×
Why the next battleground in tech will be operationalizing AI at the edge
Written by
Published on
Join our daily newsletter for breaking news, product launches and deals, research breakdowns, and other industry-leading AI coverage
Join Now

The AI edge computing revolution: As organizations increasingly embrace AI and machine learning, the focus is shifting toward operationalizing AI at the edge and far edge, presenting both challenges and opportunities for technology leaders.

  • The rapid growth in AI workloads is putting immense pressure on data centers, driving the need for more powerful and efficient infrastructure solutions.
  • Edge computing is emerging as a key trend across industries, with predictions suggesting that by 2025, 75% of enterprise-generated data will be created and processed outside traditional data centers or the cloud.
  • The push towards edge AI is driven by the need for real-time decision-making, lower latency, and reduced bandwidth costs in various applications across manufacturing, utilities, retail, healthcare, and transportation sectors.

Infrastructure challenges and innovations: The evolving landscape of AI and edge computing is forcing organizations to adapt quickly to new hardware requirements and shorter technology lifecycles.

  • AI workloads are becoming increasingly complex, requiring more powerful chipsets and infrastructure that quickly become outdated.
  • Technology refresh cycles are shrinking dramatically, with infrastructure that previously lasted 5-7 years now needing replacement in a matter of months.
  • Edge and far edge computing solutions are becoming more feasible at scale, thanks to advancements in edge management and orchestration platforms (EMO) that enable zero-touch provisioning and upgrades in remote locations.

Industry applications and adoption: Edge AI is finding applications across a wide range of industries, driving efficiency, improving safety, and enhancing customer value.

  • Asset-intensive industries like manufacturing, utilities, and logistics are leveraging edge sensors and IoT devices paired with AI for real-time decision-making.
  • Healthcare providers are implementing data-driven clinical decision support (CDS) solutions at the edge to improve patient care.
  • Physical security systems are utilizing computer vision at the edge to detect and report safety and security incidents more efficiently.

Overcoming implementation challenges: Organizations face several hurdles in implementing edge AI solutions, but new technologies and partnerships are emerging to address these issues.

  • The vendor ecosystem for edge AI is highly fragmented, making it difficult for IT departments to integrate and maintain edge devices at scale.
  • Companies need to consider supply chain transparency, asset management, and lifecycle management when deploying edge AI solutions.
  • Edge management and orchestration platforms are enabling IT teams to monitor, update, and manage edge devices remotely, making far edge deployments more feasible.

Preparing for the future of AI: As the AI landscape continues to evolve rapidly, organizations must adopt flexible strategies to stay competitive and leverage new innovations.

  • The pace of AI innovation requires companies to be agile and ready to incorporate new solutions quickly, even if they come from unexpected sources.
  • While generative AI has brought increased attention to the field, traditional AI and machine learning expertise remains valuable for organizations looking to operationalize AI solutions.
  • Partnering with experienced technology providers and leveraging pre-trained models and fine-tuning expertise can help organizations accelerate their AI journey and stay ahead of the competition.

Implications for business strategy: The shift towards edge AI and the rapid pace of innovation in this space have significant implications for how organizations approach their technology strategies and investments.

  • Companies need to be prepared for shorter investment cycles and more frequent updates to their AI infrastructure.
  • Collaboration with experienced partners and leveraging existing solutions can help organizations quickly operationalize AI and gain a competitive edge.
  • As edge AI becomes more prevalent, businesses will need to reassess their data processing and analytics strategies to take full advantage of the benefits offered by edge computing.
Operationalizing AI at the edge — and far edge — is the next AI battleground

Recent News

Nvidia’s new AI agents can search and summarize huge quantities of visual data

NVIDIA's new AI Blueprint combines computer vision and generative AI to enable efficient analysis of video and image content, with potential applications across industries and smart city initiatives.

How Boulder schools balance AI innovation with student data protection

Colorado school districts embrace AI in classrooms, focusing on ethical use and data privacy while preparing students for a tech-driven future.

Microsoft Copilot Vision nears launch — here’s what we know right now

Microsoft's new AI feature can analyze on-screen content, offering contextual assistance without the need for additional searches or explanations.