QyrusAI Named a Visionary in Gartner® Magic Quadrant™ for Agentic Software Quality Assurance Platforms – Read More

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Modernizing a mainframe is rarely a technology problem. 

Moving workloads off a legacy platform is a solved problem. What stalls programs is that nobody can say with confidence what the existing system does, and nobody can prove the new one does the same thing. 

The analyst view is moving the same way. In June 2026, Gartner predicted that more than 70% of mainframe exit projects started in 2026 will fail to deliver their intended benefits, citing an overestimation of what generative AI tooling can do. Its guidance favors in-place modernization and a workload-by-workload approach over a one-size-fits-all exit. 

This whitepaper sets out an evidence-first path for banks, credit unions and insurers: know the estate, decide from evidence, engineer with human validation, and prove equivalence before cutover. 

Who should read it 

CTOs, enterprise architects, and heads of engineering and QA at banks, credit unions and insurers running core or adjacent systems on mainframe-class platforms, including IBM Z, IBM i and Jack Henry Symitar environments. 

What’s inside the whitepaper 

  • Why mainframe programs stall on two separate risks, missing knowledge and unproven equivalence, and why most plans only address one. 
  • What “mainframe” actually means across IBM Z, IBM i, proprietary midrange cores such as Symitar Episys and others, and what tends to break on each. 
  • The evidence-first arc (Comprehend, Decide, Engineer, Prove) and seven dispositions, from Retain and Wrap to Replace and Retire, chosen from evidence rather than a target-state slide. 
  • A technical deep dive for architects: packed decimals, per-field EBCDIC translation, CICS syncpoint versus saga, cycle dates and control totals. 
  • How to prove equivalence: field-level reconciliation in five classes, parallel runs, signed evidence packs, and API-based testing of mainframe applications without screen scraping. 
  • The integration seam between a core and mainframe systems, where a large share of defects hide, and a four-step test pack to cover it: generate, inject, assert, break it. 
  • A phased path that starts with a proof of concept on your own code, plus the evidence US examiners ask to see. 

Modernize and Assure, Working From the Same Evidence 

QyrusAI brings understanding and proof into one continuous loop. QyrusAI Modernize builds a queryable model of your estate, with every business rule traced to its source, confidence level and owner, so decisions rest on evidence. QyrusAI Assure then proves that business behavior survived the change, through field-level equivalence testing, parallel-run reconciliation and signed evidence packs. 

Nothing reaches production on agent output alone. Human experts validate at defined gates, and the platform works alongside the transformation tools and partners you already use. 

See evidence-first modernization on your own estate 

Book a demo to see how QyrusAI can map your estate, rank what should change and prove equivalence before cutover. 

QYRUSAI at The AI Summit New York

If you’re working with AI today, you’ve probably noticed how quickly the conversation moves from possibilities to expectations. Teams are exploring new tools, building new capabilities, and making decisions about where to invest. Alongside that activity comes a practical question: how do we know AI is delivering value? 

That’s one of the conversations we’re looking forward to at The AI Summit New York. QyrusAI will be joining the event with two appearances across the Applied AI panel and the Impact Arena at the AI Playground. 

For us, it’s an opportunity to exchange ideas with others navigating the same challenges: putting AI to work, understanding its impact, and building confidence in the systems businesses increasingly depend on. 

Why These Conversations Matter 

Getting an AI initiative off the ground takes time, resources, and coordination. Understanding whether it’s working means looking beyond the initial launch to what changes for the business and the people using it. 

Are teams completing work more efficiently? Are processes becoming more reliable? Can improvements be measured and sustained as adoption grows? 

These questions matter across software delivery, quality engineering, and enterprise operations. They also help organizations make better decisions about where AI belongs, what to prioritize, and how to assess progress. 

We’re looking forward to bringing that practical perspective to New York and hearing how other teams are approaching it. 

Join Akshay Deshpande for the Applied AI Panel 

“Measuring AI Success: From Spend to Business Impact” 
Day 1 | 3:20–4:00 p.m. 

Akshay Deshpande will represent QyrusAI on this Applied AI panel, joining a discussion about connecting AI investment to meaningful business outcomes. 

The topic speaks to a challenge many organizations face: knowing what to measure as AI moves into everyday workflows. Spending and adoption can show how much activity is happening, but understanding business impact requires a closer look at the results. 

If you’re evaluating AI initiatives or thinking about how to measure their contribution, add this panel to your agenda. It’s a chance to explore a question that will continue to shape how businesses invest in AI. 

Find Us at the AI Playground 

Impact Arena (AI Playground) 
Day 2 | 1:35 p.m. 

We’ll also be taking part in the Impact Arena at the AI Playground on Day 2. 

We’re excited to contribute to this part of the summit and connect with attendees exploring how AI can support their organizations. It’s another opportunity to hear from our team and continue the conversation about moving from ideas to practical impact. 

If you’re planning your second day at the event, make time to join us at 1:35 p.m. 

See You in New York 

Events like The AI Summit New York give us a chance to compare experiences, share ideas, and learn from people working through similar decisions. 

Whether you’re exploring AI for the first time, expanding an existing initiative, or looking more closely at the value it delivers, we’d love to connect. 

Join Akshay’s panel on Day 1, catch us at the AI Playground on Day 2, and say hello. We’re looking forward to the conversations ahead. 

SAPinsider Chicago Skyline Experience

November 11–12, 2026 | Radisson Blu Aqua | Chicago, Illinois 

SAP transformation reaches far beyond the ERP system. A change to one application can affect the integrations, data, and business processes that teams rely on every day. For organizations moving toward S/4HANA, adopting clean core strategies, or exploring AI, understanding that impact is an essential part of moving forward. 

That’s why we’re looking forward to joining the SAP community at SAPinsider Summit Chicago this November. 

