Most organizations already have pieces of a modern platform in place. What’s missing is alignment.
Applications are tightly coupled, dependencies are unclear, delivery is risky, and teams are forced to work around platform and process friction. The result? Modernization starts, stalls, or never delivers the value promised.
We Deliver the Two-Sided Shift
A single journey, moving both sides of the coin together,
we advance both technology and people.
Readiness
Assess
Land
Expand
Transform
Ownership
Readiness
Document baseline application modernization factors and assess organizational readiness for agentic AI-driven business process automation.
What We See
- Applications hard to change safely; delivery slow and high-risk
- Dependencies poorly understood; teams fragmented across silos
- Data scattered across operational systems with no canonical reconciliation
- Business processes live in people’s heads — no documented source of truth
- Key business events people act on are not instrumented anywhere
- No semantic layer: field codes and statuses whose meaning is tribal knowledge
THE INSIGHT:
Readiness for modernization and readiness for agentic AI business process automation are assessed together — the same gaps block both.
PROCESS:
Business and technical discovery sessions establish needs, capabilities, and the starting readiness posture for modernization and BPA.
OUTPUTS:
Current state and direction for application modernization and preliminary readiness view for agentic AI business process automation.
ESTIMATED DURATION: 3 hours
Assess
Define a clear, sequenced modernization path and produce a readiness scorecard for agentic AI business process automation across candidate processes.
What We See
- Modernization stalls from unclear ownership
- App, data, and platform silos persist
- Priorities unclear; sequencing breaks down
- Processes and entities lack canonical definitions across systems
- Data cost and access constrain value
THE INSIGHT:
Modernization scales only when platforms, apps, data, and teams align — and the same alignment work produces the foundation for agent-operated business processes.
PROCESS (Discovery & Analysis):
Agentic AI tools like Claude Code to inventory applications and dependencies
Event Storming to model real delivery and data flows (see next slide)
Ontology and semantic modeling workshops for core business entities
Data catalog and lineage audit
Process mining on instrumented workflows where source systems support it
Shadow-the-human studies to surface workarounds and tribal practices
Assess platform and data fit; align stakeholders and priorities
OUTPUTS:
Clear, sequenced modernization roadmap
Per-process readiness scorecard for agentic AI business process automation
Foundational platform roadmap — semantics, catalog, governance, identity
Aligned teams across apps and data
ESTIMATED DURATION: 3-6 weeks
Land
Prove safe, repeatable application modernization delivery — and begin producing the semantic and documentation artifacts that seed the BPA-ready foundation.
What We See
- Codebase undocumented with no automated tests — behavior must be preserved exactly
- Hidden dependencies surface unexpectedly during refactoring
- Legacy business logic buried in stored procedures or undocumented patterns
- Team lacks AI-assisted discovery tools — ramp-up is slow and manual
THE INSIGHT:
The work that makes Land slow — comprehension, test creation, refactoring — is where AI creates the most leverage. The same work also produces the semantic artifacts that Phase 2 agents will later depend on.
PROCESS:
- Generate comprehensive test suites with agentic AI before touching production code
- Decompose the application module by module using the Event Storming model
- Apply systematic AI-assisted refactoring under the strangler pattern — AI handles mechanical transformation, engineers own architecture
- Surface business logic buried in stored procedures as explicit, documented rules
- Integrate observability, security, and document patterns ready for Expand
OUTPUTS:
- Production-ready modernized module(s) with full test coverage
- AI-generated documentation capturing codebase behavior — structured as catalog-ready semantic artifacts (field definitions, business rules, event definitions)
- Reusable test commands and patterns for subsequent modules
- Team trained on AI-assisted development workflows
ESTIMATED DURATION: 2–6 months (compressed from 4–12 months with AI assistance)
Expand
Apply proven AI-assisted modernization patterns across additional modules — and compound the BPA-readiness foundation with each iteration.
What We See
- First iteration delivered; remaining modules still on legacy stack
- AI workflows established for one team but not yet institutionalized
- Test coverage exists for landed modules; significant gaps remain
- Semantic artifacts exist for early modules but catalog coverage is partial
- Compounding ROI visible but not yet fully realized
THE INSIGHT:
Each Expand iteration modernizes more code and extends the semantic layer, data catalog, and process model — the BPA-ready foundation grows with every module.
