GCC AI-Readiness Scan by Arche Global

Based on comprehensive research across 50+ industry studies and real-world implementations, Arche has developed this critical analysis: while 70% of Indian GCCs are actively investing in generative AI and 78% are upskilling teams, dangerous infrastructure blindspots threaten to undermine these investments.

Our research reveals a troubling overconfidence bias: 44% of IT leaders believe they're AI-ready, yet data exposes critical gaps that could derail their initiatives. Through rigorous analysis, Arche has identified seven critical blindspots plaguing Indian GCCs and developed a strategic 20-question assessment framework to expose these hidden vulnerabilities.

This report represents Arche's commitment to ensuring GCC AI investments translate into measurable success rather than costly missteps.

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Section 1: GCC Context & Operational Foundations

Where's your core GCC infrastructure hosted today?

On-premises only

Hybrid (some on cloud, some on-prem)

Majority cloud; minimal legacy

Fully cloud-native, automation-first

How quickly can you provision infrastructure for new AI workloads?

14 days

7–14 days

2–7 days

<48 hours

<4 hours (fully self-service)

What best describes your current state of cost visibility for AI infrastructure?

14 days

7–14 days

2–7 days

<48 hours

<4 hours (fully self-service)

How strong is the executive/board sponsorship for AI initiatives in your GCC?

None

Some C-level interest; no funding

CXO sponsor; limited runway

Full mandate, sizable budget, governance forum

What % of AI pilots in your GCC make it into production?

0–10%

10–30%

30–60%

Above 60%

Section 2: Data Infrastructure & AI Foundations

How would you rate your ability to support real-time data for AI?

Sub-second latency, streaming ready

Batch plus some near-real-time

Only batch, little streaming

Manual, basic

Still siloed/inconsistent

Does your data governance include AI-focused policies and lineage tracking?

Yes, formal AI policies and lineage automated

Yes, but AI part is nascent

Basic governance, not adapted to AI

Informal practices, reactive

None

What percentage of your critical data is “AI ready” without major cleansing?

80–100%

60–79%

40–59%

20–39%

<20%

How mature is your ML model lifecycle automation and governance?

Enterprise-grade CI/CD, drift monitoring, rollback, explainability

Good automated pipelines for train/deploy

Some tools, manual gaps

Scripts/ad hoc

No real process

Section 3: Compute, Network & Edge

Is your infra equipped (on-prem/cloud) for high-compute, AI-centric workloads, including GPUs/AI accelerators?

Dedicated resources, always available

Available, sometimes oversubscribed

Occasional access, often bottlenecked

Rely on public cloud bursts only

Not addressed yet

Can your internal network reliably support large data/model movement for AI?

Fully AI-optimized, low latency, high bandwidth

Good backbone, minor bottlenecks

Adequate for basic use

Some slowdowns, congestion reported

Big constraint for any AI work

How mature is your hybrid cloud/edge strategy for distributed AI use?

Cloud-edge integrated, auto-orchestrated, real-time

Basic hybrid, some manual handoffs

Primarily cloud, edge unexplored

Just starting with edge pilots

No edge or hybrid architecture

What % of critical AI services have deep observability (logs, metrics, traces)?

96–100%

76–95%

51–75%

20–50%

<20%

Section 4: AI Governance, Security & Compliance

Is there a clearly defined, cross-functional AI governance board/team?

Yes, active and empowered

Exists but sporadic/weakly enforced

Discussed but not implemented

Only within IT, minimal business input

None

How is AI performance/health monitored post-production?

Continuous, automated (alerts, drift, outcomes)

Frequent manual reviews with dashboards

Infrequent, high-level checks only

Ad hoc, if problems arise

Not deliberately monitored

How automated is identity, access, and data pipeline security for AI?

Fine-grained, zero-trust, constant audit

Role-based, mostly automated

Some manual, exceptions common

Minimal, not AI-specific

None

For AI/data, does your GCC have processes for cross-border regulatory compliance (GDPR, DPDP, etc)?

Robust, multi-jurisdictional, policy-driven

Good, mostly covers major risks

Partial, ad hoc by project/region

Only as required by parent org

None

For AI/data, does your GCC have processes for cross-border regulatory compliance (GDPR, DPDP, etc)?

Robust, multi-jurisdictional, policy-driven

Good, mostly covers major risks

Partial, ad hoc by project/region

Only as required by parent org

None

Section 5: Talent, Change Management, and Organizational DNA

What’s your AI talent mix in GCC?

Significant in-house, cross-stack expertise

Growing in-house, some dependency on global teams/vendors

Balanced between local/external

Rely mostly on parent org or vendors

Barely any in-house AI talent

How prepared is your org to evolve processes/roles as AI scales up?

Formal change programs, dedicated resources

Good processes, need more AI-specific focus

Org is supportive but no structured program

Change is reactive, happens only after resistance

Not considered at all

How aligned is your GCC’s AI infra strategy with parent/global mandates?

Fully embedded/strategically aligned

Good alignment, occasional local deviations

Mixed alignment, often conflicts/turf wars

Mostly local, little real integration

No visible effort to connect with global AI vision