Stabilizing Oracle Fusion Data Flows for Global Pharma
About the Client A global pharmaceutical manufacturer operating a highly regulated, multi-region supply chain. The organization was executing a massive structural transformation, consolidating fragmented international operations onto a single Oracle Fusion Cloud environment to drive global visibility and operational agility. Business Challenges Forcing disparate regional legacy systems to sync with a centralized Fusion environment generated severe data friction. The legacy AMS vendor treated these integration failures as routine break-fix tickets, causing systemic ripple effects across the business: The Technical Drain: A massive L3 backlog trapped internal senior engineers in daily troubleshooting rather than strategic architecture work. The Operational Bottleneck: Dropped data payloads fractured supply chain visibility, forcing manual data reconciliations and risking delays on the warehouse floor. The Financial Leak: The organization funded an endless billing cycle for the same unresolved issues, treating recurring system failure as a standard operating expense. What We Did Operational shift: Replaced the reactive ticket support model with a continuous problem management framework built specifically for complex Fusion environments. Triage bypass: Eliminated standard support escalation queues and deployed enterprise architects directly to audit the middleware layer. Root cause isolation: Traced the recurring integration dropouts to their source and isolated the broken data transformation logic bridging the regional systems. Structural correction: Rewrote the underlying data mapping and integration structures instead of manually patching failed data loads every morning. Contractual enforcement: Restructured the service level agreement to mandate permanent architectural fixes before any support ticket could be marked closed. Value Delivered Stabilized the enterprise infrastructure by eradicating recurring integration dropouts at the source, securing the automated, real-time data flow required for global pharmaceutical compliance: Lowered Total Support Costs: Broke the endless vendor billing cycle for recurring break-fix tickets, transforming AMS from a reactive sunk cost into a strategic asset. Eliminated Operational Risk: Restored frictionless global supply chain operations by eradicating dropped payloads and preventing manual data entry on the warehouse floor. Reclaimed IT Bandwidth: Freed internal enterprise architecture teams from L3 firefighting, allowing them to focus entirely on global Fusion scaling instead of babysitting failed data syncs.
Advanced Oracle AMS for Global Omnichannel Fulfillment
About the Client De Beers is a global luxury jewellery retailer operating boutique stores across major cities, known for high-value, individually traceable products and a personalised customer experience. Having successfully unified order fulfilment across store, digital, and partner networks, the enterprise required an advanced maintenance model to sustain this complex omnichannel ecosystem without compromising service quality or supply chain visibility. Business Challenges The newly integrated Oracle Cloud ecosystem generated complex data flows that legacy helpdesks could not effectively troubleshoot. Maintenance providers treated integration misalignments as isolated break-fix tickets rather than structural disconnects. Order routing data and supply chain syncs frequently stalled, jeopardizing the real-time inventory visibility achieved in Phase 1. Surface-level patches applied to meet SLA response times failed to address underlying code friction, guaranteeing recurring failures. Internal IT bandwidth was heavily consumed by managing a perpetual queue of repeating integration errors. The business was unknowingly funding its own technical debt as reactive ticket volumes scaled with online channel expansion. Executive leadership lacked visibility into the margin drag caused by ongoing manual re-work and delayed financial reconciliation. What We Did Oracle Application Managed Services (AMS) were restructured into a proactive, root-cause resolution model to permanently stabilize the omnichannel architecture. Transitioned maintenance from a reactive ticketing queue to an engineering-led architectural audit model. Established a diagnostic protocol to map recurring integration failures directly to their root causes in Oracle Cloud Order Management. Re-architected specific data flow disconnects across downstream systems (Salesforce, POS, SAP) to achieve zero-recurrence stability. Rewired financial and supply chain data syncs using Oracle Integration Cloud to ensure operational metrics hit the P&L daily. Shifted AMS execution focus from standard SLA response times to long-term system stability and business continuity. Configured real-time monitoring to identify and resolve structural code friction before it impacted downstream fulfillment. Eliminated the break-fix loop, completely freeing internal IT teams to focus on scaling the digital and partner networks. Value Delivered Complete elimination of targeted recurring errors through proactive root-cause engineering and structural code correction. Significant reduction in ongoing maintenance costs by shrinking the perpetual ticket queue and retiring technical debt. More consistent automated data execution across all global offshore, store, and e-commerce operations. Reduced fulfillment delays caused by misalignment between commerce platforms and back-office supply chain modules. Improved IT workforce productivity, allowing leadership to reallocate bandwidth from system life support to strategic innovation. Stronger financial audit readiness with complete, real-time mapping of supply chain operations directly to the balance sheet. Increased visibility into structural asset health, enabling proactive architectural intervention and operational risk reduction.
