Modernization Agents With SAP & Non-SAP Connectors

KTern.AI -

In today’s increasingly complex and hybrid IT landscapes, enterprise leaders depend more than ever on the accuracy and reliability of their analytics dashboards. CXOs rarely interact directly with transactional systems like SAP ECC or S/4HANA. Instead, they rely on Power BI, Qlik, and Tableau dashboards—powered by warehouses like Snowflake and Databricks—to understand business performance, assess risk, and guide strategic decisions.

As organizations prepare for transformative initiatives such as the S/4HANA 2027 conversion, these dashboards face unprecedented risk. Changes in SAP data structures, integration layers, or ETL logic can quietly break dashboards, distort KPIs, and mislead leadership—often without immediate detection. The stakes are high: a single inaccurate KPI can trigger poor decisions, operational disruptions, or stalled modernization programs.

This is where modernization agents powered by agentic AI step in, providing an autonomous, intelligent, and continuous layer of protection across the entire analytics stack—from SAP to warehouse to BI dashboards. These agents reverse engineer decades of dashboards, monitor complex data flows, detect breakage proactively, and guide or execute remediation. They give enterprises the confidence that leadership dashboards will remain resilient regardless of how much the underlying systems change.

Understanding Agentic AI in Analytics Modernization

Agentic AI refers to autonomous software agents capable of perceiving changes, reasoning across multi-system dependencies, planning multi-step tasks, and acting with minimal human supervision. Unlike traditional AI models that simply predict or classify, agentic agents are configured with specific business and technical responsibilities.

In the analytics modernization context, agentic agents are capable of tasks such as:

  • Discovering dashboards and data models across Qlik, Power BI, Snowflake, Databricks, and SAP.
  • Reverse engineering and documenting KPIs, expressions, filters, lineage, and dependencies.
  • Continuously monitoring schemas, jobs, pipelines, and dashboard health.
  • Identifying disruptions, assessing business impact, and proposing or executing remediation.
  • Learning as the IT landscape evolves—operating not as a one-time project but as an ongoing modernization function.

This represents a paradigm shift: analytics modernization is no longer a set of disconnected manual tasks but an AI-augmented operational model that keeps your decision-making ecosystem resilient.

KTern.AI’s Dynamic, Scalable, and Configurable Agent Framework

At the core of this transformation is KTern.AI’s Dynamic, Scalable, and Configurable Agent Framework. This framework orchestrates distinct yet collaborative agent types—discovery, documentation, monitoring, impact analysis, and remediation—to create true end-to-end modernization coverage.

Dynamic

Agents automatically detect new dashboards, modified objects, and evolving data sources. They adapt to changes without requiring manual scripting, ensuring the modernization process remains evergreen even in rapidly transforming environments.

Scalable

Whether your enterprise operates 100 dashboards or 10,000, the framework can traverse and process them at scale. Dashboards across multiple regions, business units, SAP landscapes, and warehouse clusters can be analyzed and governed uniformly.

Configurable

IT directors and ERP leaders can define which systems to analyze, the depth of metadata extraction, the reporting format, lineage scope, and governance thresholds. This ensures modernization operates within enterprise control boundaries.

A practical example involves deploying a Qlik MCP Server on AWS EC2. Here, agents extract metadata, normalize expressions, map lineage, generate documentation, and flag breakages—all autonomously and in real time.

SAP + Non-SAP Connectors Powering End-to-End Visibility

Modern enterprises operate a quilt of systems. SAP ECC or S/4HANA typically serves as the system of record, while other platforms like:

  • Snowflake or Databricks power massive analytical workloads
  • Informatica, ADF, or DBT manage ETL pipelines
  • Qlik and Power BI deliver visual insights

Agentic AI agents bridge these layers through native connectors, enabling a unified, end-to-end dependency map.

For example, consider a supply chain dashboard in Qlik:

  • A dashboard visual depends on a Snowflake view
  • That view is fed by DBT logic
  • The DBT pipeline extracts data from S/4HANA CDS views
  • The CDS views are based on renamed or deprecated S/4HANA tables

Any change in SAP or ETL logic can break the dashboard—but without lineage, teams find out only when users complain.

Agentic AI stitches these layers together, enabling:

  • Complete data lineage from dashboard widget → Snowflake/Databricks → ETL → SAP source
  • Faster impact analysis
  • Accurate risk detection during SAP projects
  • Greater governance during migrations and enhancements

This visibility becomes even more critical during S/4HANA conversions.

Automated Reverse Engineering of Qlik Dashboards

Many enterprises treat Qlik dashboards as “black boxes.” Over the years, as multiple developers contribute changes, dashboards grow complex, undocumented, and difficult to maintain. Manual documentation is slow, error-prone, and usually outdated within weeks.

