Russell Sarder, CEO & Founder of AI CERTs™ – advancing global AI certification & education.
In 2025, the world will generate an estimated 181 zettabytes of data, nearly double what was produced just three years ago. This exponential growth in data is more than just a technical milestone; it signals a major change in how businesses operate and make decisions. And AI is constantly transforming the way data is being consumed and utilized. AI-driven insights are becoming indispensable at the executive level. It is changing how executives engage with information, understand hidden risks, forecast market shifts and enable precision in decision making that human efficiency cannot match.
The New Roles Of Data-Driven Executives
As data becomes not just an input but a core driver of competitive advantage, executives across the C-suite, from CFOs to CMOs and CDOs, are rethinking how they lead, collaborate and deliver value.
The CDO:
The role of CDOs has moved beyond managing data and ensuring compliance. Today, successful CDOs are strategic partners who turn information into business advantages. While the number of Chief Data Officers continues to rise, many still encounter substantial challenges in their roles.
Research from MIT Sloan shows only 47.6% of organizations consider their CDO role “very successful.”
This gap highlights that many organizations have yet to define and position this significant role. The most functional Chief Data Officers are strategic leaders who have a seat at the executive table, rather than being confined to traditional IT functions.
Forward-looking CDOs are bridging this gap by integrating AI into revenue-generating operations, from product analytics to customer segmentation, demonstrating how data can drive growth and efficiency.
The CFO:
Financial leaders are also using AI as a strategic lever. CFOs are using predictive models to anticipate change, simulate scenarios and guide capital allocation. A study by Deloitte notes that AI-driven forecasting tools are helping CFOs to plan proactively and navigate volatility with speed and precision.
This technology allows finance leaders to move from protecting company finances to actively seeking new opportunities. AI-powered analytics are surfacing hidden risks, optimizing liquidity and even accelerating audits, turning the finance function into a nerve center for real-time decision making.
The CMO:
Chief Marketing Officers are using AI to decode customer behavior and deliver hyper-personalized experiences. AI enables marketing professionals to personalize at scale, measure ROI more precisely and refine messaging based on real-time feedback.
Starbucks has transformed its marketing strategy through its AI-powered “Deep Brew” initiative, which analyzes data from 90 million weekly transactions across 33,000 stores. The company’s former CMO, now CEO, Brady Brewer, leverages this system to create hyper-personalized marketing at unprecedented scale. By analyzing purchase history, app usage, location data and even weather patterns, Starbucks delivers over 400,000 variations of personalized marketing messages daily. This data-driven approach has increased customer spending by 15% to 20% for targeted segments and driven mobile order revenue to represent 80% of all transactions in key markets.
Raw Data To Intelligent Automation
Data has no intrinsic value. It should be shaped, structured and interpreted before it becomes actionable. The journey from raw data to intelligent automation is the foundation of any AI-driven enterprise, and it begins with building trust in the data itself.
In finance, for instance, AI platforms now process thousands of transactions in real time, surfacing anomalies, mapping financial flows and revealing strategic inflexion points.
Unilever implemented robotic process automation (RPA) and AI in its financial planning and analysis (FP&A) processes to automate repetitive, labor-intensive tasks. This automation enabled the consolidation of financial data across regional ERPs, reduced human errors by 78% and accelerated the financial closing cycle from 12 to 5 business days. In Unilever’s ice cream division, IoT sensors integrated with AI platforms eliminated the need for manual cost adjustments in spreadsheets. Unilever managed a 40% reduction in the time dedicated to data integration between departments.
Predictive Modeling For Anticipating The Future
Beyond automation lies anticipation. Predictive models use past patterns to forecast future trends, while prescriptive analytics use advanced models to recommend specific future courses of action.
Deloitte partnered with a leading logistics provider facing frequent disruptions from aging conveyance equipment in its distribution centers. The solution involved outfitting assets with sensors and routing facility-wide data into a shared cloud environment. Analytics then revealed how long equipment would last across the network and flagged where maintenance could prevent breakdowns. Operations moved faster, downtime shrank and the business found itself sharper and more competitive in a demanding marketplace.
AI Insights Into Strategic Agendas
For AI-powered intelligence to deliver results, it must become part of the core decision process, not just a side project. This integration requires three key elements:
• Team alignment between data leaders (CDOs, CFOs, CMOs) to ensure consistent approaches across the company.
• Leadership understanding of what AI is capable of prevents misuse.
• Smart prioritization of projects based on business impact rather than on how advanced the technology is.
Organizations that combine these elements effectively create a positive cycle where data insights shape strategy. AI, when integrated correctly, can be a source of growth in ERP systems that further facilitate strategic processes.
Challenges To Consider
The road to data-driven decision making has its challenges. As organizations expand their data capabilities, they must address:
• Data privacy and security issues that affect both reputation and compliance.
• Scalability issues create the complexity of processing and analyzing data as the volumes grow.
• Over-automation without human oversight can miss the judgment and context needed for complex decisions.
• AI bias that can skew decisions if not carefully monitored.
• Data quality degrades as many organizations struggle with outdated systems and poor data hygiene, making reliable insights difficult.
Data As A Strategic Asset
We have entered an era where data is not just a byproduct of operations but a boardroom currency. It informs where to invest, how to compete and when to pivot. And as AI makes data more actionable, the organizations that thrive will be the ones that treat information as a strategic asset.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