Headless Manufacturing in Food Production: Why the HMA (Human, Machine, Agent) Paradigm is Replacing the Monolithic ERP
Key Takeaways The UI Bottleneck is a Critical Liability: Relying on factory floor operators to manually navigate complex software screens actively throttles production. This creates a massive, expensive gap between physical reality and digital records. The Rise of HMA (Human, Machine, Agent): True manufacturing agility requires a new operational trinity. The Human governs strategy, the Machine executes physical production, and the Agent orchestrates the gap autonomously without needing a User Interface (UI). The Composable Ecosystem Overtakes the Monolith:The future of food production relies on integrating core Cloud ERPs with specialized technologies across the value chain. This includes advanced Data & AI platforms, specification DNA engines, and industrial edge telemetry. The End of Silent Compensation:Planners and supervisors constantly execute manual workarounds to keep lines moving. Headless agents expose this hidden “data debt” by enforcing policy literally and requiring absolute “Fresh Start” data integrity. Value is Shifting to the Edges: In a high-velocity supply chain, extracting value means moving away from static mid-tier systems and pushing intelligence to the physical edge and the data layer simultaneously. The Core Problem: Why Monolithic ERP Interfaces Spoil Food Manufacturing Margins The food manufacturing sector operates in a uniquely punishing environment defined by razor-thin margins, strictly perishable inventory schedules, and highly volatile agricultural commodity pricing. In this environment, latency equals waste. Yet, while enterprise leaders invest heavily in modern physical machinery and massive cloud ERP transformations, the connective tissue between the physical machine and the digital ledger remains fundamentally archaic: it is still a human operator clicking through a static UI. This operational disconnect is precisely why so many food producers fail to realize the Return on Investment (ROI) on their technology spend. Detailed analysis in McKinsey’s report on Succeeding in the AI Supply-Chain Revolution notes that effectively deploying AI-driven models across an integrated supply chain can improve logistics costs by 15 percent, reduce inventory levels by 35 percent, and boost service levels by up to 65 percent. Furthermore, designing out food waste through intelligent routing can generate an estimated economic opportunity of over $100 billion globally. However, these staggering financial gains remain entirely inaccessible if an enterprise system is crippled by UI latency. When a multi-million-dollar production line has to wait for a floor supervisor to walk to a terminal, log in, navigate five different screens, and manually recalculate material requirements, the organization is not running a modern supply chain. It is running a highly expensive, digitized 1990s factory. This friction systematically destroys enterprise value. A recent analysis by Bain & Company on Industrial Automation: From Control to Intelligence reveals a massive structural shift in how value is created. The traditional “pyramid” of value—centered heavily around mid-tier control hardware and monolithic systems—is collapsing into an hourglass shape. The vast majority of future profit pools (over 80%) are consolidating at the two extreme ends: intelligent field devices at the physical bottom, and software/data-driven digital solutions at the top. Food enterprises are aggressively prioritizing traceability and hygiene standards as core functions, meaning the legacy, monolithic ERP sitting in the middle is rapidly becoming a bottleneck rather than an enabler. Passive conversational copilots are insufficient to solve this. First-generation generative AI took the form of these copilots, which are highly sophisticated summarization engines. They operate on a strict human-in-the-loop design paradigm where the AI advises a course of action, but waits for a human operator to execute the actual transaction. This leaves the operational speed completely unchanged. True acceleration requires removing the human from the micro-transaction entirely. The Architectural Solution: Deconstructing the Monolith with the HMA Paradigm To survive this market shift, food producers must adopt Headless Manufacturing. This is the architectural decoupling of the back-end manufacturing logic (the ERP/MES system of record) from the front-end user experience. Instead of forcing humans to interact with monolithic software screens, the architecture relies on an API-first ecosystem driven by the HMA (Human, Machine, Agent) paradigm. This mirrors the rapid