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AI Is Transforming Your Business’s Digital Ecosystem

By Tris Clark
| | 14 min read

Summary

Fully understanding ever more complex data, domains, and digital dependencies is critical to assuring everyone’s digital experiences with AI.


In our previous blog, "How To Create a Culture of Trust in Automation," we touched briefly on how Artificial Intelligence (AI) is driving new ways of automated working within NetOps. When we consider the potential impact of AI holistically, it's becoming clear that cultural adjustment is just the tip of the iceberg when it comes to AI-driven changes and opportunities that ITOps teams are sizing up. While AI industry hype is reaching a fever pitch—with some vendors making increasingly outlandish claims—the reality is that some types of AI emerging now do promise to drive a fundamental paradigm shift within IT.

AI sits alongside previous computing transformations such as mobile and cloud. Indeed, this new era may be even more transformative because AI has the potential to dynamically synthesize the benefits of what has come before (cloud, mobile, edge) with ever-growing volumes of data to create new business models, services, and products. AI-powered experiences and services could look very different from what we see today. And because of the dynamic nature of Generative AI applications, the network capacity required, and the potential for wide-ranging automation, it's clear that AI has the potential to bring massive changes to the digital ecosystems that we rely upon—influencing and affecting all kinds of domains. 

As a result, the digital landscape will become more complex, dynamic, and agile. This change will couple complexity with great opportunity. And enterprises navigating this evolving landscape will also require new ways of assuring digital experiences for customers, employees, and partners.  

What 'AI Everywhere' Means for Businesses & Services    

AI-driven insights promise to reinvent business models and how companies approach digital strategies. The decisions that AI-driven software will make will permeate all types of computing environments. During the most recent IDC Directions 2024 event, the firm's president, Crawford Del Prete, stated that the emergence of AI Everywhere will allow digital businesses to drive innovation at scale. As AI widens its reach and as data-centric platforms drive the intelligent architectures that will underpin enterprises, digital operations can be delivered at scale. He noted that "over two-thirds of U.S. GDP $18.7 trillion in 2023 is based on data-dense products and services."1

IDC estimates that by 2025, "35% of enterprises will have mastered the use of GenAI to co-develop digital products and services, leading to double the revenue growth compared to their competitors."2 Thus, our economic fortunes are increasingly intertwined with automation and AI-driven platforms. This trend is only going to accelerate. However, with inflation impacting the energy costs of computing, success will be driven by a selective and sustainable approach to data usage. 

How Data Defines Digital Success   

Businesses that overly focus on the models that power AI risk losing out on wider opportunities. In Ritu Jyoti's session "Thriving Strategy for an AI Era: Reinvent Business Models to Unlock Defensible Moats," it was noted that Large Language Models are becoming commoditized. While there is a focus on AI models today, the focus will switch to a data-centric AI approach soon.

IDC's Matt Eastwood also observed that not all data retains its value over time: "50% of data loses value in hours, and 75% of data loses value in days. You have a very mixed set of data across the edge and the core and you need to make sense out of the data."3 IDC's Del Prete cautioned that by 2026, 60% of enterprises will underperform on GenAI initiatives by failing to engineer connections between data, AI models, and applications.4 

It's clear that AI is going to play a key role in driving digital success. However, on the flipside, there is a clear risk of failure if enterprises are not thoughtful about how AI is built, connected and applied. 

Cloud and AI Drive Flexibility, Agility but Also Higher Volumes of Traffic   

Given its proven flexibility and ability to drive innovative approaches, it's not surprising that the cloud will be a key platform—likely the preferred platform—for delivering and consuming many AI services. With the rise of cloud technology, IT and Operations (I&O) teams are increasingly working with technology the organization does not directly own, and AI is further accelerating this trend.

Bob O'Donnell from TECHnalysis recently noted, "As organizations navigate the early stages of GenAI deployments, many are realizing the importance of locating their GenAI software and services near their data sources….We may also witness the emergence of multi-platform GenAI deployments. Just as companies have found benefits in using multiple cloud providers, they are likely to adopt a similar strategy with GenAI platforms, using different platforms for various applications. The race is still on, but it's clear that all the major cloud computing providers want to be (and will be) important entrants in the GenAI landscape as well."

In his recent IDC Directions session, "Optimizing Infrastructure for the GenAI Era," Matt Eastwood highlighted that 57% of applications will shift platforms next year. Additionally, 66% of decision-makers surveyed see GenAI and broader AI/ML workloads as one of their top multi-cloud networking use cases.5 

The demands of AI workloads will also place new kinds of strain on network environments. IDC's Vijay Bhagavath emphasized the technical demands that AI workflows will make on data center networks, pointing to a higher proportion of "elephant flows," i.e., the immense volume of data traversing network fabric. Job completion times will also be a critical success metric. When it comes to AI delivery, latency matters more than ever.6

The good news for NetOps teams is that AI will not only place demands on infrastructure but also be embedded in the solutions that I&O teams will use to manage this dynamic digital complexity. This integration should help alleviate some of the strain of facilitating AI workloads.

