Every year, poor communication and siloed data bleed companies of productivity and profit. Research shows U.S. businesses lose up to $1.2 trillion annually to ineffective communication, that's about $12,506 per employee per year. This stems from breakdowns that waste an average of 7.47 hours per employee each week on miscommunications. The damage isn't only interpersonal; it's structural. Disconnected and fragmented data systems mean that employees spend around 12 hours per week just searching for information trapped in those silos.
The Old Way (Siloed Tools): The application team opens their APM tool. They see slow transaction times but no obvious errors in their code. They create a ticket for the infrastructure team. The infrastructure team checks their dashboards. Server CPU and memory look fine. They blame the network. The network team checks their monitoring tools. Bandwidth is normal, and latency is low. They declare, "It's not the network!" Hours, or even days, are lost in a painful cycle of finger-pointing while the business loses revenue.
In an era where AI is driving innovation and efficiency, marketing organizations are grappling with a persistent challenge - data silos and disconnected data. These issues hinder the ability of AI to deliver accurate insights and seamless user experiences. Understanding the roots of this data dilemma is crucial, as it not only impacts data quality but also reflects broader organizational dynamics.
Slack believes it has a gold mine of data. According to the company, the conversation data between employees is said gold needed to feed AI with the right context. That data is now available within Agentforce, but also to third parties. In recent years, there has been a race to build the best LLMs and have sufficient computing power to enable AI. The latter has been achieved, and now it is time to collect the right data and context and feed it to AI agents.
The global supply chain is all about modern logistics today, but it is also fragmented and complex. With different carriers, ports, customs agencies, and logistics companies all using their own unique systems and data formats, it is a recipe for miscommunication, inefficiency, and costly delays. This lack of a common language is particularly evident in the " track and trace " process. When a container moves from a carrier's system to a port's system to a customs agency's system, the data often doesn't transfer seamlessly.
In the race to stay ahead, organizations have thrown open the doors to every AI tool under the sun. The result? AI overload. According to the Wharton School, AI spending has skyrocketed by 130% in just the past year, and 72% of companies are planning to invest even more in 2025. Yet, here's the kicker: 80% of organizations report no tangible enterprise-wide impact from their generative AI investments.