market transition toward composability. According to the Gartner® Market Guide for Manufacturing Execution Systems (MES), enterprise buyers are aggressively moving away from monolithic legacy platforms and toward Composable MES (cMES). Gartner defines true composability using the MACH acronym: Microservices, API-first, Cloud-native, and Headless. Treating enterprise software as a portfolio of interoperable, headless capabilities rather than a single massive application is the only way to achieve the speed required for modern food production. The HMA architecture executes this exact vision perfectly: The Machine (Physical Execution & Edge Data): Industrial IoT sensors, PLCs, and robotics generate live, millisecond-level telemetry. They represent the physical ground truth. The Agent (Digital Orchestration & Execution): Autonomous digital workers ingest this live telemetry. Using multi-agent orchestration, they bypass the UI entirely. Agentic AI operates autonomously, directly navigating the enterprise database and executing multi-step transactions without manual intervention. In a headless setup, the agent skips the UI completely and executes via API. The Human (Governance & Exception Management): The human operator is elevated from a data-entry clerk to a strategic governor. They operate “human-on-the-loop,” managing the parameters of the agents and handling high-level strategic exceptions that require true business intuition, nuance, and contextual awareness. Comparative Analysis: Traditional Factory vs. Headless HMA Structuring operations for future resilience requires a clear departure from legacy methods. Operational Vector Traditional Monolithic Manufacturing Headless Manufacturing (HMA Paradigm) System Interaction Human navigates complex ERP/MES screens manually. Agents interact with ERP back-end via APIs; no UI required. Architectural Model Rigid, Monolithic application suite. Composable, MACH-aligned (Microservices, API, Cloud, Headless). Data Latency End-of-shift reporting; hours of delay before margin impact is visible. Real-time, millisecond execution based on live edge data. Exception Handling “Silent compensation”; manual overrides that create no digital record. Proactive, policy-bound orchestration with full audit trails. AI Role Passive conversational copilots summarizing anomalies for humans. Autonomous digital workers actively executing multi-step workflows. Industry Deep Dive: The Active Ecosystem in Food Production To execute the HMA paradigm safely in food manufacturing, enterprises must leverage a best-of-breed technology ecosystem. Autonomous orchestration is impossible if digital systems cannot communicate seamlessly across domains. This requires a Connected Shopfloor
Establishing a Single Source of Truth with Oracle Cloud PIM: The Forevermark Story
About the Client Global leader in the diamond jewellery market and a part of the De Beers Group. Maintains a physical and digital presence across Europe, Asia, Australia, and the United States. Provides differentiated offerings tailored to local tastes and preferences across major markets while operating at a global scale. Business Challenges Sought to optimize product data and performance management as part of a rapid project implementation, initially focusing on the China market. Needed to leverage existing capabilities and knowledge within the group to establish a foundational solution. Required a scalable architecture and a reliable managed services framework to seamlessly expand to other markets and processes in the future. What We Did Deployed a Performance Management common process model leveraging Oracle Cloud. Implemented Oracle Product Information Management (PIM) to establish a streamlined New Product Development (NPD) process where master data seamlessly feeds into target systems like eCommerce and Supply Chain. Integrated Oracle Inventory and Oracle Business Intelligence functionalities via MuleSoft to co-exist securely with existing local systems. Transitioned into an ongoing Application Managed Services (AMS) partnership to continuously support the product data ecosystem, ensuring it keeps pace with the business’s evolving operational needs. Value Delivered Established a single source of truth for product data, minimizing fragmented and duplicate information across the enterprise. Improved time to market for new product launches by approximately 20% through the enhanced NPD process. Enabled continuous, actionable insights based on KPIs to improve inventory control and sales optimization. Provided sustained system stability and proactive enhancements through our continuous AMS engagement, ensuring a reliable steppingstone for greater global consistency.
