemical levels.
To address the growing data volumes and comply with regulatory restrictions on connecting OT systems to the Internet, infrastructure managers have chosen an edge computing platform, such as the one provided by Barbara. This platform enables them to effectively handle data while ensuring compliance with regulations.
The project will achieve a remarkable 900% Return on Investment in just 4 years of operation, considering the amount of chemicals saved per plant annually.
The convergence of machine learning and edge AI presents Engineers with unique challenges that require a specialized skill set beyond traditional machine learning engineering. In this webinar you will gain insights into trends and best practices in implementing Machine Learning at the Edge, from optimisation, and deployment to monitoring. Learn from OWKIN, APHERIS, MODZY, PICSELLIA, SELDON, HPE, NVIDIA and BARBARA how to:
🔒 Enhance Data Access, Security and Privacy through Federated Learning
💪 The tools, systems and structures you need to put in place for real-time AI
🚀 Improve model performance for Computer Vision
⚙️ Run successful Machine Learning Model Inference
💡 Optimize ML models for edge devices
🔒 Secure your ML models in the edge
Not all success is measured in money. Take the example of General Electric who reduced emissions by up to 14% in in their Gas Turbines by adding an Edge-AI software.
The software automatically tracks the turbine's ideal operating conditions in response to environmental factors, where traditionally, these adjustments were performed manually and could take days to complete and have much less impact on reducing emissions.
This way General Electric is preparing its products to keep operating in regions where emissions are being regulated such as Europe, USA, Canada, as well as in a climate environment which is less and less predictable every day.
Companies that fail to innovate or adapt to disruptive technologies and business models often face downfall. At Barbara, we believe Edge-AI is one such disruptive innovation that offers alternative solutions to existing products, particularly in the cloud domain, and holds immense potential for rapid improvement.
When it comes to embracing edge-AI, industrial organizations and IT departments face the crucial question: how to begin?
The key lies in the development of effective business models that justify the implementation. A recommended strategy involves bringing together developers, operational teams, and infrastructure experts to identify business processes that rely on real-time critical data. Quantifying the resulting cost and risk reductions for each process will lay the foundation for a strong business case rooted in value.
Barbara partners with industrial organizations to explore the transformative potential of Edge-AI in optimizing business processes for enhanced efficiency and sustainability.