Skan AI Explained
Understanding the context graph of work and its impact on enterprise AI
Skan AI is a startup that builds a context graph of work by observing how employees actually perform their jobs across enterprise software. This approach is considered the missing layer of enterprise AI. Skan AI has raised $63 million in Series C funding, co-led by Cathay Innovation and Dell Technologies Capital, as reported by VentureBeat.
What is Skan AI and how does it work?
Skan AI uses a context graph of work to understand how employees work across different software applications. This graph provides a detailed view of the workflows, processes, and tasks that employees perform on a daily basis. By analyzing this data, Skan AI can identify areas of inefficiency and provide recommendations for improvement, as stated on their website.
The context graph of work is built by observing employee behavior across various enterprise software applications. This approach provides a more accurate understanding of how work is actually being performed, rather than relying on traditional methods such as surveys or interviews. According to VentureBeat, this approach has attracted significant investment, with Skan AI raising $63 million in Series C funding.
The use of context graph of work has significant implications for enterprise AI. By providing a more accurate understanding of employee behavior, Skan AI can help organizations to identify areas where AI can be applied to improve efficiency and productivity. As reported by VentureBeat, this approach has the potential to revolutionize the way organizations work and make decisions.
Why does Skan AI matter?
Skan AI matters because it provides a new approach to understanding how employees work and how enterprise AI can be applied to improve efficiency and productivity. According to VentureBeat, the traditional approach to enterprise AI has focused on automating specific tasks, rather than understanding the broader context of work. Skan AI changes this by providing a more holistic view of employee behavior and workflows.
The impact of Skan AI can be seen in the way it is being adopted by organizations. As reported by VentureBeat, Skan AI has raised significant investment and is being used by a growing number of organizations. This suggests that Skan AI is seen as a key player in the enterprise AI market and is likely to have a significant impact on the way organizations work and make decisions.
The use of context graph of work also has implications for the broader AI industry. According to VentureBeat, the traditional approach to AI has focused on automating specific tasks, rather than understanding the broader context of work. Skan AI changes this by providing a more holistic view of employee behavior and workflows, which can be applied to a wide range of AI applications.
What happens next with Skan AI?
As Skan AI continues to grow and develop, it is likely that we will see significant advancements in the use of context graph of work and enterprise AI. According to VentureBeat, Skan AI is well-positioned to take advantage of the growing demand for enterprise AI solutions and is likely to play a key role in shaping the future of work.
The future of Skan AI will depend on its ability to continue to innovate and provide value to its customers. As reported by VentureBeat, Skan AI has already demonstrated its ability to attract significant investment and is being used by a growing number of organizations. This suggests that Skan AI is well-positioned for future growth and success.
Frequently asked questions
What is Skan AI?
Skan AI is a startup that builds a context graph of work by observing how employees actually perform their jobs across enterprise software.
How does Skan AI work?
Skan AI uses a context graph of work to understand how employees work across different software applications. This graph provides a detailed view of the workflows, processes, and tasks that employees perform on a daily basis.
Why is Skan AI important?
Skan AI is important because it provides a new approach to understanding how employees work and how enterprise AI can be applied to improve efficiency and productivity.
The bottom line
- Skan AI is a startup that builds a context graph of work by observing how employees actually perform their jobs across enterprise software.
- The use of context graph of work has significant implications for enterprise AI and the broader AI industry.
- Skan AI is well-positioned for future growth and success, with significant investment and a growing number of customers.
- The future of Skan AI will depend on its ability to continue to innovate and provide value to its customers.
- Skan AI is a key player in the enterprise AI market and is likely to have a significant impact on the way organizations work and make decisions.
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π Full episode transcript
$63 million is what Skan AI just raised in Series C funding, betting that watching how employees actually work is the missing layer of enterprise AI. This is a huge deal, because Skan AI's approach is all about understanding the context in which work gets done, by observing how employees perform their jobs across different enterprise software. They call it a "context graph of work", and it's essentially a map of how work flows through an organization. This matters because most AI systems are designed to optimize specific tasks, but they don't always understand the broader context in which those tasks are being performed. By mapping out how work actually gets done, Skan AI is hoping to create a more holistic view of enterprise operations, and help companies make better decisions about where to deploy AI.
The implications of this are huge, because if Skan AI is successful, it could help companies get more value out of their existing AI investments. Right now, a lot of companies are struggling to get AI to work in a real-world setting, because they don't have a good understanding of how their employees are actually using these systems. By providing that context, Skan AI could help companies unlock the full potential of AI, and that's a big deal. But enough about that for now, let's move on to our next story.
Speaking of AI in the real world, it turns out that enterprises are buying AI compute for speed, but they're not always sure what it's costing them. Across 170 enterprises, AI infrastructure has moved decisively into production, with two-thirds now running AI workloads live, and three in 10 running them at scale. But at the same time, the ability to account for what that infrastructure costs has not kept pace. In fact, fewer than half of these companies can rigorously track their AI compute costs, which is a problem because it means they're flying blind when it comes to budgeting for AI. This matters because AI is only going to become more ubiquitous, and companies need to get a handle on the costs if they're going to be able to make the most of it.
This is a real challenge, because AI infrastructure is complex and expensive, and it's not always easy to understand what you're getting for your money. But companies are prioritizing performance and GPU availability over cost, which is rational given the pressure to get AI up and running quickly. The problem is, this approach can lead to cost overruns and inefficient use of resources, which can ultimately undermine the business case for AI. So, companies need to find a way to balance the need for speed with the need for cost control, and that's not going to be easy.
Moving on, there's some interesting news on the agentic reliability front. It turns out that enterprises that have been burned by a bad evaluation are actually the most likely to remove humans from the loop, not the least. This might seem counterintuitive, but it makes sense when you think about it. When companies have a bad experience with an automated agent, they're more likely to want to take steps to prevent that from happening again in the future. And one way to do that is to remove the human element, which can introduce variability and unpredictability into the system. But this approach is not without risks, because it means relying more heavily on automated systems, which can be flawed or biased.
Finally, let's talk about agentic security, which is a growing concern for enterprises. It turns out that two-thirds of enterprises enforce scoped permissions at runtime, but barely one in five isolates its highest-risk agents. This is a problem, because it means that companies are not doing enough to protect themselves against potential security threats. And with the rise of autonomous agents, this is only going to become more of an issue. Credential sharing is also a concern, with nearly two-thirds of agent fleets sharing credentials, which can create all sorts of security risks. So, companies need to get a handle on agentic security, and fast.
And that's all for today, tune in tomorrow when we'll be exploring the latest developments in AI ethics and governance, as a new report reveals that nearly 80% of AI systems are being deployed without adequate human oversight.