DEV Community: John Stein

Oracle Cloud 26C Redwood Changes: What’s New?

John Stein — Wed, 05 Aug 2026 12:31:38 +0000

Oracle Cloud 26C continues Oracle’s push toward Redwood as the standard UX across HCM, SCM, and Financials, expanding Redwood-enabled pages, workspaces, and journeys in employee, manager, and specialist-facing flows. Many Redwood changes are auto enabled, meaning experiences switch over on August 21 without a configuration step, even though they still require impact review, training, and communication.

Opkey’s 26C Redwood Advisory and Release Advisor turn the full 26C release notes into a focused Redwood impact, testing, and change plan so HR, Payroll, Finance, Procurement, and IT leads can see which Redwood experiences change, assign ownership, and protect operations before the August 21 go-live date.

Once you know what’s changing, the harder question is how ready your organization is for it. Read our whitepaper to learn more.

Redwood experience: big themes in 26C

Across the 26C cycle, Redwood continues to move from optional UX to default experience, especially in HCM employment and workflow pages, SCM supply chain work areas, and Financials workbenches and dashboards. Oracle’s release notes call out module-level updates, but the impact is felt in how employees request services, how managers approve and review, and how specialists run operational processes day to day.

In 26C, Redwood changes include:

HCM: Employment, Person, Workforce Structures, Recruiting, Journeys, and Help Desk flows moving to Redwood, with AI-assisted experiences embedded in journeys and key tasks.

SCM: Redwood work areas across inventory, orders, supply planning, and manufacturing, with new layouts for key workbenches and exception management.

Financials: Redwood dashboards and pages for General Ledger, Payables, Receivables, and Expenses, with updated navigation and task flows.

These changes alter how users navigate, search, and complete tasks, which is why Opkey’s advisory overlays a Redwood impact lens that highlights which items change UX directly, which sit behind configuration, and which are auto enabled.

Redwood in HCM: employee and manager UX

In HCM 26C, Redwood is further embedded into core HR and people-facing flows, making it the standard experience for more employment and people management tasks. Redwood-enabled experiences include employment pages, person pages, workforce structures pages, and expanded Redwood journeys, alongside updates to recruiting and talent workflows that use Redwood layouts and components.

For HR and people leaders, these are not cosmetic changes: they alter how employees submit requests, navigate employment information, and complete onboarding and HR tasks. Opkey’s Redwood advisory flags HCM Redwood items as people-facing, tags them with testing and communication priorities, and surfaces them in “What To Do This Week” action cards so HR owners know which changes need regression testing and which need employee-facing communication before go-live.

Redwood in SCM: supply chain workspaces

SCM 26C expands Redwood in operational work areas, so supply chain planners, warehouse teams, and order management specialists increasingly work in Redwood workspaces and dashboards. These changes include new Redwood layouts for planning workbenches, inventory and fulfillment pages, and exception-handling experiences that change how users review, filter, and act on supply chain data.

Because these experiences are central to day-to-day operations, auto-enabled Redwood transitions can create confusion or productivity dips if teams are not trained ahead of August 21. In Opkey’s advisory, SCM Redwood changes are grouped by work area and severity, so SCM leads can focus on the experiences their users rely on most and align testing and training accordingly.

Redwood in Financials: workbenches and dashboards

Financials 26C similarly extends Redwood across finance workbenches and analytic dashboards, with updated page layouts, navigation, and task flows in areas like General Ledger, Payables, Receivables, and Expenses. These updates reshape how finance teams view balances, process transactions, and navigate between related tasks within the same workspace.

Many of these Redwood changes are designed to streamline tasks, but they still require impact review to confirm that approval flows, reconciliations, and integrations behave as expected in the new UX. Opkey’s advisory tags Financials Redwood items by role (controller, AP/AR, expenses) and enablement type so finance leads can see which changes need regression tests, spot checks, or communication and training.

Why Redwood 26C is hard to triage manually

Oracle’s quarterly cadence means customers see Redwood expanding in multiple product families at once, with 26C layering new Redwood experiences on top of already-active ones across HCM, SCM, and Financials. Each Redwood-related change differs whether it is auto-enabled, opt-in, or configuration-dependent, and whether it touches employee-facing UX, operational workspaces, analytics, or integrations.

When HR, Finance, SCM, and IT teams try to manually sort the 26C release notes to isolate which Redwood changes affect their users, which are auto enabled, and which require setup or service requests, they lose days in a short testing and training window. Without an impact lens, teams risk treating Redwood as “just UI” and missing high-impact changes to journeys, workflows, and workbenches that drive adoption and productivity.

What Opkey’s 26C Redwood Advisory offers

Opkey’s Oracle Cloud 26C Redwood Advisory sits between Oracle’s official “What’s New” documentation and internal planning, turning Redwood references in the release notes into a clear impact, ownership, and testing view. In a single dashboard-style PDF organized by role rather than module, it gives HR, Payroll, Finance, SCM, and IT leads:

Executive Redwood Snapshot: Key counts of Redwood-enabled pages, workspaces, and journeys across HCM, SCM, and Financials, with a breakdown of auto-enabled versus opt-in experiences.

Testing Priority: Redwood changes tagged by High, Medium, and Monitor-only impact so teams can decide which experiences need full regression, which need spot tests, and which need observation post go-live.

What To Do This Week by Role: Action cards for HR and Workforce, Finance, SCM, and IT/System Admin, tied to upcoming preview and go-live dates and focused on Redwood UX changes.

Impact by Module: Redwood-related change volume and severity across HCM, SCM, and Financials, so each team sees their slice without reading through the entire 26C catalog.

Enablement Breakdown: Redwood changes classified by auto-enabled, setup required, potential setup, and opt-in plus service request.

The advisory is available free at opkey.com and is updated in sync with Oracle’s readiness publications so the Redwood data reflects the current published state of 26C.

How to use this for 26C Redwood planning

Start by downloading Opkey’s Oracle Cloud 26C Redwood Advisory and reviewing the Redwood Executive Snapshot with HR, Finance, SCM, and IT leads, focusing first on auto-enabled Redwood transitions in employee-, manager-, and specialist-facing flows. Use the “What To Do This Week by Role” section to assign owners for HCM (HR and Workforce), Financials, SCM, and IT/System Admin, and separately identify which Redwood changes are people-facing and need communication and training plans.

Then open Opkey Release Advisor at opkey.com/release-advisor and use the prompt guide to explore 26C Redwood changes by product family, module, enablement type, or test level. The Advisor has full context on the 26C catalog and can build a targeted test plan, surface which HCM Redwood changes apply to your employee experiences, which SCM workspaces impact planners and warehouse teams, and which Financials dashboards affect core finance processes, while flagging configuration steps needed by area.

Oracle Cloud HCM 26C Release: What’s New?

John Stein — Tue, 04 Aug 2026 05:17:36 +0000

Oracle Cloud HCM 26C is a significant quarterly update across recruiting, payroll, performance management, core HR, learning, absence management, time and labor, compensation, benefits, and workforce scheduling, with changes that affect not just how HR teams configure Oracle but how employees and managers experience it every day. Teams get a short, defined window between preview and production to identify which changes need configuration, which go live automatically, and which require communication plans before they land on the workforce.

Opkey’s 26C HCM Advisory and Release Advisor turn the full release notes into a focused, risk-based testing and change plan so HR, Payroll, and IT leads can see what matters, assign ownership, and protect workforce operations before the August 21 go-live date.

Knowing what changed is only half of the battle. Building a repeatable process to test it every quarter is what actually protects your operations long-term. Get our eBook and learn how to build a faster, risk-based testing strategy for every Oracle Cloud release.

Big Themes in Oracle Cloud HCM 26C

Oracle Cloud HCM continues to advance as an integrated workforce platform, and 26C extends that direction with changes spanning across the HCM ecosystem.

Unlike SCM 26C, which carries one Critical change, HCM 26C has zero Critical items. That does not reduce the workload. With 79 High-priority changes requiring full regression and 191 items total needing action, the testing and communication burden is substantial, particularly because a significant portion of HCM changes affect the employee and manager experience directly, not just backend configuration. Changes that alter how people submit absences, view payslips, complete onboarding tasks, or navigate Redwood workflows require communication plans alongside config work, and that distinction is built into how Opkey’s advisory organizes the release.

While Oracle’s official 26C release notes call out module-level updates, Opkey overlays an impact lens that highlights which items affect payroll compliance, talent workflows, employee-facing experiences, and integration touchpoints. This matters especially for the 10 AI and agentic features in this release, which require leadership sign-off and change management planning and must not be treated as standard opt-ins.

Recruiting and Talent: The Heaviest Module in 26C

Recruiting leads HCM 26C with 67 changes; AI-assisted onboarding and outreach, activity centers, intelligent talent search, and major Redwood UX updates for interviews, offers, bulk candidate actions, and pipeline requisitions. Performance Management adds 41 changes, including six AI agent features for promotions, change-assignment workflows, succession planning, talent calibration, and broad Redwood updates to performance documents, talent reviews, and career development.

