Business leaders today are not just frustrated with the pace or quality of insights — they’re fundamentally disillusioned with the entire analytics process.
After having over 140 one-on-one conversations with data, analytics and business leaders, one thing is clear: the traditional approach to analytics is not just insufficient; it’s fundamentally unsystematic and inconsistent. It’s failing to deliver the impact that modern businesses need to thrive.
The Problem Plaguing Analytics Today
The current state of analytics is plagued by inconsistencies in both quality and speed. Variability in analysts’ experience, combined with manual, labor-intensive processes, leads to insights that are often slow, inconsistent, and unreliable. This lack of reliability erodes stakeholder trust, making it increasingly difficult for analytics teams to meet real-time business needs.
At the core of these issues is the absence of a structured workflow and the manual nature of the current workflows. Today’s analytics processes are fragmented at best, and extremely time-intensive. Analysts frequently take different paths to solve similar problems, leading to inefficiencies and variability in output quality. This unsystematic, inefficient, and unscalable approach stretches time-to-insight from weeks to months. It slows down decision-making, hinders business agility, and makes it nearly impossible to scale processes to support growing business needs.
AI as a Catalyst for Amplifying Human Expertise
AI presents a tremendous opportunity to transform analytics, but its potential cannot be realized within the confines of outdated processes. For AI to be effectively leveraged, we first need a well-structured, repeatable workflow that guides analysts from task definition to data collection to insight generation.
When integrated into a structured workflow, AI can streamline processes, reduce time-to-insight, and improve the quality of insights. This allows analysts to focus on more complex, value-added tasks that require deep contextual understanding, thereby amplifying the impact of their work. In other words, AI should not be seen as a replacement for human expertise but as a powerful enabler that enhances it.
This isn’t about advocating for a magical new approach but rather a logical reimagining of AI’s role within a structured analytics framework to drive efficiency, speed, and scalability. AI brings automation and repeatability, while analysts contribute critical thinking, contextual understanding, and the ability to interpret nuanced insights. However, it’s crucial to remember that AI cannot automate what hasn’t been clearly defined. A well-defined, structured workflow must come first to ensure AI-driven solutions address inefficiencies and elevate the overall quality and speed of insights.
Challenging the Status Quo: Lessons from Other Functions
When we look at other business functions like sales or operations, we see that they’ve successfully adopted structured workflows, followed by workflow management systems and eventually AI-enhanced automation. The belief that analytics is inherently more complex and therefore harder to streamline is a misconception that needs to be challenged. Analytics leaders must embrace the principles of standardization to transform their teams from reactive data providers to proactive business partners.
Building a Future-Ready Analytics Function
To build a future-ready analytics function, process standardization is key. By establishing a well-defined, structured workflow, analytics teams can ensure reliable data collection, which is the key enabler for leveraging AI. Once this foundation is in place, AI can streamline processes, automate repetitive tasks, provide predictive insights, and even suggest next steps based on historical data.
AI should be seen as an opportunity, not a threat. A repeatable, AI-driven process provides the consistency and speed that modern businesses demand, while enabling analysts to focus on critical thinking and contextual insights that make data actionable. This balanced approach not only makes analysts more efficient but also increases stakeholder satisfaction by ensuring insights are both timely and deeply meaningful.
Elevating Analytics Leaders to Strategic Partners
This transformation is not just about improving processes; it’s about elevating analytics leaders to a more strategic role within their organizations. By consistently delivering high-quality, timely insights, analytics leaders can position themselves as strategic partners rather than mere service providers.
This shift is crucial for analytics leaders who want to move beyond being seen as service providers. A better approach to analytics could have a profound impact across different organizational roles:
- VP of Analytics and CDAOs: They could better visualize and communicate the impact of both their work and their teams’ contributions, shifting from mere service providers to strategic business partners who drive value across the organization.
- Directors of Analytics: By industrializing and streamlining their teams’ output, they could improve customer satisfaction and increase team motivation. This efficiency would allow them to deliver more consistent and impactful insights.
- Analysts: Freed from repetitive tasks, analysts could focus more on generating meaningful insights, being consultative, and proactively contributing to business outcomes.
- Business stakeholders: They could gain faster, more reliable insights to make informed decisions. This enables leaders, like Sales or Marketing VPs, to quickly identify challenges—such as missed forecasts or underperforming campaigns—and take action to drive business outcomes.
The Path Forward
The urgency for change is clear. Embracing a new approach to analytics—one that is driven by AI and balanced with human expertise—is the only way to meet the demands of the modern business environment. The future of analytics isn’t about choosing between AI and human expertise but about finding the right balance. This harmony is essential for delivering actionable insights that drive business success and satisfy the needs of all stakeholders.
Analytics leaders must lead this transformation, positioning themselves and their teams as indispensable partners to the business. It’s time to rethink analytics for the AI era.
