AI generative models' reorganization phase has concluded - prioritizing infrastructure optimization now to avoid lagging.
The AI boom has shifted gears, moving from a chaotic flurry to a strategic pivot, as stressed by Thomas Meyer, IDC's EMEA Group VP. It's essential for businesses to tackle their AI integration methodically and wisely.
Speaking at Huawei's 2025 IDI Forum, Meyer unveiled findings from his consultancy's recent study on enterprise tech leaders embracing AI. His presentation emphasized the significance of bolstering the underlying infrastructure of an enterprise tech stack for a successful AI pivot.
Meyer warned against recklessly charging headlong into AI transition strategies. Instead, he encouraged organizations to build a solid foundation for their AI implementation.
"Now is the time for action if your organization wants to stay competitive in this new tech age," he stated. "The scramble is over; now comes the strategic pivot."
Transitioning to an AI-driven setup isn't going to be a walk in the park - it demands significant financial investments, regardless of the organizational size. The first crucial step when plunging into this AI transformation involves a selective examination of the technology's perks pertaining to specific functions within the organization.
For instance, identifying customer service roles, particularly contact centers, as an ideal target for automated processes. This harmonization would help streamline operations, increase customer satisfaction, and boost productivity.
AI adoption is gaining traction in several industries, including media, tech companies, telecom operators, and financial services. Best of all, the financial sector shows promising signs of using AI in frontline operations for practical, tangible applications.
Comparing companies based on their AI adoption speed and success, Meyer categorizes them into two groups: the 'survivors' and the 'thrivers.' The gap between these two is apparent when it comes to their AI initiatives. Successful companies invest early in chatbots and focus on fraud detection using AI technology.
IDC's findings, focusing on AI's use in financial services, align closely with the adoption rates presented by research published by UK Finance in late 2023. During the initial stage of AI's implementation, most banks found achieving a decent return on investment challenging.
It is not just about allocating resources to AI use cases; it's also about equipping organizations with the right infrastructure, as emphasized at the IDI Forum. Huawei introduced its new AI Data Lake solution, intended to enhance infrastructure visibility, extend data storage capacity, and facilitate enterprise access to relevant data for AI training and inference.
Meyer stressed, "A strong tech stack is crucial. It will render AI use cases pointless if you don't have the necessary infrastructure to back them up."
He further highlighted the challenge of creating scalable data areas and data lakes providing easy access to essential AI data. The format of data is another issue, with limitations surrounding cold, warm, and hot data storage creating obstacles, particularly when it comes to security.
The zeal surrounding the generative AI boom has left several organizations struggling to navigate its traditional pitfalls. According to Meyer, "Many of us want to fly before we can crawl, walk, and run. But to succeed in the intelligent era, you need to get the foundations right for AI running infrastructure and data."
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- To ensure a successful transition into AI-driven customer service, it's essential for organizations to identify service roles, such as contact centers, as ideal targets for automated processes, allowing for streamlined operations, increased customer satisfaction, and boosted productivity.
- As underscored at the IDI Forum, a robust technological infrastructure is indispensable for an effective AI implementation. Companies must invest in solutions like Huawei's AI Data Lake to develop better infrastructure visibility, increase data storage capacity, and provide easy access to essential data for AI training and inference.