Taking place November 11–12 at the Radisson Blu Aqua, the summit centers on “SAP ERP Transformation: Moving from Clean Core to an AI-Powered Future.” It brings SAP practitioners together for two days of sessions and peer conversations focused on transformation planning, clean core execution, and the next stage of enterprise AI.  

The Business Processes Behind the Transformation 

For SAP teams, modernization brings practical questions. How will a change affect an existing workflow? Which integrations need validation? Can the team expand regression coverage without pulling business users into another lengthy manual testing cycle? 

These questions become especially important when processes span SAP and connected applications. An order might begin in a customer portal, move through SAP, and continue into warehouse and delivery systems. Testing each application individually can leave gaps in the journey that matters to the business. 

As organizations rethink their SAP landscapes, quality engineering needs to account for those connections. Teams need a clear view of dependencies, repeatable ways to validate critical processes, and evidence that changes behave as expected before release. 

Where AI-Driven Quality Fits 

At QyrusAI, these are the challenges we’re focused on helping enterprises address. 

QyrusAI brings together capabilities across Modernize, Assure, and Operate, connecting environment discovery, automated testing, and operational insight. Its SAP testing capabilities sit alongside web, mobile, API, and data testing to support validation across connected enterprise workflows.  

For the conversations in Chicago, we’re especially interested in how teams are approaching regression testing during S/4HANA transformation, maintaining automation as applications evolve, and validating processes that cross system boundaries. 

AI adds another dimension to that discussion. As teams explore new ways to automate work and support decisions, they also need ways to evaluate outcomes and maintain visibility into how changes affect the business. Building quality into that journey gives teams a stronger foundation for adopting new capabilities. 

Join Our Sessions on November 11 

QyrusAI will be hosting two sessions on Wednesday, November 11, with Sujit Srivastava speaking at both: 

  • Customer Case Study | 10:50–11:30 a.m. 
  • In-Booth Session | 3:05–3:20 p.m. 

Add both to your event schedule and join Sujit for a closer look at QyrusAI. We’re looking forward to sharing our perspective, hearing your questions, and connecting throughout the day. 

Learning From the SAP Community 

One of the reasons we’re looking forward to SAPinsider Chicago is the opportunity to hear directly from the people doing this work. 

Every organization brings a different landscape, set of priorities, and stage of transformation. Some teams are planning their next major move. Others are managing ongoing releases or deciding where AI can deliver practical value. Sharing those experiences helps surface the details that shape successful projects: testing responsibilities, integration dependencies, maintenance effort, and the processes that need the most attention. 

We’re looking forward to exchanging ideas with SAP leaders, architects, business process owners, and quality teams about how they’re navigating those decisions. 

See You in Chicago 

If you’re attending SAPinsider Summit Chicago, we’d love to connect. Join Sujit for our customer case study, stop by our in-booth session, or come share what your team is working on. 

Whether you’re working through S/4HANA transformation, strengthening SAP test automation, or exploring how AI can support quality engineering, we’re looking forward to the conversation. 

We’ll see you in Chicago this November. 

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There’s a common assumption in enterprise AI: if you have enough data, AI will work. 

But in reality, that’s rarely the case. 

The Missing Ingredient Isn’t Data 

Most organizations today have: 

  • Vast amounts of data 
  • Modern data platforms 
  • Advanced AI tools 

And yet, many enterprise AI initiatives fail to deliver expected business outcomes. 

Why? 

Because data without context is not intelligence. 

The Problem with Disconnected Data 

Enterprise data is spread across: 

  • Applications 
  • Infrastructure 
  • Cloud platforms 
  • Business systems 

Each dataset exists in isolation, and while data may be accurate, its meaning is often incomplete. 

When algorithms ingest fragmented data, they lack the structural relationships that govern how an enterprise actually operates. A comprehensive study by the RAND Corporation found that over 80% of AI projects fail—more than double the failure rate of non-AI corporate technology projects—with researchers identifying a failure to ground models in operational context as the primary culprit. Furthermore, according to NewVantage Partners’ executive benchmark, only 29% of corporate leaders report achieving measurable business outcomes from their AI investments, pointing directly to persistent architectural silos and missing contextual relationships. 

The Enterprise AI Disconnect

Why Context Matters 

For AI to work effectively, it needs to understand: 

  • Relationships between systems 
  • Dependencies across processes 
  • How data flows through the organization 

Without this: 

  • Insights lack relevance 
  • Predictions lack accuracy 
  • Decisions lack trust 

When context is absent, teams burn enormous resources attempting to compensate with brute-force data engineering. Industry analysts at Gartner predict that at least 30% of generative AI projects will be abandoned after proof of concept due to poor data quality, missing risk controls, escalating costs, or unclear business value. Simultaneously, data scientists and engineers still spend up to 80% of their working hours manually preparing, cleaning, and reconstructing disconnected context rather than training and optimizing intelligent models. 

From Data to Understanding 

The shift organizations need is clear: moving from simply collecting data lakes to understanding how data is dynamically connected across systems, workflows, and business outcomes. 

This is where concepts like: 

  • Enterprise knowledge graphs 
  • System-level dependency mapping 
  • Real-time digital twins 

become critical. 

Context provides the semantic anchor that bridges raw technical data and business intelligence. It transforms an isolated metric or log entry into a living relationship: which application produced it, which downstream API consumes it, and which revenue-generating customer journey depends on it. 

The Role of Context in AI 

When AI is powered by continuous context:  

  • Insights become more accurate 
  • Decisions become more reliable 
  • Outcomes become more actionable 

Because the system understands not just what is happening, but why. Context provides the necessary guardrails that eliminate hallucinations, anchor agentic reasoning in verified ground truth, and give human stakeholders the transparency needed to trust algorithmic recommendations. 

