Here's how it works and why the architecture behind it matters.
Think about what a typical decision cycle actually involves. A finance analyst spots a variance that needs a planning adjustment. To act on it, they export the relevant data, rebuild the model, circulate it for approval over email, and then manually update the ERP — a process that takes days and involves three or four people, each working from data that gets a little older at every handoff. The decision that gets entered into the system at the end of that chain is based on a version of the data that no longer exists.
The reporting side of the problem is similar. When a planning team needs to distribute a monthly operations report to seven stakeholders — each requiring a version scoped to their region or business unit — someone has to build those seven versions by hand. Half a day of work, minimum. And once the reports are sent, they're static: if the underlying data changes, there's no way to push an update, so stakeholders end up making decisions from whatever snapshot they happened to receive.
Neither of these is an unusual situation. They're how most organizations manage operational decisions at scale — and both share the same structural gap: the insight lives in the analytics platform, while the action and the visibility live somewhere else.
Orchestrate Workflows handles the operational side of the insight-to-action problem: getting decisions out of Incorta and back into the systems and stakeholders that need them. There are three core capabilities.
When a decision is made inside Incorta - a planning adjustment, an approved exception, an updated allocation - Orchestrate Workflows can push that change back to the system of record directly, without requiring a manual export-import cycle or a separate integration project.
Here's what that looks like in practice. A financial planning team is reviewing December performance. Last year closed around 1,300 units. The current forecast projects 1,390. But the team knows December is peak season - demand is strong, promotional spend is already planned - and they want to push the target to 1,499 units, with revenue projected at $23,227 and marketing spend adjusted to $2,974 to support it. They update the figures, add a note to finance explaining the rationale-— this is holiday peak, the plan is aggressive, it's backed by promo spend - and save.
That's it. The numbers write back directly to the source planning system. Finance sees the updated plan. The demand gen team sees the same spend figures and the same reasoning. No export, no email thread, no reconciliation meeting a week later when someone realizes two teams are working from different versions of the forecast.
The market sometimes calls this reverse ETL. The framing is accurate but undersells the governance dimension: it's not just moving data back upstream, it's doing it within the same access controls and audit infrastructure that governed the original analysis.
Report bursting is the capability to distribute a report to many stakeholders simultaneously, with each version automatically filtered and formatted for its intended recipient. Instead of producing one version of a report that contains everything and is relevant to no one in particular — or producing twelve versions manually — the platform generates and routes them automatically based on role, region, business unit, or whatever dimension the report is organized around.
The time savings are the obvious benefit. The less obvious benefit is accuracy: when the same underlying data populates every version of a report, you eliminate the reconciliation errors that arise when different teams are working from manually produced exports at different points in time.
Decisions rarely affect only one system. An inventory adjustment touches the ERP and the procurement platform. A revenue update touches the CRM and the financial reporting system. Orchestrate Workflows connects Incorta to the rest of the stack — SAP, Oracle, Salesforce, and others — allowing a single decision or trigger inside the platform to initiate coordinated updates across multiple downstream systems. New integrations can be added as needs evolve, without requiring full re-engineering of existing workflows.
Write-back sounds straightforward. In practice, it's one of the most trust-sensitive operations in enterprise data management. Writing incorrect or stale data back to a system of record doesn't just produce a bad report — it can corrupt the authoritative data that the rest of the organization depends on.
The reason Orchestrate Workflows can do this reliably is the same reason Incorta's analytics are reliable: the data being written back was never a copy. Incorta's Direct Data Mapping technology keeps the platform connected to source-level data, so there's no reconciliation gap between what Incorta sees and what the system of record holds. The write-back is grounded in an accurate, current understanding of the data state — not in an export from last night's ETL run.
That distinction matters most in finance and supply chain, where decisions are made on the basis of yesterday's numbers and acted on against today's reality. Orchestrate Workflows keeps both sides of that equation aligned.
Most data governance thinking is oriented inward: who can access what, what can be queried, what requires approval before it's viewed. Orchestrate Workflows extends that orientation outward: who can write what back to which systems, under what conditions, with what audit trail.
This matters because write-back without governance is a significant operational risk. An analytics platform that makes it easy to push changes to source systems with no record of who authorized what is a platform that's going to create compliance problems.
