From Compliance to Foresight
Information can be deceptive; think of a textbook at primary school, often depicting the Solar System as a flat diagram of circles. In reality, the Sun is in motion, racing through the Milky Way at nearly 828,000 kilometers per hour, and planets are tracing orbits around it, resembling a DNA spiral. Each of us is carried along more than 7.2 billion kilometers every year without even leaving our seats, and that means nothing is standing still. Business data works in the same mysterious ways, with dashboards’ apparent stillness only to hide behind SQL queries and snowflake tables. Every information from an ERP exported to cells in a spreadsheet is telling a story of customers, supply chains, employees, and technologies in constant motion. Data alone is not an answer; it is a signal with a multitude of other components, all of that only awaiting interpretation.
The corporate use of data began with compliance because transactions had to be recorded, paper archives had to become digital documents, and all that still had to be kept to preserve the audit trails. Then portable document format was no longer a novelty, and people started talking about reducing not only manual work but also manual handling, using information to accelerate workflows and remove errors. The next stage was intelligence, drawing patterns from the past to anticipate what might come now and even predict the future.
Hewlett-Packard analysts saw early signals in contra-revenue patterns and rebates that hinted Hardware with high costs and low margins would one day give way to Services and Software, focusing on business lines of high margin. That story was clear as day in the numbers, a long time even before executives started to look at that possibility.
Even routine processes carry potential for insight. With the Customers we had in Portugal and Spain, the tedious task of handling paper tax certificates was reframed as a product design challenge. By simplifying flows and redesigning forms, P2P teams reduced mistakes and revealed new ways to do old things, increasing efficiencies. That was a mundane daily task with a problem that, once analyzed, understood, and reframed, became a catalyst for innovation. Today, the function of data is no longer only achieving efficiency but remaining at the center of strategic foresight, setting the direction toward resilience, innovation, and
growth.
Beyond Flat Screens: The Rise of Narrative Intelligence
As data multiplied, leaders began to realize that dashboards and reports were not providing everything they would like to see. Of course, numbers can have relevance to orchestrate, manage, and supervise the underlying processes that they represent, but only when translated into meaning. Finance, reconciliations, and month-end close processes require technical precision. In media, early digital projects at MTV were guided not by balance sheets but by page views, unique users, and engagement. Both forms of understanding data mattered, and they required human brains to sift through that information.
The new leadership challenge is not to master algorithms but to interpret how narratives are
happening and connect with their people. Narrative intelligence is the ability to translate numbers into coherent, actionable stories, and that has become as essential as financial literacy. Understanding retention rate on a chart means little until it is explained as a story of customer loyalty, brand trust, or shifting competitive dynamics.
This shift explains the rise of roles like Chief AI Officer, AI Ethicist, and AI Interaction Designer, and that’s not just technical jobs. They represent the ability to translate things, ensuring that models and outputs are comprehensible, trustworthy, and aligned with purpose. Without translation, even accurate model outputs risk being sidelined as black boxes, things that are hidden or are hard to explain. Narrative intelligence is not an accessory, or a one-time thing in a workshop; that’s daily work and connects information with action, strategy with the people being asked to execute it.
The Evolution of Analytics: From Past to Prescription
The trajectory of analyzing data, describing, diagnosing pain points, predicting trends, and prescribing actions is familiar to people handling that, long before it started to be done with the press of a button. Today, Descriptive analytics explains what happened, and Diagnostic analytics reveals why it happened. Predictive analytics suggests what might happen next, and Prescriptive analytics advises what should be done.
What perhaps distinguishes leaders is not the availability of these ways to use Data but the discipline to move deliberately across each of them. In IBM’s global delivery centers, what started as cost-driven outsourcing evolved into analytics hubs producing forward-looking insights. DXC Technology combined automation with design thinking to ensure that data processes remained human-centered.
Achieving maturity in Analytics is not a finish line on a race but a cyclical activity, like people walking on a staircase. What’s more, Diagnostic insights improve predictive accuracy. As we climb up the stairs, we have prescriptive recommendations, creating new descriptive baselines. The most resilient organizations treat analytics as a dynamic cycle of learning and not a flat, static maturity model.
Augmenting, not Replacing.
Artificial intelligence came to amplify all that cycle, and today predictive models can easily
anticipate churn, fraud, or demand fluctuations. At this point, no model decides how an enterprise should respond; the responsibility rests with human leaders who must weigh context, ethics, and long-term implications.
Looking at Retail, an algorithm can suggest price adjustments in real time, but leaders must balance those suggestions against reputational risk and customer perception. In Healthcare, predictive systems flag potential conditions, but doctors need to add context to results with patient history, creating trust and empathy. AI augments judgment but does not replace it, and this is something happening now in every field.
Risks are real, and they flag the danger of treating AI as the sole oracle and source of wisdom, rather than an instrument requiring the validation from Experts and Leaders. Those who think there’s no need to validate outputs and interpret the data that they analyze are risking losing credibility. On the other hand, leaders embracing collaboration with any emerging technologies can gain bandwidth, the routine analysis is automated, and set human energy free to create, invest in relationships, and renew.
Three Lessons for Leaders
The journey from records to foresight comes with three critical lessons. For instance, a balanced quarter end may mask shifting market dynamics, emerging competitors, or changing customer expectations, which makes the case for analytics, exposing the hidden facts that matter most. Secondly, a KPI detached from outcomes is irrelevant, so what matters really is not the percentage but the story, how it connects to empowerment, resilience, or innovation. Finally, there’s a constant need for renewal. Just as the biblical Jubilee periodically reset debts and restored balance after 49 years, metrics can fossilize, and the role of analytics is to remind leaders when it is time to refresh incentives, redesign processes, or renew culture. These lessons apply at every scale, either the personal decisions of moving to a new job or department in the same company to corporate strategy, and in the governance of global enterprises.
From Prediction to Guidance
By 2025, artificial intelligence will no longer be limited to task automation, and today people use it for companionship, therapeutic dialogue, and even meaning-making. In organizations, capability centers are evolving into strategic hubs where synthetic data allows experimentation, and analytics do more than just predict: they prescribe. The shift is subtle but will send seismic waves with predictive capabilities that provide probability, in the same way guidance leads to purpose. The big differentiator is not the model itself but the narrative wrapped around it, when Leaders must frame outputs in terms of mission, values, and strategic intent. A prescriptive model can recommend shifting supply chains, but only people at the helm of their ships can explain the move in terms of resilience, sustainability, and long-term competitiveness. Numbers alone are not building trust, but the stories that are enabled are taking that task pretty well.
Data as a Living System
Data Analytics and Artificial Intelligence mark the next chapter of the data journey, and it is reminiscent of what happened a couple of years ago with OCR, RPA, and earlier Machine Learning using Cloud Network. That was never about eliminating human judgment but just amplifying it, for better and for worse. Numbers without context are static symbols; the power to conduct analysis is what reveals the path ahead and the direction to be taken.
Organizations that thrive treat data as an evolving, spiralling, living system, soon understand that it is a dynamic set of information, depending on context, and that must be constantly renewed. This is a system uncovering motion beneath apparent stability, translating metrics into meaning, and equipping Leaders with stories their people can believe in. For business leaders, there are a couple of tasks ahead. For instance, developing narrative intelligence alongside technical capabilities, and using AI as augmentation, not substitution. That ensures metrics are still relevant and actionable before they decay. Data is not a record of what was. It is guidance for what comes next. Leaders who learn to read it as movement, meaning, and renewal will not only navigate disruption but also define the future.