The artificial intelligence (AI) boom is here — and here to stay. Companies everywhere are determining how best to apply AI within their organization, and this is especially true for generative AI (GenAI) which offers significant productivity gains, ability to craft better customer experiences, and achieve a sharp competitive edge when applied properly.
The uptick in adoption has forced many to take a close look at their current infrastructure to ensure that it can handle the new and unique demands of AI. Modern applications require modern infrastructure – so it’s no surprise that a majority (85%) of companies plan to ramp up their investments in IT modernization for the purpose of supporting particularly GenAI.
Here are three considerations to take into account when updating enterprise infrastructure:
Edge Strategy is Paramount
2023 was a breakout year for GenAI. A year later, according to McKinsey, 65 percent of organizations are regularly using the technology. That’s nearly double the percentage from the previous survey issued just ten months ago. This staggering rise occasions for a close examination of one’s edge strategy that is critical for the future success of AI initiatives.
Companies realize that a modernized edge infrastructure is crucial for faster processing and giving AI real-time access to the data it needs to provide next-level business value. In a survey, 90% of organizations said strengthening their edge strategy is a priority this year, and 72% plan to increase investments in the area.
Data Quality, Management, and Governance Should Be a Top Priority
Data quality and management help improve the performance and accuracy of AI systems especially for training and fine tuning. It also gives organizations comprehensive control of their data used in the AI lifecycle. By prioritizing data visibility and data management, they can ensure their AI systems have access to the data they need to function optimally.
Another key point is the proximity of AI compute resources to data. Within the enterprise, compute resource must be placed close to data because moving data is so much more complex and expensive, especially if it is regulated. In case data cannot be moved to a specialized AI compute provider, one has to find ways to bring specialized compute to the data location to enrich AI models.
Having the Right IT Foundation Is Vital
Of course, none of this is possible without the right IT infrastructure. Another survey found that the top challenge companies are facing when it comes to AI development is infrastructure (54%), followed by compute-related (43%) and data-related challenges (41%). Companies recognize that having the right IT foundation is synonymous with success in AI initiatives, and that’s why, more than half (59%) are cranking up their investment in infrastructure.
The hype around AI is giving way to a more practical phase in which enterprises are starting to make the adjustments necessary to get the most value out of their investments. By creating an edge strategy, prioritizing data visibility and management, and modernizing their IT infrastructure, enterprises will be well-positioned to stay competitive and fuel innovation with AI.
