Data strategy, product updates, and technical insights from the Incorta team on analytics, AI, and live enterprise data.
In Gartner's 2026 CEO and Senior Business Executive Survey, 76% of leaders said AI is the technology most likely to disrupt their industry in the next three years. But in that same survey, Gartner also found that 59% of AI pilots fail to reach production, primarily because of organizational constraints.
Amidst the feeling of certainty surrounding AI productivity, who’s stopping to check if AI agents are actually ready? When such an overwhelming majority agrees and puts real time and money into AI, that consensus can override the hard questions about timing, readiness, and what differentiation looks like down the road.
The money and urgency poured into tech investments can't make up for leadership, infrastructure, and systems that were never designed for AI in the first place. Building trust in a business’ AI workflow requires a closer look at its organizational processes and data infrastructure.
In the same survey, Gartner also notes that AI pays off when the organization around it changes too. That means redesigning processes, redefining who makes which decisions, aligning incentives, and managing well. Skip those steps, and AI just speeds up and amplifies existing inputs, good or bad.
If an AI agent takes a customer call, who owns the outcome? If a workflow changes, whose goals change with it? When nobody has answers, an AI pilot will stall. That pilot might show exciting new capabilities in a demo, but scaling it into production means changing how hundreds of people work in their day-to-day. A 2025 study from IBM notes that only 16% of AI programs have scaled across companies, despite the number of pilot programs in place.
"Garbage in, garbage out" is a common tech adage that describes how, when low-quality or incorrect information enters into a system, unreliable outcomes will result. AI can only deliver trusted answers when the basics are in place:
● Who within the organization has access to the data?
● How fresh is the data when it reaches the AI layer?
● Where did the data come from?
● What are agents allowed to do with the data?
Most organizations can't answer all four. In fact, Gartner predicts that through 2026, organizations will drop 60% of AI projects that don't have AI-ready data.
AI runs on the data it has access to. If AI agents run on stale, outdated numbers, it’ll give answers based on an older version of the business. Without trusted data, employees may revert back to building reports and analysis “off the AI grid.” As a result, AI outside a shared foundation becomes difficult to track: Nobody knows what exists, what data it’s using, or what it costs.
Providing a governed data and AI platform for its customers, Incorta has identified three moves that businesses can make to build and maintain trust in their AI systems:
1. Controlled, visible spend on a centralized platform.
When every team expenses its own tools and tokens, it gets harder to track. And when that spend is scattered, it’s virtually impossible to gauge AI performance against cost, scalability, and operating limits. Centralize AI models, tooling, and infrastructure so the CFO can see the whole bill in one place.
2. Governed data, so teams can build what they need without being a security risk.
IBM's 2025 Cost of a Data Breach report found that one in five organizations had a breach tied to shadow AI, adding an average of $670,000 to breach costs. Of those that suffered an AI-related breach, 97% didn’t have AI access controls in place. Teams are already building with AI, often in tools IT never signed off on. It can provide a short-term productivity boost, but that’s company data that is shared with unauthorized tools and can create a tremendous business risk.
Data governance gets a bad reputation as the department of "no" that restricts and slows things down. But done right, it's the reason business teams can say "yes" faster.
3. A system flexible enough to switch models.
Build systems that can swap models, using flexible APIs, abstraction layers, and portable data. On a multi-model platform, admins can decide which models are available, and teams pick the best one for each job. When a better model shows up next quarter, businesses shouldn’t need to waste time rebuilding.
With organizational and data foundation changes in place, a business can understand and rely on its AI responses. It can also look to its data to provide a hidden advantage. Competitors have access to the same models as everyone else — what they don’t have is your data.
Businesses sit on a data goldmine: years of customer conversations, support tickets, contracts, product usage, financial and planning data, and more. Public models know a lot about the world, and nothing about how specific businesses actually run. Grounding AI in governed data gives outputs that teams can actually use, unique insights no other business can copy, and the freedom to build what is needed — all on a governed foundation. With clarity on governed and usable data, visible spend, and lines of ownership, businesses can trust the AI outcomes generated for their business.
This story was produced by Incorta and reviewed and distributed by Stacker.
Manufacturing teams keep running into the same wall: the data that planning, operations, and finance rely on is always a step behind the plant floor. Ebrahim Alareqi, Principal ML Engineer at Incorta, explains why traditional ETL struggles with modern manufacturing, what “real time” should mean for different teams, and how to get AI working on a data foundation you can trust.
The main problem with most pipelines is rigidity. Traditional ETL was designed for a world where the monthly report was the finish line. Data gets extracted from source systems overnight, transformed into a star schema someone designed years ago, and loaded into the warehouse. Every link in that chain adds latency, so the data drifts from the source. A supplier delay or a production disruption can change the picture faster than the pipeline can keep up, and by the time the report arrives, it may already be obsolete.
