Daniel Salinas COO of Lakeside Software
Hello from the DEX office
I saw a demo a few weeks ago where a self-service AI-powered helpdesk chatbot was given a laptop connectivity issue to diagnose. The vendor had put it in verbose mode to show off all the work the agent was doing in the back-end. It pulled in six months of related tickets from the ITSM system and referenced four internal KB articles on wireless troubleshooting, as well as the full chat transcript from the user's last three interactions, and event logs going back 90 days. It was thorough. It was impressive. And the answer it produced was confidently, almost elegantly, wrong.
The AI didn't fail because it was stupid. It failed because it was overwhelmed. There's a term gaining traction in the AI research community for this failure mode: context rot. And if you're building or buying AI-powered ITSM, you need to understand it because it's about to become the most common reason your AI investments underperform. Read more...
Adopt Site Reliability Engineering Principles to Get Digital Workplace Operations AI-Ready
- Stuart Downes
- 9 February 2026
Strategic Planning Assumption
By 2028, 80% of enterprises will use site reliability engineering practices across their organizations to optimize product design, cost and operations, up from 30% in 2024.
Impact Brief
In the next 12 months, heads of I&O must create operational headroom to support critical AI initiatives. Traditional “keep the lights on” operations consume too many resources and fail to prevent employee frustration. By adopting SRE principles, heads of I&O can fundamentally change the economics of their support model.
The opportunity: SRE practices optimize operational efficiency by automating manual interventions, ensuring staff time is spent on engineering value rather than repetitive tasks.
The risk: Organizations that rename operations teams to “SRE” without changing the underlying principles risk failing to deliver tangible benefits and may reduce DEX.
Lakeside Software
SRE for EUC: Endpoint Reliability as the “Last Mile” of Site Resilience
Over the past two decades, the global enterprise technology ecosystem has undergone a profound structural and operational transformation. Driven by the imperative to deliver highly available, hyper-scalable, and deeply resilient digital services, organizations have fundamentally reimagined their backend infrastructures. At the absolute core of this transformation has been the widespread adoption and maturation of Site Reliability Engineering (SRE). Pioneered in the early 2000s to manage vast, complex cloud infrastructures with unprecedented precision, SRE permanently shifted the paradigm of IT operations by treating systems operations strictly as a software engineering problem. By developing systems and tools to automate operations away, SRE introduced highly rigorous, deterministic methodologies designed to eliminate manual toil and ensure that complex distributed systems remain operational under immense computational scale.
The AI-Powered Help Desk Has a Bloat Problem
The hype cycle surrounding generative AI in IT Service Management (ITSM) has focused heavily on the promise of the autonomous service desk. The prevailing narrative suggests that if organizations simply feed enough historical tickets and knowledge base articles into a large language model, the machine will eventually outpace human agents in both speed and accuracy. This vision assumes that the primary bottleneck in IT support is a lack of accessible information. However, this approach forgoes two fundamental realities of the modern enterprise desktop: the technical limitations of LLM context processing and the chaotic, non-deterministic nature of the modern endpoint.
