85% of executives know their legacy systems are blocking AI adoption. But knowing and doing are two very different things. Most organisations are stuck somewhere between “we need to modernise” and “here’s how we actually do it”. Competing priorities, unclear timelines, fear of disruption. It’s paralysing.
This playbook gives you a structured 90-day framework for executing AI-assisted legacy modernisation. Three phases: assessment and prioritisation (days 1-30), pilot project validation (days 31-60), and scaling with process refinement (days 61-90).
You’ll get actionable assessment frameworks with 6-dimension evaluation criteria, prioritisation matrices to pick which systems to tackle, team structure decision trees, and week-by-week execution milestones. This is part of our comprehensive AI legacy modernisation overview that establishes the strategic context for execution. It covers readiness assessment, system prioritisation, team composition, human-AI collaboration patterns, and reducing tech debt whilst keeping the business running.
What Does a 90-Day AI Modernisation Roadmap Actually Look Like?
Three 30-day phases. That’s it.
Phase 1 establishes your assessment and prioritisation. Phase 2 validates your tools through pilot execution. Phase 3 scales to multiple systems whilst refining your processes.
What you get from Phase 1: a legacy system inventory, AI readiness scores across 6 dimensions, a prioritisation matrix showing you quick wins vs strategic bets, and stakeholder alignment on which pilot system you’re going with.
What you get from Phase 2: tool evaluation results, a completed pilot reverse engineering or migration, validated human-AI collaboration workflows, and refined cost and timeline estimates.
What you get from Phase 3: 2-3 additional systems modernised, a documented playbook your team can replicate, a KPI tracking dashboard that’s operational, and a 6-month roadmap for your next modernisation wave.
This follows what Cognizant research calls the flywheel approach. Phase 1 operational improvements free up capital. Phase 2 tech debt reduction enables AI integration. Phase 3 growth initiatives create new revenue.
Here’s the week-by-week breakdown. Weeks 1-2 for inventory. Week 3 for scoring each system across 6 dimensions. Week 4 for validation workshops with SMEs. Weeks 5-6 for tool selection and pilot scoping. Week 7 for initial AI output generation. Week 8 for human validation against ground truth. Week 9 for refinement and final pilot assessment. Week 10 selecting additional systems. Week 11 for parallel execution. Week 12 for documenting your playbook. Week 13 for establishing KPI tracking and planning the next wave.
Executive sponsorship secured by day 5. Pilot system selected by day 20. First AI-generated output validated by day 45. Scaling decision made by day 75.
Resource allocation: 2-3 full-time engineers for the pilot, 1 architect for framework design, 1 product owner for requirements validation, and potentially a systems integrator for acceleration.
Healthcare benefits providers modernised 10,000+ COBOL screens with 3x faster migration speed. A global bank migrated 350+ digital banking journeys with 40% productivity gains and 50% fewer defects.
How Do You Assess Legacy Systems for AI Integration Readiness? (Days 1-10)
You need a six-dimension assessment framework.
Architectural elasticity: can the system flex without breaking? Data proximity: can AI access contextual data in real-time? Model volatility tolerance: can the system handle AI behaviour changes? Governance fit: does compliance allow probabilistic systems? Organisational friction: will teams resist AI workflows? Economic visibility: can you measure intelligence ROI?
Architectural elasticity scores 0-10. Monolithic systems with hard-coded dependencies score 0-3. Modular systems with clear APIs score 7-10. Middleware-heavy integration layers score 4-6 and need API facades before AI integration.
Data proximity is crucial. If your systems need 5+ data source integrations or batch processing takes over 1 hour, you’ve got poor proximity. 65% of AI initiatives fail due to data latency. Real-time APIs with co-located data score high.
The numbers tell a clear story. 85% of senior executives are concerned their existing technology estate will stop them from integrating AI. Only 24% believe their current tech estate could support consumer adoption of AI. Traditional enterprise systems weren’t designed for agentic interactions. Most agents still rely on conventional APIs which creates bottlenecks. You need a paradigm shift from traditional data pipelines to enterprise search and indexing through knowledge graphs.