The pipeline also pre-aggregates data so the analytics layer can answer questions quickly. That is where the trouble starts. Once transactions are rolled up into totals, you lose the detail people need to answer “why.” Finance can see that margin moved. It cannot see which orders or material costs caused the change.
“Real time” gets abused a lot in this industry. Most operations don’t need millisecond streams. What they need is decision-time data: information refreshed often enough that the person making a decision can act on it while it still matters.
That threshold changes by function. A plant supervisor watching throughput may need updates every few minutes. A finance team closing the books may be fine with an hourly refresh. A practical benchmark is this: every team, whether operations, finance, or planning, should be able to query the same data at full transaction detail, refreshed within minutes to about an hour of the source system, without waiting for a pipeline to rebuild. If you can do that, you know you have the speed you need.
AI has a lot of room to help here. Demand forecasting, predictive maintenance, dynamic safety stock, and anomaly detection on quality data are all strong candidates. Large language models have also gotten much better at reasoning, especially for explaining variances and writing SQL.
Choose use cases carefully, though. Many AI projects fail, and the ones that struggle usually started with a problem that wasn’t a good fit. Experimenting is fine. For production use, I’d take your time. Start with the functions where you’re struggling most and where AI can genuinely help. Don’t throw AI at every problem at once.
Traditional pipelines were not built for AI agents. A dashboard designed for steady, predictable traffic can fail when an agent fires off a hundred queries to answer one question. The bigger issues are the failure modes.
The first is the stale answer delivered with confidence. AI doesn’t know your data is old. If the pipeline runs nightly and a shipment problem starts at 9 a.m., the model will tell you everything is fine.
The second is missing detail. Without a well-established data foundation with rich context, the agent can’t reason over what’s happening, and it will give you a wrong answer with full confidence.
The third is semantic chaos. If planning and finance calculate “on-time delivery” differently and run on separate pipelines, AI will inherit that inconsistency.
The fourth is compounding error. One wrong data point feeds every answer downstream unless the pipeline gives the AI a solid source to check against.
Before you commit to an AI solution, ask your data and IT teams four things. What is our actual latency from source system to consumption? Can I drill down from any KPI to the source transaction? How long does it take to add a new data source? And how will AI access our data, and what will that cost? These questions give you a clear way to judge whether your foundation is ready.
Most of the time, you need to connect your data, not rebuild it. Start by connecting to your source systems, whether that’s SAP, Oracle, or Salesforce. Next, establish a semantic layer. That’s the key to making your data usable and consistent. Then look for cross-functional pain points by checking with other teams to see where their data problems overlap. Add AI last, once your foundational data is connected, corrected, and carrying the right meaning.
Native connectivity comes first. The tool you choose needs to connect natively to your systems and have a proven track record with them. SAP and Oracle are deep, complex schemas, so ask for evidence of real deployments.
Next, make sure transaction detail is preserved. If a platform abstracts your data into aggregates and doesn’t let you trace back through lineage, your AI agents will be stuck. Real-world agents need to go back to the source.
Speed matters too. If a solution can get you to production in days or weeks, it’s worth serious consideration. If it takes months or years, I wouldn’t pursue it in the age of AI.
Governance is the piece people underrate. Look for role-level security built into the platform. Many tools look good in a demo but leave security for your team to build later, and that’s a lot of work you don’t want to take on.
Finally, trust. AI can analyze more data points than any human could review, which makes its output hard to verify. Choose a tool that lets you see how an answer was produced so you can check it against the detail.
Two use cases come up often as a first AI project. The first is conversational variance analysis, where a plant manager or finance lead asks why a number moved and gets an answer traced to the source transactions. The second is AI-driven exception detection on supply chain data, such as flagging late supplier shipments before they break the schedule. Both can show value in about a week, and both work because the data is trusted and users can verify every answer against the detail.
Get your foundation right before you build the stack. The AI will only be as good as the data underneath it, and that data needs to be current, detailed, and connected.
Data lag is the delay between something happening in a source system, like an ERP or a supplier portal, and the moment that information is available to the people and AI tools that need it. Lag comes from nightly batch loads, multi-step pipelines, and pre-aggregated data that hides the detail behind a number.
Decision-time data is information refreshed quickly enough that the person making a decision sees the current state of the business when it matters. The required speed depends on the team. A plant floor may need minutes, while a finance close may be fine with hourly updates.
Watch the full session here:
Incorta ranked among the top six vendors for Governance in Gartner®'s 2026 Critical Capabilities. Why governance is now the real competitive moat.
Incorta has been recognized for the fifth consecutive year in the 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms. Among the results we are most proud of this year Incorta ranked among the 6 highest scoring vendors for Governance Use Case in the 2026 Gartner Critical Capabilities report - across all 20 vendors evaluated.
To us, that score reflects something we have long believed: governance is no longer a compliance checkbox. For enterprises running complex operational data, it is the foundation that determines whether analytics actually gets used - and trusted - in production.