These are functional, not cosmetic: updates to recruiting workflows, offer‑letter versioning, candidate selection, and performance‑document flows will change daily interactions for hiring managers and employees. Opkey flags these items with testing and communication priorities in “What To Do This Week” cards so recruiting and talent owners know which need regression testing and employee-facing messaging before go‑live.

Payroll Compliance and Country-Specific Updates

Payroll includes 39 changes in 26C, many of them country-specific compliance updates that require configuration before August 21. These cover Carer’s Leave, Domestic Violence Leave and Payment, PRSI Exemption Changes, and Ireland NAERSA Contribution Submission for UK and Ireland; LGPS Stringer Days, P45 Document Type, and Real Time Information encryption updates; Bahrain Unemployment Insurance; and India updates to TDS Challan Mapping and Investment Declaration.

Three High-severity Global Payroll Interface V2 and ADP baseline extractions are auto-enabled, meaning they go live on August 21 regardless of review. For global organizations, the combination of auto-enabled baseline extractions and legislative changes makes Payroll one of the highest-risk modules in 26C. Opkey’s advisory organizes Payroll updates by country and enablement type, allowing admins to quickly focus on the changes relevant to their jurisdictions.

People-Facing Changes That Require Communication Plans

Unlike other Oracle product families, HCM 26C stands out for the sheer number of changes affecting how employees and managers interact directly with Oracle. Cross-Functional brings 35 changes, such as Agent as Task Performer in Journeys, AI Recommendations for Key Communications, Express Communication Teams, and Redwood-Slack integration, that reshape daily user experience. Core HR adds 29 changes, including Redwood enabled by default across Employment, Person, and Workforce Structure Pages, plus AI-assisted job and position management.

While these updates may not require complex configuration, they can substantially change how HR specialists, managers, and employees navigate Oracle after August 21. Failing to communicate these changes before go-live risks confusion, higher HR Help Desk volume, and lost productivity gains. Opkey’s advisory separates people-facing changes from backend configuration items, helping HR and communications leads identify what needs a communication plan versus just a config ticket.

Why HCM 26C Is Hard to Digest Manually

275 changes across 12 HCM modules in Oracle 26C.
191 changes require action; 84 are auto enabled at production.
Preview: August 8 | Production: August 21, leaving a short testing window.
Compared to Oracle HCM 26B, 26C has more total changes (275 vs. 268) and more auto-enabled updates (84 vs. 66).
No Critical items in 26C, but 84 background changes increase deployment risk.
Auto-enabled updates impact Recruiting, Payroll, Redwood UX, and integrations.
Changes vary by enablement type (auto-enabled, opt-in, configuration), business impact, and testing requirements.
51% require setup, 16% may require setup, and 3% require both opt-in and a service request.
79 High-priority changes require regression testing.
HR, Payroll, and IT teams must identify configuration needs, people-facing changes, testing scope, and expiring opt-ins, making manual review slow and error-prone within the limited release window.

What Opkey’s 26C HCM Advisory Offers

Opkey’s Oracle Cloud HCM 26C Advisory sits between Oracle’s official “What’s New” documentation and your internal planning, turning release notes into an impact, ownership, and testing lens. In a single dashboard-style PDF organized by team rather than module, it gives HR, Payroll, Talent, and IT leads:

Executive Snapshot: 275 total changes, 191 requiring action, 84 auto-enabled, and 10 AI and agentic features requiring leadership sign-off.
Testing Priority: 79 High items flagged for full regression; 79 High plus 196 Medium for spot check; 196 Medium for monitor only.
What To Do This Week by Role: Action cards for IT and System Admin (before preview) and HR and Workforce (standard review), with items tagged by module, enablement type, and whether they affect employees directly.
Impact by Module: Change volume and severity across all 12 HCM modules, so each team sees only their slice without reading through the full catalog.
AI and Agentic Features: 10 new agent capabilities including the Employee Goals Assistant, Payroll Run Analyst, AI-assisted onboarding, Benefits Analyst Agent, and Talent Calibration Workspace, all flagged separately because they require change management planning, user training, and leadership sign-off.
Opt-In Expiry: Features with windows closing permanently on August 21 that require action before go-live or the choice is gone.
Enablement Breakdown: 51% setup required, 31% no action needed, 16% potential setup, 3% opt-in plus service request.

The advisory is available free at opkey.com. Opkey updates its release tracking in sync with Oracle’s readiness publications, so the data reflects the current published state of 26C.

How to Use This for 26C Planning

Start by downloading Opkey’s Oracle Cloud HCM 26C Advisory and reviewing the Executive Snapshot with HR leaders, Payroll admins, Talent leads, and IT, with immediate attention to the three auto-enabled Payroll baseline extractions and the OPA Integration discontinuation notice in Cross-Functional, both of which require action before the preview window opens July 8. Use the “What to Do This Week by Role” section to assign owners across HR and Workforce and IT and System Admin and separately identify which people-facing Redwood and AI changes need communication plans assigned to HR communications or change management owners.

Then open Opkey Release Advisor at opkey.com/release-advisor and use the prompt guide to explore 26C by module, country, enablement type, or test level. The Advisor has full context on all 275 changes and can build a targeted test plan, surface which Payroll changes apply to your countries, identify which Recruiting and Performance changes require employee communication, and flag configuration steps needed by module. The advisory is the map. The Advisor is navigation.

Download the free HCM 26C Advisory to cut your triage time and build a plan your HR, Payroll, Talent, and IT teams can act on before the window closes.

Oracle Cloud SCM 26C Release: What’s New?

John Stein — Tue, 04 Aug 2026 04:50:05 +0000

Oracle Cloud SCM 26C is a major quarterly update across planning, procurement, inventory, orders, manufacturing, maintenance, and logistics, aimed at improving usability, planning accuracy, and execution efficiency. Teams get a short, defined window between test and production to decide which changes affect their workflows. Opkey’s 26C SCM Advisory and Release Advisor turn the full release notes into a focused, risk-based testing and change plan so you can see what matters, prioritize testing and automation, and protect supply chain continuity before the 26C go-live date.

Big themes in Oracle Cloud SCM 26C

Oracle Cloud SCM continues to evolve as a unified, end-to-end supply chain platform, and 26C extends that direction with changes across Supply Chain Planning, Procurement, Inventory Management, Order Management, Manufacturing, Maintenance, Cost Management, Supply Chain Collaboration, Product Hub, and Warehouse Management.

26C is a heavier release than 26B across every measure that counts. The prior cycle carried 299 total changes across 12 modules, with 200 requiring action and 99 going live automatically. 26C adds 22 more total changes, pushes action-required items from 200 to 233, and reduces auto-enabled from 99 to 88, meaning more of this cycle’s workload lands directly on your team rather than rolling out in the background. Teams that managed 26B manually will find 26C harder to triage on the same approach.

While Oracle’s official 26C release notes call out module-level updates, Opkey overlays an impact lens, highlighting which items affect planning logic, procurement contracts, inventory controls, shipping and fulfillment flows, and integration touchpoints. This matters especially for the 10 AI agent features, Redwood experiences, and execution diagnostics in this release, which often require targeted testing and training rather than just a configuration toggle.

The Critical Change You Cannot Miss

26C carries one Critical change across all 11 SCM modules: Capture Fiscal Documents for Intercompany Drop Shipments in Cost Management. This change requires configuration before go-live and has direct implications for how fiscal documents are captured and recorded in intercompany drop ship flows. Organizations running intercompany drop shipment processes must review their Cost Management configuration and validate this flow before August 21. It is the single highest-priority item in the release.

Stronger planning, inventory, and fulfillment controls

Several 26C updates directly affect how you plan, stock, and fulfill orders. Inventory Management carries the heaviest load in this release with 86 changes, the largest of any module, including new AI agents for inventory optimization, inbound goods, and warehouse operations, alongside Redwood UI updates for cycle counting, picking, and receiving. Supply Chain Planning adds 33 changes covering safety stock integration with inventory optimization plans, pegging data visibility, and new planning constraint options that alter how planned orders are generated and sequenced.

For supply planning, operations, and logistics teams, these are not optional reviews. Changes to planning tools, stocking logic, or warehouse workflows can alter decision-making and KPIs like fill rate and on-time delivery. In Opkey’s advisory, these items are tagged as high impact for regression testing and surfaced in “What To Do This Week” action cards so owners know they require attention before go-live.

Redwood UX and automation in supply chain execution

Oracle Cloud SCM 26C advances Redwood UX and automation across supply chain execution in ways that standard release notes typically understate. Procurement carries 74 changes in this cycle, a significant portion of which are Redwood UI updates covering contract fulfillment, supplier qualification, negotiation workflows, and buyer assignment rules. Order Management adds 58 changes including Redwood updates to transit time calculation, ship method scheduling, and configure-to-order diagnostics, alongside six new AI agents covering claim settlement, order exceptions, sales order correction, and dock scheduling.

