Enterprise Conversational Intelligence in Weeks with AI | Agentic AI Podcast by lowtouch.ai

Welcome back to the Agentic AI podcast. Today we're really going to move past the hype cycles, the demos. >> I would prove for concepts. >> Exactly. We're getting right into the core challenge that I think every technology leader is facing right now. How do you get these high-value action-taking AI workers into the enterprise and do it fast without opening up massive security risks or kicking off these huge year-long projects? >> That really is the pivotal question. It's called so much progress for years. Enterprise leaders, especially your CTOs, your CIOs, they've been operating under this one big assumption. >> Which is? >> That to get real AI intelligence, you have to do a source-treuth approach. >> Terror everything down. >> Serent all down. >> All right. >> You build applications, deploy massive new server farms or dedicate 18 months to redesigning a workflow that already works. >> That's the roadblock, right? This idea that speed means you sacrifice control or if you want control, you have to sacrifice speed. >> You can have both. >> They want what's being called a true digital worker, not just another chatbot for the website. >> No, not a Q&A machine. They want a resilient agent that underscends intent, executes complex tasks, and this is the key, operates safely inside their own firewalls. >> And that is the whole mission of our deep dive today. We're unpacking a really, well, a pretty radical new architectural approach that promises to deliver this advanced intelligence across your existing apps. >> And in a timeline that frankly sounds almost impossible at first. We're talking weeks, not years. >> Weeks, specifically the sources we dug into are proposing a timeframe of just four to six weeks. >> Four to six. >> To get these agentic digital workers live. So let's just, let's start right there. How is that even plausible? What's the architectural magic trick here? >> I think the genius of it is that it leverages what you already have instead of fighting it. >> Yeah. >> All the logic, the data, the really mission critical workflows. >> Yeah. >> Already in your current applications in APIs. >> Already there, they're crested. >> Exactly. So this methodology says, why rebuild that? Just put an intelligent layer on top that can access and execute it. >> So the central claim is you can deploy this whole conversational AI layer, full automation, without rewriting a single line of your core business systems. >> Precisely. And the how is critical for both security and speed. It's all about deploying secure, private, agentic AI appliance. >> Okay, appliance isn't interesting word. This isn't just software as a service in the cloud. >> Not at all. This isn't a shared tenant model. This appliance sits on site in your data center or securely inside your private cloud, your VPC or VNet. It's a dedicated intelligent layer running right on top of your existing infrastructure. >> We have to pause on that four to six week number because it sounds revolutionary. I mean, you look at industry data, something like 70% of big modernization projects run over budget. >> Oh, easily. And over time. >> By 30% or more. And they often take two, maybe three years, just to get off the old systems. So if you skip the rewrite, what kind of risk are you actually eliminating? >> You're basically eliminating the entire architectural risk profile. And the time savings, they aren't just in the initial deployment. Think about it. You're avoiding dependency mapping, all that regression testing that comes to the rewrite, the procurement delays. >> A domino effect. >> It is. If the agent just connects to an established tested API on a legacy system, the enterprise gets what it's always wanted. Interoperability without disruption. That speed comes from preservation. >> That distinction is so important. Let's get really clear on this term, digital worker. Because we keep saying it's not a chatbot. What is the fundamental technical difference? >> A chatbot is, well, it's designed for a synchronous back and forth, Q and A. It processes language, looks something up and gives a response. It's basically a very, very smart search engine. >> Okay. >> But a true digital worker is different. It's stateful and it's transactional. It manages a process that has multiple stages, sometimes over days. >> You mean example of that statefulness? What does that look like? >> Okay. Think about a full PTO request. A chatbot can tell you the policy. A digital worker initiates the request. It calls a finance API to check your accrued hours. Then it validates those hours against an HR policy. It reads from a runbook and SharePoint. >> So it's already talking to two different systems? >> At least. Then it submits the draft request to service now. And then it holds that state maybe for three days. While it waits for a manager to approve it via email, the agent manages and tracks that entire multi-day multi-system transaction from beginning to end. >> Right. That's the action taking piece. That's what delivers the actual automation