玩偶姐姐

I recently attended a series of marketing-centric webinars hosted by industry-leading enterprise data cloud vendors, all proclaiming that the business intelligence (BI) dashboard is unofficially dead and that Generative AI-based cloud data platform interfaces would provide a renaissance, if not a clear path to redemption for the future of business intelligence. Further, some recent architectural efforts I’ve been associated with also suggested that many large enterprises in multiple verticals struggle with data operations and the need for dynamic, outcome-driven interaction with data beyond investments in data lake houses, medallion or lambda architectures, and semantic models. All of these are foundational to making BI useful.

After some reflection, I was forced to consider the very real possibility that the BI landscape had not really diversified significantly in at least a decade. To be fair, industry leaders like Microsoft and Salesforce (Power BI and Tableau respectively) according to Gartner, have a significant in the BI client arena and provide tooling that is sophisticated and evolved enough to engage business decision makers with compelling data and data visualizations yet, as I noted in Data trust and the evolution of enterprise analytics in the age of AI, 58% of business decisionmakers rely on gut feel or experience rather than data and information.

While much of the issue is data trust, a larger portion is also based on the need for democratized inquiry, interaction, discovery and most of all, time-to-execution. Creating a semantic layer that is chained to static dashboards doesn’t really provide a significant advantage for anyone who needs to operate at the speed of business. The new basis of competition and market differentiator is not just time-to-insight, it is also time-to-execution. According to , unlocking data with a path to execution as opposed to aggregating and storing data means moving from “systems of record to systems of action.” So, what is the path forward to enabling both insight and action?

Traditional business intelligence is characterized, if not defined by, static dashboards and periodic reporting cycles and is evolving to dynamic, real-time measurement systems. Enterprises invest heavily in data cloud platforms like Snowflake and Databricks to develop medallion and lambda-based lake house architectures under the assumption that creating a malleable “gold” layer will facilitate semantic flexibility in the client and actionable insights.

The reality, however, is not quite that flexible and lacks the urgency of a generative AI interface’s ability to query, visualize and most of all execute agentic workflows. Leading data cloud vendors naturally rose to the occasion with solutions like Snowflake’s and Databrick’s , yet maturity and organizational readiness are key. reveals that “GenAI is not replacing business intelligence; instead, it’s leveling the playing field as all BI vendors integrate generative and agentic AI capabilities.”

What is key here is that the basis of competition and differentiation lies in how vendors deploy these capabilities to transform business measurement paradigms and enable observable and measurable business outcomes. The transition is not only evident in terms of OEM feature support, but also in terms of competitive time-to-action independent of platform features. This is driving enterprises to accelerate business outcomes through combined cognitive intelligence and automation, both agentic automation and generative AI.

Progressive organizations are moving past traditional KPIs to embrace what industry analysts call . These AI-powered metrics adapt to changes in business conditions that dashboards don’t. They provide predictive rather than historical insights and integrate with operational workflows. The result is a clear transformation from high-latency indicators that characterize what happened in snapshots to near-real-time indicators that predict and prescribe with insights that recommend and even execute specific actions. Time-to-action is the new normal and the high watermark for business intelligence, but a generative AI interface alone won’t accomplish this for enterprise business.

The multi-agent measurement revolution

The most significant advancement in business outcome measurement comes from multi-agent AI systems that can coordinate across business functions and ecosystems to provide holistic performance assessments. indicates that successful AI transformation requires moving “from use cases to business processes” and “from siloed AI teams to cross-functional transformation squads.” This approach enables comprehensive measurement across entire business ecosystems rather than isolated departmental metrics.

Whole ecosystem measurements, simulations and actions take business intelligence to the next level. Consider the impact of shifting organizations from static dashboards to dynamic prompt interaction for query and analysis, to agentic workflows triggered by interaction, discovery and broader business ecosystems events with actions brokered by agentic automation. This is a fundamentally different way to engage with data, and it drives the power dynamics in business decision-making down to the lowest common denominator with true, outcome-driven objectives, measures powered by agentic automation and dynamic, on-demand insights.

Progressive organizations are pursuing this through what veteran industry thought leader and architect Eric Broda calls the ““. An agentic AI mesh is a modular, governed system for managing distributed AI agents across teams, tools and data. This architecture enables continuous, real-time measurement of business outcomes while maintaining the transparency and oversight necessary for enterprise governance.

The measurement capabilities of these systems are unprecedented. Multi-agent frameworks can track traditional financial metrics while simultaneously measuring human-AI collaboration effectiveness, process optimization rates and adaptive capacity signals. PwC’s trailblazing adoption and use of demonstrates this integration: They that “people who regularly use AI tools demonstrate productivity gains in their business functions” and can “focus on more strategic work and bring more value to clients”. What about the convergence of these capabilities into a single interface?