Safeguarding Global PLM Operations with Oracle Agile Managed Services: The Infinera Story
About the Client Founded in 2000 and based in San Jose, California. Manufactures Wavelength division multiplexing (WDM)-based packet optical transmission equipment and IP transport technologies. Provides optical transport systems, packet-optical networking solutions, and software-defined networking (SDN) capabilities to global network operators. Business Challenges Faced critical stability issues in the production environment where Agile PLM Process Extensions (PXs) would unexpectedly cease functioning. Required the IT team to reactively restart production application servers during active business hours. Experienced a direct negative impact on the productivity of business users due to recurring system failures. What We Did Optimised business processes within Oracle Agile PLM and established a new clustered Sandbox instance. Developed integrations with external systems to retrieve essential manufacturer part details, such as Years to End-of-Life (YEOL) and country of origin. Automated user deactivation processes and updated manager information for active users. Delivered a proactive AMS framework encompassing regular security and vulnerability assessments, applying the latest RUPs across DEV, QA, and PROD environments, and executing optimised server restarts. Collaborated directly with Oracle Support to address Averify issues and obtain critical bug fixes for the Agile PLM application. Value Delivered Enabled the calculation of Supply Chain Risk Management (SCRM) ratings to help the business mitigate supplier risks and determine BOM resiliency. Drastically reduced the workload for application administrators through automated procedures. Safeguarded the PLM system from vulnerabilities and eliminated disruptive downtime through continuous AMS engagement. Consistently achieved top customer satisfaction ratings operating as the client’s go-to expert for Agile PLM enhancements and support.
Oracle EPM in 2026: Why CFOs are forcing operations and finance to share one brain
Key Takeaways The spreadsheet era is officially a liability. The Gartner official research on Financial Planning and Analysis confirms that Extended Planning and Analysis (xP&A) is no longer optional. Enterprises failing to connect operational drivers to financial outcomes face a measurable agility deficit. True agility requires architectural overlap. The value of Oracle Enterprise Performance Management (EPM) Cloud is not just migrating data to cloud servers. It requires deploying Planning, Consolidation (FCCS), Account Reconciliation (ARCS), Costing, and Reporting modules so tightly interlocked that data moves through them without human translation. Siloed planning drains enterprise value. Operations running disconnected from finance creates a heavy drag on overall efficiency. Insights from the Forrester Enterprise Architecture research blog emphasize that cohesive, outcome-driven investment strategies are required to maintain competitive margins in highly volatile markets. Asset-heavy industries have the most to gain. Manufacturing, Pharma, and Food & Beverage are the primary proving grounds for this technology. A detailed McKinsey analysis on Enterprise Performance Management highlights that navigating a volatile macroeconomic environment requires deep integration between real-time operational data and financial models to prevent severe margin erosion. Narrative Reporting changes the audit game. By layering Oracle Narrative Reporting directly on top of Financial Consolidation and Close, companies eliminate the manual reconciliation of board decks and statutory filings. This entirely removes the risk of copy and paste errors. Why Do CFOs Struggle with 30-Day Financial Reporting Lags? Chief Financial Officers struggle with 30-day reporting lags because operational data and financial data live in disconnected systems. Furthermore, controllers spend weeks manually matching thousands of transactions in spreadsheets rather than utilizing automated reconciliation, causing massive delays in scenario modeling and margin visibility. A recent shift in enterprise volatility has exposed a glaring weak point across the corporate landscape. Companies have spent the last decade modernizing their core Enterprise Resource Planning (ERP) and Product Lifecycle Management (PLM) systems. This modernization generates terabytes of high-quality operational data every single day. Yet, when it comes to the actual financial planning and the month-end close that drives the business forward, the majority of that work is still exported into disconnected offline models. That disconnect has a real cost, and it is highly measurable. When a supply chain team adjusts inventory forecasts based on sudden raw material shortages, finance often does not see the margin impact until the end of the month close process. The delay is compounded by accounting teams manually reconciling bank feeds against sub-ledgers. Two teams working off two entirely different versions of the future is exactly how strategic agility disappears. A CFO who used to get judged mainly on the accuracy of historical reporting is now being judged on whether the business can actively model its future. As outlined in the Deloitte perspective on the Future of Financial Planning and Analysis, finance transformation is shifting from analyzing past performance to actively shaping the future through rapid scenario modeling. When the operational systems, the reconciliation tools, and the planning systems do not share one unified logic, somebody in the middle reconciles them by hand. That manual patch creates a 30-day reporting