Summary
- Enterprise AI is shifting from model training to real-world inferencing at scale.
- Private AI is becoming critical as organizations prioritize privacy, security, and data control.
- Low-latency infrastructure matters more as AI moves into real-time business applications.
- Existing network and regional infrastructure can help support distributed AI deployments.
- Early use cases in healthcare, customer support, research, and retail show AI’s practical value today.
Private AI and edge inferencing are rapidly becoming essential components of modern enterprise AI infrastructure. We’re at the stage where AI has moved well beyond experimentation and is now considered critical to business strategy and competitive advantage. Yet, as organizations begin to operationalize AI, new questions are emerging around performance, privacy, scalability, and infrastructure.
David Driggers, CEO of Cirrascale, recently joined me for a conversation in which he shared his perspective on how enterprise AI is evolving and why inferencing, not model training, is becoming the next major phase of AI adoption. Our discussion explored the growing importance of private AI environments, the role of latency-sensitive applications, and how existing infrastructure assets may create new opportunities for enterprises and service providers alike.
Here are five key insights from our conversation.
Insight #1: AI has entered the inferencing era
For the past several years, much of the focus around AI has centered on training large language models. But as Driggers shared, the market is rapidly shifting toward inferencing, which is the process of running AI models in production to generate outputs, recommendations, and actions.
According to Driggers, this shift is being driven in large part by the rise of agentic AI. Unlike traditional AI applications that respond only when prompted, AI agents can continuously perform tasks, gather information, and support workflows in the background. As organizations deploy these capabilities more broadly, inferencing workloads are growing significantly faster than many anticipated.
For enterprise organizations, this represents an important transition. The challenge is no longer simply gaining access to AI models, but rather, determining how to operate them reliably, securely, and cost-effectively at scale.
Insight #2: Privacy and data sovereignty are driving new AI requirements
As enterprises begin integrating AI into daily operations, concerns around privacy and data governance are becoming increasingly important.
Many organizations are comfortable experimenting with public AI platforms. However, when AI systems begin interacting with proprietary code, customer information, financial data, research, or internal business processes, organizations often become far more cautious about where that information resides and how it is processed.
This is fueling interest in private AI environments that provide greater control over data, prompts, and outputs. Rather than sending sensitive information into shared public environments, organizations are looking for ways to keep AI workloads closer to their users, applications, and data sources.
For many enterprises, private AI is quickly becoming a strategic requirement rather than simply a technical preference.
Insight #3: Latency matters more than ever
Driggers believes that as AI becomes embedded in business processes, performance expectations will change, and the importance of latency will come to the forefront.
Some AI applications, such as robotics, automation systems, and operational technologies, require real-time responses. Others, including customer support assistants, employee productivity tools, and business applications, may be more tolerant of slight delays but still depend on fast, predictable performance.
In either case, latency directly impacts user experience and business outcomes.
The closer AI infrastructure is to users and data, the more responsive those applications become. This is one reason organizations are beginning to evaluate AI infrastructure differently than traditional cloud workloads. Location is no longer simply an infrastructure consideration — it is becoming a business consideration.
As AI adoption expands, the ability to deliver low-latency inferencing will become increasingly important for organizations looking to maximize the value of their investments.
Insight #4: Existing infrastructure creates new opportunities
One of the most interesting themes from my conversation with Driggers was about the role existing infrastructure can play in supporting enterprise AI initiatives.
Many organizations already possess significant investments in connectivity, distributed facilities, and regional infrastructure. Rather than building entirely new environments from scratch, he believes that these enterprises may be able to leverage existing assets to support private AI deployments.
Furthermore, according to Driggers, this opportunity extends beyond enterprises themselves. Service providers, telecommunications organizations, and regional infrastructure operators are uniquely positioned to help bring AI capabilities closer to end users through existing network footprints and facilities.
As demand for private AI grows, infrastructure that was originally built to move data may increasingly become infrastructure that enables AI.
Insight #5: Early enterprise AI use cases are already emerging
While organizations continue to explore future use cases, they are already finding practical applications today.
In higher education and research environments, AI agents are helping accelerate research processes, literature reviews, and information gathering. Tasks that once required months of manual effort can often be completed in a fraction of the time.
Customer support organizations are using AI to augment employees, provide guidance during customer interactions, speed up onboarding, and improve service quality. Rather than replacing workers, these systems are helping teams become more productive and effective.
Healthcare organizations are exploring ways to leverage AI as an assistant for clinicians, helping surface information, support decision-making, and reduce administrative burdens.
Retail and quick-service environments are also emerging as strong candidates for AI adoption, particularly where speed, customer experience, and operational efficiency are critical.
Across all of these examples, a common pattern is emerging: Organizations are using AI to augment human expertise and streamline workflows rather than simply automate jobs.
The future is distributed AI
Perhaps the most important point that Driggers brought up is the fact that AI infrastructure is becoming increasingly distributed.
As enterprises seek lower latency, greater resilience, stronger privacy controls, and better user experiences, centralized approaches alone may not be sufficient. Distributed inferencing architectures can help organizations bring AI closer to users while improving performance, availability, and scalability.
The organizations that begin planning for these requirements today will be better positioned to support the next wave of AI adoption.
At GDT, we help organizations evaluate the infrastructure, connectivity, security, and operational requirements needed to support AI initiatives at scale. Whether you’re exploring private AI environments, edge inferencing strategies, or broader AI infrastructure modernization efforts, our team can help you build a foundation for what’s next.
