Ravula AI

AI Readiness & Opportunity Mapping

Get a ranked list of AI plays with ROI, risk and effort—ready to execute.

  • Identify 3–5 initiatives with clear payback
  • Quantify impact on cycle time, cost, and quality
  • Produce an actionable 90-day road map
  • Baseline current-state processes and data readiness

Who this is for

Ops, IT, Finance, and Compliance leaders at mid-market firms with stalled pilots or scattered AI experiments. Decision-makers who need clarity on where AI will deliver the fastest ROI.

Typical titles:

  • • VP Operations / COO
  • • Chief Technology Officer / VP IT
  • • CFO / VP Finance
  • • Chief Compliance Officer / VP Risk
  • • Director of Process Improvement

Trigger phrases you might be saying

  • ""We don't know where to start with AI."
  • ""Too many manual handoffs and repetitive tasks."
  • ""Data is messy; leadership wants ROI."
  • ""We've tried AI pilots but nothing stuck."
  • ""Need to prioritize AI investments across departments."
  • ""Can't measure if our AI experiments are working."

Business outcomes

Cycle time reduction

15–30%

Identified in target processes through automation opportunities

Cost-to-serve reduction

20–40%

Potential in scoped workflows through AI augmentation

Prioritized initiatives

3–5

With effort, ROI, and risk scoring ready for execution

ROI clarity

Quantified

Expected payback periods and investment requirements for each opportunity

What we deliver

  • Current-state process + data inventory

    Documented workflows, data sources, and integration points across target functions

  • Opportunity scorecard & ROI model

    Ranked list of AI opportunities with effort, risk, and expected return calculations

  • 90-day execution plan

    Prioritized roadmap with owners, milestones, and success metrics

  • Baseline KPI measurements

    Current performance metrics to track improvement against

  • Data readiness assessment

    Evaluation of data quality, accessibility, and integration requirements

How it works

Step 1

Discover

Stakeholder interviews, data sampling, and KPI baseline measurement. We map current processes, identify pain points, and assess data availability.

Step 2

Design

Map opportunities to workflows and architecture. We score each opportunity on ROI, effort, and risk, then sequence them for maximum impact.

Step 3

Prove

Pilot specification, success metrics, and governance gates. We define how to validate each opportunity and measure success.

Timeline & effort

Duration

2 weeks

From kickoff to final deliverable presentation

Your team's time

4–6 hours

Total stakeholder time across interviews and reviews

Light data pulls required: We work with sample data and existing documentation. No heavy data engineering needed upfront.

Pricing bands

$10,000–$30,000

Fixed-fee diagnostic based on scope (sites, functions, complexity)

Pricing factors:

  • • Number of business functions assessed (1–3 functions: $10–15K; 4–6: $15–25K; 7+: $25–30K)
  • • Multi-site operations (add $2–5K per additional site)
  • • Data complexity (simple structured data vs. complex multi-source integration)
  • • Industry-specific requirements (regulated sectors may require additional compliance review)

KPIs we move

Each opportunity is mapped to specific Universal Chart of Accounts processes and their associated KPIs:

Cycle time

Cost per transaction

Error rate

Throughput

Forecast accuracy

SLA adherence

First-pass yield

Capacity utilization

Time-to-value

Customer satisfaction (CSAT)

Employee productivity

Data quality score

Tech stack & integrations

Tool-agnostic approach. We work with your existing systems and recommend solutions that integrate seamlessly.

Common integrations:

  • • Major ERPs (SAP, Oracle, Microsoft Dynamics)
  • • CRMs (Salesforce, HubSpot, Microsoft 365)
  • • Ticketing systems (ServiceNow, Jira, Zendesk)
  • • Data warehouses (Snowflake, BigQuery, Redshift)
  • • LLM providers (OpenAI, Anthropic, Azure OpenAI)

Our approach:

  • • No vendor lock-in—we recommend best-fit solutions
  • • API-first architecture for easy integration
  • • Works with cloud, on-premise, or hybrid environments
  • • Security and compliance considerations built-in

Risks & safeguards

Shadow IT proliferation

Risk: Teams deploy AI tools without governance, creating security and compliance gaps.

Safeguard: We establish centralized policies, access controls, and approval workflows as part of the governance framework.

AI hallucinations and errors

Risk: LLMs generate incorrect information, leading to business decisions based on false data.

Safeguard: We design evaluation harnesses, retrieval-only patterns for critical workflows, and human-in-the-loop checkpoints.

Data leakage and privacy

Risk: Sensitive data exposed through AI tools or training processes.

Safeguard: Clean-room data patterns, red-team security testing, and compliance validation (HIPAA, GDPR, SOC 2) before deployment.

Caselets

Industrial Distributor

Challenge: Manual warehouse picking processes causing delays and errors.

Solution: Identified 26% picking-time reduction opportunity via AI-powered slotting optimization and copilot SOPs for order fulfillment.

Impact: $180K annual labor savings, 15% improvement in order accuracy, faster same-day shipping capability.

Regional Bank

Challenge: KYC (Know Your Customer) case preparation taking 4–6 hours per case.

Solution: Identified 35% faster case prep opportunity using retrieval-augmented document summarization and risk scoring automation.

Impact: $250K annual compliance team productivity gain, improved customer onboarding experience, reduced regulatory risk.

Frequently asked questions

How do you quantify ROI without full data access?

We triangulate from sample data pulls, industry benchmark ranges, and process analysis. We validate assumptions in stakeholder interviews and refine estimates. For pilot opportunities, we define success metrics to validate ROI post-implementation.

Do we need a data lake or modern data infrastructure first?

No. We start with available systems and define the minimal data path for each opportunity. Many high-ROI AI wins can be achieved with existing data sources. We'll identify data readiness gaps and sequence infrastructure improvements as needed.

What if we've already tried AI and it didn't work?

We analyze what went wrong in previous attempts—often it's a mismatch between use case and technology, lack of change management, or insufficient data quality. We design opportunities that address these failure modes and include adoption strategies.

How do you prioritize opportunities across departments?

We use a weighted scoring model: ROI potential (40%), effort/complexity (30%), strategic alignment (20%), and risk (10%). We also consider dependencies and sequencing—some opportunities unlock others. Final prioritization is done collaboratively with your leadership team.

What happens after the 2-week diagnostic?

You receive a prioritized roadmap and can choose to implement opportunities yourself, engage us for implementation support (see Agent Workflow Design), or bring in our AI Governance team for compliance frameworks. No obligation to continue.

Can this work for regulated industries (healthcare, finance, government)?

Yes. We include compliance considerations (HIPAA, FINRA, FDA, etc.) in our assessment and identify opportunities that meet regulatory requirements. We can also connect you with our AI Compliance & Governance service for deeper regulatory frameworks.

What's the typical ROI on the diagnostic itself?

Clients typically see 5–10X return on the diagnostic investment within 12 months by implementing the top 2–3 opportunities. The diagnostic pays for itself by avoiding low-ROI initiatives and accelerating time-to-value on high-impact opportunities.

Ready to identify your AI opportunities?

Book a 20-minute fit call to discuss your challenges and see if this diagnostic is right for your organization.

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Last updated: November 2025