lag. This is a delay that modern enterprises simply cannot afford. What is the Architecture of Overlap in Oracle EPM Cloud? The architecture of overlap in Oracle EPM Cloud refers to deploying distinct financial modules, such as Planning, FCCS, ARCS, and Narrative Reporting, on a single unified data model. This eliminates manual data transfers, ensuring that operational updates instantly trigger financial recalculations across the entire enterprise. The true Return on Investment of Oracle EPM Cloud is not just that it sits on scalable infrastructure or reduces on-premise server costs. It is that its individual modules are designed to completely overlap. This creates a closed-loop system where data flows seamlessly from operational input to final regulatory output. When correctly architected, there is no daylight between these five core engines. We call this the Oracle EPM Module Matrix. Oracle Enterprise Planning (The Forward View): This is where xP&A lives. It ingests operational drivers directly. When the supply chain alters procurement volumes or shifts a manufacturing routing, this module instantly calculates the forward-looking financial impact. It allows businesses to run complex what-if scenarios in minutes. For example, if a company wants to model the impact of a ten percent tariff increase on raw materials, the Planning module will automatically adjust the cost of goods sold across the entire product portfolio. Oracle Profitability and Cost Management (The Margin Engine): Blunt, top-down cost allocations no longer work in a complex global market. This module traces exact overhead costs, including freight, warehouse space, utility usage, and machine hours, down to a specific customer or product SKU. By abandoning the peanut butter approach to generic cost allocation, businesses can identify their truly profitable products and their hidden loss leaders. It pushes those refined profitability metrics back into Planning to inform future pricing strategies. Oracle Account Reconciliation (ARCS – The Data Cleanser): The silent killer of financial agility is the manual reconciliation of balance sheet accounts. ARCS sits directly beneath the consolidation process, automating high-volume transaction matching across bank feeds, sub-ledgers, and corporate systems. When ARCS flags an exception, it workflows the issue directly to the right analyst with rule-based auto-match capabilities. Without ARCS cleaning and certifying the data, consolidation stalls, and planning models run on inaccurate actuals. Oracle Financial Consolidation and Close (FCCS – The Anchor): This engine handles the complex global reality of enterprise accounting. FCCS covers multi-GAAP reporting requirements, multi-currency translations, and automated intercompany eliminations. FCCS automates the mechanical heavy lifting of the month-end close so controllers can focus on strategic variance analysis rather than data entry. Crucially, certified actuals from FCCS instantly feed back into the Planning module to recalibrate the rolling forecast automatically. This creates a perfect loop between historical truth and future projections. Oracle Narrative Reporting (The Output): A board of directors needs the story behind the numbers rather than just a spreadsheet. This module sits directly on top of the other four. It blends structured financial data with unstructured commentary, ensuring
Poland’s largest pharma manufacturer moved its Oracle Agile PLM to Azure
About the Client Polpharma is Poland’s largest pharmaceutical manufacturer and a market leader in Central and Eastern Europe, with operations reaching into the Caucasus and Central Asia. For more than 85 years, it has delivered medicines to the standards of quality and regulatory compliance that pharma demands. Business Challenges Polpharma’s Oracle Agile PLM ran on ageing on-premise servers, and that put its product data at risk. A single server failure or system crash could wipe out critical product data, with no guarantee of a timely recovery. The legacy hardware and supporting components limited reliability and performance, and the risk grew every year the servers stayed in place. Keeping that infrastructure running took steady effort and cost, and left little room to scale or modernise. Any move had to protect what was already working. Agile PLM held years of configuration, custom developments and integrations, including the packaging documentation workflows InspireXT had built with Polpharma earlier. What We Did InspireXT rehosted Polpharma’s Oracle Agile PLM on Microsoft Azure like for like, so the platform moved without functional change. We installed Agile PLM on Azure and replicated it in its current state, so users opened the same system they had used the day before. We carried the existing configuration and custom developments across untouched. That meant no rework, no retesting of new functionality and no added risk. We preserved every integration with upstream and downstream systems and re-established each one in Azure, so business processes ran without interruption. We mirrored the overall architecture and environment layout in Azure, giving Polpharma’s team a familiar setup on a modern platform. This built on earlier work with Polpharma, where InspireXT redesigned the packaging documentation lifecycle in Agile PLM and connected internal teams, suppliers and manufacturing partners on one platform. We continue to support the platform through a managed services model covering application support, system monitoring, security, performance tuning and integration support. Value Delivered Polpharma’s product data is now protected on Azure, and the move caused no disruption to