A future without dashboards?

As businesses increasingly seek faster, clearer, and more actionable insights, a fundamental shift is underway in how we interact with data. Traditional dashboards, once the cornerstone of performance monitoring, are being re-evaluated. In their place, new technologies are emerging that promise a more intuitive and proactive approach to decision-making.

Imagine a workday in 2030: instead of sifting through dashboards and KPIs, professionals are guided by AI-driven summaries that highlight key business developments and suggest tailored next steps. These insights are contextual, timely, and delivered through natural, conversational interfaces. What was once a manual search through charts and tables becomes an intelligent dialogue—seamless, targeted, and embedded in daily workflows.

This evolution challenges the role of dashboards as we know them. What if we no longer needed to look at a dashboard at all? What if insights simply found us?

In this article, we explore the limitations of traditional dashboards and examine how emerging approaches, such as conversational business intelligence, data storytelling, and AI-driven nudging, are redefining the future of data interaction.

Dashboards have long been the go-to tools for visualizing business data. They offer snapshots of key metrics, aiming to help teams monitor performance and make informed decisions. However, in many organizations, dashboards are falling short of these goals.

One common issue is information overload. Instead of highlighting what's truly important, dashboards often present a flood of data, leaving users to sift through and interpret the numbers themselves. This can be overwhelming and counterproductive. A study by Zenloop found that 35% of customer experience professionals spend excessive time navigating through numerous dashboards filled with too much information, leading to fatigue and decreased productivity.¹

Dashboards frequently display data without sufficient context, making it challenging to understand the significance of the information or what actions to take. Without clear explanations or narratives, users may misinterpret data or overlook critical insights. This gap between data presentation and actionable insight can hinder effective decision-making.

Even when dashboards successfully highlight issues, they often stop short of suggesting next steps. Users are left to determine the appropriate actions, which can delay responses and reduce efficiency. This separation between analysis and action means that valuable insights may not translate into timely or effective decisions.

While dashboards are valuable tools, their current implementation in many organizations leads to information overload, lack of context, and a disconnect between insights and actions. To enhance decision-making, it's crucial to evolve these tools to provide clearer, more contextualized, and actionable information.

Read all about how we can help you streamline your Actionable Insights: AI insights - KPMG Belgium

So, let’s look ahead! The shift to actionable insights without dashboards is already happening. Conversational Business Intelligence – is where you interact with your data in a conversational style, through an AI system. And it’s already being built by organizations with the right infrastructure.²

Emerging ways to present data include data storytelling, nudging, and providing personalized action lists. Let’s see how AI might build further upon these ways to interact with data.

Data storytelling is the art of transforming complex data analyses into compelling narratives that inform decision-making and inspire action. It combines data, visuals, and narrative to contextualize information, making it more accessible and engaging for diverse audiences.

For instance, an operational customer service team might be much more motivated by seeing a smiley, representing the customer sentiment, rather than a complicated chart with statistics and (lagging) targets.

At its core, data storytelling involves three key components:

1. Data: Accurate and relevant data serves as the foundation, providing the factual basis for the story.
2. Visuals: Charts, graphs, and other visual elements illustrate data trends and patterns, aiding in comprehension.
3. Narrative: A coherent storyline ties the data and visuals together, providing context and highlighting the significance of the insights.
   

This approach enables stakeholders to grasp complex information quickly, facilitating informed decision-making.

AI can take this a step further, generating a narrative like, "Sales dipped by 10% last quarter, primarily due to decreased demand in the European market." This approach leverages natural language generation to provide context-rich explanations, enhancing understanding and retention.

Data-driven nudging is a strategy that leverages data analysis to design and implement subtle interventions—known as "nudges"—aimed at influencing individuals' choices and behaviors without restricting their freedom of choice. Rooted in behavioral economics, this approach utilizes insights from data to understand decision-making patterns and then strategically adjusts the choice architecture to steer individuals toward more beneficial outcomes.

