How IT decision-makers in the US can partner with Hexaware to lead the Oracle transformation wave

Introduction: The Oracle imperative in 2025

For decades, Oracle has been a backbone for enterprise applications: ERP, HCM, SCM, databases, EBS, PeopleSoft, CPQ, and more. Today, CIOs and IT heads are under pressure to not only modernize Oracle landscapes (move to cloud, reduce cost, eliminate technical debt) but also to embed intelligence, automation, and autonomy into those landscapes.

To stay competitive, organizations in the US are asking:

• Can Oracle apps generate proactive insights or decisions, not just house data?
• Can we reduce operational toil via autonomous agents?
• How quickly can we migrate (or modernize) without major disruption?
• Which providers have both deep Oracle domain credibility and an AI-first mindset?

Hexaware positions itself at that intersection. Their proposition: not just Oracle modernization, but Oracle + AI delivered via platforms like Vibe Coding, RapidX™, and Tensai®. The promise is a quantum leap in agility, cost efficiency, and future readiness.

In this piece, I’ll walk you through:

1. Hexaware’s core Oracle + AI value proposition
2. Deep dives into case studies and outcomes
3. Architecture patterns and use cases (agentic AI + Oracle)
4. Industry-specific applications (US-centric)
5. RFP / evaluation questions IT decision-makers should ask
6. Practical prompt & agent templates
7. A concluding call-to-action for US enterprise CIOs

Hexaware’s Oracle + AI value proposition

A. Oracle services built for the AI era

Hexaware’s Oracle Services practice is not just about legacy upgrades or cloud lifts. They frame it as an AI-enabled transformation. Their capability stack encompasses:

• Advisory & strategy   defining AI-enabled target architectures, data and governance blueprints
• Application services   full implementation of Oracle Fusion, HCM, CPQ, ERP
• OCI infrastructure & migrations   lift/shift, replatform, and managed operations
• Legacy modernization & support   PeopleSoft, EBS, custom modules
• AI & automation overlay   embedding agentic AI, intelligent operations, and accelerated development

What differentiates Hexaware is that the AI layer is first among equals, not a bolt-on afterthought.

B. Core AI-enabled platforms

Hexaware invests in platforms that turn Oracle systems into living, responsive ecosystems:

• Vibe Coding   an AI-native delivery model where development, testing, and deployment are accelerated with AI agents.
• RapidX™   purpose-built agentic AI for software engineering tasks (automated builds, tests, maintenance).
• Tensai®   a platform for intelligent operations, monitoring, remediation, and CX automation layered on production systems.

These platforms allow a customer to migrate Oracle workloads and leapfrog them into a self-optimizing, autonomous environment.

C. Migration accelerators & risk mitigation

Hexaware offers a migration accelerator for Oracle apps moving into OCI. This helps reduce friction, compress timelines, and minimize rollback risk.

They also point to real-world metrics (in their published case studies) to back their claims   which I’ll cover next.

Hexaware Case study: evidence of excellence

Hexaware’s credibility hinges on outcomes, not just claims. Below are highlighted case studies with analysis of how Hexaware has delivered in real Oracle + AI transformation contexts.

A. HR & Finance transformation in 20 weeks

Hexaware claims a “turnkey SaaS transformation in just 20 weeks,” deploying HCM, Payroll, and Finance modules across a complex global environment.
This is remarkable in the Oracle space, where such transformations often take 9–18 months.

Why this matters to US IT decision-makers:

• Compressed timeline reduces disruption and risk
• Success in cross-functional modules (HR + Finance) indicates platform integration confidence
• A “turnkey” offer signals end-to-end accountability

If you’re evaluating partners, ask Hexaware to show you the detailed breakdown of weeks across design, build, testing, go-live, and stabilization.

B. PeopleSoft → OCI for a global law firm

In another case, Hexaware migrated a law firm’s PeopleSoft HCM on-premise setup into OCI. Their published outcome includes:

• 30% lower unscheduled downtime
• 20% boost in HR efficiency

These are non-trivial metrics. Downtime is a hard KPI for CIOs. HR efficiency gains reflect process automation, not just infrastructure.