Where QyrusAI Fits In 

This is where QyrusAI introduces a connected approach across the enterprise application lifecycle. As an agentic platform, QyrusAI creates a real-time, unified representation of your enterprise to ground AI systems in operational reality. 

At the center of this capability is QyrusAI’s Live Enterprise Knowledge Graph, which continuously discovers enterprise assets, monitors live system interactions, and maps dependencies across services, databases, APIs, and business workflows. 

By combining: 

  • Continuous asset discovery 
  • Dynamic dependency mapping 
  • Knowledge graph intelligence 
The Context Bridge

QyrusAI provides the continuous context layer that enterprise AI systems require[cite: 2]. This intelligence is operationalized across three core pillars:  

  • Modernize: Unpacks legacy architectures, assesses technical debt, and models real-time dependencies so modernization and portfolio decisions are driven by complete system context. 
  • Assure: Delivers continuous, automated testing across web, mobile, API, SAP, and complex data pipelines, verifying data contract integrity and application behavior before changes reach production. 
  • Operate: Brings operational intelligence across AIOps, observability, BizOps, and FinOps, directly correlating technical signals and operational anomalies with business outcomes. 

This enables organizations to:  

  • Build more reliable, hallucination-resistant AI models 
  • Improve explainability and governance across automated decisions 
  • Accelerate time-to-value from AI investments 

Frequently Asked Questions (FAQs) 

  1. Why do enterprise AI projects fail even with massive data lakes?

Data volume is not the same as data understanding. Data lakes store raw data in isolated silos without architectural or business relationships. Without contextual mapping, AI models cannot determine which processes, transactions, or services that data actually influences. 

  1. What is an Enterprise Knowledge Graph, and why does AI need it?

An Enterprise Knowledge Graph is a dynamic, semantic model that connects disparate data assets, applications, and workflows based on real-world relationships. It provides AI models with deterministic context, preventing hallucinations and enabling autonomous agents to reason accurately about complex systems. 

  1. How does QyrusAI provide context to enterprise operations?

QyrusAI uses continuous discovery and automated dependency mapping to build a Live Enterprise Knowledge Graph. This connects raw telemetry, data pipelines, and infrastructure to the specific business workflows they support.

  1. How does contextual intelligence improve AI governance and explainability?

When AI makes a recommendation or triggers an automated remediation, context reveals the exact dependency path and reasoning behind the decision. This transparency makes models auditable, safe, and trustworthy for enterprise leadership.  

The Bottom Line 

AI doesn’t fail because of a lack of data. It fails because of a lack of context. 

The question that helps you separate experimentation from real impact is no longer: 

“Do we have enough data for AI?” 

It is: 

“Do we understand our data well enough for AI to work?” 

Ready to move from disconnected data to contextual intelligence? 

Discover how QyrusAI connects your data, systems, and business outcomes into a living operational Knowledge Graph.

Talk to an Expert 

Technology spend is no longer predictable by looking at last month’s bill. Cloud, SaaS, on-premises infrastructure, and AI workloads now move too quickly, consume too differently, and depend on too many disconnected systems for traditional FinOps reporting to keep up. 

This whitepaper explains why enterprises must move beyond visibility and build an architecture for cost predictability — one that connects billing data with telemetry, attribution, dependency context, AI usage signals, and decision intelligence. 

Why Cost Visibility Should Be Your Priority 

Most enterprises can see what they spent. Far fewer can explain why spend changed, which teams or applications drove the increase, or what will happen before the next invoice arrives. 

The gap is not just a tooling issue. It is a data architecture issue. Predictable technology spend requires more than dashboards. It requires a connected model that understands usage, ownership, dependencies, utilization, AI token economics, and planned engineering change.

98% 

of FinOps practitioners now manage AI spend. 

90% 

manage SaaS spend as FinOps expands beyond cloud. 

29% 

estimated IaaS/PaaS waste in 2026, driven by growing workload complexity. 

What You’ll Learn in This Whitepaper 

  • Why historical cost forecasting fails in dynamic cloud, SaaS, hybrid, and AI environments 
  • How AI workloads introduce new attribution challenges through tokens, agents, retries, reasoning steps, and shared API usage 
  • The five root causes of technology cost unpredictability 
  • Why billing data alone cannot support finance-grade forecasting 
  • How QyrusAI combines cost data, telemetry, attribution, dependency mapping, and AI intelligence into a cost predictability architecture 
  • How enterprises can detect anomalies, explain cost drivers, and take action before spend becomes a month-end surprise 

Built for Leaders Who Need Predictable Technology Spend 

This whitepaper is designed for CTOs, CIOs, FinOps leaders, cloud leaders, engineering executives, finance stakeholders, and technology operations teams responsible for governing spend across modern, multi-source technology estates. 

How QyrusAI Helps Enterprises Move from Visibility to Predictability 

QyrusAI helps enterprises unify technology cost data across cloud, SaaS, on-premises infrastructure, licenses, and AI providers. By enriching billing data with telemetry, utilization signals, dependency maps, and AI usage instrumentation, QyrusAI turns cost reporting into cost intelligence. 

  • Unified cost data: Normalize spend across cloud, SaaS, AI providers, software licensing, and on-premises infrastructure. 
  • Telemetry-enriched attribution: Connect cost to real usage, resource efficiency, application behavior, and ownership. 
  • AI-powered forecasting: Forecast spend at enterprise, business unit, application, service, and resource levels. 
  • Anomaly intelligence: Detect cost deviations, identify root causes, route them to the right owners, and recommend actions. 
  • AI cost attribution: Track token, model, feature, agent, retrieval, and retry-level consumption to understand AI unit economics. 
  • Change impact simulation: Understand the financial effect of infrastructure, workload, and AI architecture changes before they happen. 

