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      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.

      The challenges of dashboards

      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.

      Too much data, not enough clarity

      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

      Lack of context and actionability

      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.

      Disconnect between analysis and action

      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

      Peter Van den Spiegel
      Peter Van den Spiegel

      Partner, Head of Lighthouse | Advisory

      KPMG in Belgium

      The good thing about dashboards

      Despite their challenges, dashboards are an integral part of many organizations: from an operational level all the way through strategic reporting. Here are two elements that are difficult to replace:

      1. Dashboards as tools for transparency and alignment

      Dashboards offer a centralized view of key performance indicators (KPIs), facilitating transparency across departments. They enable teams to align on objectives, monitor progress, and identify areas needing attention. This shared visibility fosters a culture of accountability and continuous improvement.

      2. The human element in decision-making

      Dashboards provide data, but human judgment remains crucial in interpreting and acting upon that information. Understanding organizational dynamics, employee morale, and customer sentiments often requires insights beyond what dashboards can offer. As Kim Morrish aptly noted, subtle indicators—like employee engagement levels—are vital for timely interventions and are not always captured in dashboard metrics.

      A future without dashboards?

      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: crafting narratives from numbers

      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.

      Nudging: gentle prompts toward better decisions

      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

      Personalized action lists: tailored tasks for targeted outcomes

      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. 

      Can AI really replace the controller, data scientist, engineer?

      As AI continues to advance, its capabilities are reshaping traditional roles within organizations. While AI can automate many tasks, the complete replacement of roles such as business controllers, data scientists, and data engineers is nuanced.4

      Business controllers: from number crunchers to strategic advisors

      AI can automate routine tasks like financial reporting and variance analysis, allowing business controllers to focus on strategic decision-making. By handling data aggregation and initial analysis, AI enables controllers to interpret results, provide context, and advise on business strategies. This shift transforms the role from transactional to strategic, emphasizing the importance of human judgment in interpreting AI-generated insights.

      Data scientists: collaborators with intelligent systems

      AI tools can handle data preprocessing, model selection, and even some aspects of predictive analytics. However, data scientists are essential for defining problems, ensuring data quality, and interpreting results within a business context. Their expertise guides AI applications, ensuring that insights are relevant and actionable. Rather than being replaced, data scientists are becoming collaborators with AI, leveraging its capabilities to enhance their analyses.

      Data engineers: architects of AI infrastructure

      While AI can assist in tasks like data pipeline creation and maintenance, data engineers are crucial for designing and overseeing the infrastructure that supports AI systems. Their role involves ensuring data integrity, security, and accessibility, which are foundational for effective AI operations. As AI systems become more complex, the need for skilled data engineers to manage and optimize these systems remains critical.

      How will we trust AI?

      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.

      Transparency and explainability

      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.

      Human oversight and accountability

      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.

      Ethical governance and stakeholder engagement

      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.

      Where are we today?

      In 2025, the integration of AI into business operations has accelerated, signaling a shift toward more intuitive and automated decision-making processes. However, the complete transition to a dashboard-free environment remains a work in progress.

      AI adoption across business functions

      Recent surveys indicate a significant uptick in AI utilization across various business domains. According to McKinsey, 78% of organizations have incorporated AI into at least one business function, with IT, marketing, and sales leading the way. Notably, the use of generative AI has also seen a substantial increase, with 71% of respondents reporting regular usage in at least one function.5

      Emergence of agentic AI

      The concept of agentic AI—autonomous systems capable of making decisions and performing tasks without human intervention—is gaining traction. These systems leverage advanced techniques like reinforcement learning and deep learning to adapt and optimize processes dynamically. Such capabilities are instrumental in moving beyond static dashboards to more responsive and personalized decision-support tools.

      Challenges and considerations

      Despite these advancements, several challenges hinder the full realization of a dashboard-free future. Data integrity remains a critical concern; ensuring that AI systems have access to accurate and relevant data is paramount for effective decision-making. Moreover, the need for human oversight persists, especially in complex scenarios where contextual understanding and ethical considerations are essential.6

      The road ahead

      While the vision of AI-driven, dashboard-less decision-making is compelling, organizations must navigate the transition thoughtfully. This involves not only technological upgrades but also cultural shifts, workforce training, and the establishment of robust data governance frameworks. As AI continues to evolve, its role in transforming business intelligence will undoubtedly expand, paving the way for more agile and informed strategies.

      What must organizations do to enable this future?

      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.

      Cultivate a culture of data and AI literacy

      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.

      Establish robust data governance frameworks

      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.

      Align AI initiatives with business strategy

      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.

      Foster cross-functional collaboration

      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.

      Implement ethical AI practices

      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.

      Invest in scalable and flexible data & AI infrastructure

      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.

      So, can we get rid of dashboards?

      Even in 2030, in the imagined future where AI can do a lot of work for us, dashboards will play a role in decision-making. Dashboards were good for:

      • Giving us control
      • Building shared understanding
      • Creating rituals of review

      Even in 2030, executives will want a bird’s-eye view—a visual validation. But the value of a dashboard has shifted: from static representation to dynamic guidance. Dashboards won’t be the centerpiece. They’ll be sidebars—supporting actors to AI-driven advisors.

      Find out how we can help you on your journey to a data and AI powered organization.

      Share your thoughts! If you have any questions, feel free to reach out! 

       

      Author: Han van der Ven, Manager Advisor