value. >> That's it. >> Okay. So that clears it up. Speed by avoiding migration, value by enabling stateful cross-system action. Let's move to the non-negotiables for any enterprise. Technical flexibility and what we're calling the control imperative because for a CTO, speed is great, but it's totally relevant if they lose security or governance. >> Absolutely. >> Yeah. >> And this is where the design of that private AI appliance really shines. They knew that to get any trust, the solution has to fit into the existing security framework. But before that, we should probably address the elephant in the room. TCO. >> Total cost of ownership. >> Right. >> Running a whole private AI infrastructure maybe even hosting your own private LLMs that sounds incredibly expensive. Much more than just using a scalable cloud service. >> It sounds like it, but the calculation completely shifts when you're dealing with regulated or highly sensitive data for say a big bank or a defense contractor, the potential cost of one compliance breach or even just the legal overhead to negotiate a third-party data use agreement in the cloud. >> That alone can be a huge number. >> It dwarfs the operational cost of running the infrastructure privately. So this model is laser focused on eliminating compliance risk. And for those industries, that assurance is priceless. So the TCO is justified because the risk mitigation is so high. That makes sense. Okay, so let's look at how this appliance connects to everything. The source material mentions it supports something called the Model Context Protocol or MCP, but it says it's optional. >> Yes. >> For those of us not deep in the weeds of model standards, what is MCC and why would deploying it be a time sync? >> MCP is essentially a standard, a defined way for AI agents and models to communicate their capabilities and context securely. And it's great for brand new environments. But trying to deploy it across a huge, diverse, sprawling enterprise application landscape, you'd have to do integration work on every single existing system. That's a massive project in itself. It completely defeats the four to six week promise. >> So the secret sauce for speed is being able to just bypass that requirement? >> Absolutely. >> So what does the fast path look like in practice? >> The fast path is, well, it's beautiful in simplicity. The appliance can evoke existing enterprise APIs directly. >> With zero modifications. Zero changes to the underlying code. If you already have a REST API for updating a jury ticket or querying service, now the agent is just securely configured to use that exact endpoint with its existing security tokens. The architecture is totally preserved. >> And that leads us right to control. We're talking about integrating powerful, generative AI. The promise is that zero data leaves the environment. How is that isolation actually enforced? >> It's enforced by the physical or virtual boundary of the company's own network. The whole stack, the agent, the workflow engine, the private AI infrastructure that manages context and memory, it all runs fully inside the company's domain. The data stays put, whether it's querying a database or analyzing a document. >> And that immediate isolation is what makes it compliant from day one. You mentioned specific features that would satisfy CSILE. It's not enough to just say we're secure. You have to fit into their existing standards. >> Absolutely. And the inclusion of tools like Open Search and Prometheus for logging and monitoring, that's not an accident. >> It's a very intentional strategic move. It means this AI layer isn't some shadow IT project running in a corner. It plugs directly into the existing security operation center, the SOC. They're framework for auditing and monitoring. We're talking about enforcing existing enterprise SSO, role-based access control, and providing complete searchable audit trails of every single action an agent takes. >> That's the goal then, maintain total control, but deliver speed. The governance is built in, not bolted on after the fact. Okay, so we know how it works, we know where it runs. Let's pit it to what it can actually do. After this super fast four to six week roll out, what can these digital workers automate? >> They're really built to handle what you'd call high value cognitive load. They don't just search for keywords, they perform deep reasoning. So they can read vast libraries of internal policies and runbooks, reason over huge amounts of unstructured and structured data at the same time. >> Both at once. >> Concurrently, yes. And they automate those complex multi-step workflows that used to need a dedicated human analyst, just to piece everything together. >> But those complex processes, they often require a human check point and approval. If the agent is transactional, how does it handle that? >> That's handled through what's called HITL, or Human in the Loop functionality. The agent is trained to understand the workflow governance. So if a policy change needs a manager sign off, the agent does all the prep work. >> It gets everything ready. >> Gathers all the