Explore QyrusAI for FinOps

Learn how enterprises can build a cost predictability architecture for the AI era — and how to give Finance and Engineering a shared, actionable view of what spend is doing, why it is changing, and what can be done before the cost occurs. 

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October 26, 2026 | Madison, Wisconsin 

Every SAP change comes with a practical question: will the business processes people rely on still work as expected? 

That’s a conversation worth having in person. QyrusAI is heading to Madison, Wisconsin, on October 26, 2026, for the ASUG Wisconsin Chapter Fall Meeting and Social to explore how AI can help teams approach SAP change with greater confidence. 

From planning modernization initiatives to testing everyday updates, SAP teams have plenty to balance. A change in one application can affect integrations, data, and workflows across the business. Understanding those connections—and validating them—is essential to moving forward. 

QyrusAI brings application discovery, AI-powered testing, and operational intelligence together to help teams see the bigger picture. In Madison, we’re looking forward to conversations about: 

  • Understanding dependencies across SAP and connected applications. 
  • Strengthening testing for critical, end-to-end business processes. 
  • Reducing repetitive manual work as systems evolve. 
  • Connecting modernization decisions with software quality and operational reliability. 

We also want to hear your side: what’s taking too long, where testing gets complicated, and what your team hopes to improve next. 

If you’ll be joining the ASUG community in Madison, let’s connect. Bring your questions and your toughest SAP testing challenges—we’d love to talk them through. 

See you in Madison on October 26! 

Most enterprise IT environments weren’t designed. They accumulated. A system that ran fine on its own five years ago has since picked up two integrations, a merger’s worth of inherited applications, and a handful of workarounds nobody documented. None of that shows up as a problem, until someone tries to change it.  

That’s the real challenge behind modernization: it’s rarely about deciding whether something needs updating. It’s about knowing what else gets affected when it is. Application discovery and dependency mapping is how enterprise architects, IT operations teams, and CIOs answer that question, by building an accurate picture of what’s actually running across an environment and how each piece depends on the rest, before anything gets touched.  

What Application Discovery and Dependency Mapping Actually Means  

Application discovery and dependency mapping is really two linked steps. Discovery identifies what exists — the applications, services, servers, and infrastructure running across an environment. Dependency mapping traces how those pieces connect: which service calls which database, which application sits on top of which infrastructure, and which business process breaks if a given component goes down.  

That second part carries more weight than it first appears to. A relationship just means two systems exchange traffic, they talk to each other. A dependency is directional: it means one system’s failure will break another. A payment gateway and an authentication service might have a relationship in a topology diagram, but if the authentication service fails, the payment gateway is the one that stops working, not the other way around. Knowing which direction that failure travels is what determines who gets paged first during an incident, and what teams prioritize before a planned change.  

Static inventories and manually maintained configuration management databases (CMDBs) were built for slower-moving environments. They capture what’s there at a point in time, but they don’t hold up once infrastructure starts changing weekly instead of quarterly, which is now the norm, not the exception, for organizations running hybrid cloud and containerized workloads.  

Why This Is Harder Than It Used to Be  

Enterprise environments have gotten more complex faster than the tools tracking them have kept pace. A typical environment today spans hybrid cloud infrastructure, containerized microservices, distributed APIs and SaaS applications, and infrastructure that scales continuously rather than on a fixed schedule. Layer in a few acquisitions or a legacy system nobody’s fully documented, and the gap between what’s on paper and what’s actually running starts to widen.  

The numbers back this up. Gartner has found that only 25% of organizations extract meaningful value from their CMDB investments, the rest are working from data that’s incomplete, stale, or simply not trusted enough to act on. Separately, Forrester research cited by Virima found that 54% of IT professionals report unknown assets in their environments, systems nobody currently tracks are still running, still connected to something else, and still capable of breaking a change nobody saw coming.  

That visibility gap has a direct line to project risk. Application dependencies are one of the most commonly cited concerns IT teams raise about cloud migration projects, and budget overruns tied to unplanned rework are common enough that they show up in nearly every year’s IT leadership surveys. Periodic discovery scans and manually updated spreadsheets can’t keep up with environments that change this often, which is why the shift underway across the industry is toward continuous, automated discovery instead.  

From Discovery to a Living Model: The Knowledge Graph  

A one-time discovery scan produces a snapshot, accurate the day it’s run, and progressively less accurate after that. Continuous discovery instead produces something closer to a living model: infrastructure changes get detected automatically, dependencies stay current as the environment evolves, and the resulting map doesn’t need to be manually reassembled every time someone asks for it.  

The more useful shift is what happens to that discovery data next. Rather than sitting in a dashboard as a point-in-time diagram, discovered assets and their dependencies can feed into a persistent knowledge graph, a structured, queryable model of the environment that stays current as things change. That’s a meaningfully different architecture than “run a scan, export a report.” It turns discovery into infrastructure other systems can build on: incident response tools that can trace an issue back through its dependency chain automatically, planning tools that can simulate the impact of a proposed change, and automation that can act on accurate, current context instead of a document that was correct six months ago.  

Put together, the pattern looks like a pipeline rather than a single tool: discovery identifies what’s running, dependency mapping traces how it connects, a knowledge graph holds that model in a form other systems can query, and from there, automated and AI-driven operations become possible because they’re finally working from something trustworthy. Skip the first two steps, and everything built on top of them inherits the same blind spots.  

Where This Shows Up: Cloud Migration  

Cloud migration is where the cost of skipping discovery becomes concrete fastest. Moving an application to the cloud is rarely just moving the application. It may depend on a database that isn’t moving on the same timeline, communicate with a service that stays on-premises, or support a larger business process that spans systems nobody thought to check.  

