Insights

There is an AI bubble, but it is not driven by a lack of value in the technology itself, the gap comes from how it is being implemented. Many initiatives underestimate what it takes to move from experimentation to meaningful results. AI is often positioned as a plug-and-play product instead of the final layer of a broader data and operational foundation. As investment in AI continues to accelerate, the distance between expectations and measurable outcomes has become harder to

In 2026, AI is no longer experimental. It shows up in everyday tools, appears in planning discussions, and is often expected by leadership teams. At the same time, many organizations are realizing that AI is not always the right answer. What separates teams making progress from those feeling stuck is not access to technology. It is having solid data foundations and clarity around how decisions are made. When those are missing, even well-intentioned AI initiatives struggle to

Conversational Business Intelligence (BI) helps teams interact with data using natural language while still getting answers grounded in real business rules. Instead of digging through dashboards or waiting on analysts, executives, sales teams, and operators can ask clear questions through a chatbot and receive answers immediately. Sisifo’s Conversational BI connects directly to trusted data models, existing BI tools, and governed data lakes. The result uses the same definitio

Most companies experimenting with generative AI are running into the same wall: their pilots look impressive in demos but fail to deliver value in real operations. According to a recent MIT study, only 5% of GenAI pilots make it past the prototype phase. The surprising truth is that these failures aren’t caused by “bad AI.” They happen because teams try to build quick chat-based tools instead of fully integrated systems that align with the way the business actually runs. This

Many organizations feel pressure to adopt AI, but most aren’t sure how it fits into their daily operations. Leaders hear promises of automation and transformation, while teams feel overwhelmed by jargon and unsure whether their data is even ready. The truth is that implementing AI doesn’t require huge budgets or futuristic systems. What it needs is a clear understanding of the data a company already has and a realistic view of where AI can make work easier, faster, or more co

For many companies, Excel is the comfort zone. It’s familiar, flexible, and has “worked for years.” But when teams rely on spreadsheets to reconcile data or build recurring reports, Excel stops being a tool and starts becoming infrastructure. That’s where things break down. At scale, manual spreadsheets slow teams down, introduce inconsistencies, and make reporting depend on whoever last touched the file. The habit persists because it feels comfortable, not necessarily becaus

When a company loses trust in its data, every decision slows down. This is what we call the Distrust Gap. It is the space between what the data says and what people believe it says. The gap usually forms when different teams filter, transform, or visualize data in their own ways. Each version appears correct on its own, but together they create competing “truths.” Once this happens, alignment becomes difficult, even for companies with strong technical foundations. The goal is

Cloud infrastructures rarely fail for technical reasons. More often, they fail financially. Over time, as teams change and projects evolve, costs accumulate, dependencies are forgotten, and workloads keep running “just in case.” Sisifo helps organizations audit, redesign, and govern their cloud and data environments so that costs reflect actual business value. Across industries, we’ve seen redundant pipelines, orphaned servers, duplicated storage, and dashboards querying tera