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Is AI reshaping the enterprise network?

Is AI reshaping the enterprise network?

5 minute read
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Is AI reshaping the enterprise network?
Updated: December 14, 2023
July 12, 2023
Updated: December 14, 2023
5 mins

It’s clear we’re not putting the Artificial Intelligence toothpaste back in the tube. The last year has only seen an acceleration of AI concepts and experiments becoming applicable to enterprise workflows. If it’s improving outcomes and profitability, why not push forward, right? Uh… right? Well, this is not the place to argue for that philosophy and its potential realities. However, we can certainly speak on the complexity that AI/ML driven process adds to the network that underpins these applications and services. Network automation, artificial intelligence...with every amazing step forward, a new thread of complexity to weave into the fabric of enterprise networking. Somewhere, right now, a network engineer is grappling with the additional demands these processes place on an already massively complex system. Let's explore what's making their day more difficult.  

Note: Most of the challenges discussed here apply across industries, but it’s helpful to frame these issues practically, hence the industry-based lens. Understanding the problem in its true context is the first step toward finding solutions. 

Just What the Robot Ordered - Improving Healthcare Outcomes with AI

We’ll start with where improving outcomes is most personal - our health. Hospitals, clinics, and other medical facilities have been jolted into embracing technologies they were more conservative about pre-pandemic. The strain on healthcare systems and growing patient needs and expectations spurred this embracing of technology-based solutions to medical problems.

Doctor using iPad to make use of AI

Democratizing care access via more and better telehealth services, automated patient imaging and diagnostics, remote patient monitoring, robot-assisted surgery, automated drug administration, patient record updates… it’s clear that done right, these initiatives can take pressure off historically overworked doctors, nurses, radiologists, and technicians and improve healthcare outcomes. “Done right” is key, I’m sure you’ll argue, when you’re the one attached to an IV and a computer is selecting your medication and dosage. 

So, what lies beneath these health-tech marvels? Here are just three points to consider: 

  • Data management: Training AI models requires large amounts of data; in a healthcare setting, this data is highly sensitive. To maintain data integrity, it must be stored, managed, and accessed appropriately. The volume of this data, as more healthcare processes are subject to machine learning efforts, makes governance complicated.
  • Connectivity requirements: Continuous connectivity and low – extremely low – latency (close to real-time) data processing is a matter of life and death in a healthcare setting. Teams simply cannot compromise on computational resources or network resilience to make this happen.  
  • Regulations and oversight: With care in the Cloud, Electronic Health Records must be adequately protected and secured. As your attack surface grows, cybersecurity threats increase, and regulatory and legal bodies raise compliance requirements, attentive and proactive adherence is simply not optional. From HIPAA and FISMA in the US, to the European Health Data Space, the pressures from all sides make navigating regulations and keeping healthcare networks secure a daunting task.   

Knowledge is power, and AI makes this power accessible - Automated for Education

Our frameworks for learning are fundamentally changing with the mainstream use of AI technologies. It can start with simply automating repetitive tasks – grading, scheduling, planning - to free up time for educators. However, it also transforms the way students learn and what they learn. AI can create personalized learning paths and curriculums based on student preference or ability, speed up tailored feedback, and use historical data to track progress and determine knowledge and skills gaps more accurately. Generating educational content has become faster thanks to AI, and access to education has improved with AI-powered translation. Chatbots available 24/7 means students can ask questions whenever they need to, and get valuable answers.  

Students at a university using a laptop to connect to the network

What does this mean for network engineers managing networks for universities, schools, and Edutech companies? 

  • It’s a BYOD party: With AI as a tool, an effective student is a connected student. Campuses are bound to be flooded daily with different devices – laptops, tablets, smartphones, smartboards - connecting to the network. This device diversity complicates the consistent compatibility and performance requirements placed on the network. It's essential that the network is set up to handle a Bring-Your-Own-Device policy that balances security with students and staff having the right access to portals, or AI tools they might need.  
  • Connected, everywhere: Remote and asynchronous learning, like it or not, is becoming a standard offering by educational institutions. The need for secure VPNs, the provision of low-bandwidth solutions for students who need it, and the need to solve technical support issues remotely all add to the complexity of managing these networks. If it’s imperative that all students and staff benefit from AI technology, despite their on-campus or remote status, the network infrastructure must consider all these factors. 
  • Scalability and Flexibility: As AI/ML initiatives grow within a school or university, network engineers must plan for scalability and flexibility. They need to design networks that can accommodate increasing computational and data requirements. This may involve adopting cloud-based solutions or implementing scalable infrastructure to handle expanding AI/ML workloads. Network engineers must also consider future technology advancements and plan network architecture accordingly. 
Groupe de masques 26

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The Price of Innovation - AI and Financial Services

Though somewhat stymied by heavy regulation, Finance/Fintech enterprises have long sought to harness the power of AI to wade through data and make better predictions, optimize processes, improve customer experience, and enhance services. 