Enterprise analytics projects fail more often than the industry likes to admit. Not because the technology doesn't work, or because the dashboards aren't well designed. They fail because the data underneath can’t be trusted.
A finance leader who has been burned by a report that contradicts the one their colleague pulled an hour earlier will stop using the tool. A supply chain team that cannot trace where a KPI came from will build its own spreadsheet instead. An IT organization that cannot prove audit-ready lineage from source to dashboard will block the rollout entirely.
Enterprise environments running on Oracle or SAP run into this all the time - the data is complex and vast, user expectations are demanding, and the cost of a wrong number is significant.
Governance prevents this - so why do most enterprises still treat it as an afterthought?
Governance in enterprise analytics is less of a feature, more of an architectural commitment. When your data comes from Oracle or SAP, the security models, the business definitions, and the data lineage all need to be inherited from the source - not reconstructed on top of it. That is what Incorta was built to deliver.
We feel this year's recognition highlights the depth of that commitment across four capabilities that together define what governed analytics looks like in practice:
4-Layer Security: Row-level, column-level, object-level, and data classification security -with permissions inherited directly from Oracle and SAP rather than manually mapped in the analytics layer. When a user's access changes in the source system, it is reflected immediately in Incorta. There is no secondary security model to maintain and no risk of the two falling out of sync.
Certified Business Views: A single trusted definition for every metric across the organization. Revenue means the same thing to the CFO as it does to the regional sales lead. Inventory turn is calculated consistently whether it appears in a finance dashboard or a supply chain report. Certified business views eliminate the competing definitions that quietly undermine confidence in analytics platforms over time.
End-to-End Data Lineage: Full lineage from source ERP system to dashboard - traceable, auditable, and available out of the box. When a regulator asks where a number came from, or when an analyst needs to understand why a metric changed, the answer is available without a forensic investigation. This is not a feature most platforms offer at the depth enterprise compliance teams require.
Crowdsourced Trust Layer: Business users can rate, review, and flag data quality on certified views - giving data teams a continuous feedback loop on what is working and what is not. This shifts data confidence from a centralized IT responsibility to a shared organizational practice, and it surfaces issues before they become decisions.
Gartner evaluates Analytics Governance across all 20 vendors in the report - platforms with significantly larger market share, broader global footprints, and extensive enterprise sales organizations. Incorta ranked among the 6 highest scoring vendors for Governance Use Case.
To us, that reflects a deliberate product philosophy. Incorta was not built as a general-purpose BI tool that added governance capabilities over time. It was built for complex enterprise ERP data from the ground up -which means governance is not a layer on top of the platform. It is the platform.
For enterprises in finance, healthcare, and manufacturing - where compliance is non-negotiable, and data errors have real consequences -that distinction matters more than quadrant position.
The analytics market is moving fast. AI capabilities are being added to every platform, natural language querying is becoming table stakes, and vendor positioning is shifting quickly - as this year's Magic Quadrant illustrates, with AWS entering the Leaders quadrant, Databricks making its first appearance, and several established vendors declining on Ability to Execute.
In an environment where features converge rapidly, governance is the capability that is hardest to replicate. It requires architectural decisions made early, deep integration with source systems, and a sustained commitment to the trust layer that makes analytics usable at scale. Platforms that prioritize speed to market over structural integrity will struggle to close that gap retroactively.
For the enterprises evaluating analytics platforms in 2026, the question is not only which vendor has the most capabilities - it is which vendor's platform will still be trusted by business users a year after go-live.
That is the competitive moat governance builds - and it’s why we believe this score is the most meaningful result in this year's evaluation.
Get your own copy of the 2026 Gartner® Critical Capabilities here.
-----------------
Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner, Magic Quadrant for Analytics and Business Intelligence Platforms, Anirudh Ganeshan, Christopher Long, Edgar Macari, 29 June 2026.
Getting AI into production depends on the operational data underneath it. Lessons from our Context-Driven AI webinar on building that foundation.
Enterprises stall on AI in production because the data reaching the model is fragmented, stale, and flattened. The record of a single order is spread across the ERP, the CRM, and the fulfillment system. Batch is the default - and batch means your agent is reasoning about last night. Flattening is the one people underrate. Pre-aggregation is where the answer to "why" quietly disappears.
Every one of these ends the same way: The AI answers with confidence about a version of the business that no longer exists.
I use three words for it: live, detailed, and connected.
Live means current at the time of the question. Fresh-ish does not count. Detailed means the lowest level, and that is a requirement. If an agent is asked why margin moved from one number to another, it can’t answer from a monthly total. Connected means the relationships between your systems are part of the context itself. Miss one, and everything above it inherits the gap.
Everyone agrees you need structured, unstructured, and real-time data together. In practice, the structured operational data is the blocker. The reason has nothing to do with size. ERP schemas were built for writing transactions; they run thousands of tables deep, and every conventional route out of them flattens the data.