users, workflows or integrations. Azure backup and disaster recovery protect critical product data and support Polpharma’s regulatory obligations. Azure’s infrastructure removes the risk of a single server failure taking the platform down. There is no hardware to buy, refresh or manage. Everything gained from the earlier packaging work moved across intact: a 60% cut in packaging material preparation time, a single source of truth for packaging documentation, and full audit trails supporting 21 CFR Part 11 and GxP. Polpharma can now plan its next PLM step from a stable cloud base, with InspireXT supporting the platform. In Polpharma’s words “The migration has significantly reduced technical debt, improved platform stability and reliability, and established a strong foundation for future development. What impressed us most was not only the quality of the delivered work, but also the partnership mindset shown throughout the engagement.” – IT Solutions Architect, Polpharma
Oracle EPM FCC Optimisation: Stabilising Financial Consolidation for a Global Manufacturer
About the Client The client is a global leader in the packaging/manufacturing sector, operating with a highly complex financial and organizational footprint. To manage statutory reporting and consolidation across its various divisions, the enterprise relies on Oracle EPM Financial Consolidation and Close (FCC). While the platform is central to their financial operations, the client needed to improve its everyday usability and reliability without sacrificing the historical data, process knowledge, and baseline configurations already built into the system. Business Challenges The client’s FCC environment had grown overly complex, leading to configurations that no longer aligned with their fundamental consolidation requirements. Because reverting to manual processes was not a viable option, the business needed to address several critical hurdles within the live system: Configuration Misalignment: The existing system architecture was not optimized for core reporting, making the platform increasingly difficult to navigate, maintain, and adapt to changing business needs. Eroding User Confidence: Cumbersome usability and recurring data accuracy issues during the close cycle were undermining stakeholder trust in the final financial outputs. Risk to Live Operations: The organization needed a structured mechanism to stabilize the application and resolve priority defects without disrupting ongoing financial reporting cycles. Asset Preservation: A complete overhaul was premature; the business required a pragmatic method to fix the immediate friction points while protecting the historical data and capital investment already committed to the platform. What We Did We executed a rapid discovery phase to evaluate the current state of the application and map out immediate operational friction points. Following this assessment, we deployed a Managed Services framework to serve as an interim operating model while the client evaluated a larger, long-term finance transformation program. This Business as Usual (BAU) support structure provided dedicated functional and technical oversight. We managed issue triage, defect resolution, configuration refinements, and direct user assistance. By focusing on resolving material issues related to consolidation, balancing, and proofing, we maintained business continuity, improved day-to-day performance, and kept the door open for future reimplementation decisions. Value Delivered Restored Close Cycle Predictability: Stabilized the core consolidation engine, ensuring critical financial reporting periods run smoothly without system-driven delays or disruptions. Eliminated Shadow Workarounds: Drastically reduced the finance team’s reliance on manual, offline spreadsheet calculations to balance numbers, freeing up capacity for actual financial analysis. Rebuilt Stakeholder Trust: Re-established confidence in the system’s outputs by embedding stricter in-system proofing and automated validation checks, ensuring audit-ready accuracy. Protected Capital Investment: Maximized the return on their existing Oracle EPM footprint, providing a high-functioning interim environment while leadership evaluates long-term architectural changes.
Microsoft Fabric and Databricks: why CIOs don’t have to choose in 2026
Key Takeaways Only 1 in 20 large companies say their data is actually ready to support their AI initiatives, according to a 2026 Dun & Bradstreet survey. Microsoft Fabric and Azure Databricks can now read each other’s data in both directions without copying it, a capability that reached general availability at Databricks’ Data + AI Summit in June 2026. Running both platforms together lowers cost and speeds up reporting by removing duplicate copies of the same data, and it closes a real gap: 78% of executives don’t feel confident they could pass an independent AI governance audit today. Mastercard, Novo Nordisk, and PepsiCo are among the named companies already building on this exact combination. The decision left for CIOs is where the next budget dollar goes: deeper into Databricks engineering, or deeper into Microsoft Fabric and Power BI reporting. Every board is asking the same question this year: are we ready for AI. Most CIOs already know their honest answer, and it comes as a number, not a feeling. Nearly every large company already has AI initiatives running. Only 1 in 20 believe their own data can actually support that work. That gap has a boring, specific cause: the numbers a data science team trusts and the numbers a board sees have rarely been the same copy. For any enterprise already running both Microsoft Fabric and Databricks, that