These updates may not always change configuration dramatically, but they can significantly alter how day-to-day work is executed on the shop floor, in the warehouse, and in control towers. In the advisory, Opkey flags these Redwood and automation updates with pre-assigned testing and communication priorities, so teams can see where UX changes will affect SOPs, training, and change management before go-live.

Risk, compliance, and contract governance

For procurement, quality, and audit teams, 26C introduces changes that affect contractual controls and auditability. Procurement carries a Contract Compliance Workspace, contract collaboration via Microsoft SharePoint, and restricted party screening enhancements, all flagged as High severity and requiring configuration. Supply Chain Collaboration adds 19 changes including GTM restricted party pre-processing and automation agent actions for shipment tendering, which affect how outbound shipments are authorized and tracked.

Why SCM 26C is hard to digest manually

Oracle’s quarterly release model means SCM customers process four waves of change each year, with preview and production dates tightly scheduled and a standard testing window. For 26C, preview opens July 8 and production goes live August 21. That window covers 321 changes across 11 modules, with 233 requiring action and 88 going live whether your team is ready or not.

Each change differs in how it is enabled, what it touches, and how much regression is needed versus a spot check or monitoring. 45% of 26C changes require setup, 11% carry potential setup requirements, and 8% combine opt-in with a service request. When teams try to manually sort through this distribution to find the Critical item, isolate the 184 High items needing regression, and track the 10 opt-in windows closing permanently at go-live, they lose critical days in an already short testing window.

What Opkey’s 26C SCM Advisory adds

Opkey’s Oracle Cloud SCM 26C Advisory sits between Oracle’s official “What’s New” documentation and your internal planning, turning release notes into an impact, ownership, and testing lens. In a single dashboard-style PDF organized by team rather than module, it gives teams:

Executive Snapshot: 321 total changes, 233 requiring action, 88 auto-enabled, and 10 AI and agentic features requiring leadership sign-off.
Testing Priority: 1 Critical plus 184 High items flagged for full regression; 184 High plus 135 Medium for spot check; 135 Medium and 1 Low for monitor only.
What To Do This Week by Role: Action cards for IT and System Admin, Supply Chain and Operations, with items tagged by module and enablement type.
Impact by Module: Change volume and severity across all 11 SCM modules so each team sees only their slice.
AI and Agentic Features: 10 new agent capabilities including the Inventory Optimization Advisor, Planning Order Release Assistant, Inbound Goods Advisor, and Sales Order Command Center, all flagged separately because they require leadership sign-off and change management planning, not just a configuration ticket.
Opt-In Expiry: 10 features with windows that close permanently on August 21, covering Inventory Management, Manufacturing, Procurement, Maintenance, Supply Chain Planning, and Cost Management.
Enablement Breakdown: 45% setup required, 27% no action needed, 11% potential setup, 8% opt-in plus service request.
The advisory is available free at opkey.com. Opkey updates its release tracking in sync with Oracle’s readiness publications so the data reflects the current published state of 26C

How to use this for 26C planning

Start by downloading Opkey’s Oracle Cloud SCM 26C Advisory and reviewing the Executive Snapshot with supply chain, operations, procurement, and IT leads, with immediate attention to the one Critical change in Cost Management for intercompany drop shipments. Use the “What To Do This Week by Role” section to assign owners across Supply Chain and Operations and IT and System Admin, with the 10 opt-in expiry items given their own deadline tracking before August 21.

Then open Opkey Release Advisor at opkey.com/release-advisor and use the prompt guide to explore 26C by module, enablement type, or test level. The Advisor has full context on all 321 changes and can build a targeted test plan, surface which Redwood changes affect your buyers or warehouse staff and identify configuration steps needed by module. The advisory is the map. The Advisor is the navigation.

26C is a bigger release than 26B, with 233 action-required changes, one Critical compliance item in Cost Management, 10 AI agents requiring leadership decisions, and 10 opt-in windows closing permanently on August 21. The teams that come out ahead are the ones who assign ownership now rather than the week before go-live.

Download the free SCM 26C Advisory to cut your triage time and build a testing plan your whole team can act on before the window closes.

Oracle Cloud Financials 26C Release: What’s New?

John Stein — Thu, 30 Jul 2026 13:40:25 +0000

Oracle Cloud Financials 26C is a major release with 137 changes across 12 modules, going live in production on August 21, 2026, whether you act on them or not. For finance and IT teams, the main work is deciding which changes need attention, testing, and change management before that date, instead of reading long release notes.

Opkey Release Advisor helps you do that by turning the full 26C change list into a clear, impact-based testing plan for your own environment. You get a direct view of which updates affect your configurations, integrations, and business processes, and what to test and when.

Key themes in Oracle Cloud Financials 26C

Oracle Cloud Financials remains the core for global finance operations, and 26C continues that path with changes across Payables, Receivables, Project Financial Management, Tax, Fixed Assets, Cash Management, Risk Management, and cross-functional workflows.
Opkey’s advisory snapshot breaks this down into four numbers worth holding onto for the rest of this article.

Across Financials, HCM, and SCM together, Opkey’s dashboard tracks more than 700 changes for 26C. Finance, HCM, and SCM teams each get their own view rather than one long shared list.

Compliance priorities: Poland and Brazil

26C carries high compliance urgency in Financials. All three Critical changes are legislative mandates for Poland and Brazil, all three are auto-enabled on August 21, and all three require configuration before go-live. They are not optional.

JPK Audit File Reporting Update (Poland)

Oracle has updated the JPK_V7M structure to align with the latest Polish Ministry of Finance requirements. Organizations filing JPK audit reports must reconfigure their reporting setup to match the new schema before go-live. Submitting under the old structure after August 21 will produce non-compliant filings.

Polish VAT Declaration Alignment (Poland)

The VAT declaration workflow inside Oracle Tax has been updated to reflect Poland’s current regulatory format. Finance and Tax teams running Polish VAT processes need to validate their tax configuration, review period-end outputs, and confirm the updated declaration template is correctly mapped to their legal entity setup.

Brazilian Fiscal Document Workflow Update (Brazil)

Oracle has updated the electronic fiscal document (NF-e/NFS-e) processing workflow to reflect current SEFAZ requirements. Organizations issuing or receiving fiscal documents in Brazil must review their fiscal document configuration and test end-to-end document flows before August 21 to avoid processing failures at go-live.

In Opkey’s advisory, these three changes sit in a separate Critical Compliance Alert section so they cannot be lost under routine updates.

Automation and New Finance Features

26C brings a new set of agent-style features into Financials, including Fixed Assets, Payables, Project Financial Management, and cross-functional workflows. These capabilities are treated separately in the advisory because they usually need leadership review and structured change management, not just IT setup. Enabling a new agent in a live finance process is a different decision than turning on a new report or field.

Risk and configuration workload

With 109 of 137 Financials changes requiring action and 28 going live automatically on August 21, the configuration and testing workload is heavy for this cycle. Spread across 12 modules; this volume makes manual triage hard. Teams need clear direction on which items are urgent, which are configuration only, and which need cross functional sign off.

Why 26C is hard to manage by hand

Oracle’s quarterly update model gives Financials customers four releases a year, each with its own preview and production timeline and a standard testing window. For 26C, Opkey’s advisory maps Financials changes across 12 modules and tracks them alongside 84 HCM changes and 88 SCM changes that share the same August 21 release date.

Each change differs in how it is enabled, what it touches, and how much testing is needed. Manually sorting 137 Financials items to find the 109 that need action, understand what the 28 auto enabled items might affect, and locate compliance risk in Poland and Brazil is where teams lose time and control.

What Opkey’s Oracle 26C Release Advisory offers

Opkey’s Oracle Cloud 26C Advisory collects all module specific updates across Financials, HCM, and SCM and organizes them into one dashboard, so each functional team can go straight to the changes that matter to them.

The dashboard includes:

Executive Snapshot: Four key metrics on total changes, changes that need action, auto enabled items, and new agent style features.
Critical Compliance Alert: Legislative changes that go live automatically on August 21, including the Poland and Brazil mandates.
Testing Priority: Every change tagged as full regression, spot check, or monitor only, so QA planning does not start from a blank sheet.
What To Do This Week by Role: Action cards for Finance and Tax, IT, Payables, and Project and Receivables, each with owned changes and timelines.
Impact by Module: Visual breakdown of change volume and severity across all 12 Financials modules.
Agent Features: New agent capabilities highlighted separately to support leadership review.
Opt In Expiry: Features with closing windows that become permanent after August 21.
Enablement Breakdown: Summary of what is auto enabled versus what needs setup, opt in, or a service request.
Prompt Guide: Ready questions for Opkey Release Advisor, organized by role.

Why 26C matters and how to use this

Why 26C Matters

Oracle 26C is a heavier release than 26B by every action-required measure: total changes are up from 129 to 137, action-required items climbed from 91 to 109, and auto-enabled changes dropped from 38 to 28, meaning more of this cycle’s workload lands directly on your team rather than rolling out automatically.