Documents, tickets, logs, and clickstreams are easier to land by comparison. I'm not saying those layers are unimportant, or that they are simple. ERP is just the one that stalls the entire project, so it gates the rest.
Our systems of record are read directly and delivered to the AI layer. That middle box is Incorta: you don't have to reshape anything in advance, and you don't rebuild a pipeline every time the business adds a requirement, adds an entity, or changes a process. You’re connected right to live, detailed data from the source system.
Direct Data Mapping™ makes that work. It keeps your data at full fidelity, with the speed to query it, so you or your agent can drill to the smallest level with no lag.
One of my most used examples is our customer, Broadcom - they took complex data across 26 business units and consolidated it into one layer. They were able to cut data pipeline costs by 80%, and took reporting wait time from twelve weeks to nearly instantaneous. They also streamlined their SKU down to about 500 queries: about 17,000 Broadcom users log in to Incorta every day to get the data (and context) they need to do their jobs. (You can read more here)
Think about your foundation before you build the stack. Mastering and quality matter, and so does search. Each of them needs something current, clean, and connected underneath.
Watch the full session here:
Originally posted on CFODive.com. AI is making it easier for finance to connect the dots, but better forecasting still depends on people who understand the business, tech executive Mike Nader writes.
See original article on CFODive here.
Ten years ago, it would have been unusual to see a data and analytics leader reporting to the CFO. Today, that is much more common.
That is not an accident. It reflects how much the role of finance has changed.
Finance is still responsible for explaining what happened. That part has not gone away. But, increasingly, finance is also being asked to explain why it happened, what changed and what the business should do next.
The last point in particular represents a fundamental shift for finance, which historically has focused on reporting past performance with limited visibility into real-time business signals that could reshape the outlook.
Consider this example: At a manufacturing plant in Peoria, Illinois, a supplier misses a shipment of tubing needed for a packaging line. Production keeps running, but not at the rate the plan assumed. Throughput falls to 80% of forecast.
The operational problem is obvious inside the plant; the financial impact may not be obvious for weeks.
Finance will eventually see it. Revenue will come in lighter than expected. The forecast will miss. Someone at corporate will ask what happened.
The answer was sitting inside the business the entire time.
Finance has spent years getting very good at producing accurate, repeatable reporting. That work still matters. But the real value is no longer in producing another version of the same report. The value is in understanding what the numbers are telling you and how the business should respond.
Today, the need to recognize operational signals quickly is more critical than ever. Tariffs are changing cost structures. Supply disruptions are altering production plans. Shifting demand patterns are making the task of financial forecasting more difficult. A forecast that looked reasonable two weeks ago can become stale before the month is over.
The current environment gives finance a chance to rethink processes that were built for a time when every new question required another extract, another spreadsheet, another reconciliation, or another meeting. Many of those processes made sense when the cost of getting to detail was high. The question is whether they still make sense now.
This is why I think much of the conversation around artificial intelligence in finance misses the point.
AI does not magically create business insights from thin air. What it can do is help finance arrive at answers faster. But that only works if the underlying information is accessible and trusted.
It also requires judgment. That’s why I don’t get excited about someone who is great at building pivot tables. I can generate 10,000 Excel workbooks. That is not the scarce skill anymore. What I need is someone who understands the business.
The best forecasters I have worked with were not necessarily the people who spent the most time in Excel. They were the people who understood how the business actually operated.
They knew when a supplier issue was going to create a downstream problem. They knew when demand was behaving differently than it should. They knew when a number looked wrong because it did not match what they were hearing from customers, seeing in the operation or sensing from the market.
That judgment is becoming more important, not less.
The rise of AI has simply given finance more capacity to focus on higher-value work.
A lot of the routine work around data gathering, validation and report production can now be automated. That does not make finance professionals less valuable. It changes where they add value.
The differentiator is not who can build the most complex spreadsheet. The differentiator is who understands the business well enough to recognize when something is changing and what that change is likely to mean.
Finance has been very good at explaining what happened. The opportunity now is to understand why it happened while there is still time to act.
SAP ECC support is ending. What it means for your business and your data, plus the steps to take now.
Mainstream maintenance for SAP ECC 6.0 Enhancement Packages 6 through 8 ends December 31, 2027, and Enhancement Packages 0 through 5 already lost mainstream maintenance at the end of 2025. After 2027, you can pay for extended maintenance through 2030, qualify for a limited RISE-only option through 2033, or fall back to reduced support. Every path leads to the same decision: plan your move to SAP S/4HANA. This article covers the dates, your options, the migration paths, and what happens to your data along the way.
SAP ECC (ERP Central Component) is the core of SAP Business Suite 7. It has run finance, supply chain, manufacturing, and HR for thousands of companies for about two decades. SAP is ending mainstream maintenance for it on December 31, 2027.
