We are touching on the “AI paradox”: companies invest, but without a clear strategy or defined objectives, they do not see significant operational or financial returns.
If generative AI is a buzzword, in fact it has not yet conquered the majority of companies. According to a BPI study, 43% of SMEs and ETIs have adopted an AI strategy, but only 31% use a generative AI solution. Likewise, less than 30% of CEOs are driving their company’s AI agenda; leaving most initiatives without the strategic direction needed to scale. Result: almost 90% of AI projects do not go beyond the pilot stage.
We touch on one of the causes of the “AI paradox”: companies invest, but without a clear strategy or defined objectives, they do not see significant operational or financial returns. How can they overcome this paradox and obtain concrete and measurable results?
In particular by selecting differentiating use cases. Those who underpin the competitiveness of businesses. In this regard, three use cases can be considered safe values: customer service, productivity and IT operations. Here are the reasons why they are essential.
Improving the customer experience
AI has not only changed how businesses operate, it has also transformed customer expectations. They have become extremely independent. When they contact customer service, they want to get their problem resolved quickly and efficiently. Unfortunately, support engineers often spend a lot of time wading through voluminous documentation in order to meet the specific needs of each customer. Result: resolution time increases and customer satisfaction decreases.
In this context, generative AI chatbots are real “game changers”. They enable support engineers to respond effectively to customer support requests while empowering customers. Similarly, semantic vector search helps understand the intent behind a query and display relevant results even when terminology varies. It can also synthesize information from multiple sources, summarize discussion threads, and suggest next steps.
These two combined levers – chatbots and semantic search – improve the average response time (MTFR) and reduce the volume of support cases.
Increasing employee productivity
Organizations are constantly producing more data than they can effectively manage or use. This constant influx makes it more difficult to quickly access relevant information that is scattered throughout the company’s systems and various data sources. As if that weren’t enough, some of the company’s resources remain siled and fragmented. The result: outdated and unreliable information that undermines data accuracy and leads to redundant requests from employees.
Internal generative AI tools help employees quickly find relevant information and therefore increase their productivity. Indeed, they enable faster and more intuitive search and discovery in proprietary data sources.
Transforming security team workflows
As the use of SaaS applications continues to grow, attack surfaces expand. At the same time, new sophisticated threats are emerging, powered by AI. In an environment where everything is constantly moving, visibility is limited — and it’s difficult to adapt quickly. The same goes for obtaining truly actionable intelligence. Estimating the potential impact and risk level of a given threat in a given environment requires extreme concentration.
Traditional reporting methods no longer work in the face of a constant stream of new threat reports from disparate sources. Manual signal collection, documentation analysis, and lessons learned are ineffective when faced with rapidly moving and multiplying targets.
Faced with this challenge, AI assistants have a role to play. They strengthen the expertise of security analysts, increase their efficiency and reduce the manual workload associated with incident investigation and response, by streamlining the entire threat intelligence reporting process. With more time to focus on high-value tasks, analysts can now go further in understanding the relevance and impact of emerging threats.