That scale does not need to become a fire drill. A practical plan starts by downloading Opkey’s Oracle Cloud 26C Advisory and reviewing the Executive Snapshot with finance and IT leaders, with direct attention to the Critical Compliance Alert if you operate in Poland or Brazil. From there, use the “What To Do This Week by Role” section to assign owners across Finance and Tax, IT, Payables, and Project and Receivables.

Then open Opkey Release Advisor at opkey.com/release-advisor and use the prompt guide to explore 26C by module, severity, enablement type, or test level. This helps you build a focused test and change plan, with full regression on the three Critical and other high-priority items, and spot checks or monitoring for the rest.

26C is not a light release. Between the three compliance mandates, 109 action-required changes, and new agent-style features landing across Financials on the same August 21 date, the teams that come out ahead will be the ones who start triaging now rather than the week before go-live.

Download the free 26C Advisory and Readiness Checklist to see exactly what your team needs to act on before August 21.

Post-Merger Enterprise App Integration: Why Most M&A Programs Fail

John Stein — Tue, 28 Jul 2026 07:59:11 +0000

Mergers and acquisitions are tough. Among the myriad of integration tasks, integrating enterprise and cloud apps stands out. Post-merger enterprise app integration programs are massive projects and often fail for the same reasons as other large cloud and enterprise app programs: fragmented, manual processes increase timelines and risk.

The testing gap in M&A integration

No single view of real business processes

After an acquisition, you inherit two enterprise environments with overlapping but non-identical workflows. Finance closes books differently. HR runs payroll on different cycles. Procurement uses different approval paths. Supply chain maintains separate vendor masters.

Testing teams cannot validate end-to-end flows because they do not know what the real flows are across both systems.

Manual regression cannot keep pace with integration changes

M&A integration is not a one-time cutover. It is months of iterative changes; harmonizing chart of accounts, consolidating vendor lists, aligning security roles, migrating data in waves. Every configuration change introduces regression risk. Manual testing cannot keep up. Teams test the obvious scenarios and hope nothing critical breaks in production.

Cross-system dependencies stay invisible until go-live

The biggest defects show up when one enterprise triggers a workflow in another. A purchase order in the acquired company’s Oracle instance needs to update inventory in the parent’s SAP system. Payroll data from Workday feeds consolidated reporting in Oracle Financials.

These integrations are nearly impossible to test manually because dependencies span applications, security contexts, and data models.

Testing happens too late

Most M&A programs treat testing as a pre-go-live gate instead of continuous validation. By the time QA starts running test cases, integration decisions are locked in. If testing reveals a gap, the program either delays or accepts the risk and moves forward anyway.

How CALM platforms change M&A testing

Cloud Application Lifecycle Management (CALM) platforms built for enterprise apps handle this scenario differently; they discover real processes, automate regression, and validate cross-system flows throughout the integration timeline.

Process discovery across both environments

Automated process mapping shows how workflows actually run in both legacy systems. Integration teams see the complete procure-to-pay, order-to-cash, hire-to-retire flows across Oracle, Workday, SAP, or any combination.

You harmonize what exists, not what outdated documentation says exists.

Continuous regression as integration evolves

Every time integration teams harmonize configuration, migrate data, or adjust security, automated tests run across both enterprise environments to confirm core processes still work.

Testing happens throughout the program, not just before go-live. Defects get caught when they are still easy to fix.

End-to-end validation across system boundaries

Tests cover workflows that span multiple enterprises. A purchase order created in the acquired environment triggers the right updates in the parent’s financials. Payroll changes in one HR system flow correctly into consolidated reporting.

You have test evidence that integrations work; not assumptions.

Adaptive test suites

M&A timelines shift. Configurations change. Data models evolve. AI-powered tests adapt automatically so regression suites do not break every time the integration plan changes.

QA teams validate business outcomes instead of maintaining test scripts.

What it looks like in practice

A global retailer acquired a regional competitor running Oracle Cloud HCM and SCM. The parent used Workday for HR and a different Oracle instance for financials and supply chain.

Integration required consolidating vendor masters, aligning procurement workflows, and ensuring payroll from both HR systems fed a single financial close process.

The team mapped procurement and payroll processes in weeks, automated regression so configuration changes did not break workflows, validated cross-system data flows before go-live, and reduced post-merger production incidents by 80% compared to the previous acquisition.

The program went live on schedule with no critical defects in finance, HR, or supply chain.

The bottom line

M&A integration does not break at strategy. It breaks at testing; where disparate enterprise systems prove they can work together without disrupting operations.

Platforms built for cloud application lifecycle management turn testing from a bottleneck into continuous validation. Teams discover real flows, automate regression, validate integrations, and protect operations while the program evolves.

That is how you align disparate systems without breaking the business.

Beyond Efficiency: How IT Leaders Plan to Reinvest AI Time Savings

John Stein — Mon, 20 Jul 2026 12:05:42 +0000

In Q1 of this year, we surveyed over 200 IT leaders. In light of daily news about massive tech layoffs, one of the most interesting (and optimistic) findings was that IT leaders plan to reinvest freed resources from automating the enterprise application lifecycle primarily into employee experience, innovation, higher‑value work, and backlog reduction, rather than simply cutting costs.

Invest in Growth, experience and innovation

When asked how they would use hours and budget saved by automating and optimizing the management of their enterprise apps agentic AI, respondents most often chose growth, experience and innovation-oriented outcomes.

The data suggests a clear and healthy path to growth and innovation.

First, leaders want to reinvest in people: improving employee experience and adoption is a top outcome overall for their IT organization, and it’s also the most selected use of freed capacity. This ties directly to training, change management, and smoother rollouts.
Second, there is a strong push toward innovation: roughly two in five plan to channel savings into new digital products, features, or capabilities, especially in finance, manufacturing, and retail.
Third, teams see automation as a way to change the nature of IT work, not just its volume, by reskilling and moving staff away from manual configuration, testing, and firefighting into analysis, design, and continuous improvement.
Fourth, a significant share will finally tackle the IT backlog that has built up under the pressure of frequent cloud releases and limited headcount, using freed hours to retire technical debt and deliver long‑delayed enhancements.

Cost‑cutting is still on the list, but it sits behind these growth and experience oriented moves. Reducing reliance on external SIs and consultants comes ahead of broad operational cost cuts, which aligns with other findings that many see current SI usage as something they plan to significantly reduce.

Implications for your roadmap

Taken together, these findings tell a strategic story: agentic AI‑driven automation is not just about “doing the same with less,” it is about freeing up capacity to do different, higher‑impact work.

IT leaders are effectively saying: Let AI handle regression testing, impact analysis, and repetitive support tickets, and let our people focus on adoption, process redesign, analytics, and new capabilities.

In a climate where you may be under pressure to reduce headcount, this gives you a powerful alternative narrative: protect your experts, change the work. If you are planning your own AI or automation roadmap around enterprise applications, be sure to:

Frame your business case around reallocation, not only savings. Be explicit about how many hours you expect to move into employee enablement, product innovation, or backlog burn‑down.
Involve both IT and business stakeholders early, because the top uses of freed capacity are cross‑functional: adoption, experience, and new capabilities all require tight partnership.
Plan for reskilling as a first‑class workstream, not an afterthought, since over a third of leaders expect to redeploy people into higher‑value roles as automation ramps.

How Opkey helps you operationalize this shift

IT operations are under pressure, but the fastest path out is to automate the right parts of the lifecycle and reinvest the capacity you unlock.

Opkey was built for exactly this moment. Across the lifecycle, Opkey automates key tasks, including:

Testing: with 30,000+ self‑configuring test scripts that replace slow, labor‑heavy regression cycles with AI‑generated coverage, increasing coverage and shrinking patch testing from weeks to days.
Configuration: by automating change detection, impact analysis, and migration so you can ship more, break less, and stop relying on late‑night heroics.
Training Documentation: by automatically delivering role‑based, in‑app guidance that lifts adoption and turns every release into an opportunity to build capability, not confusion.

All of this is orchestrated by Argus AI, Opkey’s ERP‑native small language model designed to understand configuration, process, and change in context.

The result is simple but powerful: you reduce cost and risk, and you get a choice about what to do with the time and money you get back.

Our research tells us what high‑performing leaders are choosing:

Invest in people and experience.
Fund new capabilities.
Upgrade skills instead of cutting heads.
Finally tackle the work that has been stuck on “someday.”

If you’re being asked to cut, but you’d rather reallocate, now is the moment to put a data‑backed plan on the table.

Agentic AI and the Future of Cloud Application Lifecycle Management

John Stein — Fri, 17 Jul 2026 11:19:04 +0000

In Opkey’s 2026 State of Enterprise Application Lifecycle Management survey of over 200 IT leaders, expectations for the use of agentic AI in managing enterprise applications are nothing short of transformational: most plan to adopt it, and they’re already planning how to reinvest the time and money they expect to save.

Management of enterprise apps is already consuming IT

Before we get to AI, it is worth grounding the reality IT leaders are living in today.

