AI and networks are inextricably linked, each powering the other’s potential. While new research suggests AI is expected to enhance network operational efficiency by 40%, the relationship between AI and the network is a symbiotic one, as a robust and secure network can ensure AI innovation remains steady and not stagnated, especially as edge data centers make a comeback, bringing AI workloads closer to where the data is generated. Realising this potential requires a strategic approach that addresses the challenges and opportunities inherent in AI implementation.
AI’s influence is already far-reaching, transforming sectors from healthcare and biotechnology to manufacturing. In healthcare, AI-powered diagnostic tools are improving accuracy and efficiency, meanwhile, in manufacturing, AI is predicting equipment failures, ensuring quality control and automating tasks through robotics. As AI continues to evolve, businesses need to ensure their networks are up to scratch, otherwise they risk falling behind when it comes to innovation.
The network as a key enabler
So how is the role of the network linked to this? Networks serve as the digital backbone for AI applications, facilitating data transfer, efficient processing for tasks like model training and inference, and storage. As AI models grow in complexity, so does their reliance on high-performance and secure networks. These networks need to be reliable, automated, and simple to design, deploy, operate and optimise.
This is where AI for networking plays a pivotal role, with AI automation presenting a significant opportunity. For AI to be effective in this context, it requires access to data spanning the entire network and security infrastructure. A platform-based approach can simplify this process, consolidating data into a single location for AI processing, while taking data management, data governance and effective AI model management into account.
Today, 88% of CIOs prefer a single integrated platform for networking, AI, and security – a preference that is even stronger in financial services and technology, where 93% and 95% of respondents, respectively, ranked it as a top priority. This unified approach drives innovation by providing businesses with several key advantages.
By streamlining and automating network operations and ensuring optimal performance with AI for Networking, organisations can focus on developing and deploying AI applications, accelerating their time to market and gaining a competitive edge.
Challenges of implementation
With AI advancing at a rapid pace, organisations face a number of challenges in its implementation.
One significant hurdle is the increasing risk of cyberattacks targeting AI systems, so protecting sensitive data and maintaining system integrity is crucial. In fact, a recent CIO survey revealed that while 84% of respondents have integrated AI into their tech stack, a concerning 40% still express worries about data security.
Another challenge lies in the demanding nature of AI applications. These systems often require substantial bandwidth to process and transmit large datasets and network bottlenecks can significantly hinder AI performance and scalability. The same CIO survey found that a notable 49% of respondents face network bandwidth challenges during AI implementation.
Accessing data, along with managing its quality, classification, and governance is another challenge that organisations are facing. AI thrives when data from different parts of the business is integrated and combined, but without proper processes, a strong data culture, and awareness of data quality –along with failing to treat data as a product– frustration and unsuccessful implementations are inevitable.
Furthermore, the complexity of AI systems, particularly the emerging AI agent technology which holds the greatest promise right now, presents significant management challenges. As AI applications grow in sophistication, so does the time and resources required to manage and develop them. Organisations must invest in skilled personnel and robust tools to effectively oversee these systems.
Finally, cost optimisation is a critical consideration. Balancing the potential benefits of AI with the associated costs is essential, and businesses must carefully evaluate the expenses related to AI infrastructure, development, and maintenance to ensure a positive return on investment (ROI). This requires establishing a clear business case early on and focusing on high-value, high-impact use cases, rather than simply “adding a chatbot” to existing applications.
Lack of Trust, the adoption hurdle
To fully realise the potential of AI and overcome challenges, building trust with AI users is essential.
This trust is driven by both the accuracy of AI and its ability to transition from conversational and collaborative interactions to automation where AI agents can make certain autonomous decisions, sometimes with, sometimes without human involvement. Additionally, ensuring data privacy, security and compliance, including adherence to the EU AI Act is critical. Alongside AI safety guardrails, data governance and a strong data classification strategy, adopting Zero-Trust security models can mitigate risks by enforcing strict access controls and continuous authentication of resources.
Finally, ensuring cost-effectiveness is vital. To maximise the return on investment of AI initiatives, organisations must carefully consider costs while selecting high-value use cases that not only enhance efficiency and accelerate processes but also leverage AI to create clear differentiation and competitive advantages by focusing on new experiences and making the impossible possible. Developing a comprehensive AI strategy and focusing on real-world use cases can help prioritise investments and allocate resources effectively. By making informed decisions and tracking key performance indicators, organisations can optimise their AI investments and drive sustainable growth.
What lies ahead
As we stand on the precipice of a new era defined by AI, the relationship between AI and networks becomes increasingly critical. By embracing network platformisation and addressing emerging challenges, organisations can unlock the full potential of AI and achieve a sustainable competitive advantage. It is imperative to invest in both AI and network technologies to drive innovation, enhance efficiency, and shape the future of industries worldwide.
