Gartner: Generative AI Becomes Core Infrastructure — $644B Hardware-Driven Spend Surge by 2025

Gartner: Generative AI Is Moving from Pilot to AI Infrastructure — Expect a Hardware‑Driven Spend Surge

Think generative AI was a short experiment? Gartner’s forecast says otherwise: generative AI will become core infrastructure, and it will carry a material price tag. Gartner projects global generative AI spending will reach $644 billion in 2025, a roughly 76.4% year‑over‑year increase as organizations move from pilots and proofs‑of‑concept to widescale deployment.

The headline and what it actually means

The $644 billion figure isn’t just more cloud model licenses. Gartner’s 2025 breakdown puts the largest buckets on hardware: $398.3 billion for devices, $180.6 billion for servers, $37.1 billion for software, and $27.7 billion for services. In short, about 80% of the surge is headed into AI hardware — phones and PCs with on‑device AI, dedicated inference servers, and the networking and infrastructure that support them.

This forecast covers global spending across consumer and enterprise markets and reflects a shift from experimentation to operational AI infrastructure. That matters because hardware decisions have different procurement cycles, lifecycle costs, and governance implications than SaaS subscriptions.

Why hardware is the dominant line item

This hardware focus isn’t just technical — it’s a commercial move. Device makers are packaging AI features as standard differentiators (think on‑device assistants, real‑time transcription, image editing and predictive text). Purpose‑built inference chips in phones and edge devices reduce latency and keep some workloads off the cloud, but they also create new costs: upgraded endpoints, chip supply constraints, and ongoing firmware and model updates.

Gartner warns that manufacturers will increasingly ship AI as a default feature, accelerating adoption even when consumers didn’t explicitly ask for it. The upshot: CIOs will face a new “hardware tax” — higher capital expenditure, complex refresh cycles, and embedded telemetry that vendors may use to improve models or monetize usage.

“Ambitious internal gen‑AI projects that started in 2024 will be scrutinized in 2025, pushing CIOs toward vendor solutions for more predictable business value rather than continued heavy investment in POCs and self‑development.”

— John‑David Lovelock, Gartner

What’s driving CIOs to favor vendor solutions?

There are three practical drivers.

Model reliability and safety. Generative models still produce hallucinations (when models invent false or misleading outputs). That creates legal and compliance exposure for customer‑facing applications.
Operational cost and complexity. Training, retraining and maintaining custom models requires MLOps, data engineering and costly compute. Many organizations lack the skills and scale to run these economically.
Faster time‑to‑value. Commercial off‑the‑shelf (COTS — ready‑made vendor software) features arrive packaged with SLAs and support. They let teams move from pilot to production faster, with clearer vendor accountability.

Because of these factors, many CIOs will shift budget away from endless internal proofs‑of‑concept toward vendor‑delivered features that promise predictability, even if they concede some control.

Tradeoffs: COTS vs custom models (and when each makes sense)

COTS buys deliver speed and reduced integration risk. Custom builds can deliver strategic differentiation but demand investment across talent, tooling, and governance.

Choose COTS for functions where speed and reliability matter more than differentiation — e.g., AI for sales (proposal generation), customer self‑service, or knowledge search embedded into CRM.
Choose custom when the AI capability is core to your competitive advantage — e.g., proprietary recommendation engines, domain‑specific agents, or IP‑rich automation workflows where nuanced data ownership matters.
Hybrid approach is often best: adopt vendor features for baseline capabilities and build a narrow set of in‑house models where they create defensible value.

Downstream implications: procurement, governance and TCO

A hardware‑first reality changes procurement, governance and total cost of ownership.

Procurement: RFPs must include model behavior SLAs, telemetry limits, data portability, and exportable model formats to avoid vendor lock‑in.
Governance: Add controls for hallucination mitigation (testing thresholds and human‑in‑the‑loop rules), privacy impact assessments for device telemetry (the data devices send back to vendors about usage and health), and regulatory compliance (EU AI Act, sector rules).
TCO: Track not just acquisition costs but ongoing inference costs, device refresh cycles, and energy impacts of inference servers. On‑device inference reduces cloud bills but shifts costs into endpoints and firmware updates.