Mainstream maintenance covers the security patches, legal and regulatory updates, and full support that keep an ERP current. SAP leadership has repeatedly said the 2025 and 2027 end of mainstream maintenance dates for ECC 6 are not changing. Rimini Street
ECC keeps running after December 31, 2027 - what changes is the support behind it. Users have three options:
1. Extended maintenance (2028 to 2030). Companies can buy optional extended maintenance through the end of 2030, which adds three years of support at two percentage points on top of the existing maintenance fee. It covers security patches and limited legal updates. It adds no new features. IBsolution
2. The SAP ERP, private edition, transition option (to 2033). This option lets a select group of large, complex customers keep running ECC under a managed cloud subscription through the end of 2033. The conditions are strict. Eligible organizations must move relevant systems to SAP ERP Private Edition on the HANA database before the end of 2030, hold a RISE with SAP contract, and purchase the option under a fee-based plan. It is not a general extension of ECC support, and most mid-size companies will not qualify. SAP ECC End of Life 2027: What CIOs Need to Know Now +2
3. Customer-specific maintenance. This is the default if you do nothing. Its scope is much smaller, with no guaranteed new security patches or legal updates, so most organizations treat it as a last resort. Qyrus
For most SAP customers, yes. Extended maintenance and the transition option add time. The move to S/4HANA stays on the roadmap either way. The real question is when you start and how you'll keep the business running during the project.
Longer than most teams expect. Most S/4HANA migrations take 12 to 24 months, and full projects run 18 to 36 months once data and integrations are included. That means a company starting in late 2026 is already working against the 2027 date. Doodex
Adoption is also behind the deadline. At the end of 2024, only about 39% of ECC customers had licensed S/4HANA (Gartner, via CIO). As 2027 gets closer, experienced migration partners get harder to book and more expensive.
There are three common approaches.
The right choice depends on how customized your ECC system is, how much history you need, and how much process change you want.
This is the part most migration plans underestimate. It shows up in two ways.
Reporting can break during the move. Your dashboards and reports are built on ECC's data model. When that model changes, reports stop updating or need to be rebuilt. For a stretch measured in quarters, finance, supply chain, and operations can lose a clear view of the business.
Historical data can become hard to reach. A greenfield S/4HANA system often starts without your full ECC history. Once ECC is turned off, years of transactions can be hard or expensive to access. That's a problem for audits, compliance, and trend analysis.
Incorta connects directly to both SAP ECC and S/4HANA, so your reporting stays live the whole way through the migration. It keeps years of ECC history available after the system is turned off. It also gives you real-time analytics on your SAP data today, so you can migrate on a timeline that works for you.
Find out where your data stands before the migration starts. Get the SAP Migration Guide here.
Incorta earned 15 G2 badges in Fall 2026, including Easiest Setup, Easiest Admin and Easiest To Use in Enterprise Data Preparation.
G2 publishes its reports four times a year. Placement comes from verified reviews written by people who use the product at work, scored on satisfaction and market presence. Vendors cannot buy a badge.
Here are the headline recognitions from this quarter:
Fifteen badges in total, spanning four data and analytics categories.
The three usability badges came from the same category and the same segment: Enterprise Data Preparation, scored by reviewers at companies with more than 1,000 employees. Those are the environments where data platforms usually get hard. Dozens of source systems. Years of customizations in ERP. Security models that took a decade to build.
Enterprise data prep has a reputation for long deployments and heavy administration. Teams budget quarters for a rollout and then staff a small group to keep the pipelines healthy. Winning Easiest Setup, Easiest Admin, and Easiest To Use in that segment says the day-to-day experience holds up under real enterprise conditions.
The reason traces back to how the platform works. Incorta reads data directly from source systems and keeps the original detail intact, so teams skip the modeling and transformation layers that make traditional stacks slow to stand up and expensive to maintain. Fewer moving parts means a shorter path from connection to first dashboard, and less work to keep everything running after go-live.
The badges land in four categories that most vendors treat as separate products: Data Preparation, Analytics Platforms, Data Fabric, and Data Science and Machine Learning Platforms.
That spread matters for buyers. A typical modern data stack stitches together an ingestion tool, a warehouse, a transformation layer, a BI tool, and a separate environment for data science. Every handoff adds cost, latency, and one more place for numbers to drift apart. Being recognized in all four categories at once reflects a platform that covers that ground in one place, on live data, with the detail preserved end to end.
For teams building AI on top of their data, the same point applies. Models and agents are only as good as the data underneath them. A platform that already handles preparation, delivery, and analysis gives AI a single, governed source of context instead of five partial ones.
The Momentum Leader badge in Data Preparation and Analytics Platforms measures growth. G2 calculates it from review velocity, satisfaction trends, and market signals over time, which makes it a read on where a product is heading rather than where it has been.
Two Momentum Leader badges alongside four High Performer badges tells a consistent story: customer satisfaction is high, and more customers keep arriving.
