Striking the Balance: Automated KPIs Dashboards vs AI/ML Use Cases Experimentation — D&A Analytics Change Journey
Gaurav Sinha
Continuous Improvement Leader @ Syngenta | Strategy Deployment, Business Transformation, Productivity Delivery, Outcome-based Problem Solving
In today’s rapidly evolving business landscape, data is the backbone of decision-making, especially in supply chain management. Organizations striving to stay competitive need more than just data—they need insights, both real-time and predictive. After successfully completing Phase 1 of our centralized reporting initiative with dashboards, we’ve seen significant improvements in decision-making agility. However, as we gear up for Phase 2, a new challenge has surfaced;
Should we continue investing in dashboards, or is it time to focus more on AI/ML opportunities?
This debate brings forward two vital, yet often misunderstood, perspectives: the immediate value of dashboards and the long-term potential of AI/ML-driven insights.
Phase 1: Why Dashboards Matter—and Why ROI is Hard to Measure
Dashboards can fundamentally transform supply chain operations by centralizing reports and establishing a single source of truth. The first POC has enabled our teams to access real-time data, monitor KPIs, and improve transparency. However, we’ve encountered a familiar challenge—how do we effectively measure the ROI on dashboards?
If you’ve been in a similar situation, you know that measuring dashboard ROI can be difficult. The value they bring is often indirect: faster decision-making, increased alignment, and improved efficiency across teams. But when leaders start asking, "How much has the dashboard investment increased revenue or reduced costs?" the answers aren’t always straightforward.
Have you struggled with proving the ROI of dashboards to leadership? How have you navigated this challenge?
What we’ve realized is that dashboards aren’t just a tool for today—they’re an essential part of the broader data journey. They lay the foundation for advanced analytics, ensuring that data is accessible and usable across the organization. In fact, without the clarity and visibility dashboards provide, moving into more advanced AI/ML initiatives would be far more difficult.
Phase 2: Exploring AI/ML Use Cases —Is Your Data Ready?
As we enter Phase 2, there’s a growing interest in leveraging AI and Machine Learning to extract deeper, predictive insights. AI/ML holds tremendous potential to:
- Predict future demand and disruptions: With better forecasting, supply chain teams can optimize inventory and mitigate risks.
- Enhance operational efficiency: AI-driven insights can reveal patterns in the data that humans may overlook.
- Automate decision-making: Over time, AI models can automate routine supply chain decisions, freeing up time for strategic thinking.
But the question remains: Is your organization ready for AI/ML if your foundational data isn’t in the best shape?
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If you’re facing similar challenges, you may find that AI/ML investments are being discussed before fully realizing the benefits of dashboards. It’s tempting to want to jump ahead to the latest technology, but advanced insights can only be as good as the data they are built on. Centralized, clean, and well-structured data from dashboards is the bedrock for AI/ML success.
How are you ensuring your data is AI/ML-ready, and do you see dashboards as part of that journey?
The Data & Analytics Journey: It’s a Change Process
It’s important to acknowledge that building a Data & Analytics (D&A) capability is a journey. It’s not just about implementing the latest tools—it’s about transforming how an organization works with data over time. This journey involves a cultural shift, upskilling teams, and building confidence in the data-driven decisions enabled by dashboards, AI, and ML.
If you’re also in the midst of a D&A transformation, you know this isn’t just about technology—it’s about change management. Leaders and teams need to be ready for a long-term capability-building process that requires patience and continuous refinement.
Moving Forward: The Balanced Approach
Rather than seeing dashboards and AI/ML as opposing investments, we believe the path forward is about balance. Dashboards provide the immediate insights and transparency necessary for today’s decision-making, while AI/ML holds the promise of predictive insights for tomorrow. By investing in both, we can build a data strategy that delivers value in both the short and long term.
For those of you navigating similar discussions, how are you balancing immediate dashboard ROI with the future potential of AI/ML?
Our goal is clear: to create a data-driven supply chain that empowers leadership with the tools to make smarter, faster, and more informed decisions. Whether it’s through real-time dashboards today or predictive models tomorrow, the future of supply chain management will be defined by our ability to harness data effectively.
Thank you for taking the time to read and engage with these thoughts. I’d love to hear your views—how are you navigating the balance between dashboards and AI/ML in your own data journey? Please feel free to share your experiences and insights in the comments, as we can all learn from each other’s challenges and successes.
Senior Sales Dev Rep @ Yeeflow | Driving digital transformation via rapid app development | Workflow Automation
2moThis is a great exploration of the balance between the immediate value of dashboards and the long-term potential of AI/ML in supply chain management. I completely agree that dashboards provide the critical foundation needed for AI/ML to succeed, especially in ensuring clean and accessible data. Your emphasis on not rushing ahead with AI/ML without building the right data infrastructure first really resonates. It’s a thoughtful approach to creating a data-driven culture that balances both short-term gains and future innovations. Thanks for sharing!
Chief Learning Officer at ProSkills.training: Training-as-a-Service platform from RapidEzy Training Systems, with integrated LMS & e-learning library with thousands of readymade courses.
2moI agree
Manager - Financing at Siemens | Ex-BMC
2moIn my opinion, this debate between dashboards and AI/ML is crucial. Dashboards offer immediate visibility into operations, but AI/ML can provide deeper predictive insights and automation. The decision to continue with dashboards or invest in AI/ML might depend on the maturity of the current system and the company’s future goals. Combining both technologies could be an effective strategy—using dashboards for current-state visibility while integrating AI/ML for long-term forecasting and automation.
Navigating Transitions, Transformations & Projects | Driving Change & Operational Excellence | Empowering Decisions through Data-Backed Strategies
2moWell articulated Gaurav ! The transformation journey hinges on leveraging near real-time data analytics aligned with organizational goals. While quantifying individual report impact may be challenging, combining data-driven insights with swift action and a culture of continuous improvement yields tangible success. This collaborative effort drives: - Improved Key Metrics - Improved customer satisfaction scores - Enhanced employee engagement Data-backed results demonstrate the power of teamwork and informed decision-making.
Senior Data Scientist
2moI totally buy your point on balanced investments. Quite often it is said that spends to leverage AIML is not going to yield much until the foundational journey from descriptive to prescreptive analytics has been followed deligently in the right order. True that, but refraining to adopt something which can be game changing (though with certain degree of complexity)even with limited capacity won’t be fair either. The optimal way forward could be to determine the magnitude of investment and scale of deployement of these things as a function of need and more importantly readiness.