Red flags: no source code access, zero API availability, SME knowledge held by a single individual nearing retirement, zero documentation.
AI-powered tools can speed up the assessment process. AWS Transform analyses mainframe source code to create technical documentation, extract business logic, define data models, and analyse activity metrics. Slingshot AI agents produce specifications with up to 99% code-to-spec accuracy in days, cutting assessment timelines by up to 50%.
Understanding knowledge graph architecture for evaluating tool claims lets you do deeper technical debt analysis during this phase.
How Do You Prioritise Which Legacy Systems to Modernise First? (Days 11-20)
Plot your systems on two axes: business value (revenue impact, user count, strategic importance) vs modernisation complexity (code quality, documentation state, dependency depth).
Quick wins quadrant is high value, low complexity. These are non-critical revenue-generating systems with good documentation and modular architecture. Ideal pilot candidates. They deliver early ROI to fund subsequent waves.
Strategic bets quadrant is high value, high complexity. Mission-critical systems with poor documentation and tangled dependencies. Defer these to wave 2-3 after your pilot learnings reduce the risk.
Avoid indefinitely quadrant is low value, high complexity. Non-differentiating systems. Consider API wrapping to freeze technical debt or decommission if functionality is replaceable by SaaS.
Here’s the reality. 79% of companies will retire less than half of their technology debt by 2030. Currently 93% have retired 25% or less.
But organisations are planning to act. They plan to halve budget allocated to maintaining existing systems from 61% today to 27% by 2030, whilst boosting spending on modernising and migrating legacy systems from 26% to 43%.
Most customers follow the 80/20 principle. 80% of mainframe applications may not require functional changes, whilst 20% genuinely benefit from business-level modifications.
Anti-patterns to avoid: modernising the newest system because it’s “easier”, tackling the most important system first without pilot validation, paralysis from trying to get perfect prioritisation.
Case study: a company prioritises their customer onboarding system (moderate complexity, high revenue impact, 3-week pilot timeline) over their core ERP (high complexity, 6-month timeline estimate). They validate tools and processes before tackling the strategic bet.
Your choice between these prioritisation approaches depends on your overall modernisation strategy. Choosing your modernisation approach influences prioritisation logic differently – evolutionary strategies favour smaller, low-risk systems first, whilst big-bang approaches may justify tackling critical systems head-on.
How Do You Build Your Modernisation Team: In-House, Systems Integrator, or Hybrid? (Days 21-30)
98% of survey respondents plan to seek outside talent by using a systems integrator. Pilots built through strategic partnerships are twice as likely to reach full deployment compared to those built internally.
Go in-house when you’ve got strong internal AI expertise, unique requirements that demand customisation, long-term capability building is a strategic priority, or IP sensitivity requires internal control.
Use a systems integrator when speed to value is paramount, proven playbooks reduce risk, you have limited internal resources, or project-based scope doesn’t justify permanent hires.
The hybrid approach works for most organisations. The SI leads the pilot and wave 1 establishing patterns. Knowledge transfer occurs throughout via pair programming. Your in-house team takes ownership from wave 2 onward for sustainability and cost control.
The skills gap presents a challenge. 85% of respondents highlighted cost and availability of talent as the top impediment to modernisation. The COBOL developer community is declining whilst demand remains flat, fuelling a shortfall close to 100,000 workers.
Decision criteria: assess your current team’s AI literacy, available capacity, budget flexibility, and timeline urgency.
Typical team composition: 2-3 engineers (mix of legacy domain experts and modern stack specialists), 1 architect, 1 product owner, and an optional SI consultant.
Vendor landscape navigation informs which SIs have deep expertise with your preferred platforms and helps you evaluate code comprehension vs generation tools.
Only 18% of companies have completed continuous core modernisation. The majority report blowing their system modernisation project budgets using traditional approaches.
How Do You Execute a Pilot Project and Validate Tools? (Days 31-60)
Your pilot selection criteria: non-critical system, well-scoped boundaries, measurable outcomes, representative complexity.