If those dependencies surface after the move, the project has a problem: a service that worked fine in testing breaks in production because something it depended on didn’t come along or came along on a different schedule. If they surface beforehand, the same information becomes a planning input instead of an incident. Teams can sequence the migration around what actually needs to move together, flag the dependencies that need special handling, and set expectations before a cutover date gets missed.  

This is also where discovery connects to the rest of the modernization lifecycle rather than standing apart from it. The same dependency data that de-risks a migration is what later informs application rationalization — deciding which systems are redundant or safe to retire — and technical debt assessment, which starts from knowing what’s actually in the environment before deciding what to fix. Discovery isn’t a standalone exercise; it’s the input every later modernization decision draws on.  

How QyrusAI Approaches Auto Discovery  

QyrusAI is an enterprise AI platform built around three workspaces — Modernize, Assure, and Operate — covering how organizations understand, test, and run their IT environments. Auto Discovery sits under Modernize, where it works alongside dependency mapping, application rationalization, and technical debt analysis to help enterprise teams understand what they’re working with before they change it.  

Auto Discovery continuously scans an organization’s IT environment across cloud, on-premises, and hybrid infrastructure, identifying what’s actually running, applications, services, infrastructure, and mapping the dependencies between them. It traces those relationships upstream and downstream and feeds the results into QyrusAI’s Enterprise Knowledge Graph, building a continuously updated model of the environment rather than a static diagram that has to be manually maintained.  

That shift from periodic to continuous, knowledge-graph-backed discovery shows up in the numbers: organizations applying this approach to their modernization lifecycle have seen a 25–40% reduction in migration timelines, 20–30% of legacy applications identified for rationalization, and a 98%+ migration success rate, driven by earlier, more accurate dependency visibility rather than late-stage surprises. The goal isn’t just a more complete inventory — it’s giving teams a foundation they can actually plan against before a project starts, not one they reconstruct after something breaks.  

Frequently Asked Questions  

What’s the difference between application discovery and dependency mapping?  

Discovery identifies what’s running in an environment — applications, services, infrastructure. Dependency mapping traces how those components connect and rely on each other. They’re typically delivered together because discovery data is what dependency mapping is built from.  

Is application discovery and dependency mapping the same as a CMDB?  

No. A CMDB is a database that stores configuration item records, often updated manually or on a schedule. Application discovery and dependency mapping is the process — increasingly automated and continuous — that keeps that data current and can feed directly into a CMDB rather than replace it.  

How often should IT environments be re-discovered?  

As close to continuously as the tooling allows. Environments that scale, deploy, or reconfigure weekly can go stale within days under a periodic scan model, which is why the industry trend is toward continuous, automated discovery rather than scheduled scans.  

Does application discovery work across hybrid and multi-cloud environments?  

Modern discovery tools are generally built to span cloud, on-premises, and hybrid infrastructure in a single pass, rather than requiring separate tools per environment. That unified view is what makes dependency mapping useful — a dependency that crosses environments is invisible if the discovery tool only sees one side of it.  

What happens if you skip dependency mapping before a migration?  

Dependencies tend to surface anyway — just later, and usually as an incident instead of a planning input. A database, service, or business process that wasn’t accounted for can break after a cutover, turning a planned migration into unplanned rework.  

Understand It Before You Change It  

Modernization projects don’t start with the first change — they start with understanding what’s already there. Application discovery and dependency mapping gives enterprise architects, IT operations teams, and CIOs the visibility to plan migrations, rationalize applications, and manage change with fewer surprises along the way.  

QyrusAI’s Auto Discovery continuously maps environments across cloud, on-premises, and hybrid infrastructure and feeds that model into the Enterprise Knowledge Graph — giving teams a current, connected view of their systems rather than a static inventory. Request a demo to see how QyrusAI Auto Discovery maps your environment before you modernize it. 

At QyrusAI, we are dedicated to transforming how enterprises validate and assure their most complex application estates. 

We are proud to announce that QyrusAI has been positioned as a Visionary in the October 2026 Gartner® Magic Quadrant™ for Agentic Software Quality Assurance Platforms authored by Ross Power, Sushant Singhal and C.A. Swan. 

We believe this recognition reflects our deep commitment to addressing the emerging demand for validating not just traditional applications, but AI-generated software and the autonomous decision paths of the agents that create and operate them.  

Our AI-agentic quality platform supports comprehensive testing across web, mobile, API, desktop, data, and LLM/RAG autonomous workflows.  

Why We Believe QyrusAI Was Recognized: 

  • Innovation in Agentic Capabilities: Our platform differentiates itself through investments in evaluating AI agents, RAG pipelines, and autonomous workflows.  
  • The SEER Framework: Our proprietary Sense, Evaluate, Execute, and Report (SEER) framework automates test impact analysis, test selection, execution, and reporting across multi-application workflows.  
  • QyrusAI Pulse/Gym and QHive Memory: The platform combines QyrusAI Pulse/Gym’s AI evaluation capabilities—assessing LLM outputs and agent behaviors—with QHive Memory to support context-aware testing and validation.  
  • Bridging the Engineering Gap: Our unique positioning effectively targets traditional QA and engineering leaders alongside AI/ML teams responsible for validating LLM applications and agentic workflows.  
Gartner Subscribers can Read the Full 2026 Gartner® Magic Quadrant™ Report

 Disclaimer: 

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. 

GARTNER and MAGIC QUADRANT are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. 

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For years, IT operations has been built around a relatively simple premise: if systems are functioning as expected, the business will function as expected. That assumption shaped how teams monitored environments, prioritized incidents, and measured operational success. System uptime, response times, and infrastructure health became proxies for business performance. In simpler, more linear environments, that model largely held true. 

