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It was more than a decade ago that we started to hear the term “Big Data.” The scale and speed of information had exceeded what existing systems were designed to handle. Today, I can’t even shop without being expected to hand over my email address for a receipt. According to IDC’s long-term projections, we are now generating hundreds of zettabytes of data every year, nearly a ten thousand percent increase, and it’s still accelerating.
The inability to put that data to the best use, especially in business, has been a challenge for many years. What’s changed is the pace of business and what seems like a relentless ambition to boost workflows and improve processes using AI. It has driven the need for a new type of solution to solve a new set of problems.
Speed Has Outrun Action
The speed of business used to be manageable. Problems emerged slowly enough that, if we were organized, we could see them coming, intervene, and feel good about how we ran things. That world no longer exists.
Digital technologies have lowered the cost of launching, changing, and responding. Cloud platforms have removed friction. Global competition has become continuous. Capital markets reward speed as much as outcome. What once happened in cycles now happens as a continuous stream. And AI has removed the final constraint on pace: human involvement. Automation and machine-driven decision-making have introduced self-propagating systems where actions trigger actions faster than people can intervene and broken process effects are amplified. The gap between seeing what’s happening and being able to act has widened dramatically.
By the time information reaches leaders, bad outcomes are often already locked in. And more data actually increases accountability. There are fewer excuses for being caught out, even as execution blind spots become harder to detect and far more expensive when they surface.
We Just Need a Better Dashboard, Right?
Most business apps were designed to be excellent systems of record. To make the business feel faster, organisations did the most reasonable thing they could: they leaned harder into getting more from existing data.
As divisional leaders, we use dashboards to build custom charts and tables and talk confidently about being data driven. Predictive analytics offer better forecasts, churn prediction, likelihood of buying, and clearer prioritisation of effort. It feels like the answer to a faster world. However, no matter how advanced, dashboards and predictive models still depend on data that has already been processed, aggregated, or delayed by design.
Remember when we used to talk about speech analytics and how it evolved from batch analysis to live analytics? Initially, recordings were reviewed days or weeks after customer interactions, disconnected from the moment decisions were made. As contact centers became more automated, that lag stopped being acceptable. Insight had to move into the interaction itself; we’re now even seeing sentiment changes a sentence at a time during a conversation.
We saw the same transition play out in other areas, too:
- Security teams moved from reviewing logs after incidents to detecting threats as they emerge, because understanding an attack after the damage was never useful.
- Fraud detection shifted from post-transaction analysis to live authorization controls.
- Infrastructure monitoring evolved from periodic health checks to continuous observability and assurance as systems became too dynamic to inspect in snapshots.
In each case, the pattern has been the same. Once activity became continuous, insight had to move closer to the moment action was taken. So, why aren’t we doing this at a business level? Because up until now the need wasn’t compelling enough and the innovation wasn’t good enough.
But enterprises now run on cloud platforms. Their core systems expose live APIs. Their data is observable in motion. The problem to solve isn’t about leadership, data, or intent. It’s architectural.
Something Important Has Changed That We Weren’t Even Aware Of
Just as AI existed for decades before compute and data made it practical, the ambition of the real-time enterprise has been waiting for its moment.
Not that many years ago, enterprise finance systems were largely closed, batch oriented, and exposed through brittle SOAP or file-based interfaces. More recently, REST APIs emerged, but adoption was uneven and most organizations still treated core systems as back-office silos.
Today, cloud operational platforms expose rich, real time APIs as a first-class capability, not added later, and those platforms are now the norm rather than the exception. The data is live, the surfaces are open, and the operational mindset has shifted.
A new practical operating layer has emerged at the heart of the enterprise. Not to replace systems of record, analytics platforms, or predictive models, but to sit alongside execution itself. A layer designed to observe activity at the moment it begins, preserve context before it is abstracted away, and enable intervention while outcomes are still changeable.
The Real-Time Enterprise is no longer a vision. It is a fact.
Seeing the Business While It’s Running
For the first time, both the technology and the organizational posture exist to support continuous, in flight decisioning. Decision Intelligence Platforms are built on two capabilities most traditional systems have lacked: point of inception visibility and live per-second processing.
Instead of waiting for events to be written into a general ledger, ERP, or data warehouse, every financial and operational event is caught as it happens and automatically links to relevant business context, from inventory and pricing to customer behavior, cash position, and supply chain status. AI-enabled processing both shows and understands what’s happening to be acted upon.
By the way, the shift isn’t optional. Correlation across systems, anomaly detection, and predictive indicators must be delivered at the same cadence as the business itself. In a slower-paced world we could get away with it. Now, finance teams must anticipate liquidity shifts before they become crises, operations must adjust workflows before bottlenecks peak, and commercial leaders must respond to customer trends the moment they unfold.
Live execution decision intelligence changes the question from “What happened?” to “Do we want this to happen?”. Only one of those changes outcomes.
