Artificial Intelligence (AI) is rapidly becoming a major driver of innovation and efficiency in companies worldwide. By 2025, global investments in AI are expected to reach an impressive $200 billion, and AI could account for over 2% of global GDP.[1] These figures clearly show that AI is already having a significant impact on the corporate landscape. Around 72% of companies have integrated AI into their processes, and over 5% of digital budgets are flowing into AI-related projects.[2] With those impressive investments, it is more important than ever for companies to develop clear business cases for their AI bets as costs are clear, but benefits aren’t.
Two distinct categories of AI have emerged: Traditional AI and Generative AI (GenAI). While GenAI has captivated the market with its creative capabilities, the true cost-saving potential in the near term lies in the tried-and-tested power of Traditional AI.
Generative AI: Hype with Uncertain Returns
Generative AI, which includes advanced models capable of generating new content, code, and simulations, has fueled much excitement within the tech community. Its ability to create new software solutions, engage in complex data generation, or enhance user experiences through interactive chatbots is impressive.
However, despite its potential, GenAI’s ability to deliver concrete financial returns—especially in IT—remains uncertain. While over 65% of companies plan to increase their AI investments in the coming years, many of its applications are still in the experimental phase.
Traditional AI: The Proven Path to IT Cost Savings
Traditional AI, on the other hand, focuses on automation, predictive analytics, and process optimization. These AI tools help IT departments drive efficiency, eliminate manual tasks, and optimize operations—producing measurable cost savings.
It represents a new wave of efficiency by tackling repetitive, low-value tasks that traditionally require significant human intervention. From infrastructure management to application performance monitoring, Traditional AI automates processes that have historically been resource-intensive.
It requires heavy change management with change in processes, staff responsibility and tasks, vendor services, but when achieved, companies can drastically cut operational IT costs without sacrificing performance or service quality.
Key areas where Traditional AI can drive IT cost optimization include:
• Automating IT Monitoring : AI-powered tools can handle routine IT tasks like system monitoring, log analysis, and incident detection.
• Predictive IT maintenance: By analyzing data from IT systems, Traditional AI can predict potential system failures or performance bottlenecks before they happen. This allows IT teams to address issues proactively, avoiding costly downtime and reducing maintenance expenses.
• Cloud cost optimization: Traditional AI models can analyze cloud usage patterns to identify inefficiencies and recommend optimization strategies, such as rightsizing instances, scheduling non-critical workloads, or switching to cost-effective cloud resources.
• IT service desk automation: Traditional AI-driven chatbots and virtual assistants can automate IT service desk operations by handling common user requests, such as password resets or troubleshooting guides.
Conclusion
In the realm of IT cost optimization, Traditional AI emerges as the clear winner for businesses seeking immediate, tangible cost savings. Automating IT operations, predicting maintenance needs, and optimizing cloud usage are just a few of the ways Traditional AI can transform IT departments. While Generative AI is certainly a compelling technology for the future, organizations should focus on the proven capabilities of Traditional AI to drive efficiency and reduce IT costs today. This pragmatic approach ensures that companies optimize their IT environments without relying on speculative new revenue streams from unproven technologies
Authors: Malte Lüttenberg, Associate Consultant; Alexandre Gay , Managing Director at BG&A (Blanc Gay & Associates)
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[1] Goldman Sachs (2023): AI investment forecast to approach $200 billion globally by 2025, Available at: https://www.goldmansachs.com/insights/articles/ai-investment-forecast-to-approach-200-billion-globally-by-2025
[2] McKinsey (2024): The state of AI in early 2024: Gen AI adoption spikes and starts to generate value, Online at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#/
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