There are a number of specific AI use cases that are delivering the most value in cloud operations. According to Blanker, intelligent search and document processing have seen the most adoption as companies recognize the importance of improving information retrieval efficiency for digital transformation. organizations are starting to use AI for cybersecurity.
“For most companies, cost savings is the top metric they are looking to improve with AI-based solutions,” says Blanquera. “Additionally, customer success teams are among the most active users of AI, making customer satisfaction another important metric for many companies.”
The Human Factor: Why Local IT Support Still Matters
23.01.2025
As automation and artificial intelligence reshape IT support, on-premises service providers continue to rise to the occasion by offering personalized service, fast on-site assistance and creative solutions to complex problems, writes Ian Wham, managing director of IT support provider Innovec, on ITPro Today .
A generation ago, IT meant having a pool of loudly humming servers packed into a cabinet in a store or basement, with blinking green lights and lots of tangled wires.
Companies that didn't have their own IT manager or team relied on a local ISP helpline to call to resolve issues. If turning the terminal off and on didn't help, they'd send someone to take a look.
A lot has changed in the last ten years, not austria mobile database the replacement of on-premises servers with cloud platforms. AI has recently swept everything in its path, but companies still rely on humans physically intervening when things go wrong.
The digital revolution has undoubtedly changed the way we live and work. However, with the rise of AI, automation, and large call centers, a human approach to IT support and personal service remains not just relevant, but critical.
Limitations of Impersonal Support Models
The rise of large call centers and AI-based support systems is driven by the desire for efficiency and cost reduction. However, these models often fall short in key areas.
Lack of personalization. Call centers often use standard scripts and troubleshooting procedures. This “one-size-fits-all” approach does not take into account the nuances of individual customer needs and technical conditions.
A small business owner with a simple network faces very different challenges than a large corporation with a complex multi-site infrastructure. Call center agents, often lacking contextual knowledge, struggle to offer effective solutions.
AI, while capable of processing massive amounts of data, often lacks the insight and critical thinking skills needed to solve complex or unusual problems.
Communication barriers. Technical jargon and complex explanations can be intimidating for non-technical users. Call center agents under pressure from workload may not have the time or patience to explain solutions clearly and compassionately.