Agentic AI solves this by automatically reverse engineering dashboards.

Agents can:

  • Enumerate every sheet, object, and visualization across Qlik servers
  • Extract expressions, set analysis logic, variables, bookmarks, and filter conditions
  • Map KPIs to data fields, models, tables, and ETL sources
  • Normalize complex expressions into readable semantic formats
  • Generate documentation, lineage diagrams, and impact assessment reports

What once took teams months can now be completed in days—continuously updated as changes occur.

Why This Matters for S/4HANA 2027 Conversions

S/4HANA transformations introduce fundamental changes to SAP’s data structures, reporting logic, tables, and integration methods. Even when the business processes remain stable, the underlying technical fields, domain values, and naming conventions can shift significantly.

This puts analytics stacks at substantial risk.

Agentic AI agents play a critical role by:

1. Mapping ECC → S/4HANA changes

Agents compare legacy ECC structures against S/4HANA equivalents to identify:

  • Renamed fields
  • Deprecated tables
  • Replaced data elements
  • Modified CDS views

2. Identifying at-risk dashboards

Agents link SAP data changes to downstream warehouses and dashboards to identify which KPIs or visuals may break.

3. Producing technical and functional retrofit specs

Instead of manual detective work, agents auto-generate clear specifications outlining what needs to change.

4. Continuous post-migration monitoring

After go-live, agents validate data continuity and detect regressions immediately—before executives notice discrepancies.

In essence, modernization agents act as guardians of executive dashboards throughout the S/4HANA journey.

Towards Self-Healing Analytics Ecosystems

The ultimate objective is not just modernization, but autonomy.

Agentic AI drives this through self-healing capabilities powered by continuous detection, contextual reasoning, and guided or automated fixes.

For example:

  • If a Snowflake field used in a KPI disappears, the agent detects it instantly.
  • It traces the issue to the upstream ETL job.
  • It highlights the business impact (e.g., “Gross Margin on CXO Dashboard will misreport”).
  • It proposes remediation, such as mapping to a new field or adjusting query logic.
  • Under governed scenarios, it can automatically implement fixes.

This reduces Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) dramatically, allowing enterprises to operate analytics stacks with a level of reliability previously impossible.

Strategic Value for CXOs, IT Directors, and ERP Managers

For enterprise leaders, analytics modernization is a business-critical initiative—not just a technical exercise.

Agentic AI delivers several strategic advantages:

1. Assurance of dashboard continuity

Leaders can trust that key KPIs remain accurate during transformations.

2. Protection of institutional knowledge

Automatically generated documentation preserves years of dashboard logic that would otherwise remain tribal knowledge.

3. Governance-enabled agility

Agents enable faster modernization while maintaining strict compliance and visibility.

4. Reduced reliance on manual intervention

IT teams shift from reactive firefighting to proactive management and continuous improvement.

Analytics stability becomes a competitive advantage rather than a persistent risk.

Serving Large Enterprises with Mature BI Stacks

Large enterprises running Qlik, Power BI, Snowflake, Databricks, and SAP face unique modernization challenges:

  • Thousands of dashboards
  • Multiple global business units
  • Complex lineage spanning SAP and non-SAP sources
  • Tight timelines for S/4HANA 2027

KTern.AI’s modernization agents directly address these challenges by offering:

  • Fast, autonomous reverse engineering of dashboards
  • Cross-platform lineage visibility
  • Risk and impact analytics
  • Self-healing monitoring

These capabilities ensure that CXO dashboards remain accurate and resilient—even during high-velocity modernization.

From One-Time Projects to Continuous Modernization

Traditional modernization treats documentation, impact analysis, and remediation as project tasks. Once done, they quickly become outdated.

Agentic AI shifts modernization into a continuous, AI-driven operational process:

  • Agents discover changes as they occur
  • Documentation remains live
  • Dashboards are monitored continuously
  • Remediation cycles operate autonomously

This adaptive model aligns with today’s reality of frequent SAP upgrades, evolving warehouses, and fast-changing BI requirements.

Conclusion: The Future of Analytics Modernization

As enterprises accelerate their move toward S/4HANA and cloud-first architectures, analytics reliability becomes non-negotiable. Dashboards drive decisions, decisions drive business outcomes—and any disruption can cause cascading organizational impact.

KTern.AI’s modernization agents, powered by advanced agentic AI and enriched with deep SAP/non-SAP connectors, provide a resilient shield for analytics ecosystems. They bring intelligence, autonomy, and continuous governance to your dashboards—ensuring business continuity even as underlying systems transform.

This is the future of enterprise analytics:
where agentic AI keeps dashboards reliable, modernization continuous, and decision-making uncompromised. One dashboard at a time.