Gregory Jenelos

Mainframe Managed Service Providers (MSPs) have built impressive capabilities over the last several decades. They excel at infrastructure management, code conversion, and supporting complex hosting environments. Many have invested millions in advanced tools for code parsing, refactoring, and other technical aspects of mainframe management and modernization. Yet despite these strengths, MSPs consistently encounter the same bottlenecks that threaten mainframe modernization project timelines, profit margins, and client satisfaction.

In this article, we’ll explore the most common gaps MSPs face, how Zengines platform helps fill those gaps, and why Mainframe MSPs are partnering with Zengines.

The Critical Gaps in Current MSP Toolkits

Gap 1: Business Rules Discovery

While MSPs have sophisticated tools for parsing and reverse engineering COBOL code—they can extract syntax, identify data structures, and map technical dependencies—they lack capabilities for intelligent business logic interpretation. These parsing tools tell you what the code does technically, but not why it does it from a business perspective.

Current approaches to understanding the embedded business rules within parsed code require:

• Manual analysis by teams of expensive mainframe experts and business teams to interpret the business meaning behind technical code structures
• Line-by-line COBOL review to understand calculation logic and translate technical operations into business requirements
• Manual correlation between parsed code elements and actual business processes
• Expert interpretation of conditional logic to understand business rules and decision trees
• Guesswork about the original business intent behind complex branching statements and embedded calculations

Even with advanced parsing capabilities, MSPs still need human experts to bridge the gap between technical code structure and business logic understanding. This discovery phase often represents 30-40% of total project time, yet MSPs have limited tools to accelerate the critical transition from "code parsing" to "business intelligence."

The result: MSPs can quickly identify what exists in the codebase, but still struggle to efficiently understand what it means for the business—creating a bottleneck that no amount of technical parsing can solve.

Gap 2: Data Migration Tools

A critical step in any mainframe modernization project involves migrating data from legacy mainframe systems to new modern platforms. This data migration activity often determines project success or failure, yet it's where many MSPs face their biggest challenges.

While MSPs excel at physical data ETL and have tools for moving data between systems, they struggle with the intelligence layer that makes migrations fast, accurate, and low-risk:

• Manual data mapping between legacy mainframe schemas and modern system structures—a time-intensive process due to guesswork of trial-and-error
• Insufficient data quality analysis relevant to the business context, leading to "garbage in, garbage out" scenarios in the new system
• Manual transformation rule creation that requires expensive technical resources and extends project timelines
• Limited validation capabilities to ensure data integrity during and after migration
• Rules-based tools that constantly need to be adjusted when the data doesn’t fit the expected pattern - more often then not, this is the case.

These gaps expose organizations to costly risks: project delays, budget overruns, compromised data integrity, and client dissatisfaction from failed transfers. Delays and cost overruns erode margins and strain client relationships. Yet the most significant threat remains post-go-live discovery of migration mistakes. Today’s approach of manual processes are inherently time-constrained—teams simply cannot identify and resolve all issues before deployment deadlines. Unfortunately, some problems surface only after go-live, forcing expensive emergency remediation that damages client trust and project profitability.

The result: MSPs can move data technically, but lack intelligence tools to do it efficiently, accurately, and with confidence—making data migration the highest-risk component of mainframe modernization projects.

Gap 3: Testing and Validation Blind Spots

Once data is migrated from mainframe systems to modern platforms, comprehensive testing and validation becomes critical to ensure business continuity and data integrity. This phase determines whether the migration truly preserves decades of embedded business logic and data relationships.

Without comprehensive understanding of embedded business logic and data interdependencies, MSPs face significant validation challenges:

• Incomplete test scenarios based on limited business rules knowledge—testing what they assume exists rather than what actually exists in the legacy system
• Inefficient testing based on what the code presents versus what actually exists within the data
• Manual reconciliation between old and new systems that's time-intensive, error-prone, and often misses subtle data discrepancies
• Inadequate validation coverage of complex business calculations and conditional logic that may only surface under specific data conditions
• Reactive testing methodology that discovers problems during user acceptance testing rather than proactively during migration validation
• Post-go-live surprises when undocumented logic fails to transfer, causing business process failures that weren't caught during testing

The consequences: validation phases that stretch for months, expensive post-implementation fixes, user confidence issues, and potential business disruption when critical calculations or data relationships don't function as expected in the new system.

The result: MSPs have inadequate and non-optimized testing where teams test what they think is important rather than what the business actually depends on.