Orchestrate Workflows handles this through the same governance model that covers everything else in Incorta — same RBAC, same audit logging, same lineage tracking. Every write-back is associated with the user or process that initiated it, the data state that prompted it, and the approval chain that authorized it. The audit trail doesn't start when the data leaves the platform — it starts at the original analysis.
Finance and Close Processes
Month-end close is one of the highest-stakes, highest-friction workflows in any organization. Orchestrate Workflows supports the write-back dimension of close — pushing approved adjustments and reconciled figures back to source systems and distributing the resulting reports to the right stakeholders automatically. What used to take multiple days of manual coordination compresses significantly when the distribution and write-back steps are automated.
Supply Chain and Inventory Management
When an inventory signal requires a procurement response — a reorder, an expedite, a vendor notification — the decision needs to reach multiple systems and multiple stakeholders at once. Orchestrate Workflows handles that distribution, ensuring the ERP, the procurement platform, and the relevant team members all receive consistent, current information from the same source.
Planning and Forecasting Cycles
Planning cycles involve writing a lot of data back to source systems — updated forecasts, revised allocations, approved targets. The manual version of this process involves exporting data from one system, reformatting it, and importing it into another. Orchestrate Workflows replaces that with a governed, automated pathway that keeps planning data and operational data in sync — the same way the December forecast example above works in practice, but applied across every planning cycle the business runs.
Orchestrate Workflows is the outbound half of a complete insight-to-action capability. Incorta Builder provides the interface for human-initiated actions — apps where a user makes a decision and takes the next step. Agentic Workflows handles automated responses to data conditions. Orchestrate Workflows handles the downstream consequences of those decisions: the write-back to source systems, the distribution to stakeholders, the cross-platform coordination.
Together, they represent a complete loop: data comes in from source systems, gets analyzed and acted on inside the platform, and the results flow back out to the systems that need them.
Learn more at incorta.com/incorta-builder, or watch this demo.
The way enterprise software gets built is changing - and it's not a slow change. Teams that used to wait six months for IT to deliver an app are now describing what they need in plain language and seeing something functional the same week. That shift is real, and it's creating pressure on every analytics platform to answer a question it wasn't originally designed to answer: can your users act on data here, or do they have to go somewhere else to do it?
Incorta Builder is our answer to that question. Here's what it is, how it works, and why the data foundation it runs on makes a meaningful difference.
Most analytics platforms are very good at getting to the insight. They surface the answer, display the variance, flag the exception. What they don't do is close the loop. The moment a user needs to act on what they've found—submit an approval, update a record, trigger a downstream process, run a what-if scenario with live numbers—they leave the platform. They open a spreadsheet. They file an IT request. They wait.
That gap between finding the answer and completing the next step is where time disappears, data drifts, and the value of the original analysis erodes. By the time the action happens, the data behind it is already a version old.
Incorta Builder is designed to eliminate that gap by letting teams build the apps that complete the work—directly on top of the same live, governed data they're already analyzing.
Builder is a tightly integrated, low-code platform for creating custom AI-powered apps within the Incorta environment. Apps built in Builder aren't separate tools that happen to pull from Incorta data—they're first-class applications that run inside the platform's governance model, inherit its security controls, and stay connected to live source data without any additional pipelines or copies.
The technical foundation includes:
AI-assisted app creation and vibe-coding capabilities mean that the technical barrier to building has dropped significantly. A business analyst with domain knowledge and a clear picture of the workflow they need can get to a working prototype in days. The same process that used to require formal requirements documents, backlog prioritization, engineering resources, and a QA cycle now starts with describing what you're trying to accomplish.
There's a category of app-building tools that makes it easy to build interfaces. What's harder—and what most of them don't solve—is ensuring the data those interfaces run on is accurate, current, and governed.
Builder apps can query live data directly from Databricks, Snowflake, Oracle Apps, SAP, and Salesforce via REST API or JDBC connections, and can perform cross-platform joins for data analysis across those sources. Most analytics and app platforms connect to a warehouse or lakehouse that holds a copy of operational data. The copy is recent, but it's still a copy—it arrived through ETL, carries some lag, and may not preserve the row-level fidelity that operational decisions require.
