<div id="readability-page-1"><div>Gargi Ray, VP Finance, Group Revenue Operations, Synopsys Inc.​In my last article, we explored why 95% of AI projects stall at the prototype stage and how to bridge the gap toward integrated, governed financial workflows. But infrastructure is only half the battle. To unlock true enterprise value, we must cross the next threshold: moving beyond data retrieval—the mere &#34;finding&#34; of information—to sophisticated reasoning to help drive strategic decisions. Traditional business intelligence (BI) has reached its architectural ceiling. Over the last decade, finance departments have spent millions building robust data lakes and thousands of &#34;dashboard stories.&#34; However, these tools were designed for information retrieval, not intelligent insights. Dashboards are fundamentally descriptive; they show revenue growth or receivables increase, but they rarely explain why it happened or what the fundamental drivers behind the change were. They provide the &#34;what&#34; but leave the &#34;why&#34; and figuring out &#34;what to do next&#34; to the human expert. Dashboards typically give easy answers to standard questions: quarter-on-quarter (QoQ) revenue change, regional comparisons, cash collected to date or open receivables aging. However, the moment a complex, multivariable question is posed to an analyst—for example, &#34;Why did our QoQ sales increase in the U.S. region while our days sales outstanding (DSO) did not show improvement?”—an isolated dashboard fails to provide an answer.Historically, resolving these queries required a manual, labor-intensive process that relied heavily on human intervention and intuition of the key drivers. It required opening multiple dashboards, quantifying the effect of dozens of different factors, and using our own expertise to piece together the narrative. That process looked something like this: We reviewed the U.S. regional sales comparison QoQ and explained the top customers and orders that caused revenue growth. Then we checked the average billing and collection of the customers to review what factors contributed to the DSO, whether it was delayed billing or providing them with extended payment terms or a delayed collection.It is a process that is slow, with drivers limited by human bias, and, most importantly, it does not scale in a $10B-plus revenue environment where thousands of such variables exist across different business units.​The evolution toward agentic AI—specifically multi-agent orchestration—changes the way insights are obtained. Instead of isolated stories through dashboards, we are building autonomous agents that research and reason across different databases. At our company, this meant moving away from isolated dashboards toward a system where specialized agents with business-specific context perform complex, multistep deep dives. These agents act in a collaborative ecosystem rather than siloed tools. They are capable of &#34;reasoning&#34; through a problem. Consider the sales versus DSO problem again. In an agentic framework, the query triggers a coordinated effort: The revenue agent reviews the revenue growth and identifies the specific top-tier customers and orders that contributed to the increase. The billing agent reviews the bill plans and specific payment terms for those exact contracts, identifying any &#34;Net 60&#34; or &#34;Net 90&#34; outliers. The collections agent analyzes those specific customers' typical &#34;days-to-pay&#34; over the last 24 months to quantify the impact on the current DSO. The orchestrated system then provides a unified insight: &#34;Sales rose 15%, but DSO increased by four days because three large deals were signed with 90-day terms to secure multiyear renewals.&#34;This is not just &#34;chatting with your data.&#34; It is a move toward autonomous revenue operations. When a query enters the system, these agents don't just pull a number; they review multiple data points in multiple databases and analyze the drivers.​The goal is to provide a unified insight that previously took a team weeks to compile. In the past, analysts were responsible for navigating multiple views, requiring significant domain expertise just to find the right data point and largely were driven by known drivers. Today, the agent can perform an unbiased analysis of multiple data points to determine the true drivers of the change.​In the successful 5% of companies, AI isn't just a tool for the analyst; it is the first-pass analyst, operating at an enterprise scale previously thought impossible. The human expert moves from being the data-gatherer to the strategic decision-maker, reviewing the agent's “reasoning&#34; and executing on the insight. We are moving from a world where we spend 80% of our time gathering data and 20% analyzing it, to a world where the gathering is instantaneous and the analysis is augmented, enabling leadership to understand the precise levers available to drive growth in real time.​To transition from passive dashboards to active reasoning, the shift isn't just about better software—it’s about architecting for information flow. This begins with three actionable steps: 1. Harmonizing data definitions across bookings, sales, billing and collections so your data speaks a single language.2. Extracting logic out of manual spreadsheets and into governed database objects so that every insight is digitally accessible.3. Defining the &#34;dictionary&#34; of your business. This allows an agent to understand the meaning of a query, not just its syntax. By moving from simple data management to “context readiness,&#34; we stop building reports and start building a foundation for autonomous intelligence. True leadership in the modern enterprise is no longer measured by the automation of a report, but by the speed of the insights behind it. Stop building dashboards to tell you what happened—start building reasoning engines to tell you what to do for your next action.​​<hr/>Forbes Finance Council is an invitation-only organization for executives in successful accounting, financial planning and wealth management firms. Do I qualify?<hr/></div></div>

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Gargi Ray，Synopsys Inc. 财务副总裁，讨论了财务运营中从传统仪表板到自主人工智能的演变。虽然仪表板提供了描述性见解，但未能解释变化的根本原因。向多智能体编排的转变允许在数据库之间进行复杂推理，从而实现更快速和无偏见的分析。这一转变使分析师能够专注于战略决策而非数据收集，最终提升企业价值和运营效率

Death Of Dashboards: The Evolution From Data Retrieval To Reasoning And Insight

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仪表板的消亡：数据检索向推理和洞察的演变