Worknet

Last week, one of our customers pointed out that RAG (Retrieval-Augmented Generation) is becoming a commodity in the GenAI realm—something I’ve been thinking about for a while. We’re seeing firsthand how RAG delivers significantly more value than classic keyword search. Personally, I’ve almost stopped using Google—tools like ChatGPT and Perplexity significantly outperform traditional search engines. But when it comes to applying GenAI in businesses, RAG is just the starting point. The real game-changer lies in high-value scenarios such as: - Enforcing company guidelines and playbooks through GenAI to ensure consistent behavior. - Accessing systems like CRM, data warehouses, logging, or payments to read data and take action. These capabilities boost productivity, and the potential is massive. Excited to see where this journey takes us next! ??

Google Search vs. Gemini, or actually, Search vs. Find In my view, a GenAI RAG-based response with citations to reference documents is generally superior. With search, we get a list of relevant sources, but it’s up to us to dig through them to find the answer, often buried in one or more documents. However, many people may disagree with this. I personally believe this is more of a behavioral shift issue than anything else. For many years, we’ve become accustomed to searching for information rather than actually getting direct answers, as we can with GenAI responses. What’s your take? Check out the following Worknet.ai chatbot "find" operation that analyzes two documents and provides an answer with citations to each source document

We’ve made some major enhancements to our GenAI for customer support teams product and are now working on a website upgrade. I'd love to get your feedback on our initial brainstorming ideas for the new site. Here’s a visual that illustrates our offering: Unlike other automated resolution chatbots, Worknet goes beyond using AI agents—it also connects human experts and operates using companies’ playbooks (SOPs). The actions of human experts help train Worknet AI, expanding its automation coverage and capabilities over time. We’re considering using a QB metaphor, as CSMs and AEs often refer to themselves as the QB of the deal or the customer. Helping us emphasize that Worknet ensures the ball never drops until the job is done. What do you think? ???Any feedback would be hugely appreciated.

When we started Worknet, our goal was to reshape customer engagement. We envisioned a world where every customer or prospect could easily ask any question and receive a prompt response (within minutes) from the right person on the vendor side (not necessarily a support person) and/or from their own team. Initially, we adopted Slack and Slack Connect because they support rapid, async communication that bridges customer and vendor teams. This approach has enabled our customers to stand out by offering exceptional service and creating sustainable relationships. However, some companies have been hesitant to adopt this model, concerned that they might not be able to meet customer expectations. GenAI is already addressing this concern. GenAI allows companies to respond quickly to customers and when needed connect the right experts and teams.

Customers Prefer Self-Service When the Task Is Simple. 72% of customers have used self service portals, and 55% have used self-service chatbots. Source: Salesforce State of connected customers

Human assistance is a differentiator! 77% of customers expect to interact with someone immediately when they contact a company Source: Salesforce State of connected customers

Something I've noticed - most AI customer support solutions still function like a Google search—addressing specific point requests but missing the bigger picture. They overlook the broader context of the customer, the issue, and the goal. Worknet and a few other advanced solutions are using Agentic AI to handle tasks like finding knowledge, searching the web, surfacing customer-specific data and dynamically executing business policies like refund. And yet, most of them are not supporting integrating people and teams into the resolution process. Streamlining handoffs and enabling case swarming and collaboration are crucial, especially when input is needed from beyond the core support team. Real progress happens when we seamlessly bring together AI, people, and teams. That’s where the magic is ?? https://lnkd.in/gy4XKUv2

Will AI replace all of our jobs? Probably not in the near future. Even something relatively simple as a decent cup of coffee still requires an experienced human barista and a good coffee machine.

Every salesperson understands the importance of multi-threading. When I joined ZoomInfo after the acquisition of Komiko (acquired by Zoominfo), the sales team recognized the primary value in measuring the health of relationships based on the breadth and depth of connections. Data shows that this is a leading indicator of winning deals and is also crucial for retaining and expanding existing customers. However, most post-sales organizations do not fully embrace this approach. While CSMs and AMs focus on decision-makers, which is clearly important, customer service operations often overprotect their support and engineering teams by minimizing interactions. This may boost productivity, but it can lead to discovering churn too late in the process or missing expansion opportunities. GenAI offers an opportunity to control costs while still delighting your customers.