The Impact of AI on Enterprise Networks: A Blueprint for Success with Advisory Support | ISG

As organizations increasingly pivot toward AI, the repercussions on enterprise networks are significant and far-reaching. Leveraging AI can usher in operational efficiencies, bolster security protocols and yield actionable insights that reshape business strategies. But, to effectively navigate this kind of transformation, network leaders must fully comprehend the use cases of AI and the implications these advancements have on their infrastructure and support teams.

It can be challenging to identify the impacts, develop a holistic strategy and keep pace with demand – especially while minimizing delays in provisioning capacity and managing costs.

What Are the Emerging AI Use Cases

AI technology is becoming a cornerstone for various enterprise functions, with innovations being built on top of its capabilities. As organizations assess these advancements, several key use cases are rising to prominence:

• Enterprise GPT technology: A growing number of companies are deploying “private” GPT models to ensure data security and allow customization for unique business needs. These models help safeguard proprietary information while enhancing AI effectiveness.
• Enhanced customer and employee support: AI-driven chatbots can significantly improve user interactions across the organization by offering more natural and context-aware responses. This leads to higher satisfaction levels among both customers and employees.
• Optimized enterprise technology operations: AI is playing an increasingly vital role assisting IT teams to monitor, manage and streamline their environments. Done right, it can facilitate improved stability, strategic planning and development processes.

How to Prepare the Network Infrastructure for AI

While the potential of AI is promising, network leaders must ensure their infrastructure, processes and data management align to support key business objectives.

Here are three essential considerations for leveraging AI in enterprise networks:

1. Plan carefully for enterprise GPT implementation: To maximize the value of a private GPT instance, comprehensive training on voluminous data is required. Network teams should assess deployment strategies—whether on-premises or in the cloud—and establish data repository logistics. Be sure to plan for ongoing model retraining to maintain relevance and accuracy. Proper planning and strategic partnerships with advisory services can help organizations avoid over-provisioning costs while ensuring robust network capacity.
2. Prepare for AI-powered customer and employee support: The demand for high availability and low latency in AI-driven chatbots necessitates meticulous planning. Network teams must ensure adequate bandwidth and efficient data access to enhance service response times. Rethink multi-cloud connectivity and integrate private data center services to achieve simplicity and adaptability.
3. Invest in AI for technology operations: Although the promise of AI in monitoring and managing network operations is appealing, it requires careful planning and accurate data input. Network professionals need to invest in understanding AI/ML fundamentals—not only for deployment within their own frameworks but also to support enterprise-wide integration. Partnering with knowledgeable advisors will streamline this learning curve.

A recent ISG analysis presented at the ISG Digital Business Summit found that AI Ops are impacting managed services contract pricing, with cost reductions in the network services tower of up to 25%. However, achieving these savings relies heavily on both the technology implementation skills of the enterprise and its service providers and the readiness of the environment.

The ISG Market Lens™ 2025 IT Budgets and Spending Study indicates limited growth for enterprise IT budgets year over year, with increases expected to range from 1.1% to 2.1% across industries and regions. Considering these tight budget constraints, we expect enterprises will actively pursue cost reductions throughout the year to invest in key strategic priorities, particularly in enhancing productivity and reducing expenses.

What Network Leaders Need to Know

To position themselves for success today, network leaders need a clear understanding of the diverse applications of AI, from enhancing customer experiences to streamlining technology operations. And they need to consider the overarching impacts of AI on their teams and processes – and actively involve relevant stakeholders such as network operations, engineering, security and application teams.