Globant recently introduced AI Pods as a service offering, a departure from traditional FTE-based services. Think of it as platform orchestrated and human supervised. Agentic AI workflows run on the Globant Enterprise AI platform, a model‑agnostic accelerator with a library of prebuilt agents (e.g., CODA for the software development life cycle). Human supervision helps ensure strategic alignment and quality. Customers pay a monthly subscription for each AI Pod, which provides access to token-based, metered capacity.

Why it’s generating interest

Globant is the first major IT enterprise services player to announce an AI agent-driven “services as software” model. Its AI Pods promise several benefits that, taken together, have the potential to be disruptive: alignment with outcomes or deliverables, not effort; enhanced flexibility (“engineering streamed like content”); enhanced productivity; and more consistent output.

This offering breaks from the industry’s prevailing time and materials model by bundling a model-agnostic platform, a library of prebuilt AI agents, and human supervision, and charging for it all using tokens. The approach aims to monetize AI at the team level by turning a library of proprietary agents into a billable asset.

Stepping back, the move underscores a broader industry shift, signaling that intellectual property could become a more significant source of competitive advantage. However, several key considerations remain before such models can become truly mainstream.

What we like

• Potentially nonlinear commercial model: The Globant service offer replaces time-based billing with token-metered subscriptions, with the goal of aligning customer spending to throughput rather than effort. This could unlock nonlinear economics and embed AI-driven productivity gains into the trajectory of the service model.
• Flexible, scalable delivery: “Engineering streamed like content” would enable rapid pod spin-up or spin-down. A small supervisory crew could handle higher throughput, potentially enhancing margin leverage.
• Faster, more consistent execution: Standardized agentic workflows, reusable IP (e.g., CODA), and continuous delivery cycles could reduce variation, speed up delivery, and drive down costs.
• Model-agnostic AI platform: Globant Enterprise AI offers a catalog of prebuilt agents, shortening build cycles and enabling reuse across use cases and industries.
• Enterprise-grade quality control with human oversight: Guardrails and lean human oversight help ensure that output is compliant and aligned to client standards.

Key watch-outs and considerations for operationalizing at scale

Token transparency and predictability are critical. It is unclear how tokens are defined. Will customers really pay for deliverables or simply effort discounted for productivity gains? It will be important to monitor market response and track how well tokens align with deliverables and with customers’ perception of value. Experience from SaaS shows that consumption-based pricing needs to be transparent and predictable, to enable budgeting and CFO alignment

Customer readiness—not technology—will determine the rate of adoption. The rate of adoption will likely depend more on customers’ ability to adapt their SDLC processes and workflows in order to consume IT services in this manner. Scaling requires redefining processes, not just effective AI agents, and gaps here risk inconsistency.

Not all work types are a great fit. AI Pods are well suited to development, testing, and automation, but it’s unclear if customers will see value in switching to a new service and pricing model for work such as UX design and architecture.

Customers could face governance and platform lock-in risk. Workflow interface and escalation paths remain unclear. It’s also unclear if dependence on Globant’s tech stack will limit portability.

What it means for tech services companies

• Pricing pressure: AI-native delivery models will expand the 20%–30% renewal compression rate as clients expect built-in productivity gains.
• Displacement risk: Players that don’t adapt quickly risk churn in favor of AI-forward challengers.
• Widening growth and valuation gaps: IP-rich, AI-centric firms will likely see faster growth and attract higher multiples; conventional players may see multiple compression.
• IP, not headcount, as a driver of margin: Differentiation may hinge on proprietary agent libraries, orchestration tooling, and data pipelines.
• Accelerating platform M&A: Traditional vendors may pursue acquisitions to close AI and automation capability gaps.

How to respond

Pressure-test your account and project portfolio. Surgically identify accounts and projects where the opportunity to shift delivery and commercial models exists, especially for repeatable, effort-heavy work.

Accelerate IP-led delivery. Future margin profile may be defined by proprietary agent libraries, orchestration tools, and automated workflows—not bench size. Invest now in reusable assets and internal platforms that enable AI-assisted execution at scale.

Pilot your own “pod-equivalent” offerings. Test modular, token-based, or outcome-priced offerings in high-leverage domains like development, QA, and support automation.

Reframe client conversations around outcomes. Proactively engage with clients on pricing meters that better align with outcomes, and refine the model with customers.

Preserve strategic flexibility. Stay platform-agnostic where possible. Avoid locking delivery to a single AI stack; build a multi-model orchestration layer to retain optionality and avoid dependency traps.