AI Workflows - WisdomAI: Why we must move beyond reactive analysis
This is a guest post for the Computer Weekly Developer Network written by Soham Mazumdar in his capacity as co-founder & CEO of WisdomAI.
WisdomAI is a knowledge platform that connects into structured and unstructured datasets, enabling teams to search and analyse enterprise data to make internal knowledge bases searchable. The technology has particular relevance for platform engineering best practices and troubleshooting workflows.
Mazumdar writes in full as follows…
We’ve built impressive tools that respond to human queries and automate individual tasks, but we’re still trapped in a reactive model that creates more bottlenecks than it eliminates.
Today, when an executive needs insights about declining conversion rates, they ask for an analysis from the data team. The analyst queues the work, pulls relevant data, conducts the analysis and delivers findings three days later. By then, the trend has gotten worse… and the chance to intervene has passed. #
This slows down decision-making and limits how fast organisations can operate.
We have a scaling problem
Our AI tools are sophisticated enough. The real problem is that we’re still building workflows around human gatekeepers. Data analysts have become bottlenecks, not because they lack skill, but because the reactive model simply cannot scale. No company can hire unlimited analysts and even the best analysts can only work on one problem at a time.
This creates an “analysis gap” in my view.
In other words, the growing distance between the volume of data being generated and our capacity to extract actionable insights from it. Traditional AI workflow platforms have tried to bridge this gap by making analysts more efficient, but efficiency gains hit natural limits. We need a different approach entirely.
From reactive to proactive
AI systems will eventually work autonomously alongside humans, constantly learning and monitoring in the background. This means continuously and proactively analysing patterns, detecting anomalies and surfacing insights before problems arise. For example, no one will have to ask about conversion rates; the system will already know something is wrong and explain why.
The way work gets done will completely change. Proactive agents will operate as always-on virtual team members that never sleep, never get overwhelmed and continuously learn from every interaction and outcome. I should note that they will not replace human judgment; they will handle the repetitive monitoring and analysis work that typically consumes a massive percentage of an analyst’s time.
How to build this
Building truly proactive AI workflows means solving complex technical challenges that traditional automation platforms have yet to address. First, the system has to understand the business context deeply enough to distinguish signal from noise. A 10% drop in metrics might be catastrophic for one business unit but expected seasonal variation for another.
Second, proactive systems need autonomous learning capabilities that go beyond simple rule-based triggers. They have to identify patterns, adapt to changing business conditions and improve their signal detection over time. This means combining multiple AI techniques; from time-series analysis and anomaly detection to natural language processing for generating human-readable explanations.
Third, these systems have to integrate seamlessly with existing data infrastructure without requiring extensive setup or configuration. The best proactive AI workflows leverage existing metrics, models and business logic, making them deployable across diverse technical environments.
What changes when you’re proactive
The benefit is that teams will catch issues early and can fix them in real-time rather than discovering problems after they’ve done damage. Analysts will stop spending their time on routine monitoring and focus on strategic analysis and solving complex problems.
Even better, all departments (not just data teams) will have access to the kind of monitoring and insights that used to require a dedicated analyst. Marketing teams will understand campaign performance immediately. Operations teams will be able to spot process problems as they happen. Finance teams will be able to see trend changes right away rather than waiting for month-end reports.
Where is this heading?
I’ve seen teams that built proactive monitoring. They catch revenue problems within hours, not days.
They fix data pipeline issues before they corrupt downstream models. They spot user behaviour changes while there’s still time to adjust product strategy.
The technical infrastructure is getting there. Streaming platforms process event data with sub-second latency. AI frameworks support continuous model updating and anomaly detection. AI Agents can automatically root cause and remediate.
Yet most data teams are still building dashboards and responding to Slack messages about metric drops. They’re solving yesterday’s problems with tomorrow’s tools.
Organisations that jump on this will be able to move faster and solve different problems entirely, while their competitors are still catching up to issues that happened last week.