The state of play: whilst 30% of organisations are exploring agentic options and 38% are piloting, only 14% have deployment-ready solutions and 11% are actively using in production. 42% are still developing their agentic AI strategy roadmap, with 35% having no formal strategy at all.
Tool evaluation framework: assess code comprehension capabilities, code generation quality, human-in-the-loop workflow, cost structure, and integration effort.
Validation checkpoints: weeks 5-6 for tool selection and pilot scoping, week 7 for initial AI output generation, week 8 for human validation against ground truth, week 9 for refinement and final pilot assessment.
Success criteria: for reverse engineering projects, 95%+ business rule extraction accuracy validated by SME review. For migration projects, generated code passes 90%+ of your existing test suite with performance within 10% of legacy baseline. For documentation projects, engineers unfamiliar with the codebase can navigate the system in under 2 hours using AI-generated docs.
John Roese, CTO at Dell Technologies: “We require a material ROI signed off by the finance partner and the head of that business unit.”
Human-in-the-loop validation is non-negotiable. You need SME validation checkpoints for business logic extraction, architect review for generated code quality, security review for sensitive data handling, and performance validation against baseline benchmarks.
Common pilot failure modes: AI hallucinations in generated code (implement contract testing), insufficient context for accurate comprehension (add AGENTS.md files, MCP servers, few-shot examples), vendor tool limitations discovered mid-pilot (have a backup tool shortlist).
AWS Transform combined with Kiro supports specification-driven development. Transform provides outputs that serve as inputs for Kiro.
During the pilot phase, two methodologies prove particularly valuable for ensuring quality and consistency.
What Are Spec-Driven Development and Context Engineering for AI-Assisted Modernisation? (Days 31-60)
Spec-driven development means executable specifications become the authoritative source of truth and AI agents continuously generate code validated against those specs. It’s a fifth-generation programming abstraction, elevating developers from implementation to intent.
This contrasts with “vibe coding” which is informal iterative prompting without formal specifications that creates technical debt, inconsistent outputs, and difficulty scaling across teams.
Kiro’s spec-driven approach enables architects to design microservices, creating formal specifications for review and refinement before implementation begins.
The workflow: a business analyst writes a functional specification in plain English. Specification stored as executable artefact in version control. AI agent generates code implementation from the spec. Contract testing validates generated code conforms to specification. Drift detection monitors runtime behaviour. Human architect reviews and approves for production deployment.
Benefits: it separates planning (human strength) from implementation (AI strength), creates an auditable decision trail for compliance, enables deterministic regeneration, reduces coordination overhead, and maintains architectural consistency.
Context engineering optimises information provided to AI agents through three mechanisms.
AGENTS.md files: 200-500 line markdown documenting project context, architectural principles, testing requirements, and deployment process.
MCP servers: structured APIs providing schemas, service catalogues, and business rule repositories.
Few-shot examples: 5-10 examples per generation pattern showing input-output pairs for your specific transformation needs.
Slingshot produces living architectures that update when business rules change. The platform proposes microservice boundaries, API specifications, and data migration schemas which architects review and refine.
AI-powered approaches apply Domain-Driven Design principles to identify natural bounded contexts within legacy applications.
John Roese at Dell: “We expect you to be very clear about the processes you’re improving. You apply AI to processes, not to people, organisations, or companies.” And: “If you don’t have solid processes, you should not proceed.”
Specifications encode compliance requirements. Policy-as-code automatically validates generated code against governance rules. Drift detection ensures runtime behaviour matches declared specifications.
As you scale modernisation efforts, reducing technical debt becomes paramount.
How Do You Scale Modernisation While Maintaining Business Operations? (Days 61-90)
Use the strangler fig pattern. Gradually replace the legacy system by routing new features to modern services whilst the legacy remains operational. Progressive cutover migrates workflows one-by-one with continuous rollback capability.