According to Gartner® predictions, "by 2026, more than 90% of IT operations management tools will have embedded GenAI capabilities." Additionally, Gartner forecasted that, "by 2027, more than 40% of digital workplace operational activities will be performed using GenAI-enhanced management tools."7 

Many teams at Cisco are working on solutions to help address AI's challenges. For example, the higher bandwidth demands of AI/ML traffic led to the development of Cisco Silicon One, a unified architecture that delivers high-bandwidth routing and web-scale switching silicon. Additionally, the AI-powered forecasting capabilities of ThousandEyes WAN Insights are also helping to drive more predictable performance across network environments and for SaaS apps, assuring users with high-quality digital experiences.

So, while AI places greater demands on digital infrastructure, AI-powered hardware, software, and services will play a central role in minimizing and managing the new complexities that AI introduces.

The Rise of LEO: Digital Ecosystems Reach for the Stars   

Low Earth Orbit (LEO) satellite constellations are increasingly capturing the interest of enterprises looking to expand connectivity across their ecosystems. According to IDC's Rick Villars, there is an abundance of foundational components for AI delivery, with LEO satellite constellations being a prime example. These constellations are poised to extend enterprise connectivity in innovative ways. That is why IDC recommends that enterprises "leverage low earth orbit satellite connectivity to create a unified fabric for resilient/ubiquitous access and guaranteed data fluidity."

IDC is not alone in recognizing LEO as a rising component of the digital ecosystem.

Gartner states, "LEO satellites will be a key tool for organizations that need broadband coverage when existing solutions are inadequate." However, Gartner also states, "there will be a lot of variation in terms and conditions, as providers strive to differentiate their offerings, exploit pricing opportunities or manage demand." As a result, Gartner advises enterprises to "Buy tactically. Several new providers will emerge through 2028, some of which may be more suitable for your organization than the market incumbents. Be prepared to switch providers or test out multiple providers."8  

From Digital Dream to Dynamic Reality   

While many AI technologies are at a nascent stage, the recent rise of ChatGPT and Amazon Bedrock shows that they are rapidly becoming part of our ecosystem. There are clear steps that ITOps teams can take today to pressure-test their digital ecosystems, reexamine best practices, and ensure they are ready to reliably scale for the needs of the business. They are as follows:

  • Recognize that your cloud environment has never been static. AI will add new layers to consider. Begin evaluating which of your cloud services will be most impacted by new AI capabilities based on roadmaps shared by your chosen cloud providers.
  • Focus on differentiated data, not commoditized LLM models, when selecting AI-driven solutions. Not all data is created equal or retains the same value over time. Ensure the data you track reflects your reality—measure the right things in the right places and at the right time.  
  • Let data show you the way. Audit your infrastructure and collaborate closely with your third-party providers to "bulletproof" against points of weakness. Assure your APIs are up to the task when AI-driven traffic explodes.  
  • Be ready to reimagine workflows. The growing volume of AI, automation, and APIs increases the complexity of your digital ecosystem. Make sure you have the right workflows and quality controls in place to ensure automation and AI-driven decisions can proceed safely and reliably.
  • Automate strategically and build IT organizational competency as a services broker. Audit your team's skills versus business needs—where can automation boost your team's ability to scale? Which AI-centric skills can take your team's management of your IT portfolio to the next level? 

As we navigate the rapidly evolving landscape of AI and its integration into digital ecosystems, it's clear that the potential for transformation is immense. From enhancing connectivity with LEO satellite constellations to leveraging AI-driven insights for business innovation, the opportunities are vast. However, with these opportunities come challenges that require thoughtful planning and strategic action.

ITOps teams must recognize that their cloud environments are dynamic and ever-changing. By evaluating the impact of new AI capabilities, focusing on differentiated data, and assuring robust infrastructure, they can prepare for the demands of AI-driven traffic and automation. 


1. IDC, “The AI Moment: Welcome to the Next Era,” Crawford Del Prete (IDC Directions), Doc # DIR2024_GS1_CDP, May 2024 

2. IDC, “The New Era of Experience-Orchestrated Applications,” Mickey North Rizza (IDC Directions), Doc # DIR2024_GS7_MNR, May 2024

3. IDC, “Optimizing Infrastructure for the GenAI Era,” Matt Eastwood (IDC Directions), Doc # DIR2024_GS6_ME, May 2024

4. IDC, “The AI Moment: Welcome to the Next Era,” Crawford Del Prete (IDC Directions), Doc # DIR2024_GS1_CDP, May 2024

5. IDC, 2024 Future Enterprise Resiliency and Spending (FERS) Survey, Wave 1, January 2024 (N=881)

6. IDC, “The Networking Opportunity in AI,” Vijay Bhagavath (IDC Directions), Doc # DR2024_T1_VB, May 2024

7. Gartner, IOCS 2023 Conference Presentation, “Empowered People and Groundbreaking Innovations Deliver Your Future,” Autumn Stanish, Dennis Smith, Tom Bittman, December 5-7, 2023

8. Gartner, LEO Satellites Will Be an Essential Part of Your Future Networking Strategy, Nick Jones, Bill Ray, Bill Menezes, 27 April 2023


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