G2 badges come from verified user reviews. Reviewers confirm their identity, describe their company size and role, and answer detailed questions about the product.
Because scoring resets every quarter and recent reviews carry more weight, badges reflect how the product performs now.
Analyst reports tell you what a firm thinks of a vendor's strategy. G2 tells you what the people running the software say about it after go-live. Both are useful, and they answer different questions.
If your shortlist includes platforms that promise to unify data preparation, analytics, and AI, three questions are worth asking every vendor: how long until the first working dashboard, how many people it takes to administer once it is live, and whether business users can answer their own questions without filing a ticket. The Fall 2026 badges are Incorta customers answering those three questions.
Read the reviews behind the badges on Incorta's G2 profile, or book a demo to see how fast a live enterprise data environment comes together.
Incorta and Google Cloud turn systems of record into a foundation for analytics, Gemini and AI workflows that support real-time decisions.
Most enterprise data platforms were designed to answer a question: What happened?
But AI is increasingly being asked a different one: What should happen next?
Take a simple example. A dashboard can tell you inventory is running low. An AI-powered workflow might recommend a reorder, identify at-risk suppliers, and surface downstream impacts before they affect the business. Those outcomes depend on access to the operational data that reflects what is happening across the business right now.
As organizations move from reporting to AI-driven workflows, operational systems are becoming more than systems of record. ERP, finance, supply chain, and other operational systems contain the signals that reflect what is happening across the business right now. Making that data available to analytics and analytics and decision-support workflows is becoming critical as organizations look to respond to changing business conditions in real time.
Incorta helps organizations bring live ERP and operational data from systems such as SAP, Oracle, Workday, and NetSuite into BigQuery while preserving the relationships, hierarchies, and business rules embedded within those systems. Using Direct Data Mapping®, Incorta maintains the structure of source applications, reducing the need for extensive transformation and modeling before data can be used across reporting, machine learning, and AI initiatives.
On Google Cloud, BigQuery becomes an operational foundation where Gemini and AI-driven workflows can work from the same trusted business data. Instead of relying on separate data pipelines for reporting, forecasting, and operational workflows, teams can work from a common foundation that reflects current business conditions and keeps decision-support systems aligned.
That allows AI to operate on current business signals rather than historical snapshots. It can help identify potential supply chain disruptions as conditions change, recommend inventory actions based on current demand signals, surface financial risks before they escalate, or initiate next-step actions directly within business workflows.
The value of operational data extends beyond reporting and visibility. It creates the foundation for systems that can increasingly support decisions and actions using the same trusted business context that powers analytics today.
For data and AI leaders, the opportunity is not simply faster reporting. It is the ability to operationalize AI on top of the systems that already run the business. Instead of creating separate data architectures for analytics, AI, and future initiatives, organizations can build on a shared operational foundation that supports all three.
As AI adoption continues to accelerate, the organizations that move fastest may not be the ones with the most data. They may be the ones that can turn operational data into action.
Learn more: Explore Incorta on the Google Cloud Marketplace to see how Incorta and Google Cloud help operationalize AI on top of trusted enterprise data.
Enterprise data carries the relationships and business logic that make a company run. Learn why AI needs that context preserved from ERP to BigQuery, not just access to raw tables.
Enterprise data contains the relationships, workflows, and business logic that make organizations run. A shipment, for example, is more than just a record. It connects suppliers, inventory, forecasts, fulfillment timelines, and customer commitments. An invoice is tied to contracts, approvals, and payment terms.
As organizations deploy AI more broadly, those connections become increasingly important because AI needs to understand how the business actually works, not just what each record says. Yet the relationships that connect data across the business can become harder to preserve as information moves between operational systems, warehouses, and analytics environments. When AI can only see fragments of the underlying business process, recommendations become less reliable and harder to operationalize.
Incorta and Google Cloud help make enterprise systems usable for AI without requiring organizations to reconstruct the relationships and business logic embedded in their data from scratch.
Incorta helps organizations bring complex ERP and operational data into BigQuery without stripping away the relationships and rules that give it meaning. Using Direct Data Mapping® and its semantic layer, Incorta preserves the business structure already embedded in source systems, so teams can bring ERP and operational data into BigQuery without losing the relationships, hierarchies, and business rules that make it useful for analytics and AI.
On Google Cloud, that contextualized data becomes available in BigQuery, where it can support analytics, Gemini, and other AI-driven workloads. Instead of starting with raw replicated records, organizations start with data that retains the relationships, hierarchies, and business rules needed to make data truly AI-ready.
Together, Incorta and Google Cloud help establish a shared foundation for analytics and AI, reducing the effort required to reconstruct business logic every time a new use case emerges.
When enterprise relationships, business rules, and operational context remain intact, the same foundation can support analytics, AI, and emerging agentic workflows without requiring teams to rebuild business logic for every use case.