The enterprise environment has changed. Modern businesses depend on interconnected applications, APIs, data pipelines, cloud services, and infrastructure layers that continuously interact. A single customer action such as placing an order, completing a transaction, or accessing a service may traverse multiple systems, each with its own dependencies and performance characteristics. The relationship between system health and business outcome is no longer direct. It is mediated by complexity. 

When Complexity Breaks the Model 

While system component reliability has generally improved across isolated hardware layers, rising enterprise dependency across multi-cloud and API ecosystems means cost-per-incident is steadily increasing even as event frequency levels off. Today, 1 in 5 organizations (20%) report that their most recent major IT outage cost over $1 million. 

The Limits of System-Level Visibility 

Teams may have comprehensive visibility into individual components such as servers, services, and applications, but that visibility does not automatically explain how those components collectively support business processes. Systems can appear healthy in isolation while the business experiences degradation in aggregate. Transactions slow down, customer journeys break, and workflows stall because something within the dependency chain is no longer functioning optimally. 

Highlighting this structural blind spot, 47% of outages are reported by customers before internal monitoring catches them—meaning nearly half of all major system disruptions are first flagged by end-users rather than internal technical alerts. Furthermore, 44% of incidents are caused by “detectable” failures, representing issues that technical telemetry actively tracked, but teams failed to interpret or prioritize correctly in time. 

The Gap Between Signals and Meaning 

The challenge is not necessarily a lack of data or visibility. Most organizations already have dashboards, alerts, monitoring, and observability tools. The challenge is context. These signals describe what is happening at a system level but often do not explain what it means at a business level. Teams are therefore forced to correlate signals manually, trace dependencies, and infer impact under time pressure and with incomplete information. 

The Cost of Operating Without Context 

The gap between system visibility and business understanding has tangible and severe financial consequences. The median cost of enterprise IT downtime has jumped to approximately $15,000 per minute ($900,000 per hour), and 91% of enterprises report that a single hour of IT downtime costs them more than $300,000.  

Beyond immediate direct losses, organizations face 3x to 5x hidden costs—including lost productivity, emergency recovery expenses, and brand equity damage. In fact, 81% of technology leaders cite customer loss and churn as the most damaging direct consequence of downtime.  

Incident response can slow down because teams lack clarity about impact. Prioritization can become inconsistent because technical severity does not always correspond to business importance. Communication between IT and business stakeholders can also become fragmented because each group is working from a different view of the environment. Over time, this misalignment creates operational inefficiency and strategic risk. 

A Shift in Perspective: Toward BizOps 

Business Operations Intelligence, often discussed in the context of BizOps, brings business goals and IT operations into a shared operating context. The goal is not to discard monitoring or observability. It is to connect technical signals to the services, workflows, KPIs, and business outcomes they support. The unit of analysis moves from the individual component toward the service, workflow, transaction, and outcome. 

This broader approach is also reflected in the evolution of business observability. Rather than looking only at technical telemetry, business observability connects operational data with business metrics and context so teams can understand how technology affects business performance. 

From Components to Relationships 

This requires relational visibility rather than isolated visibility. Teams need to understand how systems are connected, how dependencies are structured, and how changes or failures propagate across the environment. More importantly, those relationships need to be mapped to business services so that the impact of an issue can be understood in terms that matter beyond IT. An alert becomes more useful when it can indicate which service, workflow, or customer journey may be affected. 

Why Static Approaches Fall Short 

Traditional approaches such as CMDBs, manual service mapping, and periodic documentation exercises can struggle to keep pace with modern environments. Systems evolve, dependencies shift, and new services are introduced continuously. A static representation can therefore become outdated between documentation cycles. For organizations operating across hybrid, cloud, and legacy environments, maintaining an accurate picture of the estate requires continuous discovery and relationship mapping. 

The Need for Continuous Intelligence 

What is required is a continuously evolving model of the enterprise that reflects the current state of systems, their relationships, and their connection to business outcomes. This changes how teams perceive their environment, prioritize work, and make decisions under pressure. The emphasis shifts from collecting more signals to creating usable context from the signals already available. 

Where QyrusAI Fits In 

This is where QyrusAI brings a business-aware operations approach into the broader enterprise application lifecycle. As an agentic platform, QyrusAI is designed to help enterprises modernize, assure, and operate their enterprise applications through a connected lifecycle.   

QyrusAI continuously discovers enterprise assets, maps dependencies, and connects technical relationships to business services. At the core of this approach is QyrusAI’s Live Enterprise Knowledge Graph, which provides a continuously observed model of applications, dependencies, and business services. This creates a shared context for understanding operational issues rather than relying solely on isolated alerts or static documentation. 

This connected lifecycle is delivered across three key pillars: 

  • Modernize: Helps enterprises discover applications and dependencies, assess technical debt, rationalize portfolios, and plan and execute modernization with greater architectural context.  
  • Assure: Focuses on testing and validation across web, mobile, API, SAP, data, and test orchestration, helping teams validate application changes before they reach production.  
  • Operate: Brings operational intelligence across AIOps, observability, BizOps, FinOps, automation, and incident response, helping teams connect technical health with business impact. Its Operate capabilities extend this context directly into operations, including BizOps, where technical signals can be connected to the business services and transactions they affect.  

 Together, these pillars create a connected view across the application lifecycle, with a shared Knowledge Graph and operational context that can flow seamlessly between modernization, quality, and operations. 

From Reaction to Clarity 

When business context is connected to technical signals, incident response can become more precise because impact is easier to identify. Prioritization can be grounded in business relevance rather than technical severity alone. Decision-making can also become faster because teams can work from a shared, contextual understanding of the environment instead of piecing together fragmented information. 

