Operations | Monitoring | ITSM | DevOps | Cloud

From AI Sprawl to Orchestration: Delivering Intelligence as a Service

Most enterprise AI deployments were never designed to coexist. They were designed to prove a point, respond to a board directive, or secure a budget. The result, two years into the generative AI cycle, is an expanding estate of disconnected models, fragmented pilots, and overlapping capabilities that collectively deliver far less value than the sum of their parts. HFS Research calls it "death by a thousand POCs". The more precise description is architectural negligence at an enterprise scale.

90 Days Isn't Enough Notice: What Predictive Churn Warning Actually Looks Like

Your customer started their renewal evaluation on a Tuesday in March. You did not know about it. Their CFO had asked the procurement lead to "look at alternatives" during a quarterly budget review. Three weeks later, a competitor's SDR was on a discovery call with their head of operations. By the time your CS platform's health score turned amber, six weeks had passed inside their building. This is what most CS leaders miss when they evaluate early warning systems.

Lorka AI and the Next Phase of Smart Digital Transformation

Digital transformation has moved past the experimentation phase. For most organizations, the question is no longer whether to adopt digital systems, but how effectively those systems can adapt, learn, and scale. Artificial intelligence has become the defining force in this evolution. What was once limited to automation and analytics is now expanding into systems capable of continuous decision-making and operational intelligence. Within this landscape, platforms like Lorka AI are positioning themselves as enablers of a more adaptive, AI-native enterprise model.

The Retention Blind Spot: Why Your Marketplace Provisions Everything But Protects Nothing

By 2030, enterprise software sales through AWS, Azure, and Google Cloud marketplaces are projected to grow to $163 billion. Compared to $30 billion in 2024, this is a 29% CAGR, and one of the most aggressive route-to-market shifts the software industry has ever seen. Add the distributor and MSP marketplaces, and the picture gets even larger.

Your Enterprise is Running AI. But Who is Governing It?

If you’ve been online in the last fortnight, you’ve probably seen ServiceNow’s “Kevin” memo, the fictional 2028 post-mortem about an enterprise where the AI agents won, the governance team was eliminated, and a single AI governance lead named Kevin spent two years filing risk assessments that were auto-resolved before anyone read them.

Why Your Agentic AI Aspirations Need to Evolve from Models to a Workflow Data Fabric

Enterprise conversations today are dominated by one phrase: Agentic AI. Across boardrooms and innovation labs, organizations are experimenting with copilots, autonomous agents, and AI bots capable of resolving tickets, recommending actions, and orchestrating complex processes. The promise is real — AI that doesn't just generate insights, but takes meaningful action. Here's the uncomfortable truth: most enterprises are architecturally unprepared for the agentic future they're trying to build.

From Health Scores to Autonomous Action: What Changes When Your CS Platform Stops Reporting and Starts Executing

Here is something worth sitting with before any AI conversation: a red health score has never renewed a contract. A CSM (Customer Success Manager) still had to open it, interpret it, write the email, log the call, and route the escalation. The dashboard told you something was wrong. Everything after that was still manual. That gap, between knowing and doing, is what agentic AI is closing. Not only by making dashboards smarter.

Before You Deploy Another Agent, Read This

Enterprise boardrooms are not debating whether to adopt agentic AI anymore. The debate has moved to a harder question: why do so many agentic deployments stall between pilot and production? ServiceNow's Enterprise AI Maturity Index 2026 puts a number to it. Most enterprises that have invested in AI tooling report that their biggest obstacle is not model quality or compute cost. It is the infrastructure that those agents are expected to operate within. The models are capable.

The Hidden Knowledge Crisis Behind Every Repeat Truck Roll in Field Service: Can AI Help?

The organization ran a farewell. Someone brought a cake. And on that same afternoon, roughly 22,000 undocumented decisions, like repair workarounds, asset-specific judgment calls, the kind of pattern recognition that only comes from two decades of showing up, quietly ceased to exist. No system captured them. No handover covered them. They left with the person. This is the operational risk that most field service leaders are misreading.

The Data Problem Hiding Behind Every Agentforce Deployment Hiccup

AI without context is a hallucination engine waiting to deliver your customers the wrong answer with complete confidence. Every inaccurate response an autonomous agent produces traces back to data that was incomplete or trapped inside a silo. This dependency elevates the Data Cloud (now Data 360)–Agentforce relationship from a standard integration to the most critical architectural investment in your ecosystem.