Edge Computing Accelerates AI Adoption: Insights from Hitachi Vantara’s Data Strategy Leader

AI Strategy at Hitachi Vantara: How Edge Computing is Transforming AI

Enterprise AI is shifting its focus. Businesses that once relied mainly on cloud-based machine learning models now face the challenges of scaling AI with centralized processing. Edge computing offers a new approach that changes how AI is deployed.

Dia Ali, Global Platforms & Solutions Leader for Data Intelligence at Hitachi Vantara, shares insights on how edge computing accelerates AI as investments grow.

What Is Edge Computing and How Does Hitachi Use It?

Edge computing processes data closer to where it’s generated, rather than sending everything to a central cloud. This approach is essential as the combination of cloud and edge computing enables real-time intelligence. Speed, agility, and immediate insights are becoming necessary for businesses to stay competitive.

With data volumes soaring — large enterprises now handle around 150 petabytes on average, expected to double by 2026 — real-time responsiveness becomes critical. Hitachi Vantara’s State of Data Infrastructure Global Report 2024 highlights how this data surge pushes organizations to move away from purely centralized systems. Edge computing is now a key driver of innovation in industries like manufacturing, healthcare, banking, and retail.

Why Is Proximity an Advantage?

Processing data near its source drastically reduces latency and boosts speed. This proximity allows data teams to gain real-time, actionable insights that improve operations and spark innovation. Edge computing also democratizes AI by enabling advanced processing at the network’s edge without massive infrastructure.

Traditional centralized designs struggle with the flood of real-time data. Sending all data to a central cloud is often impractical. Edge computing tackles this by bringing analytics closer to the source, cutting down on round-trip delays. IT leaders aiming to future-proof their systems should seriously consider moving computing tasks toward the edge.

Is Edge Computing Growth Sustainable?

Investment trends show strong momentum. IDC forecasts global edge computing spending will grow at a 13.8% compound annual rate, hitting nearly US$380 billion by 2028, up from US$261 billion in 2025. The demand for localized processing and analytics is expected to continue rising as companies seek efficiency and better data strategies.

The business impact is clear:

• Logistics firms improve delivery efficiency through real-time routing at the edge.
• Energy companies use edge-powered smart grids to balance loads and reduce outages.
• Public safety agencies apply edge video analytics for faster incident detection and response.

These examples confirm that edge computing is becoming a strategic priority across sectors.

How Do AI and Edge Computing Work Together?

Edge computing changes the way AI operates. While AI often relied on large cloud infrastructures, improvements in edge hardware now allow AI workloads to run locally. This supports applications that require low bandwidth and minimal latency.

Organizations in remote or bandwidth-constrained environments benefit greatly. Oil rigs, mines, and rural clinics can run AI models on-site to generate insights and make autonomous decisions quickly.

Manufacturing plants use edge AI to spot irregularities and predict maintenance, reducing downtime. Healthcare facilities provide real-time patient monitoring without relying on steady network connections. Banks deploy edge AI to detect fraud faster, approve transactions instantly, and offer personalized services while meeting data residency rules.

Edge computing brings AI closer to where data is created, making it more scalable and accessible.

How Essential Are Security and Compliance in Edge Computing?

Speed and insights matter, but security cannot be compromised. Edge computing enhances data protection by processing sensitive information locally, reducing exposure during transfers. Zero-trust models, encryption, and secure boot protocols limit access to authorized users and ensure only verified software runs on edge devices.

This approach shrinks the attack surface across distributed environments. It also helps organizations comply with local regulations and data sovereignty laws, especially in regions with strict personal data rules.

Security and compliance form the foundation of any successful edge deployment.

What Does the Future Hold for Edge Computing?

The key opportunity lies in seamless integration between edge and cloud systems. Platforms that intelligently move workloads based on security, latency, or regulatory needs will deliver the best returns.

Edge computing won’t replace centralized systems but will complement them, allowing businesses to act fast, reduce risk, and get timely insights exactly where they’re needed.

For those interested in expanding their knowledge on AI and edge technologies, exploring latest AI courses can provide practical skills to stay ahead in this evolving landscape.