This approach aligns with the broader direction of enterprise observability. IBM notes that business observability connects technical signals with business metrics and context, helping teams understand how IT system health affects core business outcomes. The underlying principle is straightforward: visibility becomes more valuable when teams can connect what changed in technology to what matters to the business. 

The Real Shift 

The deeper shift is in how IT operations is measured. It is no longer sufficient to know that systems are running. Teams also need to understand whether the services and business processes those systems support are running as intended. That requires visibility into relationships, dependencies, service health, and business impact.  

Frequently Asked Questions (FAQs) 

  1. What is the main difference between traditional IT monitoring and Business Operations Intelligence (BizOps)?

Traditional IT monitoring focuses on isolated component metrics such as CPU loads, server uptime, and response latency. BizOps bridges the gap between IT and business goals by connecting those technical signals directly to the workflows, transactions, services, and business outcomes they support.  

  1. How does QyrusAI eliminate manual dependency mapping?

QyrusAI utilizes its Live Enterprise Knowledge Graph to continuously discover enterprise assets, map real-time technical dependencies, and link them automatically to business services without relying on outdated static documentation or manual CMDB spreadsheets.  

  1. Why do traditional CMDBs fail in modern hybrid environments?

Modern multi-cloud and microservice architectures evolve rapidly through continuous deployment pipelines and automated scaling. Traditional CMDBs and periodic documentation exercises quickly become obsolete, creating critical blind spots during active outages.  

  1. How does QyrusAI help reduce alert fatigue for SRE and IT Operations teams?

By mapping technical anomalies within the Live Enterprise Knowledge Graph and correlating raw telemetry against actual business workflows, QyrusAI groups related alerts and prioritizes them based on true commercial impact rather than isolated technical thresholds. 

Closing Thought 

The question is no longer whether your systems are healthy. 

It is whether you understand what that health actually means for your business. 

Ready to move from system-level visibility to business-level understanding? Explore QyrusAI BizOps to see how technical signals, dependencies, services, and business impact can be connected in a single operational context. 

Talk to an Expert 

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Picture an enterprise with hundreds of applications. A few run business-critical services. Several do almost the same job. Some barely get used. Application rationalization is the discipline of sorting that portfolio: reviewing every application and deciding whether to modernize it, consolidate it, retire it, or leave it alone.  

The decision matters because modernization budgets are finite. When every outdated application becomes a modernization project, teams spend months upgrading systems that should have been merged or switched off. This guide shows a practical way to make the call, based on business role, usage and cost rather than technical age alone.  

Why an old application isn’t automatically a modernization candidate  

Ask most teams where to start, and they point at the oldest systems. The instinct makes sense. Older applications often run on aging frameworks, with limited vendor support and more security exposure.  

But age measures technical condition. It says little about how much the business relies on the application. A decade-old billing system may sit behind every invoice you send. A recently bought workflow tool may have a dozen users and no clear owner.  

Gartner’s TIME model captures this by plotting each application on two axes, business value and technical fit, then sorting it into Tolerate, Invest, Migrate, or Eliminate, as LeanIX explains. Age feeds into technical fit. But it is only one input beside maintainability, compatibility and vendor support. Enterprise architects use the result to see which applications deserve money. The same logic gives you four cases to remember:  

  • Old and business critical: modernize, carefully. The application has earned the investment.  
  • Old and low value: retire it. Modernizing it would only preserve something the business doesn’t need.  
  • New and business critical: keep investing and keep it healthy.  
  • New and low value: question it. It often overlaps with something you already own.  

Treating all four the same is how modernization budgets get spread thin.  

The four decisions a portfolio review produces  

Frameworks use different labels. TIME says Tolerate, Invest, Migrate and Eliminate. Cloud-focused models talk about rehosting, replatforming, retiring and retaining. Underneath the vocabulary, a portfolio review comes down to four practical outcomes.  

Decision  

When it fits  

Roughly maps to  

Modernize  

The business needs the application, but its technology holds it back  

Migrate  

Consolidate  

Several applications do the same job, so one survives and the others’ users and data move across  

Migrate, then Eliminate  

Retire  

The application has low value, low use or a redundant role; switch it off and keep any data you must retain  

Eliminate  

Leave alone  

It works, costs little and nobody is asking for more  

Tolerate or Invest  

  

Look at what that table implies. Three of the four outcomes reduce the amount of modernization you need to fund. Consolidation merges projects. Retirement removes them. Leaving an application alone avoids them. Cost, usage and business role drive each choice, not the year the application was built. That is why the review should come before the modernization budget, not after it.  

A worked example: three departments, one job  

Here is an illustrative scenario, not a customer result. Sales, customer support and field services each run their own application to track customer requests. Each was a sensible purchase at the time. Now each carries its own licenses, infrastructure, upgrades and support contract.  

Without a portfolio view, a modernization program sees three separate applications and funds three projects. Each team defends its own tool, and the work starts from the assumption that all three will survive.  

A rationalization review asks different questions. How many people use each application every week? What does each cost to run? Which business process depends on it? Suppose the answers show that one application covers nearly everything the other two do, while the other two serve small teams for features the first already offers.  

Now the team can consolidate: keep one application, migrate the users and data from the others, and retire two. Three modernization projects become one, or none if the survivor is already healthy. Overlap isn’t always waste, though. If one department needs a regulated workflow the others lack, the duplication may be justified, and the review should say so.  

The same thinking applies to supporting systems. Test environments multiply in the same way, which is why we’ve written about how infrastructure consolidation lowers testing costs.  

Five steps that start with evidence  

A review is only as good as the data behind it. These five steps put evidence ahead of opinion.  

  1. Build the inventory from the environment, not from surveys 

Most portfolios are bigger than the people running them believe. Zylo found that organizations underestimate their application count by about 1.7 times and their software spend by about 3 times, according to its analysis of SaaS sprawl. Ownership is scattered too. Business units control 81% of SaaS spend, while IT directly manages 15%, per Zylo’s 2026 SaaS Management Index. And Torii puts the average large enterprise at 2,191 business applications.  