Builder apps connect directly to source systems without replicating data through a transformation pipeline. The data an app displays and acts on is source-identical. There's no reconciliation gap between what the app sees and what the system of record holds.
For operational decisions, that matters. A finance app that runs a what-if scenario on numbers from yesterday is a different thing than one running on today's actuals via a live connection. An inventory app making a replenishment recommendation on data that's six hours old is making a different recommendation than one working from the current state.
One of the consistent failure modes for enterprise app democratization is governance. A platform makes it easy to build apps, usage grows, and eventually someone surfaces a compliance issue or a data leak—because the apps were built outside the governance model the organization relies on.
Builder sidesteps that failure mode structurally. Because apps live inside Incorta's platform, they inherit the same RBAC, row-level security, and audit infrastructure that governs everything else. There's no separate security model to configure, no permission mapping to maintain, no question of whether the app is respecting the same access rules as the dashboard next to it.
From an IT and data governance standpoint, this changes the conversation. Instead of managing a parallel universe of informal apps built on copied data with inconsistent permissions, you have a governed catalog of production-ready applications built on the same foundation as everything else.
Early adoption follows a practical pattern. In the initial release, IT teams, BI developers, and advanced analysts handle building and deploying apps. Business users consume them. That's the model that gets to production fastest with the least governance risk.
Over time, AI-assisted creation capabilities lower the barrier further—making it realistic for technically minded business users to build for their own teams without requiring engineering support. The goal is to make it possible for the people who understand the problem best to participate in building the solution, rather than relying entirely on a requirements handoff process that inevitably loses context.
The business case for Builder is most compelling when you look at what it replaces. Many organizations are running a mix of niche SaaS tools—lightweight CRM overlays, approval routing tools, inventory management interfaces, planning apps—each purchased to solve a specific workflow problem, each requiring its own login, its own data integration, its own training program, and its own license renewal.
Builder doesn't replace mature, full-featured platforms where the breadth of functionality justifies the complexity. It does replace the tail end of that SaaS stack—the tools that were bought because building something custom felt impossible, and that now add cost, sprawl, and integration overhead without delivering proportionate value.
The right use cases are the targeted ones: a workflow that a team runs weekly but that doesn't justify a standalone software purchase; an approval process that needs to know something about your ERP data to work correctly; a planning interface that needs to write back to a source system. Builder handles those cleanly, without adding another system to the data governance map.
Builder is one part of a larger architectural direction in Incorta Intelligence. The platform's premise -that insight and action should happen in the same environment, on the same data, under the same governance model - requires both the ability to analyze and the ability to act. Builder handles the action surface for human-initiated workflows. Agentic Workflows handles the automated ones. Orchestrate Workflows handles the write-back and downstream distribution.
Together, they represent a different answer to the question every analytics platform is now being asked: what happens after the insight? Incorta's answer is that it happens here—in the same platform, on the same data, without leaving to go somewhere else.
Many analytics vendors and thought leaders have been proclaiming that "dashboards are dead," suggesting that AI-driven chat and automated insights would render the traditional BI dashboards as obsolete. They aren't entirely wrong in terms of the sentiment. However, the reality is more transformative than the morbid tag line suggests. Dashboards aren't being replaced; they are being supercharged to serve as the foundation for the next generation of decision intelligence.
The shift we are seeing moves us away from dashboards as isolated, static tools for interactive analysis toward agentic applications and workflows. In this new era, the dashboard serves as the critical context for AI agents to understand business logic that sits in a layer above the semantic layer, navigate complex data lineage, and deliver explainable insights that can be extended to handle ad-hoc follow up questions, build advanced analytical applications and trigger actionable decision workflows.
Every mature organization began its analytics journey with a noble charter; purpose built and curated dashboards, a higher level abstraction to hold business logic for aggregated and cross fact, cross entity analysis which was the basis for charts and visual analysis. The drill down and drill across logic between these insights then served as the blueprint to get to actionable insights. However, these fell short on two counts, the need to switch to another system to take action and the repetitive process of interacting manually to find the proverbial “needle in the haystack”.
Despite these shortcomings, every organization has these golden dashboards that already have the business context codified by domain and functional experts. And, they are already the lifeblood of daily operations, where they are run every single day to make decisions. These dashboard insights serve as query patterns that supplement the semantic layer and all other context that is imputed into AI workspaces for agentic applications.