Managing enterprise apps consumes IT budgets: 64% of organizations allocate 21–50% of their total IT budget to implement and manage enterprise applications, with another 6% spending more than half their budget here.
Investment in enterprise apps is increasing: 83% say their total enterprise application investment increased year‑over‑year. And the expectation of growth continues: 80% expect budgets to grow again over the next 12 months and 86% expect spend to grow over the next 3–5 years.
At the same time, cloud release velocity keeps climbing, and already today 73% of organizations manage three or more major app releases per year; and 36% manage 12 or more. The single most challenging task IT leaders report is the time and effort to configure new features for each cloud release, cited by 51% of respondents, followed closely by identifying config changes required for business needs (46%) and understanding current business processes (45%).

The result is a landscape where application operations are expensive, change is constant, and staff are heavily consumed by low‑leverage work.

The current model is straining

As we discussed in an earlier blog, the survey shows that IT leaders are acutely aware their current operating model is unsustainable. Production instability is common: over half report experiencing production issues from configuration or process changes sometimes, often, or almost always.

Strategically, the number one burden leaders identify is “difficulty assessing the impact of changes and updates,” which 34% rank as their top strategic issue—well ahead of cost and staffing constraints, which are ranked first by only 25% of respondents.

Constant change is causing issues to show up in production. In this context, agentic AI is not a nice‑to‑have, but is the first credible way to break the cycle.

Expectations for agentic AI are sky‑high

When presented with the idea of a secure, enterprise‑grade agentic AI system that can autonomously identify process inefficiencies, recommend and validate configuration changes, generate and maintain test scripts, update documentation, and smooth post‑release support, 83% of respondents say their organization is completely or very likely to adopt it. Only 1% are “hardly likely,” and none say “not likely at all.”

IT leaders do not view agentic AI as a lateral move from today’s chatbots and copilots. 64% believe agentic AI will deliver significantly or somewhat more value than the AI tools they’ve invested in over the last several years.

The perceived payoff is not abstract. When asked how much time their teams could realistically save if the enterprise app lifecycle were automated and optimized with agentic AI: 69% estimate savings of 5,000–30,000 hours per year. For IT leaders running a 20–30 person app team, this is the equivalent of reclaiming years of human effort every budget cycle to redeploy into higher‑value work.

In short, for IT leaders, agentic AI is now a fundamental part of their strategy for managing their applications.

Where IT leaders plan to reinvest the agentic AI dividend

Perhaps the most interesting aspect of this equation is not how much IT leaders think they will save, but how they plan to use those savings.

When asked how they would reallocate hours and costs freed by agentic AI‑driven automation of their application lifecycle, IT leaders prioritize four themes:

Improving employee experience and adoption – 42%
Innovating on new business capabilities – 42%
Reskilling or redeploying staff to higher‑value work – 38%
Reducing IT backlog – 38%

Cost reduction is present, but not dominant:

36% would specifically reduce external consulting spend, and 34% would reduce overall operational cost.

This is an important signal. IT leaders are not planning to use agentic AI only to shrink their budgets; they are planning to rebalance their portfolio of work and shift from manual remediation to proactive innovation.

What this means for IT leaders in 2026

For IT leaders, the message is clear: expectations for agentic AI are extremely high and are being set by peers who are looking at the same pressures you are.

Two implications stand out:

Innovation and employee experience are as important as cost savings. The majority of leaders want to reinvest savings into better employee adoption, new business capabilities, and backlog reduction, not just budget cuts. Your roadmap for agentic AI should explicitly connect automation gains to these growth oriented outcomes.
Agentic AI must be deeply embedded in the application lifecycle, not bolted on. The pain points IT leaders highlight—configuring new features per release, understanding process and change impact, maintaining coverage and continuity—sit at the heart of cloud application lifecycle management. Tools that operate at the surface (e.g., generic LLMs) will not close that gap; the expectations being set are for domain aware, workflow embedded agents that can act across the lifecycle with guardrails.

At Opkey, we see these findings as a mandate: agentic AI for enterprise applications has to be measured by how much complexity and risk it actually removes from your change pipeline, and by how much time, budget, and talent it frees to focus on strategic priorities rather than survival.

Domain-Specific AI vs General AI: What CIOs Need to Know in 2026

John Stein — Thu, 16 Jul 2026 11:20:18 +0000

Enterprise technology is at an inflection point.

After years of experimentation, artificial intelligence; particularly large language models (LLMs) and agentic AI, is here. It’s being actively evaluated, budgeted for, and deployed across enterprise applications ecosystems that run finance, supply chain, HR, and customer operations.

For CIOs and business technology leaders, the conversation has shifted.
The question is no longer whether AI can be used—but where, how, and at what risk.

At the same time, pressure is coming from every direction:

Do more with the same or fewer resources
Reduce operational costs while keeping up with innovation
Assure system reliability while managing constant application updates
Adopt and embed AI that delivers measurable business outcomes; not just AI pilots or proofs of concept

Agentic AI promises an attractive future state of enterprise applications: systems that don’t just analyze or respond, but observe, reason, and act inside enterprise workflows. In theory, AI agents can validate configurations, run tests, detect and fix integration issues, guide users in real time, and escalate complex issues to teams when needed.

When done right, agentic AI unlocks operational efficiencies, higher quality execution, and tighter control across complex enterprise environments.

But reality is more complicated.

The Growing Gap Between AI Promise and Enterprise Reality

Despite widespread enthusiasm, many enterprise leaders struggle to convert interest into action – that is making deliberate investments in AI technology.

More than half of organizations still rely heavily on manual, in-house processes to test and maintain enterprise applications. Automation adoption remains limited, even as application complexity continues to grow. At the same time, industry research shows that a significant percentage of enterprise technology initiatives fail to meet business goals; despite increasing AI investment.

This contradiction reveals a deeper issue.

Enterprise applications are not isolated systems. They are tightly woven webs of configurations, integrations, data, workflows, and people. Even a small change; such as a quarterly application update—can ripple across finance, supply chain, payroll, and reporting.

When automation or agentic AI implementations do not account for these dependencies, the results lead to

Incomplete impact analysis
Gaps in regression testing
Unplanned outages or process failures
Manual firefighting to remediate or roll back changes

This is why many users report frustration; not because the AI system lacks capability, but because the implementation wasn’t properly planned, and the scope wasn’t clearly defined from the start.

Generic AI Advice Isn’t Enough for Enterprise Applications

A large part of the problem lies in how “AI” is defined.

The market uses a single label to describe very different capabilities: chat assistants, retrieval tools, code copilots, workflow bots, and agentic frameworks. These systems vary widely in purpose, value, and risk; yet are often evaluated as if they were interchangeable.

General-purpose LLMs excel at language understanding and generation. They are effective at summarizing, search, and conversational tasks. But they are designed to produce plausible responses, not guaranteed accurate or auditable actions.

In enterprise environments, the distinction between a general-purpose language model and a domain-specific, context-aware system is critical.

A general AI model will always provide an answer. In the absence of deep domain context, it may improvise—introducing risks such as hallucinations, misclassifications, or incomplete reasoning. In business-critical systems, these errors can lead to faulty impact analysis, poor test coverage, or incorrect configuration changes.

Why General-Purpose AI Falls Short in Enterprise Contexts

Enterprise applications introduce challenges that general AI is not built to handle:

Organization-specific configurations that fundamentally alter system behavior
Complex integrations across multiple applications
Business logic embedded in processes rather than documentation
Cascading dependencies where small changes have unpredictable downstream effects
Fragmented and inconsistent data across systems

This environment is often described as data chaos—where information is ungoverned, inconsistent, and scattered across enterprise platforms.

General-purpose AI does not inherently understand these constraints. While prompt engineering and human-added guardrails can help, they require constant supervision. Instead of reducing effort, they often shift work back to users—adding cognitive load and operational friction.

Why Domain-Specific Excels Over General Language Models

Domain-specific AI is built with a different goal: precision over breadth.

Rather than being trained to respond broadly across any topic, it is trained on high-quality, domain-specific enterprise data and structured workflows. This focused training enables accurate reasoning and reliable actions within a defined scope, prioritizing trust, repeatability, and operational safety.

At the core of this difference is inference accuracy.

What Is Inference Accuracy?

Inference is the phase where an AI model generates an output—an answer, recommendation, or action—based on a prompt. Inference accuracy measures how often those outputs match validated truth in a specific domain.

In enterprise applications:

Low inference accuracy can result in incorrect financial analysis or missed testing scenarios
High inference accuracy enables confident automation and reduced human oversight

Importantly, inference accuracy cannot be measured generically. It must be evaluated against enterprise-specific data, tasks, and workflows.

The Building Blocks of Effective Domain-Specific AI

High-performing domain-specific AI systems combine multiple layers of intelligence:

Concept understanding

Clear definitions of enterprise terms, structures, and rules

Task knowledge

The ability to perform repeatable procedures such as testing, configuration, or reconciliation

Structured reasoning

Logical connections between tasks and concepts to solve multi-step problems

Human-centered interaction

Clear instruction-following and conversational guidance aligned with user workflows

Autonomous operation

Independent execution of routine tasks, with intelligent escalation to humans

These capabilities allow AI agents to operate inside enterprise workflows, not just advise from the sidelines.

