05 June 2025
Seva Vayner, Product Director, Edge Cloud and Edge AI at Gcore
A rtificial Intelligence (AI) is transforming how businesses operate, driving innovation in almost every industry. According to a 2024 survey by Writer, 89% of enterprises are either actively using or exploring AI solutions, with AI inference playing a crucial role in deployment. Additionally, the survey found that 47% of companies have already integrated AI into customer support functions, while 45% are using AI for business process automation.
This growing adoption of AI reflects its increasing importance in modern operations and decision-making. While much of the focus has historically been on AI model training, AI inference – the process of applying trained models to real-world tasks - is now emerging as the key differentiator for companies looking to make the most of AI.
Whether it’s providing real-time customer support or improving decision-making through predictive analytics, AI inference is changing how enterprises operate by offering faster insights and automation. But as businesses look to deploy AI inference effectively, there are still several challenges to overcome. Here, we explore five key factors that make AI inference so important for modern businesses.
The shift to real-time AI processing
AI-powered applications, including chatbots, fraud detection systems, and autonomous vehicles, require real-time processing to function smoothly. Traditional cloud setups can struggle with latency, which is where edge AI comes in. By running AI inference at the edge, closer to the data, businesses can reduce delays and improve responsiveness.
Sophisticated solutions today offer scalable, low-latency solution for AI applications, whether on-premises or in the cloud. With this approach, inference workloads can operate efficiently, without being hindered by slow networks or high compute costs.
Overcoming AI scalability and cost challenges
One of the big hurdles businesses face is balancing the performance of AI systems with cost-efficiency. Traditional AI inference models often require powerful hardware, making them too expensive for widespread use. Businesses need to find ways to optimise AI processing without overloading their infrastructure or increasing costs too much.
By integrating AI inference with edge computing, companies can spread workloads more effectively, reducing their reliance on centralised cloud resources. Modern solutions provide global edge infrastructure with numerous points of presence (PoPs), enabling businesses to process AI tasks closer to their customers. This reduces latency, enhances speed, and optimises costs by minimising reliance on centralised cloud resources.
Improving data security and compliance
As more businesses adopt AI, the need for strong data security and compliance grows. Industries like telecoms, finance, and healthcare have strict regulations that require AI workloads to be processed in secure environments.
Advanced AI inference solutions now allow businesses to deploy and monitor AI models within their own secure infrastructure while still benefiting from cloud scalability. This enables companies to leverage AI technologies without compromising compliance or exposing sensitive data to unnecessary risks.
The power of open-source and strategic partnerships
Collaborations and open-source technologies are playing an increasingly important role in the successful adoption of AI inference. Open-source frameworks simplify the deployment process, while strategic partnerships ensure businesses can access the latest innovations to stay ahead of the competition.
For example, Gcore partnered with Mirantis, combining Mirantis’ expertise in cloud-native technologies with Gcore’s edge AI capabilities. This collaboration will allow businesses to optimise their AI infrastructure, simplifying deployments and improving performance. Partnerships like this are vital for addressing the growing demand for AI-driven applications and simplifying the complexities of infrastructure management.
The future of AI inference: Accessibility and automation
As AI technology evolves, the focus is shifting towards simplifying AI inference, making it easier for businesses to implement and manage. The next phase will see solutions that enable quicker, more efficient AI model deployment, without requiring deep technical expertise.
My commitment to this goal is reflected in developing platforms that allow businesses to deploy, tune, and monitor AI models with minimal technical knowledge, making AI-driven insights and automation accessible to companies of all sizes.
Conclusion
Efficient, cost-effective AI inference is a game-changer for modern businesses. It’s no longer just an add-on; it’s a strategic necessity for staying competitive in today’s digital landscape. By embracing edge computing, optimising infrastructure, ensuring data security, creating strategic partnerships, and simplifying AI deployment, businesses can unlock the full potential of AI applications in real time.
Simply put, the future of AI is happening now, and inference is at the heart of it.
