Modernizing Outsourcing and BPO Companies with AI

The future of outsourcing is about to disrupted. This article focuses on the opportunities and business mode transformations that are emerging.

The Business Process Outsourcing (BPO) industry, valued at over $300 billion in 2024 and projected to exceed $525 billion by 2030, is undergoing a profound transformation driven by artificial intelligence (AI). As enterprises demand greater efficiency, scalability, and innovation, AI technologies are disrupting traditional BPO models. Here's how outsourcing and BPO companies can modernize with AI to unlock new opportunities and stay competitive.

In a recent post from Andreessen Horowitz, a16z, a leading investment firm, their recently article Unbundling the BPO: How AI Will Disrupt Outsourced Work.

The Case for AI in BPO: Challenges with Traditional Models

Traditional BPO models rely heavily on human labor for repetitive, high-volume tasks such as customer support, claims processing, and data entry. These processes are often:

• Slow and Error-Prone: Human-driven workflows can lead to delays and inaccuracies.

• Inflexible: Seasonal fluctuations in demand and high employee turnover create inefficiencies.

• Outdated: Many BPOs operate on legacy systems that lack technological sophistication.

AI offers a clear opportunity to address these challenges by automating processes, reducing costs, and enhancing service quality. It also provides the opportunity to invent new business models and services - akin to the idea of Services as Software. Typically, these are services where you substitute and FTE, with and Agent that is an FTE that solely focuses on outcomes.

How AI is Transforming BPO Operations

AI's ability to productize workflows traditionally dominated by manual labor is revolutionizing the BPO industry. Key areas of transformation include:

1. Automating Repetitive Tasks

AI-powered tools like robotic process automation (RPA) can handle mundane tasks such as invoice reconciliation, claims processing, and payroll management. For example:

• Companies can use AI to manage transportation invoices, reducing fraud and error while saving time.

• In healthcare, generative AI improves revenue cycle management by decreasing claim denials and accelerating processing times.

2. Enhancing Customer Support

Voice AI agents are now mature enough for large-scale deployment, enabling faster response times and personalized interactions. These systems autonomously handle common queries while escalating complex issues to human agents.

These Agents can handle complex interactions and access 3rd party software, rather than through a fixed process flow.

3. Leveraging Unstructured Data

Large Language Models (LLMs) excel at processing unstructured data—emails, reports, or internal documents—unlocking new possibilities for BPOs:

• Summarizing lengthy content like legal or financial documents.

• Analyzing data for trends and insights.

• Automating report generation and validation tasks.

• Write documents, insights and with LLM Reasoning models

4. Improving Scalability

AI redefines scalability by enabling enterprises to internalize tasks previously outsourced to BPOs. This shift enhances operational control while reducing reliance on external vendors.

One of the challenges that BPO firms have is that to increase revenue, firms have to hire more people to conduct the required work. This linear equation maintains lower margins (even though there may be some automation). More clients mean more costs, customised processes for each client, data specific to each client.

LLMs and Generative AI, with the use of Agentic Meshes enables the scaling of outsourcing offerings, without hiring more people. Agents also enable more flexible orchestration of tasks (not prescribed or all defined up front).

Knowledge-Focused Use Cases: The Role of LLMs

Large Language Models (LLMs) represent a pivotal technology for knowledge discovery within the BPO sector. Their ability to process unstructured data opens up transformative possibilities:

LLMs for Knowledge Work & Unstructured Data

One of the untapped knowledge areas within most enterprise (up to 80%) is unstructured data. This data sits in documents, spreadsheets, PowerPoints, PDF files and other similar structures. Never has it been possible to mining these corporate assets, remember, data is supposed to be a strategic asset, and most data has not been available to exploit.

Some of the key areas that LLMs enable are:

• Natural Language Interaction: LLMs allow users to interact with AI using natural language queries rather than requiring structured data or predefined queries. This makes them ideal for handling unstructured data like text documents, emails, and reports.

• Automation of Knowledge Tasks: LLMs can automate report writing, data analysis, and communication tasks in FP&A (Financial Planning & Analysis) and accounting workflows.

• Insight Generation: Future integration of LLMs into workflows can enable novel insight generation, guidance, validation of financial data, trend analysis, anomaly detection, recommendations, and automation of routine accounting tasks.

• Artifact Creation: LLMs can generate financial reports, analyze market data, provide financial advice, or automate invoice processing.

• Search Within Internal Data: Professionals can use LLMs to search internal documents or databases efficiently—a critical capability for FP&A teams managing vast amounts of unstructured information.

• Summarization: Summarizing lengthy financial reports or legal documents saves time for professionals needing quick access to key insights.

• Validation & Compliance: Ensuring accuracy in financial reports or compliance documents is another area where LLMs excel.

Transforming Knowledge Work

LLMs have the potential to transform knowledge work by enabling users to interact with AI using natural language. This means that instead of requiring structured data and predefined queries, LLMs can process and understand unstructured data, like text documents, emails, and reports.

