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Get a clear, evidence-backed view of which AI initiatives are worth investing in, without wasting time on hype or untested assumptions. It’s a structured, ownership-driven process that highlights what works, and what doesn’t.
4-week fixed engagement
A short timeframe to deliver actionable insights, not endless exploration
Defined use-case shortlist
We highlight only the AI opportunities that make a tangible difference
Feasibility and data check
Expose operational bottlenecks and integration risks before deployment
Audit and assumptions
Concrete, executive-ready report showing potential value and decision criteria
Put your project into our ROI Calculator and walk away with a grounded go/no-go on your AI investment.
The experts guiding AI projects from idea to clarity, showing exactly where effort creates value.
Alexey Karankevich
AI Innovation Lead
15+ years
Leads end-to-end AI initiatives, blending system architecture, machine learning, and engineering leadership. An MITx MicroMasters graduate in Data Science.
Yariv Z. Levy
AI Strategy Advisor
20+ years
Drives AI adoption across pharma, consumer goods, and consulting. PhD in AI (UMass Amherst), MSc (EPFL). Led AI projects at Roche, Nestlé, and ELCA.
Discover the full team driving Oxagile’s AI ROI discovery services
No theory or generic research, just concrete, actionable outputs and guidance for confident investment decisions.
Opportunity map
Identify AI-ready workflows and prioritize by expected impact.
Systems readiness
Assess data quality, API access, and integration feasibility first.
ROI modeling
Estimate costs, efficiency gains, and risk-adjusted value.
Risk assessment
Map GDPR, HIPAA, SOC 2, and ISO 27001 exposure early.
Decision pack
Prioritize next steps and provide a solid go/no-go basis.
AI decisions touch both business outcomes and technical realities. Our AI ROI discovery service equips each stakeholder with the insights they need to act confidently.
(CEO, COO, GM, CFO)
Defensible ROI: defined value logic for AI investments
Prioritized backlog: opportunities ranked by impact
Go/no-go decision: transparent assumptions
(CTO, Architect, Security)
Feasibility and constraints: technical realities mapped
Integration gaps: what needs to change to enable AI
Security: compliance and operational limits defined
Our AI Discovery service guides organizations through structured, risk-managed, outcome-driven AI initiatives. It helps leaders make investment decisions confidently and avoid common pitfalls.
Enterprises actively evaluating AI opportunities
Companies seeking clear ROI and prioritization
Teams adopting AI with governance and risk controls
Leaders accountable for budget and measurable outcomes
Exploratory experiments without defined goals
Free-form brainstorming sessions without commitment
Teams already locked into a fixed AI vendor or solution
Initiatives with no access to relevant data or decision-makers
Are you exploring ideas, automating workflows, tackling complex AI R&D, or deploying a ready-made assistant? Select the path that matches your needs, and we’ll guide you to the right next step.
We show you how to calculate ROI for AI projects by estimating time savings, efficiency gains, and reduced manual effort across your workflows, comparing baseline performance with projected improvements. This creates a clear, data-driven view of the value AI can deliver before any investment is made.
The cost of AI implementation starts around $500-$1,000 per developer monthly. A single-workflow pilot – agent setup, integrations, and evaluation harness – typically runs weeks of engineering time, not months. Multi-workflow deployments scale based on systems integrated, data readiness work, and orchestration complexity.
On returns: a well-scoped pilot should show measurable signal within 90 days, with a 30% cycle time improvement as a reasonable minimum threshold. Meaningful ROI, build and run costs included, typically lands around the nine-month mark for deployments that hit their pilot benchmarks. The discovery engagement replaces “it depends” with a number specific to your workflows, data, and infrastructure – before any build commitment is made.
Typically, workflow owners, system experts, and decision-makers from relevant units participate. Their input controls realistic mapping, reliable AI project cost estimation, and outcomes you can trust when planning next steps.
We flag blockers — data gaps, integration dead ends, compliance constraints — before they become sunk costs. Depending on what surfaces, we either tighten the scope, shift to a workflow with cleaner data access, or route complex cases toward R&D. Either way, you leave with a cost estimate tied to actual findings, not clean-slate assumptions.
Through structured interviews, data analysis, and prototype testing, we confirm that your workflows, tools, and expected outcomes align with reality. This also supports accurate AI development cost estimation, so decisions are based on validated inputs before any build begins.
We access only the data necessary to evaluate AI opportunities. All information is secured, anonymized where appropriate, and processed in line with regulatory requirements and enterprise standards, with safe and responsible AI analysis.
