Founded in 2018, we focus exclusively on enabling Enterprise AI @ scale. In a very short span of time, we have become one of the fastest growing companies in America.
We focus on questions that matter to businesses with big ambitions, empowering them to elevate outcomes across their value chain.
Mid-level Senior
Mumbai
Mid-level
Remote
Decide What to Build, When to Build It, and How to Fund It
Airo’s Enterprise AI Roadmap (eAIR) is a decision-led engagement that helps leadership teams move from fragmented AI pilots to a prioritized, fundable AI pipeline.
eAIR enables CIOs and business leaders to clearly define what to build, how to sequence initiatives, and where to invest—before AI decisions become expensive to correct.
Unlike traditional AI strategy efforts that focus on vision or maturity models, eAIR forces sequencing, ROI trade-offs, and funding decisions so AI can scale with confidence, not rework.
eAIR helps leadership teams move from abstract AI ambition to concrete business intent. By aligning priorities, constraints, and success metrics early, it ensures AI investments are grounded in outcomes, risk appetite, and funding discipline not experimentation for its own sake.
Assess enterprise readiness across data, platforms, governance, and operating models. Identify and prioritize use cases based on feasibility, value, and time-to-impact. We assess how ready the enterprise actually is to run AI in production—not just experiment with it. This includes data quality and availability, platform maturity, governance readiness, and operating model fit.
Define the platforms, data foundations, and integration patterns required to support scale without prematurely locking into tools or vendors. We help define the architectural foundations needed to support enterprise- scale AI. This includes data platforms, integration patterns, cloud and on-prem considerations, and how AI systems will interact with existing enterprise applications. The outcome is a technology roadmap that supports scale and flexibility without forcing early, expensive platform commitments.
Embed governance and risk considerations into prioritization decisions, ensuring responsible AI adoption from the outset. We help organizations factor security, compliance, auditability, and model risk into AI prioritization and sequencing decisions. This ensures that regulated, sensitive, or high-impact use cases are approached with the right controls reducing downstream rework, delays, and compliance exposure.
Prepare teams and operating models to absorb AI investment decisions and execute them effectively across business and IT. We help organizations prepare teams, roles, and operating models to support AI execution clarifying ownership, decision rights, and collaboration between business, IT, and data teams. The goal is adoption with accountability, not dependency on a few specialists.
A structured assessment that benchmarks readiness across technology, data, governance, and skills highlighting where investment will (and won’t) pay off.
Quantify value before you invest. Our ROI- driven framework ranks AI initiatives based on cost savings, revenue impact, risk reduction, and implementation complexity.
Sequencing patterns that show how validated pilots can realistically scale informing roadmap decisions with execution realities
Once priorities are approved, move from idea to working AI solutions in 8–12 weeks, not months. Our factory provides a repeatable execution model that enables rapid pilots with measurable outcomes, de-risking enterprise AI investments.
Blueprints and operating models to establish a centralized AI CoE that drives consistency, innovation, and continuous improvement across the organization.
Applied eAIR framework to assess 400+ processes and align them under a unified AI strategy.
By applying enterprise-level sequencing and ROI trade-offs, eAIR revealed higher-impact opportunities beyond maintenance.
Leveraged Maturity360 diagnostics to move from "Experimenting" to "Optimizing" maturity.
better ROI from AI investment faster time-to-value once execution begins potential operational cost savings over five years
faster time-to-value once execution begins
potential operational cost savings over five years
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