A spreadsheet that people fill in by hand will miss much of that. Automated discovery gives you one application inventory that works as a single source of truth for every later decision.  

  1. Score usage, cost and business role 

For each application, record how often people actually use it, what it costs to run (licenses, infrastructure and support) and which business capabilities depend on it. Owners understandably defend their tools, so measured usage settles debates that opinion can’t.  

  1. Map dependencies before you decide 

A low-usage application can still feed a critical process. Map what each application connects to, including data feeds, integrations and shared infrastructure, before you assign an outcome. Finding a dependency before the decision is cheap. Finding it after the shutdown is not.  

  1. Decide per application 

Assign one of the four outcomes to each application in scope. Start with one business capability or department rather than the whole portfolio, so every decision stays reviewable. If a decision involves moving an ERP, plan the testing early; our guide to performance testing for a stable SAP S/4HANA go-live shows what to check. Whichever application survives a merger will also absorb new users and data, so it needs solid regression coverage. Here is how automated regression testing improves release quality.  

  1. Sequence by risk and payoff 

Retire clear duplicates first. They free up budget quickly and carry the least risk. Consolidations come next. Then modernize the critical applications, funded in part by what the earlier steps released. Revisit the plan each quarter as usage data changes.  

Retiring an application without breaking what depends on it  

Retirement looks like the easy win. It isn’t always. Legacy applications often feed reports, manual exports and indirect integrations. Those links can stay hidden until the system is gone, according to CIGen’s decommissioning guide.  

The opposite failure is the zombie application. Teams mark it retired and restrict access, but the database stays online and old integrations keep running. The savings never fully arrive, and nobody is sure the system can be switched off, a pattern that Archon describes in its decommissioning framework.  

A safer retirement follows four checks:  

  • Discover: confirm what the application does today, who uses it and what data it holds.  
  • Map dependencies: list every upstream and downstream connection, including undocumented ones.  
  • Analyze impact: model what changes for connected systems and business processes before anything is switched off.  
  • Preserve knowledge: archive required data and record the business logic, so the organization keeps what the application knew after it is gone.

That last check matters more than most plans admit. When the people who understand an old system move on, the application itself is often the only record of how a process really works. Capture that before shutdown.  

Why a one-time cleanup doesn’t last  

A rationalized portfolio starts drifting the day you finish. Business units buy most SaaS themselves, so new tools rarely pass through central review. AI is speeding that up. BetterCloud reports that 22% of SaaS applications are now AI-powered, up from 7% a year earlier.  

New applications arrive faster than an annual review can catch them. Rationalization works best as a continuous practice: assess usage, overlap and cost as applications enter the portfolio, not once a year. Continuous assessment also keeps the inventory current, so the next decision starts from evidence instead of another survey.  

How QyrusAI supports application rationalization  

QyrusAI is a single AI platform that unites application modernization, software testing and autonomous IT operations. It organizes that work into three connected workspaces. Qyrus Modernize discovers, maps and modernizes the enterprise estate. Qyrus Assure validates every change through AI-driven test automation. Qyrus Operate detects and resolves issues in production. All three share one Knowledge Graph of the estate, so what one workspace learns informs the other two.  

Application rationalization sits inside Modernize, alongside application discovery, dependency mapping, legacy modernization, cloud migration intelligence and technical debt analysis. QyrusAI Application Rationalization looks across the whole portfolio and weighs how each application is used, what it costs to operate and what role it plays in the business. That gives teams the context to choose between modernizing, consolidating and retiring, instead of judging by technical age alone.  

Four capabilities support the decision:  

  • Auto-discovery and dependency mapping build the inventory and the connection map from the live environment, which reduces reliance on manual surveys and on a few subject-matter experts.  
  • Digital Twin impact analysis predicts the ripple effects of decommissioning an application before anyone switches it off.  
  • Knowledge retention keeps business logic and data context available in the Knowledge Graph after a system retires.  
  • Continuous assessment helps stop redundancy from building up again after the first review.  

QyrusAI’s targeted outcomes include decommissioning 20–30% of redundant legacy applications and achieving 15–20% infrastructure cost savings through right-sizing environments. These are targets, not guarantees, and results depend on the starting portfolio.  

Frequently asked questions  

What is application rationalization?  

It is a structured review of an organization’s application portfolio that decides what to modernize, consolidate, retire or keep as it is. The decision rests on usage, cost and business role.  

How is it different from application modernization?  

Rationalization decides which applications deserve which treatment. Modernization is the technical work of upgrading or re-platforming the ones you chose to keep and improve. Running the review first keeps you from modernizing systems you should have retired.  

When should a company start?  

Common triggers include mergers and acquisitions, a planned cloud migration, budget pressure, the retirement of a legacy system and a sudden rise in departmental or AI tools. Any of these tends to expose overlap.  

How many applications should a review cover at once?  

Start with one business capability or department. A narrower scope keeps the data reviewable and the decisions defensible, and you can extend the same method to the next area.  

What are the main risks of retiring an application?  

The biggest ones are hidden dependencies, data-retention obligations and user adoption. Mapping dependencies, archiving required data and planning for the people who use the application reduce all three.  

Make the right change in the right place  

The goal of modernization isn’t to make everything newer. It is to make the right change, in the right place, for the right reasons. Application rationalization gives you the evidence to do that: which applications to modernize, which to merge, which to retire and which to leave alone.  

QyrusAI Application Rationalization brings usage, cost and business context into that decision, backed by discovery and dependency mapping across the estate. Request a demo to see how QyrusAI can help you decide what to modernize and what to retire.