A supply chain model can work from the same operational context used by an AI agent. A financial forecast can incorporate the same business relationships already used for reporting and analysis. Rather than creating separate pipelines, semantic definitions, and data models for every new initiative, organizations can extend a trusted business structure across analytics and AI.
For data and AI leaders, that operational foundation creates a more scalable path to operationalizing AI. New analytics, AI, and agentic use cases can build on the same trusted data foundation rather than requiring separate pipelines, semantic definitions, or data models for every initiative.
As enterprises move beyond experimentation, helping AI understand how the business works is becoming just as important as giving it access to the data in the first place. That understanding is what enables organizations to move from reporting on the past to acting in the present.
Learn more: Download Onramp to the Integrated Data Superhighway for AI Agents to explore how Incorta and Google Cloud help create a trusted foundation for enterprise AI. Or find Incorta on the Google Cloud Marketplace to get started.
Copying data isn't enough for AI. Incorta and Google Cloud preserve the relationships, rules and context that make enterprise data AI-ready.
For years, data teams focused on making enterprise data more accessible. Data replication solved an important problem: access.
Once data reached modern cloud platforms, teams could analyze it, report on it, and build applications around it. For many analytics initiatives, that was enough.
AI introduces a different set of requirements.
Analytics and AI consume data differently. Analysts can interpret missing context, reconcile inconsistencies, and apply business knowledge on their own. AI systems rely much more heavily on the structure embedded in the data itself.
A replicated purchase order may contain all of the underlying data, but not the relationships that connect it to suppliers, inventory, approvals, and downstream business processes. When those relationships are lost, AI can still retrieve information correctly, but struggle to reason across systems, workflows, and decisions.
Incorta helps organizations bring complex ERP and operational data into BigQuery without stripping away the relationships, hierarchies, and business rules embedded within it. Using Direct Data Mapping® and its semantic layer, Incorta preserves the business structure already embedded in source systems, reducing the need to reconstruct that logic after data has been moved.
On Google Cloud, BigQuery provides a scalable environment where operational data, analytics, and AI can work from the same foundation. Google’s Gemini models and other AI-driven workloads can build on the structure already preserved within the data instead of requiring teams to recreate it.
AI-ready data becomes more valuable as organizations extend the same foundation across analytics, AI, and future workloads. By preserving business structure at the data layer, Incorta collaborates with Google Cloud to make it possible to support AI assistants, forecasting, analytics, and future agentic workflows from the same trusted foundation.
When the relationships and business rules behind enterprise data remain intact, the same foundation can support analytics, AI, and future use cases without creating new pipelines, semantic definitions, or data models each time requirements change. That means teams can spend less time reconstructing logic and more time extending a shared data foundation into new analytics and AI workflows.
For data and AI leaders, that trusted foundation creates a more scalable path to operationalizing AI. New analytics, AI, and agentic use cases can build on the same trusted business structure, reducing the effort required to bring AI into additional workflows, teams, and processes.
The value of AI-ready data extends beyond analytics. It creates the foundation for systems that can increasingly support decisions and actions across the business.
Learn more: Download Onramp to the Integrated Data Superhighway for AI Agents for a deeper look at how Incorta collaborates with Google Cloud to bridge data movement and AI readiness. Or explore Incorta on Google Cloud Marketplace to get started.
Over half of finance leaders in our poll still rely on Excel. Why the habit persists, and what it's quietly costing every team that depends on it.
During our latest webinar with Workday Adaptive Planning, we asked the audience a simple question: what data challenge is causing you the biggest headache right now? Fifty-five percent said the same thing. Their data lives in Excel or some other non-centralized source.
Nobody on the call was shocked. Ben Pierce, General Manager of Workday Adaptive Planning, walked through the pattern right after the results came in: a finance team needs a view their BI tool doesn't have, so they pull data out of the ERP, drop it into a spreadsheet, and spend the next several hours building VLOOKUPs and pivot tables just to get back to a starting point. Most of the effort goes into wrangling the data instead of actually analyzing it.
Analysts have been predicting Excel's decline for years. Gartner has forecast that by 2026, more than 70% of finance organizations will have moved off spreadsheets as their primary planning tool. But the Association for Finance Professionals' 2025 FP&A Benchmarking Survey tells a different story: 96% of finance practitioners still use spreadsheets weekly for planning, and 93% use them weekly for reporting, even at companies that already run a dedicated EPM platform.
Read those two data points side by side and the gap is hard to miss: trhe prediction and the reality aren't close.
And we get it! Excel is fast, familiar, and flexible in ways purpose-built systems often aren't. Nearly every finance professional already knows how to use it, so there's no training curve and no waiting on IT. When a forecast needs updating before a Monday meeting, Excel is usually the quickest way to get there.
The problem shows up later, once that spreadsheet becomes the system of record for a decision instead of a scratchpad for one.
A few things happen once Excel becomes the default workflow instead of a backup option.