Key KPIs CIOs should track

Latency and availability (end‑user experience).
Hallucination rate (percentage of outputs flagged as incorrect or misleading) — test and benchmark regularly.
Cost per inference (cloud and edge) and overall TCO per user.
Time‑to‑value (from purchase to measurable business outcome).
Model drift and retraining cadence (how often models degrade and need updates).

Practical checklist for procurement and RFPs

Require open APIs and documented integration points.
Demand exportable model formats or data portability clauses.
Specify acceptable hallucination thresholds and remediation SLAs.
Limit device telemetry: require explicit consent, anonymization, and contractual limits on data use.
Include clauses for firmware/model updates, security patches and end‑of‑support timelines.
Ask for clear pricing models: cost per inference, per‑device licensing, and overage terms.
Insist on third‑party audit rights or independent model‑validation reports where risk is high.

Two short vignettes

Sales acceleration with COTS AI. A mid‑market software vendor integrated a vendor Copilot into its CRM to automate first drafts of proposals. Sales cycles shortened by 20% and reps reported higher win rates. The company avoided building a custom model, preferring the vendor’s regular updates and support.

Telemetry tradeoff gone wrong. A retail chain deployed AI‑enabled handhelds with active device telemetry for performance tuning. Without strict contractual limits, the vendor collected usage patterns that exposed customer behavior to a third party. The retailer incurred reputational damage and had to renegotiate telemetry rights — a costly lesson in procurement oversight.

Risks to watch

Data privacy and regulatory exposure (consumer telemetry and model outputs).
Energy and sustainability costs for inference servers and edge chips.
Supply chain constraints for AI accelerators and NPUs.
Vendor lock‑in and reduced interoperability between AI agents and enterprise systems.
Skill shortages in MLOps and data engineering.

Actionable next moves for CIOs

Revisit budgets now: shift runway from experimental projects to AI hardware and inference costs where appropriate.
Inventory endpoints: estimate what percentage of devices will require AI‑capable chips in 12–24 months.
Update procurement templates with the checklist above and include model behavior SLAs.
Run a governance sprint: add hallucination testing, telemetry policies and privacy reviews to your compliance playbook.
Prioritize modular architectures: separate edge inference from orchestration to preserve swap‑out flexibility.
Pick 1–2 strategic in‑house projects to invest in model capability and MLOps as a talent and IP play.
Set measurable KPIs and report progress monthly to the executive team and board.

Board questions CIOs should be ready to answer

What percentage of our endpoints will need AI‑capable chips within 24 months?
Run an inventory today and model three adoption scenarios (conservative, baseline, aggressive). Tie each scenario to cost, privacy and operational plans.

How are we limiting vendor telemetry and protecting customer data?
Require contractual telemetry limits, anonymization, and narrow‑scope data usage clauses. Use audits to enforce compliance.

What’s our fallback if a key AI vendor underdelivers on reliability or increases prices?
Maintain modular integrations, insist on data portability, and build export paths for critical models. Have a contingency budget for migration or in‑house replacement.

Final perspective

Generative AI is shifting from experimentation to platform — with a hardware‑heavy price tag. That changes how organizations budget, procure and govern AI. COTS features will accelerate adoption and deliver quick wins, but they also concentrate power in vendor hands and surface new risks around telemetry, hallucinations and long‑term costs.

Smart leaders will respond with a hybrid strategy: use vendor‑packaged AI for immediate business outcomes, invest selectively in internal capabilities for strategic differentiation, and harden procurement and governance to keep control of data, costs and compliance. Gartner’s forecast isn’t just a spending warning — it’s a timetable. Prepare the budget, tighten the contract language, and measure the right KPIs so generative AI becomes an advantage, not an unmanaged cost.