How Zengines Fills These Critical Gaps

Zengines has built AI-powered solutions that directly address each of these critical gaps in MSP capabilities. Our platform works alongside existing MSP tools, enhancing their technical strengths with the missing intelligence layer that transforms good modernization projects into exceptional ones.

Zengines Mainframe Data Lineage: Bridging the Business Logic Intelligence Gap

While parsing tools can extract technical code structures, Zengines Mainframe Data Lineage translates that technical information into actionable business intelligence:

• Automated Business Logic Interpretation: Our AI doesn't just parse COBOL—it understands what the code means from a business perspective. Zengines automatically identifies calculation logic, conditional business rules, and decision trees, then presents them in business-friendly visualizations that eliminate the need for manual interpretation.
• Intelligent Business Rules Extraction: Transform months of manual analysis into minutes of automated discovery. Zengines maps the relationships between data elements, business processes, files, and embedded logic, creating comprehensive documentation of how your mainframe actually implements business requirements.
• Visual Logic Flow Mapping: Interactive visualizations show not just technical data flow, but business process flow—helping teams understand why certain calculations exist, when conditional logic triggers, and how business rules cascade through the system.
• Contextual Information based on Analyst Research: Automatically generates business-readable information that explains the "why" behind technical code, enabling business analysts and technical teams to collaborate effectively without requiring scarce mainframe expertise.

MSP Impact: Transform your longest project phase into your fastest. Business logic discovery that previously required months and years of expert time now completes in days with comprehensive information that your entire team can understand and act upon.

Zengines AI Data Migration: Solving the Data Intelligence and Migration Gap

Our AI Data Migration platform transforms data migration from a risky, manual process into an intelligent, automated workflow:

• Intelligent Schema Discovery and Mapping: AI algorithms automatically understand both source mainframe schemas and target system structures, then predict optimal field mappings with confidence scoring. No more guesswork—get AI-recommended mappings that you can validate and refine.
• Automated Data Quality Analysis: Comprehensive data profiling identifies quality issues, completeness gaps, and anomalies before migration begins. Address data problems proactively rather than discovering them post-go-live.
• AI-Powered Transformation Rule Generation: Describe your transformation requirements in plain English, and Zengines Transformation Assistant generates the precise transformation syntax. Business analysts can create complex transformation rules without needing technical programming expertise.
• Automated Load File Generation: Execute data mapping and transformation into ready-to-use load files that integrate seamlessly with your existing migration tools and processes.

MSP Impact: Accelerate data migration timelines by 80% while dramatically reducing risk. Business analysts become 6x more productive, and data migration transforms from your highest-risk project component to a predictable, repeatable process.

Comprehensive Testing and Validation: Closing the Validation Loop

Zengines doesn't just help with discovery and migration—it ensures successful validation:

• Business Rules-Based Test Scenario Generation: Because Zengines understands the embedded business logic, it informs test scenarios that cover the actual business rules in the legacy system, not just the ones you know about.
• Automated Data Reconciliation: Zengines offers helpful tools like reconciliation to expose discrepancies between source and target systems, with intelligent filtering that highlights meaningful differences while ignoring irrelevant variations.
• Comprehensive Audit Trail: Complete documentation of what was migrated, how it was transformed, and validation that it works correctly—providing confidence to stakeholders and regulatory compliance where needed.

MSP Impact: Transform validation from an uncertain phase into a systematic process that focuses on exceptions. Reduce validation timelines by 50% while dramatically improving coverage and reducing post-go-live surprises.

The Integrated Zengines Advantage

Unlike point solutions in the mainframe modernization ecosystem that address isolated problems, Zengines provides an integrated platform where business logic discovery, data migration, and validation work together seamlessly:

• Light into the Black Box: Transparency into the most challenging system in the enterprise: mainframe and midranges.
• Connected Intelligence: Business rules discovered in the lineage phase automatically inform data migration mapping and validation scenarios
• End-to-End Visibility: Complete traceability from original business logic through final validation, providing unprecedented project transparency
• Unified Data Source: Single source of truth for business rules, data transformations, and validation results

This integrated approach transforms modernization from a series of risky, disconnected phases into a cohesive, intelligent process that dramatically improves outcomes while reducing timelines and risk.

The Business Impact: Why MSPs are Choosing Zengines

Accelerated Project Delivery

MSPs can deliver 50% faster overall project completion times. The discovery and data migration phases—traditionally the longest parts of modernization projects—now complete in a fraction of the time.

Improved Profit Margins

By automating the most labor-intensive aspects of modernization, MSPs can deliver projects with fewer billable hours while maintaining quality. This directly improves project profitability.