Solving the Data Scarcity Challenge

One of the hardest problems in domain-specific AI is data scarcity.

Enterprise application knowledge is rarely available in public datasets. Much of it lives in internal documentation, historical changes, and the experience of employees and consultants.

Modern domain-specific AI systems address this through automated pipelines that:

Retrieve enterprise-relevant knowledge using structured taxonomies
Filter content based on domain relevance and technical depth
Generate realistic, domain-specific questions
Produce factually grounded answers through repeated validation cycles

This process creates high-quality, in-context training data that significantly improves inference accuracy and reduces hallucinations.

What CIOs Need to Know in 2026

By the end of 2026, AI will no longer be evaluated as an innovation initiative—it will be assessed for its effectiveness to improve operational infrastructure.

For CIOs, this marks a fundamental shift in responsibility. AI decisions now directly affect system reliability, compliance, cost structures, and business continuity. There is less room for trial and error, especially as AI becomes more deeply embedded in core enterprise applications.

Three realities define the AI landscape CIOs are stepping into:

AI Risk Is Now Enterprise Risk

When AI systems influence configuration decisions, testing coverage, release readiness, or user guidance, errors no longer stay isolated. A single incorrect inference can cascade across finance, supply chain, HR, and reporting systems.

In 2026, CIOs will be accountable not just for AI adoption; but for:

Accuracy and auditability of AI-driven decisions
Downstream impact on business operations
Governance models that balance autonomy with control

AI that cannot explain or validate its actions will increasingly be viewed as a liability, not an accelerator.

General AI Skills Are No Longer Enough

Early AI strategies focused on broad capabilities: chat interfaces, copilots, and generic assistants. While useful, these tools stop short of what enterprise applications demand.

CIOs must now evaluate whether AI systems:

Understand enterprise-specific business logic and workflows
Reason across configuration and integration dependencies
Operate safely within compliance and data boundaries

In 2026, the competitive gap will widen between organizations experimenting with general AI and those investing in domain-specific intelligence aligned to their core business systems.

Automation Without Trust Will Stall

As AI systems become more autonomous, user trust becomes the gating factor for scale.

If users are required to constantly double-check AI outputs, manually correct recommendations, or intervene during releases, AI adoption will plateau. The promised efficiency gains will never materialize.

CIOs need to prioritize AI systems that deliver:

Measurable inference accuracy in enterprise contexts
Consistent behavior across updates and environments
Clear escalation paths when AI encounters uncertainty

Trust, not novelty, will determine which AI initiatives survive beyond pilot stages.

AI Posture Will Matter More Than AI Speed

The most successful organizations in 2026 will not be those that adopted AI first—but those that defined a clear AI posture early.

This includes decisions around:

Where AI is allowed to act autonomously
Which enterprise domains require domain-specific models
How accuracy, risk, and governance are measured over time

Choosing specificity over generality—before AI exposure increases—will give CIOs greater control as AI adoption deepens.

What CIOs Should Evaluate When Choosing an AI Strategy

Before committing to AI-driven automation, CIOs should ask:

Does this AI understand our enterprise applications or just general language?
How is accuracy measured for enterprise-specific tasks?
Can it operate within our compliance and deployment constraints?
Will it reduce manual effort—or introduce new forms of oversight?

The wrong choice can compound risk and erode trust. The right choice creates a scalable foundation for intelligent automation.

Final Thoughts: From AI Adoption to AI Posture

In 2026, success with AI will not be defined by speed of adoption; but by quality of execution.

Organizations that rely on general-purpose AI for enterprise-critical systems risk operational instability. Those that define a clear AI posture, rooted in domain specificity, inference accuracy, and enterprise context; are better positioned to turn AI into a durable competitive advantage.

This shift—from experimenting with AI to operationalizing it responsibly; is where real value emerges.

For a deeper framework, including best practices and a step-by-step approach to deploying domain-specific AI in enterprise applications.

When Keeping the Lights on Becomes a Million-Dollar Line Item

John Stein — Wed, 15 Jul 2026 12:44:32 +0000

The 2026 State of Enterprise Application Lifecycle Management survey makes one thing plain: production issues with enterprise applications are recurring costs that measure in millions. Over half of IT leaders report that configuration or process changes cause production issues at least sometimes, often, or almost always, and many place the annual cost of those incidents between $500K and $5M or more. For a CIO and other executive leaders, this is a material drag on operating margin.

At the same time, 64% of organizations now allocate between 21% and 50% of their total IT budget to implementing and managing enterprise applications. Yet, despite the fact that organizations are making significant investments in managing their apps, they still have costly outages when changes go live.

Why Production Issues Are Still Endemic

The survey points to a structural imbalance between the pace of change and the operating model used to manage it.

Cloud vendors and internal teams are shipping constantly.
65% of organizations manage three or more major releases per year across their key applications, and 8% handle 12 or more. That is effectively a continuous-change environment.
Your teams are flying semi-blind.
Across all strategic burdens, “difficulty assessing the impact of changes and updates” is ranked number one, ahead of cost, staffing constraints, and even integration complexity. In other words, changes are being approved and deployed without a clear, data-driven view of where they will break things.
The most expensive parts of the lifecycle are also the most fragile.
Integrations are the top cost driver (61%), followed by ongoing management of those integrations (37%), configuration (34%), and testing (34%). Those are precisely the areas most likely to trigger an outage when something goes wrong.

These are symptoms of an outdated operating model for enterprise applications in a cloud-native, integration-heavy world.

What This Means in Business Terms

The survey’s findings translate into four core business realities.

Run-the-business spend is crowding out change-the-business investment.
With 64% of organizations dedicating up to half their IT budget just to implement and manage enterprise applications, a growing share of the technology spend is locked into maintenance, releases, and firefighting. That leaves less room for innovation, new revenue-enabling capabilities, or strategic acquisitions of digital assets.
Operational risk is under-managed relative to its cost.
43% of leaders say reducing implementation and operational risk is an “extremely important” outcome, yet more than half still experience regular production issues from changes. The risk-adjusted cost of each release remains high, and the variability in outcomes makes forecasting and performance commitments difficult.
Consulting and overtime are becoming hidden tax categories.
Integrations, configuration, and testing, which are the top cost drivers, frequently require bursts of specialist consulting and internal overtime around each release. Those peaks don’t necessarily show up as a single budget line, but they accumulate as a tax on every transformation initiative.
AI expectations are high, and they are pointed at savings, not just speed.

83% of respondents say they are completely or very likely to adopt agentic AI for managing their enterprise applications, and 69% estimate it could save between 5,000 and 30,000 hours annually. When asked how they would use freed resources, leaders prioritize employee experience, new business capabilities, backlog reduction, and reduced consulting and operational costs. That is a clear mandate to shift from labor-based scaling to automation-based scaling.

In short, the status quo is a compound-interest problem: every year you add more apps, more integrations, and more releases, but you are still managing them with processes built for a slower, less interconnected environment.

How Opkey Reduces Business Risk

Opkey is built to address exactly the areas the survey identifies as high-cost, high-risk: integrations, configuration changes, testing, and release velocity. For executive leaders, its value shows up in fewer incidents, reduced external spend, and more capacity for strategic work.

Reduce the cost and frequency of change-related incidents

Opkey automatically discovers end-to-end business processes across applications like Oracle, Workday, Salesforce, ServiceNow, and SAP, and maps them to the tests required for each release. When a change is proposed or a vendor releases a new update, Opkey’s agentic engine identifies which configurations, processes, and integrations are impacted and triggers targeted, automated tests before anything hits production. This directly attacks the top strategic burden in the survey—difficulty assessing change impact—and reduces the 52% of organizations that currently see frequent production issues from changes.

Financial impact: fewer outages, shorter incident durations, and less lost productivity across finance, HR, supply chain, and customer-facing functions.

Convert manual and consulting-heavy testing into autonomous coverage
Because Opkey auto-generates and maintains regression tests from observed business processes, your teams no longer need to hand-author scripts for each release or pay consultants to rebuild tests after every major change. This aligns with the 56% of leaders who want to reduce or significantly reduce their reliance on SIs and consultants, as well as those who see testing and configuration as top cost drivers.

Financial impact: lower SI invoices, reduced overtime around release windows, and more predictable release costs.

Bring integration risk under control

Given that 61% of respondents name integrations as their highest cost driver—and 37% highlight ongoing integration management—Opkey’s ability to validate cross-application flows is critical. It tests real business processes that span multiple systems (for example, order-to-cash, hire-to-retire, procure-to-pay) so that integration failures are caught in pre-production rather than in the middle of a quarter-end close or a peak sales period.

Financial impact: fewer revenue-impacting integration outages and lower spend on emergency fixes and war rooms.