data, fills out the forms, validates the inputs, and then it escalates the approval, using whatever system the enterprise already has. An email trigger, a Slack notification, you name it. Then the agent just waits. It holds the state of the transaction, tracks the human's response through an API, and only proceeds once that human sign off is logged and verified. >> That perfectly explains the time to value proposition for a decision maker. It really balances all the key concerns. You get speed with four, six weeks to go live. You get control and safety because everything is inside your perimeter. And critically, you get architectural preservation, no migrations, no rewrites. >> And that ability to avoid a multi-year, very expensive model. modernization cycle while immediately adding an intelligent layer. That is the huge financial win. It means you can give your existing often siloed but business critical systems, your Gira, ServiceNow, SharePoint, SalesForce, a single unified intelligent front door. You just talked to it in natural language and the agent translates that into action across the whole stack. Exactly. And this isn't just theory, this is actually happening now. Let's dig into some specific real world examples of how these agents are generating value by bridging those organizational silos. Yeah, let's move past the generic list. Take IT operations. One deployment we looked at had agents automatically handling level one password resets and VPN access requests. High volume low complexity stuff. The perfect target. By integrating with the SSO logs, the ticketing system and the policy docs, they took the meantime to resolution for those tickets from 18 minutes down to under 45 seconds. That freed up 30% of the tier one staff to handle real complex problems. That's a measurable outcome that justifies the investment almost instantly. What about something more complex like finance or admin automation? Another organization used these agents to generate cloud cost insights on demand. Now this is a classic task that spans silos. It's always a nightmare. It is. You have to query structured data from the finance ledger compared to unstructured runbooks about resource allocation and analyze data from the cloud providers billing APIs. The agent unified all of that. It reduced the time for their quarterly cloud spend reconciliation from five days down to about two hours. And what about the internal pain points, HR compliance? Absolutely. Think about unifying complex HR processes. An employee needs an instant policy aligned answer. That might mean searching the HRAS, sharepoint for the actual policy document, and maybe a ticketing system for the right form. These agents consolidate all that and ensure every answer is not just fast, but policy aligned and traceable. That's vital for compliance. The implications of these deployments really do change the entire AI conversation for tech leaders. This feels like a real shift from endless experiments and pilots. To reliable, broad enterprise adoption. It moves the mindset away from long, painful redesign cycles to rapid value delivery. And it shows the technology is mature. It's an integrated solution that leverages what you own, rather than a proof of concept that threatens to break your existing architecture. And this entire approach, it seems anchored in a really clear vision, one focused on practical use. As Rijith Krishnan, one of the key voices in this space articulated. The goal is to simplify AI automation using platforms built for secure private deployment. Right, prioritizing data privacy and security above all else. That's the key. His core message is so critical here. The breakthrough isn't just a slightly better AI model. The real breakthrough is the platform built for secure deployment inside your perimeter. That's when it enables the speed. The future of enterprise AI. It really relies on building a smarter, agentec layer over the systems you already know and trust. You don't need to rebuild. You just need that secure, agentec layer that can understand intent, reason across all your data, and take action safely inside your environment. It moves AI from being a strategic possibility on a slide deck. To a practical, deployable reality in a shockingly short amount of time. It simplifies automation, accelerates transformation, and it removes the biggest barrier to entry that data privacy risk. That's the balancing act every modern I.K. leader is trying to pull off. It is. So here's the final question for you, the listener, to take away and think about. Considering this claim four to six weeks time to value which legacy application in your organization, that one silage system that everyone dreads using, which one could most immediately benefit from a unified secure conversational experience. Think about the biggest process bottleneck you have right now. The one that's costing you real time and money, that's your starting point. That's the path to achieving true enterprise automation. Thank you for joining us on the Agentec AI podcast. We'll see you next time as we continue to explore the practical side of agent solutions.