The demand for intelligent devices capable of making real-time decisions at the source has become a business necessity, offering dramatic improvements in operational efficiency, customer satisfaction, and innovation. Enter edge AI.
In this blog post, we offer a snapshot of The Ultimate Guide to Edge AI: The Playbook for Edge AI Business Transformation, featuring key highlights and takeaways from our guide on how edge AI is transforming businesses, and both practical and strategic insights on how to leverage this groundbreaking technology.
In The Ultimate Guide to Edge AI, you’ll discover:
- The multi-billion dollar edge AI market opportunity
- A breakdown of the technologies powering edge AI
- An "Edge AI Maturity Assessment" checklist
- Top use cases and customer success stories
- A roadmap to help you turn edge AI into a powerful tool of growth and innovation
- Build vs. buy insights and analysis
- And more
Using Edge AI to Solve Business Problems
Product managers and business executives are feeling the pressure to put AI into more products to meet consumer and industrial demand for intelligent devices. Edge AI — running AI algorithms directly on devices in the physical world rather than relying on the cloud — has emerged as a groundbreaking technology that can bring those goals to fruition.
Consider a manufacturing plant where edge AI-powered sensors can detect and predict equipment failures before they occur, or a clinic offering more personalized and timely patient care with wearable devices capable of detecting anomalies and alerting healthcare providers instantly.
These are real on-device applications that are being built today, along with thousands of others, and we are just at the starting point. The use cases for edge AI are limitless.
Demystifying Edge AI and Understanding its Potential
Edge AI involves deploying intelligent algorithms to run directly on edge devices such as sensors, cameras, and industrial controllers rather than relying solely on centralized cloud servers. This approach enables real-time data processing, which is essential for applications requiring low latency, enhanced privacy, energy efficiency, and immediate decision-making.
Market Momentum and the Business Case for Edge AI
Global edge AI growth and projections
The global edge AI market size is expected to be worth around 163 Billion USD by 2033, up from 19 Billion USD in 2023.
Real-World Edge AI Case Studies and Success Stories
Healthcare
Disease insights and management: Hyfe
Examples of edge AI in healthcare include Hyfe, which uses edge AI to extract actionable insights from coughs. Hyfe imported the world’s largest cough dataset into Edge Impulse, generating a cough-detection model capable of fitting on an Arm Cortex-M33 processor.
By leveraging Edge Impulse’s turnkey Digital Signal Processing (DSP)-based extractors, Hyfe is successfully creating AI models that are not only effective in cough detection but also compatible and efficient on various devices, including high-performance hardware with low latency and small memory footprints. With this success, the company now supplies the Hyfe SDK to the Nordic nRF52-powered ActiGraph LEAP wearable, among other devices.
Manufacturing
Manufacturers continuously monitor equipment performance by analyzing data from sensors and IoT devices in real time. Edge AI algorithms enable the detection of anomalies, potential failures, and recommend preemptive maintenance actions, helping to minimize downtime and maximize asset utilization.
Consumer products and services
Poly, an HP company
In 2023, Poly, a subsidiary of HP, released the Voyager Free 60 (and 60+) earbuds. Poly Voyager Free 60 earbuds can now be controlled using voice commands, allowing users to answer or ignore an incoming call by saying “Answer” or “Ignore,” respectively.
HP used Edge Impulse’s platform to give HP engineers the tools to fast-track their
model development and deployment phase. With Edge Impulse’s help, HP was able to collect keyword data, train a production-grade ML model, and deploy it into their own custom workflow in just months.
Software and Hardware Powering Edge AI
Software market size and projections
Edge AI software solutions are becoming an integral part of modern digital infrastructures. This growing market encompasses a wide range of tools and platforms designed to enable AI inference and training on edge devices, from smartphones and IoT sensors to industrial equipment.
The global market for edge Al software is projected to grow from $1.1 billion USD in 2023 to $4.1 billion USD by 2028.
Take a look at the Top Ten Platforms for Developing Edge AI Applications
Hardware market size and projections
According to MarketsandMarkets, the global edge AI hardware market is expected to be worth $54.7 billion USD by 2029
Edge devices and processors form the backbone of edge AI implementations, enabling local processing and decision-making across a wide range of applications. These devices range from specialized AI accelerators and neural processing units (NPUs) to more general-purpose systems on chip (SoCs) optimized for edge computing.
Getting Started with Edge AI
Assessing edge AI readiness
The Edge AI Maturity Assessment Checklist can help you assess your organization’s readiness for a successful edge AI journey. This checklist will guide you in evaluating key areas essential for implementing and scaling edge AI initiatives. Whether you’re at the start or looking to advance existing efforts, this guide will provide actionable insights to support informed decision-making and strategic planning.
Edge AI Maturity Assessment Checklist
Access the complete checklist now.
Developing an edge AI roadmap
Our edge AI roadmap checklist is your compass, designed to guide you through crucial steps of planning, implementing and optimizing your edge AI strategy. Let’s build your future by exploring the key components of an effective edge AI roadmap.
Access the complete edge AI roadmap now.
Build vs. buy — making the right call for your edge AI strategy
Many organizations will face a critical decision regarding their edge AI strategy: Build vs. Buy. In the early days of edge AI, the DIY approach was often the only option, requiring companies to assemble a complex puzzle of components, tools, and resources.
DIY is a challenging approach that demands extensive domain expertise to be feasible. Developing and maintaining customized solutions often require significant time and resources, which is impractical for most organizations.
End-to-end edge AI platforms offer a more streamlined path to implementation.
An end-to-end platform offers multiple benefits including:
- Cost Efficiency and Predictability
- Faster Time to Market
- Scalability
- Integration Capabilities
- Enhanced Security Measures
- Intellectual Property Assurance
- Optimization Features
Turn Challenges into Opportunities with Edge AI
Organizations that successfully integrate edge AI into their core operations and product offerings will be well-positioned to drive growth, enhance customer experiences, and maintain a competitive advantage in an increasingly AI-driven world.
Maximize the edge AI opportunity by embracing these key strategies:
- Assess your edge AI readiness
- Develop an edge AI strategy
- Carefully consider the build vs. buy decision
- Prioritize data strategy
- Focus on user-centric designs.
- Implement robust security measures
- Continuously learn and adapt
Download The Ultimate Guide to Edge AI: The Playbook for Edge AI Business Transformation for detailed insights and analysis.