With the latest capabilities of large language models, the future of last mile analytics consumption will be transformed. It will see dashboards serve as a stepping stone to building vibe coded AI applications. The goal of this evolution isn't just to make prettier charts; it's to shorten the cycle from data to action. By embedding agentic insights and workflows into purpose built analytical applications, modern analytical platforms can now autonomously surface anomalies, identify key drivers, and even trigger business actions like sending a PDF report to stakeholders in their team messaging or collaboration platforms of choice. But, through it all, a handful of curated and governed dashboards serve as a key ingredient to an evolving context layer fueling the AI applications.
Dashboards are no longer the end of the analytical journey; they are the starting knowledge base for a more intelligent, conversational, and automated future.
When we talk about generative AI, the conversation usually centers on writing emails, drafting marketing copy, or answering trivia. But if you look at Anthropic’s recent deployments for Claude, it's clear they are building something entirely different: a fully functional, autonomous data analyst that lives inside your tech stack. Claude has moved past simply "reading" data. It is now executing code, querying live databases, and operating directly inside the spreadsheet tools you already use. Here is why Claude is fundamentally changing how we work with data.
The biggest limitation of early AI models was that they were terrible at math and choked on massive datasets. If you uploaded a 10MB server log and asked a model to find the anomalies, it would try to read every single line of text, flooding its context window, driving up your API costs, and likely hallucinating the answer.
Claude handles this entirely differently using its Code Execution Sandbox.
Instead of trying to "read" the 10MB log file as text, Claude acts like a human engineer. It writes a Python script to parse the log, executes the script in a secure, isolated container, filters out the noise, and returns just the 12 lines of critical errors. You get an accurate answer in a fraction of the time, and because the raw data never hits the language model's main context window, token costs drop by up to 98%.
Data analysis is useless if the data is out of date. While you can easily upload static CSVs or PDFs to Claude, its real power lies in the Model Context Protocol (MCP) and real-time enterprise connectors.
If you are a financial analyst, Claude can connect directly to platforms like FactSet, S&P Capital IQ, or your company's proprietary Salesforce CRM under governed access controls.
Instead of exporting a pipeline report, you can simply ask Claude to identify "at-risk deals for Q1." Claude will write a script, query your live CRM, cross-reference the data with recent engagement metrics, and present a targeted list of accounts that need intervention. The analysis is dynamic, live, and instantly actionable.
You shouldn't have to leave your workspace to talk to an AI. Anthropic recently deployed Claude add-ins directly into Microsoft 365.
If you need to build a financial model, you don't have to prompt an AI in a browser and try to copy-paste the formulas back into a spreadsheet. Claude operates natively inside Excel. It can pull in live data feeds, write complex formulas, and audit existing logic across linked workbooks.
Even better, Claude's ecosystem shares context. Once you finish running a sensitivity analysis in Excel, you can open PowerPoint, and Claude will already know the insights you just discovered, allowing it to immediately draft a pitchbook based on the exact numbers you just finalized.
We are transitioning from using AI as a sounding board to using AI as an execution engine. With a 1-million token context window, progressive skills that understand complex file structures, and the ability to run actual Python code on the fly, Claude isn't just summarizing data anymore—it's actively doing the analytics work for you.
But this raises a massive question: how exactly does Claude navigate multiple data sources from disparate systems, like merging financial billing data with CRM marketing campaigns, and how can it possibly handle enterprise databases with billions of rows and hundreds of columns while being also cost efficient?
The short answer is context orchestration. Using the Model Context Protocol (MCP) and other tools and protocols, Claude doesn't try to cram an entire data warehouse into its memory. Instead, it acts as an intelligent router, authenticating seamlessly across your systems, writing optimized SQL or Python scripts to push the heavy computation down to the database & lakehouse level, and only pulling the final, aggregated insights back into its context window for reasoning. We will dive deep into the exact architecture of querying billion-row datasets and orchestrating cross-platform analytics in our next post, so stay tuned!
The dust has settled on Google Cloud Next 2026, and the takeaway is unmistakable: the era of retrospective analytics, characterized by static dashboards and "rearview mirror" reporting is officially over.
We have entered the age of Agentic Analytics & Actions.
