AI Strategy & Advisory
AI strategy, governance, and organizational readiness. The connective tissue that aligns every other ASC engagement, from the data foundation to validated prototypes to systems in production.
- Phases
- 05
- Engagement models
- 03
Strategy before software
AI programs stall not because the models fail, but because the organization was never ready to run them: no roadmap, no governance, no one upskilled to own the outcome. AI Strategy & Advisory sets the direction the rest of ASC executes against. We run the AX Transformation Engine, a continuous loop of ASSESS, BUILD, TRAIN, and EMBED that scores readiness across five pillars, stands up production-ready AI infrastructure, upskills your teams to AI-native practices, and embeds ongoing AI leadership. Each cycle deepens maturity, adds use cases, and expands across departments, so AI lands as a durable capability rather than another pilot that never ships.
A five-phase advisory engagement
Five phases move you from an honest readiness baseline to a governed, adopted AI capability. Each phase produces concrete artifacts your teams own, and the engagement can start at any phase that matches where you are today.
Phase 01 / 2-4 wk
AI Readiness Assessment
An evidence-based baseline of where you can realistically run AI today, scored across five pillars: data, technology, talent, process, and culture.
- Data audit: availability, quality, lineage, and access to the data AI initiatives depend on
- Technology audit: infrastructure, tooling, and integration readiness for AI workloads
- Talent audit: in-house skills, gaps, and the roles needed to own AI in production
- Process and culture review: where AI fits real workflows and how ready the org is to adopt it
- Scored readiness baseline with a prioritized gap analysis and quick-win shortlist
Tooling
The connective tissue across ASC
AX does not run in isolation. It sits across Data Engineering & Analytics (foundation), AI Prototyping & MVP Development (validation), and Custom AI Development (scale), keeping strategy, governance, compliance, change management, and upskilling aligned as the work moves from readiness baseline to systems in production. The destination is a shift up the maturity landscape, from AI-Aware and AI-Enabled to AI-Native and AI-Autonomous operations. Select a service line to see how AX threads through it.
Concern / Strategy · 2-4 wk
AI Readiness Assessment
An evidence-based baseline of where you can realistically run AI today, scored across five pillars: data, technology, talent, process, and culture.
What it threads through delivery
- Data audit: availability, quality, lineage, and access to the data AI initiatives depend on
- Technology audit: infrastructure, tooling, and integration readiness for AI workloads
- Talent audit: in-house skills, gaps, and the roles needed to own AI in production
Instruments
How we engage
All 5 phases
Full Advisory
The end-to-end engagement, from readiness assessment through compliance, for organizations standing up an AI capability from the ground up.
Single phase
Targeted Phase
Start where you are. Drop in for one phase, for example governance or upskilling, when the rest of your AI program is already underway.
Ongoing
Advisory Retainer
A standing advisory relationship that keeps strategy, governance, and adoption aligned as your roadmap evolves and AX threads across delivery.
The advisory architecture: frameworks, not infrastructure
AX is the connective tissue across the other three service lines, so its ecosystem is a stack of frameworks and instruments rather than servers and pipelines. It rests on assessment instruments, builds strategy artifacts on top, codifies governance, maps it to the regimes you operate under, and lands through enablement. Select any layer to see the real instruments, frameworks, and regimes it carries.
Layer 05 of 05 / Foundation
Assessment Instruments
The evidence base everything rests on: a maturity scorecard across data, tech, talent, process, and culture.
What runs here
Works with the other service lines
Strategy that lands
The ASC Measurement Framework anchors every engagement to one North Star: cost per AI use case across the full lifecycle, normalized by complexity. From there we track economic outcomes (time to ROI, dollars saved per use case, per-team cost reduction) and the outcomes beneath them: speed to value, decision quality, risk reduction, process efficiency, and innovation rate. On the client journey those are set at a baseline during assessment and tracked as the organization moves toward an AI-native operation. The dev-tooling figure below is industry context, not an ASC metric, and it is why governance and upskilling are non-negotiable parts of the engagement.
92%
Client retention as executive sponsors become internal champions
3
Markets covered with native compliance: US, KR, JP
~48%
Of AI-generated code carries vulnerabilities, why governance is core
Industry context
Start with AI Strategy & Advisory
Tell us where you are, and we will scope the first engagement around the outcome that moves your business.