Generative AI

Generative AI is a class of artificial intelligence that creates new text, images, code, audio, or video from a prompt, rather than classifying existing data. It powers tools like ChatGPT and Copilot, and now sits inside most modern BPO automation workflows.

Large language models, trained on trillions of tokens, sit at the core. They learn statistical patterns, then predict the next token when a user submits a request. That predictive step is what makes the output feel “new” even though nothing is truly invented.

For outsourcing buyers, generative AI has shifted the conversation from headcount to hybrid teams. A single agent, backed by a co-pilot, now handles customer support ticket volumes that used to require three seats. Vendors price seats and API calls together.

Adoption has accelerated fast. McKinsey’s 2024 State of AI survey found that 72% of surveyed organisations reported using AI in at least one business function, up from 55% the year earlier, with generative AI driving most of the production shift.

Key takeaways

• Generative AI produces novel content from prompts using pretrained foundation models.
• Adoption has moved from pilots to production inside contact centers and back offices worldwide.
• The NIST Generative AI Profile lists 12 unique risk categories that enterprise buyers now govern in vendor contracts.
• BPO providers now price generative AI capacity alongside seat-based fees, blending managed services with token-metered inference.

How it works

A generative AI system takes a prompt, breaks it into tokens, and passes those tokens through a transformer neural network. The model predicts the most likely next token, repeats the loop, and returns a fluent response conditioned on its training data.

Three components define almost every current system:

1. A foundation model pretrained on public and licensed data.
2. A fine-tuning or reinforcement-learning step that shapes tone and safety.
3. A retrieval layer that grounds answers in the client’s own knowledge base.

The retrieval layer matters most in outsourcing. It lets a Manila contact centre pipe a client’s product catalogue, SLA rules, and refund policies into the same model without retraining. Answers stay on-brand and auditable.

Modality is expanding fast. Text-only chat has been joined by multimodal systems that accept images, code, and speech. That widens outsourcing use cases from ticket routing into document review, video moderation, and voice QA.

Latency and cost are the two constraints operators watch. Frontier models can add seconds of response time and cents per thousand tokens — enough to matter inside a live voice call.

Vendors route simple queries to smaller open-source models and reserve premium models for edge cases.

Governance runs alongside. The NIST AI Risk Management Framework, released January 2023 and updated with a Generative AI Profile in July 2024, is the reference most enterprise buyers cite in RFPs alongside data privacy standards.

Examples

Real deployments already spread across every outsourcing sub-vertical. The pattern is consistent: a copilot handles first-draft output, a human reviews, and the vendor bills for both the seat and the model tokens consumed on a per-transaction basis.

Concentrix rolled out generative AI copilots across parts of its 450,000-seat contact-centre network in 2024, cutting average handle time on complex tickets. TDCX and Teleperformance disclosed similar deployments in the same year.

In finance and accounting outsourcing, Genpact embedded generative AI into invoice-processing and reconciliation flows, reducing manual keying volume. TaskUs and Alorica publicly reported generative-AI-assisted QA sampling across trust-and-safety programmes.

Healthcare BPO providers Ubiquity and Sutherland use generative AI for medical-scribing pilots, transcribing patient encounters into structured notes.

Early 2024 results point to a 30–50% cut in clinician documentation time, though HIPAA-grade data security controls remain a live procurement question.

Retail and e-commerce firms Amazon, Sea Group, and Lazada now embed generative AI in customer-support chat, product-description drafting, and returns triage.

Their Philippines-based outsourced contact-centre partners deliver these workflows at scale, blending generative first drafts with local-language human review to keep tone and factual accuracy on brand.

Software-heavy BPOs use GitHub Copilot and Anthropic Claude inside developer teams — a shift that has moved offshore delivery centres in India and the Philippines from pure staff augmentation toward output-based contracts.

Related terms

• Artificial intelligence: the parent field that includes generative and non-generative approaches.
• Machine learning: the training method most generative models rely on.
• Natural language processing: the branch that handles text understanding and generation.
• Chatbot: a conversational interface that is now almost always generative-AI-backed.
• Robotic process automation: rule-based automation that pairs with generative AI in hybrid workflows.

FAQ

Is generative AI the same as artificial intelligence?

No. Generative AI is one branch of the wider artificial intelligence field. Other branches handle classification, prediction, and pattern recognition, while generative AI focuses specifically on producing brand-new content in response to a prompt.

What are the main risks of generative AI in outsourcing?

The biggest risks are data leakage, hallucinated answers, and biased output reaching customers.

NIST’s July 2024 Generative AI Profile catalogues 12 risk categories, from confabulation to intellectual property exposure. Vendors mitigate through retrieval grounding and human review.

Does generative AI replace call-centre agents?

Not yet. Most 2024 and 2025 deployments use copilots that assist agents rather than replace them wholesale. Vendors report handle-time savings and higher first-contact resolution rates, though a human still owns the customer relationship.

How is generative AI priced by BPO vendors?

Pricing usually stacks a per-seat fee with a per-token or per-transaction API charge, though larger vendors increasingly bundle capacity into outcome-based contracts tied to CSAT or first-contact resolution rate.

Ready to explore outsourcing options? Browse OA’s directory of vetted providers to find a partner suited to your operation.