During the coexistence phase, both mainframe and new systems operate simultaneously. This three-phase approach (transform, coexist, eliminate) allows organisations to gradually replace monolithic applications with microservices.
Feature flags and progressive rollouts: canary deployments test new services with 5-10% of users first, feature toggles enable quick reversion if performance degrades, A/B testing compares legacy vs modern service quality side-by-side before full cutover.
Week-by-week for weeks 10-13: Week 10 select 2-3 additional systems from your prioritisation matrix. Week 11 parallel execution on multiple systems. Week 12 documentation of playbook for team replication. Week 13 establish KPI tracking dashboard and plan your 6-month roadmap.
Maintaining operations during transition: dual-run legacy and modern systems with data synchronisation, rollback plans at every milestone, and business continuity testing.
Implementation tactics: start with read-only operations that don’t risk data corruption, progress to low-risk writes, finally migrate transactional workflows only after proving stability.
Risk mitigation: maintain legacy system operational throughout, implement circuit breakers that automatically route to legacy if modern service latency exceeds thresholds, and schedule cutovers during low-traffic windows with your full operations team on standby.
Toyota is using an agentic tool to gain visibility into vehicle ETA at dealerships. The process used to involve 50-100 mainframe screens. Jason Ballard, VP of Digital Innovations: ” The agent can do all these things before the team member even comes in in the morning.”
Scaling team composition: promote pilot engineers to tech leads for wave 2 teams, hire additional engineers trained on established patterns, and rotate domain SMEs across multiple modernisation efforts.
Process refinement from pilot learnings: tighten prompt templates based on what worked, expand AGENTS.md context based on what was missing, automate validation gates that were manual in the pilot, and update cost estimates with actual data.
Track four key metrics: modernisation velocity, code quality trends, cost efficiency, and business impact.
Slingshot agents enable self-healing pipelines reducing ops risk by 20-30%. Industry leaders complete their first pilot in 60-90 days, then ship subsequent systems every 4-6 weeks.
How Do You Reduce Tech Debt Without Breaking Production Systems? (Days 61-90)
Flywheel strategy sequence: Phase 1 operational improvements generate 10-20% cost savings. Phase 2 tech debt reduction reduces maintenance burden by 30-40%. Phase 3 growth initiatives unlock new revenue.
Business impact: increased IT agility (73%), enhanced operational visibility (74%), workforce productivity (66%).
AI can help businesses achieve modernisation plans in 30% less time and for 30% lower costs.
Incremental debt reduction tactics: extract well-defined modules first, wrap legacy code with modern APIs before replacing internals, increase test coverage before refactoring, and refactor small sections validated by existing test suites.
AI-assisted refactoring: use code comprehension tools to extract business rules and dependencies, generate comprehensive test suites from legacy code behaviour, validate AI-generated refactorings with humans, and use contract testing to ensure refactored code maintains interface compatibility.
Production stability safeguards: implement observability before refactoring, deploy refactorings behind feature flags, use shadowing to compare legacy vs refactored outputs, and maintain legacy code in production until refactored version proves stable over a 2-4 week burn-in period.
Technical debt prioritisation: security vulnerabilities, performance bottlenecks blocking AI adoption, maintainability issues causing high defect rates, and architectural rigidity preventing composability.
Rollback plans and data safety: database schema changes use expand-contract pattern, data migration scripts tested against production-like datasets, and automated rollback procedures documented and rehearsed.
How AI knowledge graphs turn legacy code into structured intelligence provides structured understanding of code dependencies and business rules that inform safe refactoring boundaries.
Slingshot generates infrastructure as code aligning with AWS Well-Architected Framework for security, reliability, and performance efficiency.
Selecting the right platform determines execution success.
What Platforms Support Automated Legacy Migration? (Tool Selection)
AWS Transform for mainframe accelerates mainframe application modernisation by transforming monolithic COBOL into microservices. It analyses complex COBOL codebases in hours or days.
Publicis Sapient Slingshot automates safer pathways by decomposing monoliths into incremental slices. It produces living architectures updated when business rules change. Unlike generic scanners, it connects outputs to business capabilities.