How GenAI-based analytics shift the balance of power from static dashboard to dynamic interaction and action

In my most recent architectural experiences with data, analytics and AI solutions in large enterprises, the demand signals for insight democratization and accelerated time-to-action are everywhere. The convergence of GenAI-based analytics and agentic workflows has also gained validation from leading industry analysts. IBM’s recognition as a leader in the 2025 IDC for Business Intelligence and Analytics Platforms specifically highlights the importance of “traceability and explainability of their AI responses” and “building out their semantic layer to ensure accurate responses”. This emphasis on governance and explainability reflects the enterprise requirements for trustworthy autonomous measurement systems.

The opportunity is that a shift to advanced measurement and dynamically cognitive workflow automation means enterprise BI is really all about enterprise agency and it is empowered with dynamic action capabilities that are unprecedented. Organizations with advanced measurement capabilities can identify opportunities faster, respond to market changes more effectively and optimize operations continuously rather than periodically. This ultimately means that we need to accelerate a shift from what was solely time-to-insight to time-to-action. To accomplish this, we need to rethink outcome-based measurement and focus on the outcome after the insight through action in near real time.

The result is what — 20% to 30% gains in productivity, speed to market and revenue that transform entire organizations.

Irrespective of how much advanced technology we apply to business decision-making, it’s all about trust and the need to interactively “trust but verify” the insights that dashboards reveal. We clearly need to build trust through transparent measurement. We’ve discussed the role of trust in business intelligence dashboards and how a balance of “go with your gut” and the data itself results in actions for most business decision-makers.

The reason for this common behavior resides in the static nature of most BI implementations. While users can manipulate data, they can’t really challenge the data effectively without human interaction. That level of dynamic inquiry is key to trusting the insight and taking action. Trust in AI-driven measurement systems requires transparency, explainability and robust governance frameworks. 

The regulatory environment reinforces this requirement for implicit and explicit trust. The imposes stringent requirements on high-risk AI systems, with non-compliance resulting in fines up to 7% of global revenue or 35 million euros. Organizations implementing autonomous measurement systems must ensure they meet these transparency and accountability standards. exemplifies practical implementation of trustworthy AI measurement. Their comprehensive “Responsible AI” training program ensures that AI-augmented employees understand both the capabilities and limitations of autonomous systems. This combination of technology deployment and human oversight creates measurement frameworks that are both powerful and trustworthy.

The future of business measurement: Emerging capabilities and market evolution

The business intelligence market is evolving rapidly toward fully integrated, autonomous measurement systems. This evolution includes advanced capabilities like multimodal analytics, cross-platform agent orchestration and real-time collaboration between human and artificial intelligence.

The integration of GenAI-based and agentic capabilities creates unprecedented measurement opportunities. Organizations can now not only track what just happened or what might happen, but can also implement autonomous systems that continuously optimize performance based on real-time measurement feedback. This creates pivot-ready, self-optimizing business processes that enhance their own effective outcomes over time.

Industry analysis and real-world implementation examples suggest organizations should prioritize three strategic initiatives:

1. Invest in GenAI-based analytics platforms that democratize data access across the organization. The goal is to enable every business user to access sophisticated analytics without technical barriers.
2. Implement agentic workflows on an autonomous basis for high-value, basic business processes where autonomous optimization can deliver measurable returns. Focus on vertical use cases linked to OKRs and KPIs. Organizations should start with pilot opportunities and test agentic simulations in specific domains before scaling across the enterprise.
3. Develop integrated measurement frameworks that capture both traditional business metrics through BI data products and new indicators of AI-human collaboration effectiveness.

The emergence of GenAI-based analytics and downstream convergence with agentic workflows represents more than technological evolution — it’s a fundamental transformation of how organizations understand and optimize their performance.

Organizations that successfully implement these capabilities will gain significant competitive advantages through faster decision-making, continuous optimization and deeper business insights. However, success requires comprehensive strategies that address technology implementation, organizational change management and governance frameworks simultaneously.

The future belongs to organizations that can successfully integrate human intelligence with artificial intelligence to create measurement systems that are simultaneously more powerful, more accessible and more trustworthy than anything previously possible. The convergence of GenAI-based analytics and agentic workflows makes this future not just possible but inevitable for organizations committed to data-driven excellence in an AI-powered world.

This article was made possible by our partnership with the IASA . The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time, as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the, the leading non-profit professional association for business technology architects. 

This article is published as part of the Foundry Expert Contributor Network.
Want to join?