Enhanced Client Experience

Clients appreciate faster time-to-value and reduced business disruption. Comprehensive business rules documentation also provides confidence that critical logic won't be lost during migration.

Competitive Differentiation

MSPs with Zengines capabilities can bid more aggressively on timeline and cost while delivering superior outcomes. This creates a significant competitive advantage in the marketplace.

Risk Mitigation

Better understanding of business logic before migration dramatically reduces post-implementation surprises and costly remediation work.

The Strategic Imperative

As the mainframe skills shortage intensifies—with 70% of mainframe professionals retiring by 2030—MSPs face an existential challenge. Traditional manual approaches to business rules discovery and data migration are becoming unsustainable.

The most successful MSPs will be those that augment their technical expertise with AI-powered intelligence. Zengines provides that intelligence layer, allowing MSPs to focus on what they do best while dramatically improving client outcomes.

The question isn't whether to integrate AI-powered data intelligence into your modernization methodology. The question is whether you'll be an early adopter who gains competitive advantage, or a late adopter struggling to keep pace with more agile competitors.

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Your new core banking system just went live. The migration appeared successful. Then Monday morning hits: customers can't access their accounts, transaction amounts don't match, and your reconciliation team is drowning in discrepancies. Sound familiar?

If you've ever been part of a major system migration, you've likely lived a version of this nightmare. What's worse is that this scenario isn't the exception—it's becoming the norm. A recent analysis of failed implementations reveals that organizations spend 60-80% of their post-migration effort on reconciliation and testing, yet they're doing it completely blind, without understanding WHY differences exist between old and new systems.

The result? Projects that should take months stretch into years, costs spiral out of control, and in the worst cases, customers are impacted for weeks while teams scramble to understand what went wrong.

The Hidden Crisis: When Reconciliation Becomes Archaeology

Let's be honest about what post-migration reconciliation looks like today. Your team runs the same transaction through both the legacy system and the new system. The old system says the interest accrual is $5. The new system says it's $15. Now what?

"At this point in time, the business says who is right?" explains Caitlin Truong, CEO of Zengines. "Is it that we have a rule or some variation or some specific business rule that we need to make sure we account for, or is the software system wrong in how they are computing this calculation? They need to understand what was in that mainframe black box to make a decision."

The traditional approach looks like this:

1. Business analyst discovers the discrepancy
2. Tickets get raised to investigate the difference
3. Someone (usually an expensive SME) digs through legacy code
4. Weeks or months pass while they navigate thousands of lines of COBOL
5. Finally, an answer emerges—but it spawns three new questions
6. The cycle repeats

The real cost isn't just time—it's risk. While your team plays detective with legacy systems, you're running parallel environments, paying for two systems, and hoping nothing breaks before you figure it out.

The Root Problem: Legacy Systems as Black Boxes

Here's what most organizations don't realize: the biggest risk in any migration isn't moving the data—it's understanding the why behind the data.

Legacy systems, particularly mainframes running COBOL code written decades ago, have become black boxes. The people who built them are retired. The business rules are buried in thousands of modules with cryptic variable names. The documentation, if it exists, is outdated.

"This process looks like the business writing a question and sending it to the mainframe SMEs and then waiting for a response," Truong observes. "That mainframe SME is then navigating and reading through COBOL code, traversing module after module, lookups and reference calls. It’s understandable that without additional tools, it takes some time for them to respond."

When you encounter a reconciliation break, you're not just debugging a technical issue—you're conducting digital archaeology, trying to reverse-engineer business requirements that were implemented 30+ years ago.

One of our global banking customers faced this exact challenge. They had 80,000 COBOL modules in their mainframe system. When their migration team encountered discrepancies during testing, it took over two months to get answers to simple questions. Their SMEs were overwhelmed, and the business team felt held hostage by their inability to understand their own system.

"When the business gets that answer they say, okay, that's helpful, but now you've spawned three more questions and so that's a painful process for the business to feel like they are held hostage a bit to the fact that they can't get answers themselves," explains Truong.

The AI-Powered Revolution: From Reactive to Predictive

What if instead of discovering reconciliation issues during testing, you could predict and prevent them before they happen? What if business analysts could investigate discrepancies themselves in minutes instead of waiting months for SME responses?

This is exactly what our mainframe data lineage tool makes possible.

"This is the challenge we aimed to solve when we built our product. By democratizing that knowledge base and making it available for the business to get answers in plain English, they can successfully complete that conversion in a fraction of the time with far less risk," says Truong.

