Blog Infrastructure Strategy in the AI Era
By Carmen Taglienti / 15 Aug 2024 / Topics: Data and AI Modern infrastructure Change and training
As AI becomes more prevalent across industries, organizations need to change how they’re thinking about their infrastructure strategy. In this blog, Insight Chief Data Officer Carmen Taglienti explores AI’s impact on the infrastructure space, including how AI has disrupted the traditional approach to infrastructure renewals.
Ready for AI? Your infrastructure might not be.
There are many compelling reasons to modernize IT infrastructure. Over time, legacy infrastructure can hold organizations back by contributing to resource drain, productivity lags, tech debt, reduced agility and other obstacles to innovation. Modernizing infrastructure can help organizations overcome these challenges and get back to driving positive business outcomes.
Today, the desire — and the pressure — to adopt innovative Artificial Intelligence (AI) solutions is one of the top drivers of infrastructure modernization. Organizations maintaining legacy infrastructure often find that their infrastructure is not equipped to support AI workloads. This is because, from networking to storage to compute, AI requires a different kind of infrastructure than traditional IT.
Did you know? Infrastructure that’s not optimized to support new technologies such as gen AI is the #3 challenge inhibiting innovation in 2024. Explore other 2024 IT trends and priorities in this survey.
Incorporating AI goals into your infrastructure strategy
Infrastructure modernization as a means for AI adoption starts at the data center. Modernizing the data center by adopting modern approaches, such as multicloud or hyperconverged infrastructure, enables organizations to begin infusing AI and machine learning into their operational and strategic processes.
When adopting modern platforms to support AI solutions, organizations should make workload alignment a top strategic priority. To get the most value out of AI, it’s imperative for organizations to strategically align AI workloads with the right platforms that will maximize agility and ROI. And remember: ROI can encompass more than just spend.
In this case, maximizing ROI could entail eliminating tech debt or distributing workloads to the point of presence — meaning, where the data lives — to optimize processing or deliver a better experience, which can improve an organization’s ability to address other needs in the future.
That said, the reality is that most organizations are not in the position to say, “Hey, let's write a check and get all the latest and greatest infrastructure because we want to start doing AI.” Renewal cycles, licensing and agreements — these are contractual roadblocks that can prevent modernization in the here and now.
On a practical level, organizations must wait for legacy contracts to expire before they can dive into new ones for modern technologies. Still, it is beneficial for organizations to be thinking about modernizing infrastructure well ahead of renewal dates, as this prevents rushed decision-making that can lead to technical debt.
How AI has disrupted the renewal cycle
In fact, AI has changed the way organizations think about infrastructure renewal, as it requires new types of hardware, software and data management. Let’s examine this through the lens of computing. Until recently, organizations would go through the normal aging of hardware infrastructure and capacity.
Then, at the end of the renewal cycle, organizations would look to buy the latest modern infrastructure to support their computing needs. AI complicates this. Now, organizations must take into account high-performance computing models, inferencing and training of data driven by AI workloads.
How people impact AI infrastructure strategy
Adopting AI poses a competency lift to infrastructure teams that organizations must take under consideration when building their infrastructure strategy. Organizations should consider whether their infrastructure teams have the skills, knowledge and experience to design, architect and support AI infrastructure that can effectively run AI workloads.
If there are skills gaps or a lack of bandwidth, organizations need to factor filling those gaps into their strategy.
Survey says: In this year’s Insight-sponsored Foundry survey, gaps in technology skills and knowledge ranked as the top challenge inhibiting innovation for the third year in a row. View the survey to learn more about where IT leaders stand in 2024 and beyond.
Also, organizational culture plays an important role in AI infrastructure strategy. Organizations with a culture that embraces innovation, data-driven decision-making and adoption of impactful technology are more likely to have a clear picture of what they are looking to achieve with AI and, as follows, better-positioned to adopt AI in a way that hits those goals.
A culture of collaboration is also key. IT infrastructure spans across an organization’s IT environment and so infrastructure strategy should be a collaborative effort that spans across IT leadership — Chief Information Officers, Chief Data Officers, Chief Technology Officers and people in similar positions should all be involved.
Optimize your infrastructure strategy to support AI. Learn more about how Insight can help your organization lay the groundwork for AI transformation.
Carm Taglienti
Chief Data Officer & Data/AI Portfolio Director and Distinguished Technologist, Insight
Carmen has more than 25 years of experience as a cloud computing, data science, data analytics and data management expert. As a principal cloud and AI architect for Insight, he focuses on cloud-based business solutions across multiple industries and technical domains. He also serves as an adjunct professor at Northeastern University Khoury College of Computer Sciences and the College of Professional Studies teaching graduate courses in data science, cloud computing and analytics. Carmen is a published author and speaks at industry and technology events.