1. EBS on OCI for a global manufacturer

In the EBS domain, Hexaware delivered managed services including monitoring, patching, and operations to drive operational excellence. It’s not just migration; it’s ensuring the EBS estate runs smoothly and evolves.

The value here is in risk mitigation and long-term stability   a key concern for ERP-heavy industries.

D. Oracle CPQ implementation & landscape modernization

Hexaware also highlights a CPQ implementation tightly integrated with both EBS and Cloud applications.

This demonstrates their strength in complex hybrid architectures   where US enterprises often live with both legacy and cloud systems, requiring seamless integration across systems.

Architecture patterns: Oracle + agentic AI for US enterprises

To make this a real “how do we do it” guide, here are architectural patterns (with examples) that you should evaluate. As CIO/CTO, consider which patterns align with your maturity, risk appetite, and business goals.

Pattern A: Agentic process orchestration above Oracle SaaS

What it is: Place a layer of AI agents (e.g. built via RapidX™) that orchestrate across Oracle applications, triggering flows, automating decisions, and escalating exceptions.

Use cases:

• Quote-to-cash automation: Agent triggers CPQ, validates orders, posts to ERP, notifies finance
• Employee onboarding: Agent reads from HCM, assigns tasks, tracks forms, escalates missing info
• Expense audit: Agent reviews expenses submitted, flags anomalies, triggers reimbursement

Value: Users see a conversational or agentic interface, not raw forms. The Oracle system becomes responsive and “smart” rather than passive.

Pattern B: RAG (Retrieval-Augmented Generation) over Oracle data

What it is: Build vector stores over Oracle data (transactional, master, logs), combine with LLMs, and enable agents to answer queries. Use Hexaware’s data platform or integrations for ingestion and governance.

Use cases:

• Finance analyst asks, “Why did revenue dip in region X last quarter?”   the agent retrieves relevant tables, performs simple analysis, and synthesizes narrative
• HR manager asks, “List compliance gaps in benefits enrollment in 2024”
• Support teams ask, “Which orders are stuck by pricing exceptions across CPQ/ERP?”

Considerations:

• Data governance is critical   maintain lineage, masking, permission controls
• Choose vector store and embedding strategy carefully
• Use agentic agents to break complex queries into subqueries

Pattern C: Intelligent operations via Tensai®

What it is: Monitor Oracle workloads on OCI, detect anomalies (e.g. service latency, database errors), and have Tensai® automatically run remediation playbooks or open tickets.

Use cases:

• Auto-restart of hung services
• Patch orchestration during off-hours
• Auto-scaling or resource adjustment upon load anomalies
• Log analysis, trend detection and forecasting

Value: Reduces mean time to repair (MTTR), lowers run-team workload, automates mundane incident handling.

Pattern D: AI-native custom extensions with Vibe Coding

What it is: Use Vibe Coding to rapidly build Oracle extension modules (microservices, UI widgets, connectors) backed by AI-assisted development and testing.

Use cases:

• Custom page in Oracle Fusion UI with AI summarization
• Microservice for external system integration
• Chatbot UI for internal users to query Oracle modules

Value: Shorter development cycles, fewer defects, embedded test/QA pipelines using AI.

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Industry-specific applications (US focus)

Below are examples of how different US industries can leverage Oracle + AI through Hexaware’s model. These help situate the above patterns in familiar verticals.

Financial services / banking

• Credit decision assistant: an agent ingests Oracle Banking (or Oracle Financial Services products) data, external credit scores, and internal KPIs, then recommends approval or escalates.
• Risk & compliance surveillance: agents monitor transaction anomalies, flag suspicious activity, automatically generate audit reports.
• Client profitability analysis: RAG agent digests revenue, cost, transaction records within Oracle ERP and presents recommendations.

Healthcare & life sciences

• Regulatory compliance assistant: agents check Oracle HCM, finance, supply chain for compliance deviations (e.g., HIPAA, FDA) and raise remediation tasks.
• Clinical trial supply planning: integrate Oracle SCM with forecasting agents to optimize supply chain and reduce waste.

Manufacturing & industrials

• Predictive maintenance orchestration: agents monitor operational logs from Oracle EBS/ERP/IoT integration, schedule maintenance, trigger purchase orders automatically.
• Order flow automation: agents route orders between CPQ, ERP and logistics systems, optimizing for cost, lead time, and capacity.