Here's how it works:

1. Intelligent System Understanding

AI algorithms ingest your entire legacy codebase—COBOL modules, JCL scripts, database schemas, and job schedulers. Instead of humans manually navigating 80,000 lines of code, pattern recognition identifies the relationships, dependencies, and calculation logic automatically.

2. Business Rule Extraction

The AI doesn't just map data flow—it extracts the underlying business logic. That cryptic COBOL calculation becomes readable: "If asset type equals equity AND purchase date is before 2020, apply special accrual rate of 2.5%."

3. Self-Service Investigation

When your new system shows $15 and your old system shows $5, business analysts can immediately trace the calculation path. They see exactly why the difference exists: perhaps the new system doesn't account for that pre-2020 equity rule embedded in the legacy code.

4. Informed Decision-Making

Now your team can make strategic decisions: Do we want to replicate this legacy rule in the new system, or is this an opportunity to simplify our business logic? Instead of technical debugging, you're having business conversations.

Real-World Transformation: From 2 Months to 0.4% Exception Rate

Let me share a concrete example of this transformation in action. A financial services company was modernizing their core system and moving off their mainframe. Like many organizations, they were running parallel testing—executing the same transactions in both old and new systems to ensure consistency.

Before implementing AI-powered data lineage:

• Each reconciliation question took 2+ months to resolve
• SMEs were overwhelmed with investigation requests
• The business team felt dependent on scarce technical resources
• Project timelines were constantly slipping due to reconciliation delays

After implementing the solution:

• Business analysts could research discrepancies independently
• Investigation time dropped from months to minutes
• The team achieved a 0.4% exception resolution rate
• Project risk was dramatically reduced

"The business team presents their dashboard at the steering committee and program review every couple weeks," Truong shares. "Every time they ran into a break, they have a tool and the ability to answer why that break is there and how they plan to remediate it."

The New Reconciliation Methodology

The most successful migrations we've seen follow a fundamentally different approach to reconciliation:

Phase 1: Pre-Migration Intelligence

Before you migrate anything, understand what you're moving. Use AI to create a comprehensive map of your legacy system's business logic. Know the rules, conditions, and calculations that drive your current operations.

Phase 2: Predictive Testing

Instead of hoping for the best, use pattern recognition to identify the most likely sources of reconciliation breaks. Focus your testing efforts on the areas with the highest risk of discrepancies.

Phase 3: Real-Time Investigation

When breaks occur (and they will), empower your business team to investigate immediately. No more waiting for SME availability or technical resource allocation.

Phase 4: Strategic Decision-Making

Transform reconciliation from a technical debugging exercise into a business optimization opportunity. Decide which legacy rules to preserve and which to retire.

"The ability to catch that upfront, as opposed to not knowing it and waiting until you're testing pre go-live or in a parallel run and then discovering these things," Truong emphasizes. "That's why you will encounter missed budgets, timelines, etc. Because you just couldn't answer these critical questions upfront."

Beyond Migration: The Ongoing Value

Here's something most organizations don't consider: this capability doesn't become obsolete after your migration. You now have a living documentation system that can answer questions about your business logic indefinitely.

Need to understand why a customer's account behaves differently? Want to add a new product feature? Considering another system change? Your AI-powered lineage tool becomes a permanent asset for business intelligence and system understanding.

"When I say de-risk, not only do you de-risk a modernization program, but you also de-risk business operations," notes Truong. "Whether organizations are looking to leave their mainframe or keep their mainframe, leadership needs to make sure they have the tools that can empower their workforce to properly manage it."

The Bottom Line: Risk Mitigation vs. Risk Acceptance

Every migration involves risk. The question is whether you want to manage that risk proactively or react to problems as they emerge.

Traditional reconciliation approaches essentially accept risk—you hope the breaks will be manageable and that you can figure them out when they happen. AI-powered data lineage allows you to mitigate risk substantially by understanding your system completely before you make changes.

The choice is yours:

• Continue the cycle of expensive, time-consuming reconciliation archaeology
• Or transform your approach with intelligent, self-service system understanding

Ready to Transform Your Reconciliation Process?

If you're planning a migration or struggling with an ongoing reconciliation challenge, you don't have to accept the traditional pain points as inevitable. AI-powered data lineage has already transformed reconciliation for organizations managing everything from simple CRM migrations to complex mainframe modernizations.

Schedule a demo to explore how AI can turn your legacy "black box" into transparent, understandable business intelligence.