What we witnessed this year was a fundamental shift in how organizations interact with information. We are moving toward a world where data doesn't just sit in a warehouse waiting to be queried; it serves as the nervous system for autonomous, intelligent agents that reason, plan, and act.
Here are the key themes from Google Cloud Next 2026 and what they mean for the future of your data strategy.
The star of the show was the evolution of Vertex AI (now called Gemini Enterprise Agent Platform). We are moving beyond simple chatbots to sophisticated AI agents capable of executing multi-step workflows. These agents maintain context, invoke APIs, and interact with enterprise tools to achieve specific business objectives, and evaluate their performance & outcomes all while maintaining policy, security and governance.
In this new paradigm, the "user interface" is shifting. Instead of a human analyst spending hours digging through a BI tool, an agent can be tasked with an objective- such as "optimize supply chain routes for cost and carbon footprint" -and autonomously perform the analysis and execution required to make it happen.
For decades, the dashboard has been the "holy grail" of BI. Google Cloud Next 2026 signaled a pivot: while dashboards remain useful for high-level monitoring, they are no longer the primary way we consume data.
In traditional BI, the workflow looks like a broken relay race:
By the time step five happens, the data is stale. Agentic Analytics collapses this entire chain into a single, continuous loop.
The new standard is conversational and context-aware systems. These interfaces generate insights and recommended actions dynamically based on real-time intent. This requires a radical rethink of data modeling - shifting away from rigid, pre-aggregated schemas toward flexible, responsive data environments that can answer questions on the fly.
With advancements in Gemini Enterprise, the barrier between structured data (SQL tables) and unstructured data (emails, PDFs, videos) has finally collapsed. Organizations can now reason across their entire knowledge base within a single analytical framework. This convergence allows for a more holistic understanding of business operations, where a contract’s text is just as "queryable" and actionable as a transaction’s dollar amount.
Agentic AI cannot function on "yesterday’s data." If an agent is making autonomous decisions, any latency in the data pipeline becomes a liability. The shift from retrospective to operational analytics means that data platforms must now handle low-latency queries and integrate seamlessly with live operational systems.
The common thread through every presentation at Next 2026 was that AI is only as good as the data foundation it stands on. As enterprises rush to adopt Gemini Enterprise Agent Platform and Gemini models, many are hitting a familiar wall: data complexity and ETL (Extract, Transform, Load) bottlenecks.
This is where Incorta becomes the critical "missing link" in the agentic workflow.
To power the future Google is building, organizations need three things that Incorta is uniquely designed to provide:
As we saw at Next, the industry is hitting a "Production Gap." While 79% of enterprises have adopted AI agents, only about 11% have them running in production. The bottleneck isn't the AI model; it’s the actionability of the data.
For an agent to take a high-stakes action, it needs more than a best guess. It needs contextual certainty. This is where Incorta’s Direct Data Mapping™ becomes a competitive moat. By providing a direct pipeline to the ERP and CRM systems where business actually happens, Incorta gives agents the data - and the full context - they need to act. Without this, agents are essentially flying blind on cached data, leading to the hallucinations or errors that keep 88% of agent pilots from ever reaching the production line.
We are quickly moving toward multi-agent systems. Imagine a "Finance Agent" and a "Marketing Agent" negotiating a budget adjustment in real-time based on a sudden surge in customer demand. This level of orchestration requires a shared, high-performance data fabric - a single source of truth that is accessible not just to people, but to the autonomous systems running the entire company.
Google Cloud Next 2026 made it clear that the competitive advantage of the next decade won't come from having the best AI model—it will come from having the most accessible, actionable data foundation. In 2026, "Time to Insight" is a vanity metric. The only metric that matters is "Time to Action." By leveraging Google’s agentic framework on top of Incorta’s real-time data foundation, organizations are finally closing the loop between what they know and what they do. The dashboard isn't dead, it's just been promoted from a static report to a real-time command center for an autonomous enterprise.
As we move toward a future of autonomous business execution, the goal is no longer just to "see" your data, but to empower your systems to use it. By combining the orchestrational power of Google Cloud with the agile data architecture of Incorta, enterprises are finally ready to turn the promise of Agentic Analytics & Actions into a production-grade reality.
Want to learn more about how to prepare your data architecture for Agentic AI? Contact our team today.