Turn AI expectations into measurable savings

The survey shows that 83% of organizations are likely to adopt agentic AI for lifecycle management and expect to save thousands of hours per year, redeploying those hours into employee experience, new capabilities, and backlog reduction. Opkey operationalizes this vision by using AI not just as a chatbot but as a set of agents that discover processes, generate tests, analyze impact, and maintain documentation automatically.

Financial impact: concrete hour savings that you can quantify, report, and reallocate, plus a credible story to your board about how AI is reducing your run-the-business cost base.

Build vs. Buy: The Enterprise Cloud Application AI Decision

John Stein — Mon, 13 Jul 2026 14:18:22 +0000

Most enterprises pour money into generative AI and see almost nothing come back. The new Opkey’s report, “Build vs. Buy: The Enterprise Cloud Application AI Decision,” pulls together three major studies to show why that happens; and what the highest performing teams do differently when they bring AI into Oracle, Workday, and other cloud apps.

The GenAI Divide: Who actually gets value?

The report opens with a simple split: a small set of organizations turn AI into real outcomes, while almost everyone else stays stuck in pilots and proofs of concept. You’ll see how often integrated AI projects reach production, and how that compares to the narrative you hear in boardrooms and conferences.

It also contrasts executive optimism with what quality engineering leaders report from the front lines. The gap between “we have an AI strategy” and “we have AI in daily production use” turns out to be much wider than most teams expect.

The blind spot inside ERP and cloud apps

The research digs into where enterprises do try AI; and where they don’t. You’ll get a taste of how rarely teams apply GenAI to ERP testing and cloud application operations, even though those systems run finance, HR, supply chain, and procurement.

At the same time, the report hints at how little of the overall test portfolio most organizations automate today, and what that means when Oracle and Workday ship quarterly changes on a fixed schedule. The numbers around this gap are sharp enough that most readers end up rethinking where they point their AI budget.

Build vs. buy: a 2x difference

Without giving away every chart, the research makes one pattern hard to ignore: organizations that buy or partner for cloud AI capabilities move faster and reach production far more often than those that build everything themselves. The ratio isn’t subtle, and it stays consistent across industries.

The report doesn’t just say “buy”; it shows how deployment timelines, success rates, and long‑term adaptability change when teams rely on purpose-built platforms instead of one‑off internal projects. You’ll also see why speed doesn’t just matter for convenience; it influences risk, shadow AI use, and competitiveness.

Why internal builds keep stalling

The paper sketches three recurring failure modes in internal AI builds; around integration, learning, and workflow coverage; without turning into a how‑to manual in the blog. Think of this as a preview: you’ll recognize some of these patterns from your own projects, but the detailed breakdown, examples, and comparison table live in the full report.

How Opkey CALM platform changes the question you should ask

Under the covers, the research points toward a different way to frame the whole discussion. Instead of asking, “How do we add AI to testing?” or “How do we add AI to configuration?”, the top performers ask, “How do we bring AI into the entire cloud application lifecycle?”

That’s where Cloud Application Lifecycle Management (CALM) comes in. Opkey’s CALM platform uses domain‑specific AI across configuration analysis, impact assessment, testing, and training for Oracle, Workday, and other ERP systems. So, when you read the report’s build‑vs‑buy data, you can see how a lifecycle platform like CALM fits into the “buy/partner” side of the story; without this blog turning into a product datasheet.

Most AI decks look great in theory. This report shows what actually happens in practice; where projects stall, where they break through, and how cloud application leaders decide when to build and when to buy.

Why Human-in-the-Loop Is Critical for Enterprise AI

John Stein — Thu, 09 Jul 2026 07:05:28 +0000

Artificial intelligence (AI) is no longer just an experimental or buzz-driven concept. It has moved beyond generative use cases such as text and image creation into a new class of systems known as agentic AI, systems that can plan, reason, make decisions, and execute actions across complex, multi-step workflows with minimal human intervention. These systems are increasingly being applied to real business scenarios, from enterprise operations to automated decision-making.

However, greater autonomy also introduces greater risk. That’s why responsible AI practices, like human oversight, governance, and domain-specific intelligence are essential for enterprise success. To learn how organizations can safely scale agentic AI across ERP, HCM, and enterprise applications, explore Opkey’s whitepaper for practical insights on building secure, governed, and enterprise-ready AI systems.

What is Human-in-the-loop?

HITL refers to an approach in which humans actively participate in the operation, supervision, and decision-making of automated or AI-driven systems. Rather than allowing AI to function in complete isolation, HITL ensures that human judgment is applied at critical stages of the AI lifecycle to improve accuracy, safety, accountability, and ethical alignment.

In the context of artificial intelligence and machine learning, HITL means that humans are deliberately involved at one or more points in the workflow—such as data labeling, model training, evaluation, validation, or real-time decision review. This involvement is especially important in scenarios where errors are costly, context matters, or decisions carry regulatory, financial, or ethical implications.

At its core, HITL creates a structured feedback loop between AI systems and domain experts who understand what “good” looks like in real-world conditions. Humans contribute their expertise by reviewing outputs, correcting mistakes, providing annotations, and guiding model behavior when confidence is low or ambiguity exists. Over time, this feedback helps AI systems learn from real operational contexts rather than relying solely on static training data.

Human-in-the-loop machine learning is therefore a collaborative model that combines the scalability and speed of machines with the contextual understanding, reasoning, and accountability of humans. By integrating human input throughout the AI lifecycle, HITL improves not only model performance but also trust, adaptability, and long-term reliability—making it a foundational design principle for enterprise-grade and responsible AI systems.

Why Do You Need a Human-in-the-loop in Agentic AI Systems?

Agentic AI systems can make incorrect assumptions, propagate errors at scale, or take actions that are misaligned with business rules, compliance requirements, or human intent. Without proper oversight, what appears efficient in theory can quickly become unpredictable in practice.

AI Agents Can Fail in Real Enterprise Environments

In controlled demos, AI agents often appear capable and reliable. However, once deployed in live enterprise systems—such as ERP workflows, testing automation, customer support, or finance operations their limitations become apparent.

Agents may enter repetitive execution loops, misinterpret business rules, or take actions that technically follow instructions but fail to account for business context.

For example, an agent automating a financial workflow might repeatedly retry a failed transaction without understanding downstream dependencies or compliance constraints. Without human oversight, such failures can propagate quickly, impacting data integrity, system stability, and business outcomes.

Synthetic Data Alone Does Not Reflect Enterprise Reality

Many agentic AI systems rely heavily on synthetic or AI-generated data to scale training and evaluation. While synthetic data is useful for bootstrapping models, it cannot fully capture the variability, edge cases, and exceptions present in real enterprise operations.

Over time, models trained primarily on synthetic data risk “model collapse,” where they reinforce their own assumptions and biases instead of learning from real-world behavior.

In enterprise applications—where processes evolve, regulations change, and user behavior is unpredictable—humans are needed to inject real feedback, validate outputs, and correct drift that synthetic data cannot reveal.

LLM-as-a-Judge Is Not Sufficient on Its Own

To scale evaluation, many teams use an “LLM-as-a-judge” approach, where one language model evaluates or ranks the output of another. While this can accelerate testing and reduce manual effort, it introduces new risks when used in isolation.

LLM judges often struggle with complex, domain-specific enterprise

content. They may reward verbose or confidently worded responses over factual correctness, misinterpret nuanced requirements, or fail to detect subtle but critical errors.

In enterprise scenarios—such as compliance validation, release approvals, or automated decision-making—these evaluation gaps can lead to false confidence in flawed outputs. Human reviewers are therefore necessary to audit high-impact decisions, validate edge cases, and ensure that evaluation criteria align with real business priorities.

Human-in-the-Loop as a Safety and Control Mechanism

In agentic AI systems, HITL is not about micromanaging every decision. Instead, it acts as a targeted control layer—stepping in when confidence is low, risk is high, or context is ambiguous.

By involving humans at critical checkpoints, enterprises can prevent cascading failures, maintain accountability, and ensure that autonomous systems remain aligned with business rules, regulatory requirements, and organizational intent.

In practice, HITL transforms agentic AI from an experimental capability into a reliable, enterprise-ready system.

How Does Human-in-the-Loop Work?

In the Human-in-the-Loop approach, humans interact with the system at defined stages of the AI lifecycle and add value to it. These interaction points are intentionally designed to improve model quality, reduce risk, and keep AI behavior aligned with real business requirements.

Human Input During Data Preparation and Training

One of the most common ways humans interact with HITL systems is by providing labeled training data. In enterprise machine learning, this often involves domain experts—such as finance, supply chain, testing, or compliance teams—reviewing and annotating data, so the model learns what is correct, acceptable, or risky in a real business context. Unlike generic datasets, enterprise data requires human interpretation to capture exceptions, edge cases, and evolving rules that automation alone cannot infer.

Human Review and Validation of Model Outputs

Humans play a critical role in evaluating model performance once an AI system is operational. Rather than relying only on automated metrics, enterprise teams review AI predictions or actions to determine whether they meet business expectations. This might include validating automated test results, reviewing AI-generated recommendations, or approving decisions before they impact live systems. Human feedback helps identify gaps that are invisible in offline testing but surface in real workflows.