There is a global move toward mobile-first banking, and it's key that banks rise to the occasion in terms of customer experience; AI can personalize and enhance these experiences with chatbots, customized product recommendations, and improvements based on customer analytics. AI is also used for better fraud detection and prevention, alerting teams to anomalies in transaction patterns quickly, and ultimately protecting the business.  

A woman holds a credit card, ready to complete an online financial transaction

So, what do these wonderful advancements mean for teams operating the IT networks that allow these financial institutions to run?  

  • Security: A hugely increased attack surface is a risk that comes along with implementing AI-based processes. Financial institutions are a target for direct cyber-attacks, with an average cost of $5.97 million per breach (not to mention consumer trust and brand reputation). Network teams must have end-to-end visibility of the network that AI-driven applications run across in order to secure them appropriately.  
  • Regulatory compliance: This is a heavily regulated industry. Working with large amounts of customer data as part of AI initiatives means ensuring that data is properly processed, handled, and stored. When these regulations change to cover AI use cases, enterprises must ensure they are compliant come next audit. For example, an improperly segmented network can have you failing your next PCI audit (see how Network Assurance can help you there).  

Artificial intelligence and machine learning are too powerful to leave any industry completely untouched. In just these three examples, the emerging patterns are clear. Enterprises need to understand the impact of AI/ML on their IT networks; the business-critical backbone of their operation. If we expect engineering teams to maintain resilience and performance to support the future-focused enterprise, they need tooling that is up to the challenge. 

AI with network assurance as a measure to continuously validate network state is a tool network teams can use to meet this challenge. To see how IP Fabric can offer end-to-end enterprise network visibility without making your life more complicated, try a self-guided demo today or request a tailored demo from our team.  


Is AI reshaping the enterprise network?

It’s clear we’re not putting the Artificial Intelligence toothpaste back in the tube. The last year has only seen an acceleration of AI concepts and experiments becoming applicable to enterprise workflows. If it’s improving outcomes and profitability, why not push forward, right? Uh… right? Well, this is not the place to argue for that philosophy and its potential realities. However, we can certainly speak on the complexity that AI/ML driven process adds to the network that underpins these applications and services. Network automation, artificial intelligence...with every amazing step forward, a new thread of complexity to weave into the fabric of enterprise networking. Somewhere, right now, a network engineer is grappling with the additional demands these processes place on an already massively complex system. Let's explore what's making their day more difficult.  

Note: Most of the challenges discussed here apply across industries, but it’s helpful to frame these issues practically, hence the industry-based lens. Understanding the problem in its true context is the first step toward finding solutions. 

Just What the Robot Ordered - Improving Healthcare Outcomes with AI

We’ll start with where improving outcomes is most personal - our health. Hospitals, clinics, and other medical facilities have been jolted into embracing technologies they were more conservative about pre-pandemic. The strain on healthcare systems and growing patient needs and expectations spurred this embracing of technology-based solutions to medical problems.

Doctor using iPad to make use of AI

Democratizing care access via more and better telehealth services, automated patient imaging and diagnostics, remote patient monitoring, robot-assisted surgery, automated drug administration, patient record updates… it’s clear that done right, these initiatives can take pressure off historically overworked doctors, nurses, radiologists, and technicians and improve healthcare outcomes. “Done right” is key, I’m sure you’ll argue, when you’re the one attached to an IV and a computer is selecting your medication and dosage. 

So, what lies beneath these health-tech marvels? Here are just three points to consider: 

  • Data management: Training AI models requires large amounts of data; in a healthcare setting, this data is highly sensitive. To maintain data integrity, it must be stored, managed, and accessed appropriately. The volume of this data, as more healthcare processes are subject to machine learning efforts, makes governance complicated.
  • Connectivity requirements: Continuous connectivity and low – extremely low – latency (close to real-time) data processing is a matter of life and death in a healthcare setting. Teams simply cannot compromise on computational resources or network resilience to make this happen.  
  • Regulations and oversight: With care in the Cloud, Electronic Health Records must be adequately protected and secured. As your attack surface grows, cybersecurity threats increase, and regulatory and legal bodies raise compliance requirements, attentive and proactive adherence is simply not optional. From HIPAA and FISMA in the US, to the European Health Data Space, the pressures from all sides make navigating regulations and keeping healthcare networks secure a daunting task.   