Most enterprise AI initiatives don't fail because of the AI. They fail because of the data.
Companies have invested heavily in machine learning, business intelligence, and generative AI - but the operational data that would actually make those investments useful is still trapped inside complex ERP systems, fragmented pipelines, and legacy architectures that were never designed with agents in mind.
That's the problem Incorta and Google Cloud solve together. And as a Google Cloud partner, Incorta is a foundational part of how enterprises get from raw operational data to production-ready AI agents - fast.
Every analytics initiative starts with the same assumption: the data will be there when you need it. In practice, it rarely is.
Traditional ETL processes strip away the transaction-level detail that AI and analytics actually need. ERP databases like SAP and Oracle can contain 10,000+ tables. Reverse-engineering them into usable pipelines takes months—sometimes years. And once you've built those pipelines, they're fragile. A vendor patch or schema change can break the whole thing, and the institutional knowledge required to fix it often lives with just one or two engineers.
The result: data latency measured in hours or days, partial data sets, and AI models that can't act on your most valuable operational information.
Incorta solves this with Direct Data Mapping™ (DDM)—patented technology that connects directly to source systems like Oracle Fusion, SAP, Workday, and Salesforce, ingests data in its native form (3NF), and delivers 100% of it to Google BigQuery without traditional ETL. No deconstructing. No manual transformations. No data loss.
With over 240 pre-built connectors and automated schema detection, Incorta eliminates the engineering bottleneck entirely. What traditionally takes 18–24 months can happen in weeks. Hormel Foods, for example, replaced an entire Oracle data warehouse with an Incorta-to-BigQuery pipeline in just 10 weeks.
Delivering data faster is only part of the story. The bigger shift happening right now is agentic AI - and it requires something fundamentally different from what most data architectures were built to provide.
A dashboard needs aggregated, high-level trends. An AI agent needs something else entirely:
Granular data. Agents need transaction-level detail to analyze exceptions, detect anomalies, and take precise action. Pre-aggregated data makes their reasoning shallow and incomplete.
Real-time access. Whether an agent is reconciling financial entries, processing invoices, or optimizing supply chain flows, it needs to act on current data—not a batch from last night.
Business context. Raw numbers aren't enough. Agents need to understand why an invoice belongs to a vendor, how an expense rolls up into a cost center, how a shipment ties to an order. Traditional architectures flatten or abstract this context away, leaving agents blind to business logic.
Incorta addresses all three with a semantic layer built directly into its platform. When data is ingested via DDM, Incorta automatically captures source relationships and keeps them intact - translating complex ERP data models into logical, business-centric datasets. Business schemas organize these relationships, standardize calculations, and enforce governance, so every downstream consumer—whether a dashboard or an AI agent—is working from the same trusted definitions.
Incorta calls this enterprise truth: the verified, context-rich operational knowledge that accurately represents how your business actually works. It includes live data from ERP, CRM, and HR systems; historical records; business logic; and trusted third-party information: delivered to BigQuery as a continuously updated foundation for intelligent automation.
Once enterprise truth is in BigQuery, Google Gemini Enterprise can do something genuinely powerful with it.
Gemini Enterprise is Google's secure, enterprise-grade environment for building, deploying, and managing customizable AI agents at scale. Using the Agent Development Kit (ADK), developers can define agents that connect to BigQuery, reason over live enterprise data, and take autonomous action.
Here's what that looks like in practice.
A financial analyst at a large enterprise might spend hours manually reviewing invoices for discrepancies against purchase orders and contracts. With an Incorta-powered Gemini agent, the workflow changes entirely:
The same agent can generate reports, create visualizations, and answer business questions: all from a single interface, grounded in real operational data.
This is what separates production-grade agentic AI from a compelling demo. The intelligence is in the model. The reliability is in the data foundation.
The joint Incorta and Google Cloud solution brings together:
The result is a data pipeline that goes from months to weeks, AI agents grounded in operational reality rather than generic model training, and an analytics architecture that scales across departments without duplicating effort or fragmenting governance.
If your AI initiatives are stalling at the pilot stage, the bottleneck is almost certainly the data - not the model. Incorta and Google Cloud give you a clear, faster path to fixing that.
'Incorta Launches Intelligent Accounts Payable for Google Cloud': read more →
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