Time disappears into data prep instead of analysis. The AFP numbers above aren't describing a minority workaround. They're describing how most finance teams spend most of their week, and it's rarely on the parts of the job that actually move the business forward.
Trust erodes. UK research from Sixthfin, covered by The Fintech Times, found that 67% of firms still run their financial close in Excel, and roughly a third of finance leaders admit they don't fully trust the reliability of their own close figures. When a number can't be traced back to its source with confidence, every conversation built on that number starts from a weaker position.
Errors compound quietly. Spreadsheets have no native audit trail. There's no built-in way to see who changed a formula, when, or why. A single broken link or copy-paste error can sit undetected for a full reporting cycle, and by the time it surfaces, it's already shaped a decision.
The close gets harder every month, not easier. Every one of those workarounds gets rebuilt from scratch next period. Nothing compounds in the team's favor. The same manual work just repeats.
Every attempt to force finance teams off spreadsheets by fiat runs into the same wall: Excel solves a real problem, and ripping it away without solving that problem first just pushes the workaround somewhere else.
The actual fix is giving finance direct, governed access to live operational data, so the spreadsheet stops being the only place that data can be shaped and explored. During the Incorta Builder launch webinar, this showed up directly in the forecast and planning app demo: users got an editable, grid-based planning interface that felt like Excel to work in, drag, fill, adjust assumptions, but every number underneath it was live, governed, and tied back to its source. Saving a plan wrote directly to the underlying database instead of creating another disconnected file that only one person understands.
That's the shift: not eliminating the spreadsheet experience finance teams are comfortable with, but connecting it to a foundation where the data is trustworthy, current, and traceable by default.
One in five finance leaders say untrustworthy data is their biggest challenge. Here's why trust breaks down, why AI exposes a weak data foundation, and what trustworthy data really requires.
At Incorta's "Beyond the Close" webinar, 20% of attendees pointed to untrustworthy data as their biggest challenge, right behind the 55% stuck in Excel. Twenty percent might sound manageable next to a majority-vote problem. It isn't. It means a meaningful share of finance leaders are making decisions on numbers they don't fully believe, and saying so out loud in a room full of peers.
Mike Nader, Field CTO at Incorta, put the underlying issue plainly during the session: finance and accounting teams have to be able to communicate in a common way, and that starts with agreeing on what a number actually means. Without shared rules and shared context, two people can look at the same figure and mean two different things by it.
Data doesn't become untrustworthy on its own. It happens gradually, through a specific set of failures that show up in almost every finance function at some point.
Systems get connected by hand instead of through a governed pipeline, so every export is a fresh opportunity for something to drop or duplicate. Business rules live in someone's head, or in a spreadsheet formula nobody's touched in two years, instead of in a place the whole team can see and reference. There's no lineage, so when a number looks off, nobody can quickly trace it back to the transaction that produced it. And definitions drift. One team's "active customer" is another team's "customer with an order in the last 12 months," and nobody notices until the two reports don't match.
Any one of these is manageable. Stacked together, they're exactly why a fifth of a finance audience will say out loud that they don't trust their own numbers.
Cloudera's 2026 Data Readiness Index surveyed 1,270 IT leaders and found that while 85% believe they have a clear data strategy, only 18% consider their data fully governed. Informatica's CDO Insights 2026 study of 600 global data leaders found that 76% say their organization's AI governance can't keep pace with how employees are actually using AI day to day. And Grant Thornton's 2026 AI Impact Survey found that 78% of business executives lack confidence they could pass an independent AI governance audit within 90 days.
The same governance gap showing up in finance teams' Excel workflows is showing up across every function racing to adopt AI, and finance has more exposure than most, because nearly every decision in a company eventually shows up as a financial outcome.
Point an AI model at ungoverned, disconnected data and it won't hesitate the way a skeptical analyst would. It'll answer confidently, using whatever pattern it finds, whether or not that pattern reflects what's actually happening in the business. The output looks polished. The trust problem is just harder to see until something goes wrong.
Fixing this isn't about finding a smarter model. It's about giving finance a data foundation that's governed at the source, not cleaned up after the fact.
That means data landing exactly as it looks in the system of record, with no undocumented transformation on the way in. It means rules and business logic applied consistently, in a shared semantic layer instead of scattered across individual spreadsheets. It means every number staying traceable, so a finance user can walk from a summary figure back to the line-level transaction that produced it, in a few clicks, without filing a ticket. And it means that same foundation, and the same governance, applying whether a person is asking the question or an AI agent is.
That last point matters more every quarter. An agent answering a finance question needs to inherit the same row-level security and the same audit trail a human user would get from a dashboard. Otherwise, adding AI just adds a new, faster way to act on bad data.
Finance teams didn't lose trust in their data because they weren't paying attention. They lost it because the systems underneath weren't built to keep pace with how fast the business changes. Closing that gap takes a foundation that's governed from the moment data lands, not a faster way to query whatever's already there.