Kiro generates microservice specifications and source code from AWS Transform outputs. It generates production-ready microservices following modern development standards.
Platform selection criteria: legacy technology stack compatibility, cloud target options, migration approach alignment, human-in-the-loop workflow, and cost structure.
Tool categories: code comprehension platforms, code generation platforms, hybrid spec-driven environments, and integration layers.
Tool selection criteria provide detailed comparison covering Dextralabs, Allganize, Augment Code, and others, helping you navigate the vendor landscape.
Build vs buy vs integrate: build custom tooling when requirements are unique, buy platforms when playbooks exist and time-to-value is needed, integrate existing tools when legacy systems can be extended rather than replaced.
Proof of concept best practices: evaluate 2-3 platforms in parallel during your pilot, use the same pilot system for fair comparison, assess human-in-the-loop workflow fit, and validate cost estimates with actual usage.
Why Are Your Competitors Modernising Faster and What Can You Learn? (Acceleration Patterns)
Decision speed: fast-moving competitors secure executive alignment within 2-3 weeks, not 2-3 months. They establish clear priorities through ruthless scoping. They empower teams to make implementation decisions without escalating every choice to steering committees.
Risk tolerance: successful modernisers pilot quickly and learn from failures. They allocate 10-20% of initial budget to “expected waste”. They measure progress by learning velocity (insights gained per sprint) not just delivery velocity.
Adequate investment: competitive modernisation requires dedicated teams, not part-time assignments. Realistic budgets reflecting actual costs. Multi-year commitment with incremental funding gates.
Strategic partner leverage: fast modernisers use SIs for acceleration on waves 1-2, transition to in-house teams by wave 3, and maintain SI relationships for specialised needs.
Competitive velocity benchmarks: industry leaders complete their first pilot in 60-90 days (vs 6-12 months for laggards), ship subsequent systems every 4-6 weeks after processes stabilise (vs quarterly cadence), and achieve 50%+ legacy portfolio modernisation in 18-24 months (vs 36+ months).
The gap is widening. Only 17% of enterprises are confident their existing infrastructure can support agentic AI. Just 28% believe their existing tech estate is sufficient for meeting changing customer expectations.
Acceleration anti-patterns: analysis paralysis from perfect planning, attempting multiple pilots simultaneously, under-investing in team capability, and big-bang planning.
Organisational enablers: executive sponsorship with board visibility, dedicated product owner for modernisation, bi-weekly steering committee reviews, and public internal communication.
Learning from failure: document what doesn’t work as rigorously as what works, conduct blameless retrospectives after each sprint, and maintain decision logs explaining key choices.
Two-year timeline: competitive disadvantage compounds, talent flight accelerates, AI adoption remains blocked, and compounding costs escalate.
Cognizant research warns: “Those who move with both purpose and precision will be equipped to thrive in the fast-approaching AI-driven world.”
FAQ Section
Can we compress the 90-day roadmap to 60 days?
Possible for simpler systems but it introduces risks. Inadequate assessment leads to wrong system selection. Insufficient tool validation causes vendor lock-in regret. Rushed pilot execution produces low-quality outputs.
Recommendation: maintain the 90-day timeline for your first pilot to establish patterns thoroughly. Compress subsequent waves to 60 days once processes are proven.
Should we hire AI specialists or train existing engineers?
Hybrid approach delivers best results. Hire 1-2 senior AI specialists to establish patterns, select tools, and design frameworks. Simultaneously train existing engineers through pair programming. Rotate domain SMEs across modernisation efforts. Budget for ongoing AI tooling training as platforms evolve rapidly.
How do we maintain business operations during migration?
Use the strangler fig pattern described in the scaling section above. New functionality routes to modern services whilst legacy handles existing features, with progressive rollout, data synchronisation, and feature flags enabling instant fallback if needed.
What if our pilot project fails?