Human Feedback as a Continuous Learning Signal

HITL systems are designed to learn from human corrections and feedback over time. When users flag incorrect outputs, override AI decisions, or adjust recommendations, that feedback becomes part of the system’s learning loop. This allows AI models to adapt to changing data, new business policies, and real-world variability—something static training alone cannot achieve.

Active Learning to Focus Human Effort Where It Matters Most

In active learning setups, the AI system selectively asks for human input when it encounters uncertainty or unfamiliar scenarios. Instead of labeling all data, humans are engaged only for high-impact or ambiguous cases. This approach makes HITL scalable for enterprise environments by concentrating human effort where it delivers the greatest improvement in model performance.

Reinforcement Learning Guided by Human Judgment

In reinforcement learning scenarios, AI agents learn by taking actions and observing outcomes. Humans provide feedback on whether those actions are acceptable, safe, or aligned with business goals. This guidance is especially important in agentic AI systems that execute multi-step workflows, where a single incorrect action can have downstream consequences across enterprise applications.

Benefits of HITL

Human-in-the-loop is not just a safeguard—it is a practical design choice that helps enterprises deploy AI systems that are accurate, trustworthy, and fit for real-world use. When implemented correctly, HITL delivers measurable benefits across model performance, governance, and risk management.

Improved Accuracy and Operational Reliability

HITL enables AI systems to improve continuously by incorporating human corrections and feedback into their learning process. In enterprise applications, domain experts can identify incorrect assumptions, edge cases, or anomalous behavior that automated systems often miss.

Stronger Ethical and Accountable Decision-Making

HITL makes this possible by allowing humans to review, override, and document decisions when automated outputs fall into ethical or contextual gray areas.
Human intervention creates an auditable trail that records why a decision was changed, who approved it, and under what conditions.
This is important in regulated industries where accountability, compliance, and external scrutiny are unavoidable.

Greater Transparency and Risk Control

HITL introduces transparency by embedding human oversight into both development and production environments.
By reviewing high-risk decisions, monitoring agent behavior, and validating outcomes, enterprises can identify technical, legal, ethical, or operational risks before they cause downstream impact.
In sectors such as finance, healthcare, and large-scale enterprise operations, HITL functions as a safety net—ensuring that automation enhances decision-making without removing human control or responsibility.

Enterprise-Ready AI, Not Experimental Automation

HITL bridges the gap between experimental AI capabilities and enterprise-grade systems.
It allows organizations to move faster with automation while maintaining trust, governance, and control.
Rather than slowing innovation, human-in-the-loop enables AI systems to operate with confidence in environments where accuracy, accountability, and transparency are non-negotiable.

Compressing Implementation Timelines: How AI Reshapes the Economics of Enterprise Application Delivery

John Stein — Wed, 08 Jul 2026 12:24:55 +0000

Enterprise application delivery for Oracle and Workday is under compounding pressure. Clients are demanding faster timelines and predictable outcomes, while most SIs are still operating delivery models built on spreadsheets, manual effort, and individual heroics. The result is a pattern SI leaders know well: overruns, rework, and margin erosion on the very programs meant to drive growth.

AI-driven, connected delivery is beginning to change those economics in meaningful ways. Compressing an implementation from 18 months to 9–12 doesn’t simply save time—it fundamentally reorders how work gets done, how risk is managed, and how value is recognized.

Why speed and predictability now define implementation success

The commercial pressure on timelines

Enterprise applications underpin the operational core of modern organizations—finance, HR, supply chain, and operations—in an environment where competitive cycles have shortened and stakeholders expect continuous, real-time insight. That urgency translates directly into the commercial terms of SI engagements.

Clients are pushing for:

Shorter implementation windows so business value lands within the budget cycle.
Tighter, fixed-fee or risk-sharing models that limit their exposure to overruns.
Delivery approaches that look and feel modern—iterative, transparent, and data-driven.

In that context, an 18-month, waterfall-style implementation with repeated unplanned extensions is nearly impossible to position as a win—regardless of how capable the final system proves to be.

Clients’ shrinking tolerance for overruns

Most CIOs and transformation leaders have lived through at least one difficult implementation. They carry a clear institutional memory of budget creep, change orders, and internal political fallout. That experience shapes their expectations—and their appetite for risk—going into the next program.

They are less willing to accept:

This is just how implementation goes” as an explanation for delays.
SIs showing up late in the program with requests for additional funding to cover rework.
Black-box delivery where it’s difficult to see how decisions are made and why issues emerge.

SIs that can credibly demonstrate—not merely promise—faster, more predictable delivery will earn an enduring advantage: not only in competitive bids, but in the long-term account relationships that define practice growth.

Quantifying the impact of AI-accelerated delivery

Effort reduction in discovery and design

Discovery and design are where AI changes the economics first. These early stages have historically consumed a disproportionate share of senior consultant time:

Running and documenting workshops
Consolidating requirements from scattered artifacts
Manually building initial designs and configuration workbooks

AI-driven tools can ingest client documentation, workshop notes, and questionnaire responses—then produce first-cut designs for enterprise structures, security models, and core processes. Consultants still validate and refine, but they begin from a structured baseline rather than a blank page.

When SIs compress early discovery and design, the downstream effects are significant:

The bid model becomes more competitive without relying on unrealistic utilization assumptions.
Senior talent spends more time on high-value advisory conversations and less on transcription.
The program reaches configuration and visible progress earlier, boosting stakeholder confidence.

That early compression also reduces a subtler risk: misaligned expectations. When clients can react to tangible design outputs rather than abstract slides, scope decisions are grounded in reality from the start.

Timeline compression across the implementation

The same AI and automation approach extends downstream:

Design artifacts can be translated into configuration inputs more quickly and consistently.
Configuration pipelines reduce the manual overhead of environment promotion and regression.
Test suites are generated and executed based on actual configurations and process risks, not static spreadsheets.

When every phase—design, configuration, testing—shifts from manual, document-based execution to a connected, automated delivery flow, total implementation timelines compress substantially.

For SIs, this timeline compression has two big economic implications:

It increases annual project throughput without a proportional increase in headcount.
It reduces the time during which a project can be derailed by external factors (organizational changes, budget cuts, leadership turnover).

The aggregate effect: more revenue recognized per unit of delivery capacity, and meaningfully lower exposure to the tail risks that define long-running, complex programs.

The delivery factory model: Templates, patterns, and implementation libraries

Underlying these efficiency gains is a more fundamental shift in what SIs treat as their core IP. By applying AI to capture and systematize what their best consultants do, leading practices are building a “delivery factory” model—one that transitions the SI’s value proposition from brilliant individuals to structured, scalable, and predictable delivery.

In this model, practices deliberately build and curate:

Industry-specific templates for enterprise structures and security, tuned to Oracle and Workday.
Process patterns that encode best practices for finance, HR, supply chain, and other domains.
Pre-built design and test assets that can be adapted instead of recreated for each client.

These assets are not static documents sitting in a knowledge portal. They are living components that AI agents can reference, adapt, and extend during discovery, design, configuration, and testing.

The result is a consistent baseline for every new engagement: you don’t reinvent the wheel; you start from a proven pattern and customize where it truly matters.

Knowledge capture at scale

The second dimension of the factory model is how institutional knowledge compounds over time. Every engagement produces additional examples of:

Successful designs and configurations for specific industries or operating models.
Common integration patterns and data migration techniques.
Edge cases and exceptions that need special handling.

In a traditional delivery model, this knowledge is trapped in individuals or buried in project archives. In an AI-enabled model, it becomes structured training data that actively improves the next engagement:

AI agents learn which patterns work well in which contexts.
Recommendations improve as more engagements feed the system.
Rare but important scenarios become easier to catch and handle, because the system has “seen” them before.

This compounding feedback loop is what distinguishes a delivery factory from a delivery team: the system gets more capable with every engagement, rather than resetting when experienced people move on.

How Opkey can help

Consider the typical profile of an enterprise application implementation delivered without AI acceleration:

3–4 months of intensive discovery and design, with heavy senior involvement and manually crafted artifacts.
6–9 months of configuration, integration, and iterative testing, with significant rework as gaps and misalignments surface.
3–6 months of stabilization, hypercare, and clean-up as issues emerge in UAT and production.

Now compare that with an AI-enabled delivery model supported by Opkey:

Discovery and design are compressed to weeks instead of months, driven by AI-assembled designs and standardized questionnaires.
Configuration cycles become more predictable because they are fed with structured, validated designs and supported by environment pipelines.
Testing keeps pace with change, thanks to auto-generated, risk-based test suites that update as configuration evolves.
Hypercare is shorter and less chaotic because more issues are caught earlier and the implementation is better aligned with real business processes.

The result is a program that closes in the 9–12 month range rather than extending to 18 months or beyond—with fewer executive escalations, less unplanned rework, and a smoother transition into steady-state operations.