Knowledge is power, and AI makes this power accessible - Automated for Education

Our frameworks for learning are fundamentally changing with the mainstream use of AI technologies. It can start with simply automating repetitive tasks – grading, scheduling, planning - to free up time for educators. However, it also transforms the way students learn and what they learn. AI can create personalized learning paths and curriculums based on student preference or ability, speed up tailored feedback, and use historical data to track progress and determine knowledge and skills gaps more accurately. Generating educational content has become faster thanks to AI, and access to education has improved with AI-powered translation. Chatbots available 24/7 means students can ask questions whenever they need to, and get valuable answers.  

Students at a university using a laptop to connect to the network

What does this mean for network engineers managing networks for universities, schools, and Edutech companies? 

  • It’s a BYOD party: With AI as a tool, an effective student is a connected student. Campuses are bound to be flooded daily with different devices – laptops, tablets, smartphones, smartboards - connecting to the network. This device diversity complicates the consistent compatibility and performance requirements placed on the network. It's essential that the network is set up to handle a Bring-Your-Own-Device policy that balances security with students and staff having the right access to portals, or AI tools they might need.  
  • Connected, everywhere: Remote and asynchronous learning, like it or not, is becoming a standard offering by educational institutions. The need for secure VPNs, the provision of low-bandwidth solutions for students who need it, and the need to solve technical support issues remotely all add to the complexity of managing these networks. If it’s imperative that all students and staff benefit from AI technology, despite their on-campus or remote status, the network infrastructure must consider all these factors. 
  • Scalability and Flexibility: As AI/ML initiatives grow within a school or university, network engineers must plan for scalability and flexibility. They need to design networks that can accommodate increasing computational and data requirements. This may involve adopting cloud-based solutions or implementing scalable infrastructure to handle expanding AI/ML workloads. Network engineers must also consider future technology advancements and plan network architecture accordingly. 
Groupe de masques 26

Get IP Fabric

Request a demo and see how end-to-end visibility can revolutionize your network operations. 
Free Demo | Zero Obligation
Request a Demo

The Price of Innovation - AI and Financial Services

Though somewhat stymied by heavy regulation, Finance/Fintech enterprises have long sought to harness the power of AI to wade through data and make better predictions, optimize processes, improve customer experience, and enhance services. 

There is a global move toward mobile-first banking, and it's key that banks rise to the occasion in terms of customer experience; AI can personalize and enhance these experiences with chatbots, customized product recommendations, and improvements based on customer analytics. AI is also used for better fraud detection and prevention, alerting teams to anomalies in transaction patterns quickly, and ultimately protecting the business.  

A woman holds a credit card, ready to complete an online financial transaction

So, what do these wonderful advancements mean for teams operating the IT networks that allow these financial institutions to run?  

  • Security: A hugely increased attack surface is a risk that comes along with implementing AI-based processes. Financial institutions are a target for direct cyber-attacks, with an average cost of $5.97 million per breach (not to mention consumer trust and brand reputation). Network teams must have end-to-end visibility of the network that AI-driven applications run across in order to secure them appropriately.  
  • Regulatory compliance: This is a heavily regulated industry. Working with large amounts of customer data as part of AI initiatives means ensuring that data is properly processed, handled, and stored. When these regulations change to cover AI use cases, enterprises must ensure they are compliant come next audit. For example, an improperly segmented network can have you failing your next PCI audit (see how Network Assurance can help you there).  

Artificial intelligence and machine learning are too powerful to leave any industry completely untouched. In just these three examples, the emerging patterns are clear. Enterprises need to understand the impact of AI/ML on their IT networks; the business-critical backbone of their operation. If we expect engineering teams to maintain resilience and performance to support the future-focused enterprise, they need tooling that is up to the challenge. 

AI with network assurance as a measure to continuously validate network state is a tool network teams can use to meet this challenge. To see how IP Fabric can offer end-to-end enterprise network visibility without making your life more complicated, try a self-guided demo today or request a tailored demo from our team.  


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