Treat pilots as learning exercises, not production commitments. Document failure modes systematically. Conduct blameless retrospectives focusing on process improvement. Adjust tool selection, team composition, or system choice based on learnings. Communicate lessons learned transparently to stakeholders. Pivot to an alternative approach with refined understanding.
Over 40% of agentic AI projects will be cancelled by 2027 due to infrastructure obstacles.
How do we know if a system is too complex to modernise with AI?
Red flags: zero source code access, business logic embedded in undocumented stored procedures, dependencies on deprecated platforms with no modern equivalents, or SME knowledge held by a single individual who’s unavailable.
For highly complex systems: start with API wrapping to create modern interfaces without touching internals, build knowledge graphs to map dependencies and business rules, and execute a smaller pilot on an isolated module first.
What’s the ROI timeline for legacy modernisation?
The flywheel approach delivers incremental ROI across three phases over 18 months. Full payback is typically 18-24 months with ongoing compounding benefits.
How often should we update our modernisation roadmap?
Quarterly roadmap reviews with monthly progress check-ins. Monthly check-ins assess progress against plan, adjust resource allocation and priorities within the current quarter, and escalate issues to the steering committee.
Quarterly reviews update the 6-12 month roadmap, incorporate lessons learned from completed waves, adjust system prioritisation based on changing business context, and re-validate ROI assumptions with actual results.
What governance is needed for AI-generated code?
Multi-layer governance framework: specification review before code generation, generated code review before merge, contract testing validates conformance to specifications, security review for sensitive modules, performance benchmarking against baselines, and drift detection monitors runtime behaviour vs declared specifications.
Human-in-the-loop validation requires AI outputs to be continuously validated by domain experts.
How do we handle resistance from engineers who fear AI replacement?
Position AI as a tool that augments engineering capabilities rather than replacing engineers. Position spec-driven development as elevating engineers from implementation to intent. Demonstrate the human-in-the-loop workflow where engineers review and refine AI outputs. Involve engineers in tool selection and process design. Celebrate engineers who effectively leverage AI tools. Provide training and pair programming to build AI literacy.
Moderna named its first chief people and digital technology officer, combining HR and technology functions. Tracey Franklin: “We need to think about work planning, regardless of if it’s a person or a technology.”
What happens if we don’t modernise in the next two years?
Compounding competitive disadvantage. Slower feature velocity as maintenance burden consumes greater engineering capacity. Talent flight accelerates as best engineers leave for modern tech stacks. AI adoption blocked permanently as agentic workflows require modernised infrastructure. Compounding costs escalate exponentially as legacy expertise becomes scarcer. Eventual forced big-bang replacement at much higher cost and risk.
By 2030, AI-powered consumers could drive up to 55% of spending. Organisations have limited time to address legacy modernisation.
How do we measure success beyond technical metrics?
Business outcome KPIs: time-to-market for new features, defect rates and mean time to resolution, customer satisfaction scores for modernised workflows, revenue per engineer, and AI adoption rate across the organisation.
Organisational health metrics: engineer satisfaction and retention, recruitment pipeline for modern skills, cross-functional collaboration effectiveness, and innovation velocity.
Financial metrics: total cost of ownership reduction, modernisation cost per system, opportunity cost avoidance, and competitive position maintenance.
Should we modernise on-premises or migrate to cloud simultaneously?
Depends on your strategic priorities and constraints. Cloud-first approach when your business seeks elasticity, scalability, and modern cloud-native services. It combines modernisation with infrastructure transformation.
On-premises modernisation when regulatory constraints prevent cloud migration, existing infrastructure investments are substantial, or your organisation lacks cloud expertise.
Hybrid recommendation: modernise code and architecture first using cloud-ready patterns deployed on-premises initially. Migrate to cloud as a second phase once modernisation stabilises.
This 90-day implementation playbook provides the tactical execution framework for AI-assisted legacy modernisation. By following the phased approach – assessment and prioritisation, pilot validation, and scaling with process refinement – you establish repeatable patterns that transform legacy systems from barriers to enablers of AI adoption. Start with your pilot system selection this week.