Unlike traditional dashboards that present data for users to interpret, data-driven nudging proactively delivers personalized prompts or suggestions at critical decision points. AI can analyze behavioral patterns and operational data to deliver timely suggestions. For instance, if customer engagement drops, the AI might prompt, "Consider reaching out to clients who haven't interacted in the past week." Such nudges are grounded in behavioral economics and have been shown to effectively influence decision-making when personalized and context-aware.³

Data-driven personalized action lists are curated sets of tasks or recommendations tailored to an individual's specific role, objectives, and real-time context. Unlike traditional dashboards that present a broad array of data visualizations, these action lists distill complex information into clear, prioritized actions, facilitating more efficient and effective decision-making.

While dashboards offer a comprehensive view of various metrics, they often require users to interpret the data and determine subsequent actions. In contrast, personalized action lists streamline this process by:

• Enhancing focus: By presenting only the most pertinent actions, users can concentrate on tasks that have the highest impact.
• Reducing cognitive load: Simplifying complex data into actionable items minimizes the mental effort required to interpret information.
• Improving efficiency: With clear directives, users can act promptly, reducing the time between data analysis and implementation.
• Increasing accountability: Specific action items can be tracked and monitored, fostering a sense of responsibility and ownership.

Looking ahead to 2030, advancements in artificial intelligence are poised to revolutionize the creation and utilization of personalized action lists. Agentic AI systems, characterized by their ability to plan, reason, and adapt autonomously, will enable:

• Real-time adaptation: AI will continuously analyze data streams to update action lists dynamically, ensuring recommendations remain relevant as situations evolve.
• Predictive insights: By forecasting potential outcomes, AI can prioritize actions that preemptively address challenges or capitalize on emerging opportunities.
• Seamless integration: AI-driven action lists will be embedded within daily workflows, delivering suggestions through preferred communication channels, such as email or collaboration platforms.
• Enhanced personalization: Leveraging comprehensive user profiles, AI will tailor action lists to individual preferences, work styles, and performance histories.

These developments will not only increase productivity but also redefine how decisions are made within organizations. As AI systems become more sophisticated, they will shift from being tools that support decision-making to active participants in the decision-making process. 

In a future where AI replaces traditional dashboards, trust becomes paramount. For organizations to rely on AI-driven insights, they must ensure these systems are transparent, accountable, and aligned with human values.

Trust in AI begins with transparency. Explainable AI (XAI) aims to make AI decisions understandable to humans, addressing the "black box" nature of many algorithms. By providing clear explanations of how decisions are made, organizations can foster confidence among users and stakeholders.

While AI can process vast amounts of data efficiently, human oversight remains crucial. Implementing human-in-the-loop systems ensures that AI outputs are monitored and validated by humans, combining the strengths of AI with human judgment.

Establishing ethical guidelines and involving stakeholders in AI development processes are essential steps toward building trust. Engaging diverse perspectives helps identify potential biases and ensures that AI systems align with organizational values and societal expectations.

Transitioning to a future where AI-driven insights replace traditional dashboards requires deliberate organizational changes. It's not merely about adopting new technologies but about reshaping culture, processes, and governance to fully harness AI's potential.

Organizations must prioritize data and AI literacy across all levels. This involves training employees to understand and effectively use AI tools, fostering a culture where data-driven decision-making is the norm. By sharing success stories and highlighting how data has driven improvements, companies can encourage adoption and reduce resistance to change.

Effective AI systems rely on high-quality, well-governed data. Implementing comprehensive data governance ensures data integrity, security, and accessibility. This includes defining clear data ownership, establishing data standards, and ensuring compliance with relevant regulations.

AI adoption should be closely aligned with the organization's strategic objectives. This means identifying areas where AI can add the most value and integrating AI initiatives into the broader business strategy. By doing so, organizations can ensure that AI efforts are purposeful and contribute to overall goals.

Successful AI integration requires collaboration between various departments, including IT, operations, and business units. Encouraging cross-functional teams to work together on AI projects can lead to more comprehensive solutions and facilitate knowledge sharing across the organization.

As AI becomes more integral to decision-making, organizations must ensure ethical considerations are at the forefront. This includes developing clear ethical guidelines for AI use, implementing rigorous testing for bias in AI models, and ensuring transparency in how AI-driven decisions are made. Regularly reviewing and updating these practices is essential to align with evolving ethical standards and regulations.

Building a scalable and flexible Data & AI infrastructure is crucial for accommodating future growth and adapting to changing business needs. This involves selecting technologies and platforms that can evolve with the organization, ensuring long-term sustainability of AI initiatives.