Retail & e-commerce

• Omnichannel pricing & bundling agents: recommend bundle discounts via CPQ + demand forecasting agent.
• Return automation: agents ingest return requests (customer service), analyze eligibility via Oracle ERP, generate credits or push tasks.

Legal & professional services

• Revenue recognition assistant: agents audit billing, engagement, contract terms in Oracle, propose adjustments, and feed into recognition schedules.
• Talent & utilization insights: HR agents pull utilization, performance, forecasts, and propose staff reallocation.

These models show how Oracle + AI is not hypothetical   it’s a generation shift in enterprise operations.

Evaluation & RFP questions for Oracle + AI partners

When engaging Oracle service providers (including Hexaware), IT decision-makers in the US should demand clarity on the following:

Strategy & roadmap

1. AI vision & architecture: How do you architect agentic AI + Oracle? Which modules, systems, and data pipelines are involved?
2. Governance & compliance: How do you preserve data lineage, audit trails, masking, and model explainability?
3. Reference metrics: Provide prior client metrics (downtime reduction, efficiency improvement)   hexaware publishes specific numbers (e.g. 30% downtime reduction).
4. Partner ecosystem: Which LLM / AI / vector store partners do you work with?

Migration & technical execution

1. Acceleration tools: Do you use RapidRe2 (or similar) to speed Oracle migrations?
2. Risk & rollback strategy: How do you manage rollbacks, phased migration, dual-run environments?
3. Integration capabilities: How do you integrate CPQ, legacy modules, custom code, and external systems?

AI & agentic capabilities

1. Scope of agent autonomy: Which tasks can agents run without human oversight vs those that always require human review?
2. Model lifecycle & versioning: How do you version, retrain, roll back, and monitor models in production?
3. Explainability & audit: How do you provide traceability from agent actions to data sources, embeddings, and prompt versions?

Operations & support

1. Run & operate model: Post-implementation, how do you support Oracle + AI operations? (patches, upgrades, incident response)
2. SLAs & performance guarantees: What are your SLAs around uptime, agent accuracy, and remediation time?

Cost & ROI

1. Cost breakdown: Provide transparent cost for infrastructure (OCI), AI compute, vector stores, licensing, and services.
2. ROI cases: Show expected payback period, TCO reduction, quantum gains (e.g., effort saved, error reduction).

People & training

1. Capability transfer: How do you train in-house staff to manage AI agents and Oracle systems post-go-live?
2. COE presence: Do you have an Oracle + AI Center of Excellence staffed with experienced resources?

By demanding specificity on these points, you protect your project from “AI washing” or overhyped promises without substance.

Prompt & agent template library for Oracle agents

Below are operational prompt/agent templates you can adapt or ask your partner (Hexaware team) to build. Each is designed for enterprise Oracle usage.

Note: Replace references to your data sources, APIs, and system endpoints accordingly.

Prompt A: HR Onboarding Agent

You are an HR Onboarding Assistant with access to Oracle HCM (Core HR, Benefits, Payroll).  

Given an employee ID:

1. Summarize role, team, manager, and hire date.  
2. Show pending onboarding tasks and due dates.  
3. Flag any missing documents.  
4. If exceptions exist, generate a ticket with title, owner, and suggested resolution.  
5. Provide a JSON output for integration with your ticketing system.

 

Prompt B: Finance Reconciliation Agent (AP/AR)

You are a Finance Reconciliation Agent. You receive Oracle AR & AP transaction data and bank statements.  

1. Match transactions.  
2. Identify mismatches and categorize (e.g. memo difference, missing invoice).  
3. Propose top 3 action items to resolve exceptions.  
4. Format output as JSON for further automation or review.

 

Prompt C: Ops Triage Agent (monitor + self-heal)

You are an Operations Triage Agent hooked into OCI metrics and Oracle application logs.  

If service latency or error rate exceeds thresholds:

1. Diagnose likely cause (CPU, I/O, database locks).  
2. Summarize findings in short paragraph.  
3. Run remediation playbook (e.g. restart, scale, flush cache).  
4. If remediation does not resolve, create an incident ticked with logs.  

Provide log excerpts and metrics in the output.