PROCESS:
Roll out AI-assisted discovery and test generation to additional modules
Standardize agentic AI workflows and prompt libraries across the team
Extend the data catalog and semantic layer with each module’s entities, events, and rules
Refine the business process model as additional workflows are modernized
Enable all team members on AI-assisted development — selection, review, validation
OUTPUTS:
Multiple modules modernized using consistent AI-accelerated patterns
Governed semantic layer and data catalog covering a widening footprint of the business
Expanded process model coverage — more of the business becomes Phase 2–ready with each iteration
Faster delivery per module as team AI fluency increases
ESTIMATED DURATION: 3–12 Months
Transform
Make AI-augmented application modernization durable and establish the governance foundation for agentic AI business process automation.
What We See
Part 1
- Modernization progressing but dependent on Capstone IT Solutions guidance
- AI-for-engineering usage inconsistent across team members
- No formal governance for AI-generated code quality and review
- No established governance model for AI operating on business data and processes
- Data governance and identity infrastructure built for humans, not agents
Part 2
- Foundation exists on paper — semantic layer, catalog, governance, identity — but unproven against a real agent in production
- Leadership wants evidence that the modernization investment translates to BPA capability
- Client team ready to participate in agent operation but needs a first working example
- Gaps in data freshness, identity edge cases, or audit coverage only surface under real use
Part 1
PROCESS:
- Formalize AI-augmented engineering — prompt standards, review workflows, output governance
- Establish data governance for agentic AI operation — catalog ownership, semantic-layer stewardship, event instrumentation standards
- Define identity and access foundations for non-human agents — machine identity, scoped permissions, credential rotation, audit infrastructure
- Formalize the business process model as a governed artifact with named owners and change control
OUTPUTS:
- Durable AI-augmented engineering team operating independently
- Governance model covering AI engineering output and the foundations for agent-operated business processes
- Identity, access, and audit infrastructure ready to support non-human agents in production
ESTIMATED DURATION: Runs across the Transform window (1–3 years)
Part 2
THE INSIGHT:
The first agent is a proving deployment, not full BPA. Its job is to validate the foundation, surface the gaps real production exposes, and create the reference case that de-risks Phase 2 — scaled agentic AI business process automation across the business.
PROCESS:
Select the first BPA candidate from the readiness scorecard — high readiness, bounded scope, meaningful exercise of the foundation
Design the pilot agent: inputs, outputs, human-in-the-loop review, failure modes, rollback
Deploy to production with full observability, audit, and named operator accountability
Extract lessons on data, semantic, identity, and governance gaps revealed under real use
OUTPUTS:
First agentic AI business process automation live in production — narrow scope, fully governed
Validated BPA-readiness foundation proven against a real agent in real use
Gap list and roadmap defining what Phase 2 scaling will require
Reference deployment that anchors the Phase 2 conversation
ESTIMATED DURATION: Runs within the Transform window (few months, parallel to governance work)
Ownership
Sustain AI-augmented modernization and agent-ready operations as a durable organizational capability — and establish the handoff into Phase 2 agentic AI business process automation delivery.
What We See
- AI tool usage depends on individuals, not documented team practices
- Semantic layer and data catalog decay without stewards — BPA readiness erodes
- The pilot agent from Transform lacks clear operator accountability
- New engineers cannot onboard to AI workflows without heroics
- Modernization stalls when key contributors leave
OWNERSHIP MODEL:
- App teams own AI-assisted delivery outcomes and code quality
- Senior engineers govern AI engineering output and architectural decisions
- Data and semantic stewards own the catalog, ontology, and event instrumentation that enable agent operation
- Process owners maintain the domain models, decision rules, and escalation logic that agents follow
- Agent operators are named, accountable humans responsible for deployed agent behavior in production
- Security defines AI code review standards and audit requirements across both engineering and agent operation
- Leadership owns AI tooling investment, team capability roadmap, and the Phase 2 roadmap
OUTPUTS:
- Durable capability — AI-augmented engineering and the first agent in production, both owned by the organization
- Organization structured to absorb the next wave of AI tooling and additional agents without disruption
- A foundation ready for Phase 2: data, semantics, processes, governance, identity, and audit in place and owned
ESTIMATED DURATION: Ongoing (continuous improvement and evolution)
App Modernization Case Studies