copy problem closed this summer. What’s left now is a budget question: where the next dollar of data investment goes. Why only 1 in 20 companies say their data is ready for AI A recent survey from Dun & Bradstreet found that 97% of large organizations already have active AI initiatives running. Only 5%, about 1 in 20, say their data is actually ready to support that work. Cayetano Gea-Carrasco, the firm’s chief strategy officer, drew the real distinction: a company doesn’t need its entire data estate to launch a pilot or a single AI use case. It needs that discipline to run AI reliably across the workflows that keep the business moving, onboarding, compliance, risk management, customer operations. That’s a much higher bar, and it’s the one 95% of large companies haven’t cleared yet. That has a real cost, and it’s measurable. Grant Thornton’s 2026 AI Impact Survey found that 78% of executives don’t feel confident they could pass an independent AI governance audit with ninety days’ notice. Two teams keeping two versions of the same number is exactly how that confidence disappears. A separate IBM Institute for Business Value study found that two out of three CIOs and CTOs are already being held accountable for AI systems they don’t fully control. The accountability showed up before the tooling to manage it did, and that’s exactly where a CIO’s own job gets harder, whatever the board deck says about strategy. None of this is really new pressure, just louder pressure. Deloitte’s 2026 Global Leadership Technology Study found that 79% of IT leaders now list driving business outcomes, not keeping systems running, as their top priority. A CIO who used to get judged mainly on uptime is now getting judged on whether the business actually trusts what comes out of the data underneath it. That’s a harder thing to be graded on, and it’s exactly the grade the readiness numbers above are describing. For a large group of companies, this gap has a specific, familiar shape. Databricks tends to be where a data science or engineering team does its real work, training models, cleaning up messy source data, building pipelines. Microsoft Fabric, and Power BI running inside it, tends to be where the rest of the business actually sees those numbers, in dashboards, in board decks, in reports a CFO signs off on. When the two systems don’t share one copy of the data, somebody in the middle reconciles them by hand, and that manual patch is exactly the pattern Kupriyanova describes. That’s the version of the AI-readiness problem worth solving first, because as of this year, it has an actual fix behind it. The ROI case for running Microsoft Fabric and Databricks together Microsoft Fabric has been able to read data governed inside Azure Databricks for a while now, without copying anything. This June, the reverse became true too: Azure Databricks can now read data sitting in Microsoft Fabric’s OneLake, a capability that reached general availability at Databricks’ own Data + AI Summit rather than staying a preview. That’s the plumbing. What it’s actually worth is a different question, and answering it honestly means netting the benefit against what it still takes to get there, not just listing the upside. For a company already running both platforms: Lower infrastructure cost, but only once both directions are actually configured and adopted, not automatically the day the announcement lands. The direction where Azure Databricks writes its own tables into OneLake is still in public beta, so full symmetry isn’t universal yet. Faster reporting, but only as good as the data underneath it. Zero-copy access moves a bad number to a board just as fast as a good one. This removes a delay. It doesn’t fix a data-quality problem. Lower audit exposure, but governance still has to be set up on both sides. One shared copy of the data doesn’t mean one shared set of permissions around it. Somebody still has to decide who can see what, on each platform, deliberately. For a company that only runs one of the two today and is deciding whether to add the other: The cost of adding the second platform drops, since doing so no longer means re-architecting the first one or building a custom pipeline just to connect them. The switching risk drops too. Adding Microsoft Fabric doesn’t require pulling data science workloads off Databricks, and adding Databricks doesn’t require giving up Power BI reporting already built on Microsoft Fabric. The timeline drops, from months of custom integration work to whatever it takes to turn on a feature that already exists. None of this is
The Active ERP: How Agentic AI Accelerates Oracle Supply Chain Operations
Key Takeaways The Copilot Bottleneck: Conversational AI only summarizes data. Supply chains still rely on human operators to manually execute the actual transactions, leaving operational speed completely unchanged. The Risk of Automated Chaos: Deploying autonomous AI against degraded master data does not create efficiency. It executes flawed logic at an uncontrollable, systemic scale. Gartner ties this directly to data hygiene, and McKinsey’s own research finds that most organizations see no measurable AI return precisely because of weak data and governance foundations, not the technology itself. The End of Silent Compensation: Planners and buyers constantly, manually correct bad ERP data to keep orders moving. Autonomous agents expose this hidden “data debt” because they enforce policy literally. Outcome-Driven Execution: Deploying specialized digital workers, such as Buyer’s Navigator and Smart Yield, shifts the business from reactive firefighting to proactive, automated execution and absolute compliance. Enterprise