In the BPO industry, thin margins, strict SLAs, and constant scalability demands are everyday realities.
Liorant offers AI-powered agents for BPO service delivery providers and tailored AI solutions for BPO support operations vendors that directly target these challenges. We focus on sharp, measurable outcomes – reducing cost per transaction, accelerating service delivery, and improving quality at scale.
Our solutions and AI agents for BPO operations work alongside your teams to handle high-volume tasks. This ensures you meet client expectations even as volumes surge.
Additionally, we assess the AI Impact on Your Operations to protect your margins and elevate your service delivery.
AI Solutions for BPOs
A new Approach for BPO Leaders
This offering is designed for BPO leaders intent on driving efficiency and growth through AI
BPO CEOs & Managing Directors
Operations & Delivery Leaders
Workforce Management & QA Leaders
Client Account Directors
Technology & Automation Heads
If you’re responsible for delivering large-scale BPO services and seeking an edge through AI, this is for you.
The Reality of Modern BPO Operations
These challenges squeeze profitability and make consistent delivery harder. It’s no surprise 88% of BPO providers believe AI is essential to stay competitive going forward. Leaders know that doing more with less is the new norm – and that’s where AI comes in.
Thin margins under pressure
Profit margins average 10–15%. There is little room for errors or inefficiencies.
High volumes and strict SLAs
Providers handle large transaction volumes. They must meet tight service level agreements. Peak periods put extra strain on resources.
High attrition and training costs
Annual staff turnover can reach 30–40%. This increases hiring and training costs and puts service quality at risk. Retaining skilled agents is an ongoing battle.
Multi-client, multi-process complexity
A BPO may serve dozens of clients and processes, each with unique requirements. Standardizing operations without losing client-specific nuance is difficult.
Increasing client scrutiny on quality, data, and compliance
Enterprise clients demand transparency and adherence to regulations. Compliance overhead (data security, audits, etc.) can further squeeze margins.
Our Methodology
Where AI Actually Creates Value in BPO
AI isn’t a future concept for BPO – it’s here now, driving real improvements across the BPO value chain. AI technologies are being applied end-to-end in different areas.
Customer Service Operations
AI chatbots and conversational AI solutions for BPO contact centers handle routine inquiries.
Quality & Training
AI monitors interactions for quality and compliance. It creates training content and coaching tips to upskill agents.
Knowledge & Self-Service
AI-powered knowledge bases deliver instant answers. Customers resolve issues faster. Agents find information quickly.
CX & VoC Analytics
AI analyzes calls, chats, and emails. It detects customer sentiment. It uncovers trends and improvement areas.
Finance & Accounting (AP, AR, R2R, FP&A)
AI for financial BPO services automates invoice processing, reconciliations, and reporting. By extracting data and flagging anomalies, AI accelerates finance processes with high accuracy.
Generative AI for BPO is helping to draft responses, summarize customer calls, and even create first-draft reports. This frees humans for higher-value work.
Forward-thinking BPO providers are deploying AI across industry-specific processes beyond customer service and finance.
For example,
- AI-driven automation for healthcare BPO is speeding up insurance claims and patient data handling.
- AI-driven cost reduction strategies for logistics BPO operations are optimizing shipment scheduling and supply chain back-office tasks.
- AI-driven predictive analytics for property investment BPO firms help them analyze market data for clients.
- Manufacturing-focused providers use AI-powered BPO for manufacturing efficiency to improve procurement and production support services.
- AI for business development in BPO is helping to define new patterns of potential customers.
Our Approach: AI That Improves Unit Economics
AI tied directly to cost per transaction
We link each AI initiative to your cost-per-interaction and per-unit costs.
ROI before scale
No massive bets on unproven tech. We prove the ROI at small scale first – demonstrating cost reduction or quality gains in a pilot.
Human oversight by design
We build human-in-the-loop oversight into our solutions from day one.
The AI for BPO Adoption Framework
To successfully adopt AI across your BPO operations, we guide you through a structured multi-phase journey. This AI for BPO Adoption Framework delivers value at every step while controlling risk.
Identify High-Impact Opportunities
Deploy Quick Wins
>Discovery & Validation. We work with your teams to identify high-volume, repetitive, SLA-critical processes.
>Value & ROI Modeling. For each candidate, we model the potential impact. We define how much we can reduce cost per interaction. And how might AI-assisted workflows lower Average Handle Time (AHT) or cut error and rework rates.
What scalability constraints can AI relieve?
We quantify ROI through efficiency gains and margin improvements. We build a clear business case for each opportunity such as training bottlenecks or IT limits.
Decide the Right AI Strategy
AI Suitability Decision
We evaluate the best-fit AI solution for the problem.
>Should you deploy ready-made AI copilots or use existing AI platforms?
>Or is a custom AI model justified for large-scale or client-specific processes?
>We help decide build-vs-buy, ensuring you invest wisely for the scope and scale required.
