For years, companies have relied on Business Process Outsourcing (BPO) providers to handle customer operations—repetitive and often complex tasks that are difficult to automate and require significant manual effort. BPOs promised efficiency and cost savings, enabling companies to scale without building massive in-house teams.
And the market is enormous. By the end of this year, global BPO revenue is expected to reach $414.81 billion.
But despite its popularity, the BPO model is showing cracks. The reality of managing BPOs is complex and costly. With turnover rates of 30-45%—triple the industry average—BPOs operate in a perpetual cycle of hiring and training that undermines consistency and quality.
Perhaps most problematic is the loss of control and visibility. Companies receive filtered performance data while multiple management layers separate BPO agents from the businesses they serve. This disconnect creates misaligned incentives, reduced accountability, and ultimately delivers inconsistent service that frustrates customers.
Now, AI is poised to change everything.
How AI is redefining customer operations
AI is fundamentally changing the landscape of customer operations by automating manual, repetitive tasks once thought exclusive to human agents. Advanced technologies like Large Language Models (LLMs) for unstructured document processing and data reconciliation are rapidly evolving, allowing AI to reach new heights of efficiency and accuracy.
AI agents are already automating tasks at scale, cutting costs, boosting margins, and delivering faster response times. Companies are increasingly turning to AI for:
- Automated content moderation: Analyzing text, images, and videos to detect and enforce policy violations, including harmful content and prohibited items.
- Counterfeit & fraudulent listing detection: Comparing product visuals and descriptions against authentic databases to identify and remove counterfeit goods.
- Fraud and scam detection: Monitoring user behavior, listing patterns, and transaction histories to flag suspicious activity and protect users.
- Automated identity and document verification: Using Optical Character Recognition (OCR), biometrics, and other technologies to authenticate documents, detect fraudulent attempts, and conduct user verification.
- Automated appeal management & dispute resolution: Categorizing and prioritizing cases, generating summaries, and recommending resolutions to streamline case handling.
- Smart logistics & order management: Optimizing shipping, tracking, and fulfillment processes for greater efficiency.
AI agents provide round-the-clock support, delivering faster response times, enhanced security, and a more consistent user experience.
Challenges to full AI adoption
While AI holds enormous potential, it has yet to deliver the sweeping transformation many anticipated. Technical limitations continue to prevent fully autonomous AI deployment, and the traditional distribution of work across departments and BPOs does not facilitate meaningful AI integration.
Technical challenges
Despite significant advancements, AI remains imperfect. Key technical challenges preventing full AI adoption include:
- AI hallucinations & unreliable outputs: LLMs can produce incorrect responses due to gaps in training data or ambiguous prompts.
- Lack of context: AI struggles when relevant information is fragmented across systems or unstructured, making accurate decision-making difficult.
- Privacy, security & transparency concerns: AI-driven decisions can be difficult to audit and may introduce security risks if not properly managed.
- Bias & discriminatory decision-making: AI can perpetuate or amplify historical biases in training data, damaging user trust and marketplace integrity.
These challenges can quickly escalate when AI is deployed at scale, leading to reputational damage, regulatory and compliance violations, revenue loss, and business disruptions.
Organizational challenges
The traditional distribution of work across departmental silos and BPOs presents a major barrier to effective AI implementation. Often, AI implementation is managed by technology teams, while Operations teams own the business problem, and BPO agents handle day-to-day challenges. This fragmented approach prevents AI systems from effectively automating entire workflows.
For AI to drive real impact, ML engineers need to work closely with Operations teams and agents to deeply understand the problems being addressed and ensure AI outputs align with business expectations. Achieving this requires rethinking team structures and fostering collaboration across all stakeholders. Only by aligning AI development with real-world challenges can companies deploy systems that deliver consistent, meaningful results.
Unlocking AI’s potential today: The hybrid AI-human model
To unlock the potential of AI today, it needs to be strategically blended with human expertise. The most effective approach is a hybrid AI-human model where:
- AI handles high-volume, repetitive tasks efficiently.
- A small team of expert human agents manages complex edge cases and ensures quality control.
- Systems intelligently escalate cases to human agents when needed.
This approach allows companies to achieve the efficiency gains AI promises while maintaining accuracy, trust, and exceptional user experiences.
How the hybrid model outperforms BPOs
Where BPOs once promised efficiency and cost savings, the hybrid AI-human model now delivers. Companies no longer need to deal with BPOs that aren’t fully aligned with their business objectives, instead, a hybrid model carries out tasks quickly and consistently with AI, while a small team of human agents ensures high-quality outputs in every situation.
Transitioning from BPOs to AI-driven operations
To stay competitive and scale operations effectively, companies need to embrace AI-driven models today. By combining AI with human expertise, organizations can reduce costs, enhance customer experiences, and boost efficiency.
Companies can set up internal teams to design and deploy their own hybrid model (as outlined in the white paper: Scaling online marketplace operations: The power of AI-human collaboration). Alternatively, they can partner with a specialized partner like Unitary for a fully managed solution that mitigates risks, eliminates complexity, and delivers guaranteed results.
Unitary partners with global organizations to deliver AI-powered efficiency with human-level accuracy. Our proprietary model blends AI agents with Unitary human agents, providing high-performance customer operations that integrate effortlessly with existing tools, just like an in-house team member, removing the need for complex integrations.
Unitary takes ownership of the model’s results, with SLAs on accuracy and response times. On average, our partners achieve:
- 30%+ cost savings upfront, with even greater efficiency at scale
- 5X faster response times compared to their BPO
- 97% accuracy on complex, multi-step tasks
- 24/7 content moderation and support, enhancing platform safety and user experience
Reduce costs, improve customer experiences, and start scaling efficiently.
Book a consultation now to discover how Unitary can drive efficiency and growth for your business.