leaders are currently caught in a costly AI trap. The market has sold them on the promise of autonomous, self-healing supply chains, but what many have actually purchased and deployed are conversational copilots. Gartner has begun warning against exactly this substitution, cautioning enterprises about vendors rebranding legacy automation as agentic AI, when true agentic AI requires goal-oriented reasoning, cross-application orchestration, and persistence that simple automation scripts do not have. The transition from passive software systems to active, autonomous execution is not merely a version upgrade. It represents a fundamental shift in the physics of enterprise operations. Oracle’s recent deployment of Agentic AI provides the robust architectural foundation required for this shift, moving the enterprise away from static ledgers. However, unlocking this capability requires a complete re-evaluation of how a business engineers its operational workflows. Transitioning from a passive ERP to an active, executing ecosystem exposes massive, hidden business risks that leaders must mitigate before they automate. Why are conversational copilots failing to accelerate supply chain operations? To understand why the enterprise is hitting the structural ceiling of current AI models, we must separate the concept of intelligence from the concept of execution. First-generation generative AI in the enterprise took the form of the copilot. These tools are highly sophisticated summarization engines. They can read a fifty-page vendor contract, flag a delayed inbound shipment, or draft a standardized email to a supplier. However, they remain fundamentally passive. They operate on a strict human-in-the-loop design paradigm. The AI observes the data, interprets the anomaly, and advises a course of action, but it waits for a human operator to execute the actual transaction. Consider a sudden, localized demand spike that threatens to stock out a regional distribution center. A conversational copilot might alert the demand planner to the anomaly hours or days faster than a traditional reporting dashboard. Yet the demand planner remains the ultimate operational bottleneck. They must still manually swivel across multiple Oracle Fusion screens, manually recalculate the material requirements, override the system’s static lead times based on their own institutional knowledge of logistics bottlenecks, and manually execute the purchase order adjustments or inventory transfers. The copilot did not solve the systemic friction. It merely reported on it faster. The business is still paying for human execution, and the speed of the supply chain remains limited by the speed at which an operator can type and click through a user interface. True acceleration requires removing the human from the micro-transaction entirely. What is the hidden cost of silent compensation in enterprise master data? Most complex, global organizations are secretly running two completely different supply chains simultaneously. There is the digital supply chain residing in the Oracle ERP, defined by static lead times, rigid master data, strictly enforced bills of materials, and predefined capacity constraints. Then there is the physical supply chain that human operators manage in reality. Every single day, buyers, demand planners, and floor supervisors perform an act of silent compensation. The master data in the ERP might state that a specific supplier in Southeast Asia has a fourteen-day lead time. However, the veteran buyer knows that during the monsoon season, port congestion always pushes that lead time to twenty-one days. To ensure the factory does not run out of raw materials, the buyer manually pads the purchase order date. In another department, a hardware engineer knows a legacy component in a bill of materials is technically flagged as obsolete in the system, but they know it is physically viable for one last production run, so they manually override the system flag to keep the line moving. These human operators keep the business running, but their manual workarounds create zero permanent systemic record. Because the buyer manually intervened, the material requirements planning engine still believes the lead time is fourteen days. It will continue to generate flawed procurement recommendations for every subsequent order. The gap between operational reality and the digital system of record grows wider every shift. The business becomes entirely dependent on the undocumented memory of its employees rather than the logic of its ERP. Book a 30-minute Agentic Readiness Audit and see exactly where your Oracle data breaks down before an AI agent finds out for you. Book the session How does raw AI automation threaten the stability of Oracle Fusion ERP? If you remove the human compensator and hand the operational keys to an autonomous AI agent, you trigger an immediate systemic crisis. An autonomous agent operates human-on-the-loop. It does not just summarize, it executes multi-step workflows. It is given an objective, such as rebalancing inventory to prevent a stockout, and it breaks that objective down into discrete tasks, navigating the system to achieve the goal.However, an AI algorithm does not possess a veteran buyer’s undocumented institutional knowledge. It does not know which system values to distrust. It takes the degraded master data literally. If you unleash an agent on an uncalibrated supply chain, it does not increase efficiency. It automates systemic chaos, authorizing bad spend based on outdated compliance files, routing critical orders based on flawed lead times, and causing stockouts at a scale and speed humans cannot catch or manually reverse.