 

Prompt D: CPQ Pricing Bundle Agent

You interface with Oracle CPQ and Product Catalog. Given a customer and quote:  

1. Propose 3 intelligent bundle/discount options aligned with margin rules.  
2. For each, return JSON payload to update quote.  
3. Provide rationale (product mix, margin vs discount trade-off).

 

Prompt E: Governance / Explainability Agent

You are an AI Governance Agent. For any decision or summary produced by agents, provide:

– Data sources consulted  

– Embedding version and vector store  

– LLM model name and version  

– Confidence score  

– Flag if confidence < 0.6 as “requires human review”  

Return output annotated with provenance block.

 

You can chain or cascade these prompts to build more complex agents   e.g. first use Prompt A to fetch HR context, then feed it into Prompt C for cross-app orchestration.

Risks, challenges & mitigation for US enterprises

No transformation is risk-free, especially when introducing AI agents. Here are common concerns and mitigation strategies:

• Agentic overreach / “hallucination”   enforce thresholds and fallback to human review. Use explainability agents.
• Data privacy & compliance (e.g. CCPA, HIPAA)   agent actions should respect data zones; log all access.
• Security & identity governance   treat agents as service principals with defined permissions.
• Cost overruns in AI compute   monitor usage, cap agent budgets, choose efficient models.
• Model drift & accuracy decay   schedule retraining, validate outputs, implement rollback.
• Agentic “washing”   demand proof that agents truly act (multi-step tool use), not just text generation. (Gartner warns over 40% of agentic AI projects will be scrapped by 2027 due to overpromise)

You should include in your contract an “agentic performance SLA”   e.g. ≥ 95% accuracy on defined tasks, retraining thresholds, remediation accountability.

Putting it all together: a 12-month transformation roadmap (executive view)

Here’s a high-level roadmap for US enterprises adopting Oracle + AI with Hexaware:

Phase	Duration	Key Activities	Governance Focus	Milestones
Discovery & strategy	2–4 months	Inventory, use case ideation, data & governance design	AI roadmap, pilot selection	Signed charter, prioritized use cases
Pilot / PoV	2–3 months	Pick one use case, build Vibe / RapidX prototype	Evaluate results, refine metrics	Pilot go-live, ROI measure
Core migration & rollout	4–6 months	Migrate core modules (ERP, HCM) using RapidRe2	Phased cutovers, fallback safety nets	Go-live for core modules
AI overlay & extension	3–4 months (parallel)	Build agentic agents, integrate operations, scale RAG	Model ops, agent governance	Agent pilot + usage ramp
Scale & optimize	Ongoing	Add new agents, optimize workflows, retrain models	Cost governance, maturity audits	Efficiency gains, cost reductions
Operate & evolve	Continuous	Maintain, upgrade, monitor, govern	SLA reviews, agent audits	Sustained performance and innovation

Throughout, you should embed governance reviews, stakeholder demos, and measurable KPI tracking (see next section).

Key metrics & ROI guardrails (for US CIOs)

To judge success, here are metrics you should demand:

• Time to go-live for modules (weeks)   e.g. 20-week transformation benchmark
• Downtime reduction (%)   benchmark vs published 30%
• Process automation uplift (%)
• FTE / effort savings in run / test teams
• Incident resolution time (MTTR)
• Agent accuracy or error rate
• Cost per AI compute / agent run
• ROI payoff period (months)

Before signing contracts, ask your vendor to run a predictive ROI or TCO model and validate it via pilot.

Conclusion

In 2025, Oracle infrastructures alone are table stakes. The real frontier is Oracle + AI   where systems anticipate, act, and adapt. For US IT leaders, the choice is not just which Oracle partner you pick, but whether they can deliver agentic AI with credibility, accountability, and domain depth.

Hexaware’s proposition   combining deep Oracle experience, AI-native platforms (Vibe Coding, RapidX™, Tensai®), and migration accelerators like RapidRe2   represents a credible option to leapfrog competition.

If you’re a CIO/CTO in the US evaluating Oracle modernization:

• Use the RFP questions above to vet AI claims
• Request direct references from the case studies above
• Propose a small PoV pilot for a key process
• Insist on an agentic performance SLA

Demand transparency on cost, model governance, and security