AI Strategy & Audits

The Problem with AI Strategy

Most AI strategy conversations happen at the wrong level of abstraction. Executives ask "how do we use AI?" Engineers want to build something immediately. The result is either paralysis or premature investment in the wrong capabilities.

An AI strategy audit cuts through that. We start with your actual business operations — the workflows, data, and decisions that drive your results — and work backward to identify where AI creates the most leverage.

What We Audit

Business Operations Analysis

We spend time with your operational teams understanding how work actually happens — not how the org chart says it happens. We're looking for:

• High-volume, repetitive decision processes
• Workflows with significant human bottlenecks
• Data-rich processes where insights aren't being extracted
• Customer-facing processes where personalization could drive conversion
• Back-office operations where errors are costly

Data Infrastructure Assessment

AI systems are only as good as the data they run on. We assess:

• What data exists, where it lives, and what quality it's in
• Whether data is structured, semi-structured, or unstructured
• Current data pipelines and their reliability
• Data governance and compliance constraints
• Gap analysis: what data you need to collect that you currently aren't

Competitive & Capability Analysis

We benchmark your AI maturity against your competitive landscape:

• What are your competitors doing with AI (where visible)?
• What do best-in-class organizations in your vertical look like?
• Where is the industry heading, and what investments position you best?

Technology Audit

We review your current technical stack to understand:

• What integrations are possible without major infrastructure changes
• Where technical debt may limit AI adoption
• Build vs. buy decisions for each identified opportunity
• Internal vs. vendor-hosted model considerations

The Deliverable: Your AI Implementation Roadmap

The audit produces a prioritized implementation roadmap with:

For each identified opportunity:

• Business case (current state, future state, gap)
• Effort estimate (low/medium/high)
• ROI model (conservative, moderate, aggressive scenarios)
• Data requirements and current readiness
• Technical approach and vendor recommendations
• Implementation timeline

Overall roadmap:

• 90-day quick wins that generate early momentum
• 6-month foundational investments that enable later capabilities
• 12-18 month transformational initiatives
• Resource and budget requirements at each stage

The result is an actionable plan — not a theoretical whitepaper, but a specific sequence of investments with clear expected returns.