Human-in-the-Loop Design
For each chosen AI solution, we design how humans and AI will collaborate. We set clear review points, such as agent checks on AI-suggested answers.
Define the optimal strategy for each high-impact area
We define exception-handling rules when AI is unsure. We also set client accountability and approval steps for AI-driven decisions. The goal is to maintain accountability and transparency with clients at all times.
Design for Frontline Operations
Practical design
Translates strategy into practical designs that work on the ground for your agents and managers
Scope & Roadmap
Together, we plan a pragmatic adoption roadmap. We usually start with assistive AI that supports human agents. Next, we move to partial automation where AI handles some steps. Finally, we deploy end-to-end automation for well-defined tasks.
UX & AI Interaction Design
We design intuitive interfaces and workflows for how your frontline will use the AI. This may include an agent-assist toolbar that suggests answers in real time.
AI can also automate routine actions inside back-office systems. The focus is on usability – making sure the AI fits seamlessly into daily operations.
Deployment& Integration at Scale
Requirements & Operational Scenarios
We define detailed requirements and user stories. They cover all scenarios the AI must handle.
Data Strategy
Data fuels AI. We use your historical data to train models where needed. We set up ongoing data pipelines. We also enforce strict client data isolation.
Architecture Design
We design an architecture that aligns with your enterprise IT landscape. For BPOs, that often means a multi-client AI architecture (one platform serving multiple client accounts securely).
Deliver Measurable Gains
Baseline Build
We first stand up the core AI capabilities as a baseline. Examples include agent copilots that support agents during live calls or chats. We automate interaction summaries to remove after-call work.
Core Development
Building on the baseline, we develop more advanced workflow automation and decision-making logic. For example, we automate full data entry workflows. We route tickets and calls to the best agents. We use AI to prioritize work queues, so urgent tasks and high-value customers come first.
Control Risk & Quality
Evaluation & Testing
We subject the AI solution to stringent testing before any broad deployment. We test accuracy using historical data. We verify SLA compliance, such as response times.
QA & Risk Acceptance
We involve your operations teams and clients in validating AI output quality, beyond internal testing. Together, we define acceptable error rates and edge-case behaviors.
Govern, Deploy & Adopt
Governance & Compliance
We establish governance frameworks covering how the AI is monitored and controlled day-to-day. We set clear data usage rules. We define what data the AI can and cannot access. This aligns with client contracts and privacy laws.
Deployment & Rollout
Rather than a big-bang approach, we advise a phased rollout of the AI solution. We can sequence deployments by team, process, or client.
Change Management
We help prepare your workforce to embrace the AI tools. We run enablement sessions for agents and supervisors. Everyone learns how to use the new AI tools and workflows.
This cultural adoption piece is critical for getting the most out of the technology.
Optimize & Scale
Performance & Safety Monitoring
We continuously track key metrics. These include cost per interaction, response times, accuracy, and exception rates.
Monitoring & Feedback
Beyond system metrics, we gather feedback from agents and feedback from clients.
Continuous Improvement
Armed with real-world data, we iteratively refine the AI solution. This may include workflow improvements.
Capability Scaling
Finally, we help you scale the solution to more processes, more clients, and more geographies. The modular architecture we design makes it easier to replicate the AI capability elsewhere.
What This Delivers for BPO Leaders
Higher SLA compliance
Faster onboarding
Reduced attrition impact
Scalable, repeatable delivery
Stronger client trust
Why Liorant
Deep understanding of BPO value chains
We speak BPO fluently. We understand BPO workflows end to end. This includes front-end sales and AI for business development, such as analyzing prospects or tailoring proposals. It also includes back-office transaction processing.
Governance-first execution
Deploying AI in a BPO isn’t just an IT project – it’s an operational transformation. We bring a governance-first mindset to execution. That means from day one we consider compliance requirements, risk management, and stakeholder sign-offs.
AI aligned to delivery economics
We don’t do AI for AI’s sake. Every solution improves your delivery economics. It lowers cost per ticket.
Multi-client, enterprise-grade design: BPO providers juggle multiple clients and strict data segregation. Our architectures are enterprise-grade, built to handle multi-client environments securely.
How We Typically Engage
AI Opportunity & ROI Assessment
We begin with a focused discovery of your operations.
Scaled Deployment
Upon successful pilot validation, we scale up. Our team works with your IT and operations teams. We fully integrate AI into your workflows. The solution scales to handle more volume, more users, and multiple client processes.
Pilot & Validation
Next, we execute a pilot project on a selected use-case. We focus on actionable insights.
Continuous Optimization
After go-live, we don’t disappear. We provide ongoing support and optimization. We run regular business reviews.
Start with Margin Clarity
Ready to transform your BPO operations with AI and protect your margins? Start with Margin Clarity. Let us begin by quantifying the impact AI can have on your specific business.
Contact Liorant for a tailored AI opportunity assessment. See where AI for BPO can cut costs, improve quality, and give you a scalable delivery advantage.
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