This indicates that LLMs can be used to automate and augment various tasks in FP&A and accounting, such as report writing, data analysis, and communication and numerous use cases for FP&A departments and companies:

• In the future, knowledge workers hope to use LLMs to support novel insight generation, guidance, validation, automation of tasks, and further integration of LLMs into their workflows using their context and data. This suggests that LLMs can be used to analyze financial data, identify trends, provide recommendations, and automate routine accounting tasks.

• LLMs can be used for creation of artifacts, to find or work with information, to get advice, or for automation. In FP&A and accounting, this could translate to generating financial reports, analyzing market data, providing financial advice, and automating invoice processing or reconciliation.

• Workers want to use LLMs to search within their own data, such as internal documents, emails, and databases. This is particularly relevant for FP&A and accounting, where there is often a large volume of unstructured data that needs to be analyzed.

• LLMs can be used to summarize lengthy content, such as financial reports or legal documents. This can save time and effort for FP&A and accounting professionals who need to quickly understand large amounts of information.

• LLMs can be used to analyze data and discover new insights. This could involve identifying trends in financial data, detecting anomalies, or forecasting future performance.

• In the future, participants wanted to be able to perform information tasks using their own data, especially searching, summarizing, and analyzing. This further emphasizes the potential of LLMs to enhance knowledge discovery within organizations.

• LLMs can be used to validate data or reports, ensuring accuracy and compliance. This is crucial in FP&A and accounting, where errors can have significant consequences.

• Participants described future scenarios where LLMs would perform tasks for them, such as handling calendars, providing status updates, and automating routine processes. This highlights the potential of LLMs to automate various tasks in FP&A and accounting, freeing up professionals to focus on more strategic activities.

In essence, LLMs can help FP&A and accounting professionals to extract valuable insights from unstructured data, automate routine tasks, and improve decision-making. They can act as intelligent assistants that can understand natural language queries, process complex information, and provide actionable recommendations.

Expanding FP&A Use Cases with RAG

Retrieval-Augmented Generation (RAG), a cutting-edge AI methodology that combines neural language models with external knowledge resources, further enhances knowledge discovery in FP&A. By leveraging RAG technology alongside LLMs, organizations can unlock the full potential of unstructured data:

1. Automating Knowledge Retrieval

RAG (Retrieval Augmented Generation) systems retrieve the most relevant information from vast datasets in real time. For FP&A teams:

• RAG-powered chatbots can answer complex queries like "How will reclassifying Customer Service costs affect gross margins?" by pulling from internal policies or IFRS guidelines36.

• Financial institutions can use RAG to analyze market trends or forecast performance by integrating real-time economic indicators18.

2. Processing Unstructured Data

Unstructured data—such as call center records or PDF reports—is often difficult to analyze. RAG systems transform this data into structured formats compatible with vector databases:

• Techniques like "data chunking" break large text into manageable segments for efficient retrieval25.

• This enables FP&A teams to automate processes like invoice reconciliation or financial report generation.

3. Enhanced Decision-Making

RAG systems provide actionable insights by combining historical data with real-time updates:

• For example, a system might retrieve recent financial performance metrics alongside industry benchmarks to guide strategic planning78.

• By integrating RAG into their workflows, FP&A teams gain access to more precise recommendations tailored to their unique datasets.

4. Ensuring Compliance

Compliance is critical in finance. RAG-powered solutions validate financial reports against regulatory standards while maintaining strict privacy protocols:

• Systems deployed on private clouds ensure sensitive data remains secure during processing.

The Rise of AI-First Startups

Startups leveraging AI are disrupting traditional BPO models by offering scalable solutions that unbundle services into specialized products. These startups bring:

• Agility: Rapid innovation without legacy constraints.

• Cost Efficiency: Reduced overhead costs compared to labor-intensive models.

• Outcome Focus: Delivering measurable results such as improved accuracy or faster turnaround times.

Examples include companies like Decagon and Tennr automating workflows across logistics and healthcare industries.

Modernizing Legacy BPOs: A Roadmap

For established BPO companies to thrive in an AI-driven landscape, they must embrace modernization through the following steps:

1. Build an "Institute of AI"

Create dedicated units focused on researching cutting-edge AI applications tailored to client needs while training employees to work alongside AI systems.

2. Adopt a Human-AI Hybrid Model

Combine human expertise with AI capabilities:

• Use AI for repetitive tasks while humans focus on complex interactions requiring empathy or creativity.

• Implement tools like AI-powered CV shortlisting or automated interview scheduling in recruitment processes.

3. Transition to Product-Based Models

Shift from labor-dependent billing models to product-first approaches leveraging AI for scalable solutions.

Future Outlook: The Human + AI Continuum

The future of the BPO industry lies in seamlessly integrating human expertise with advanced AI systems. As Kimberly Tan from Andreessen Horowitz notes, "Human + AI Agents" will dominate the next wave of outsourcing innovation. This hybrid model enhances efficiency while unlocking new growth opportunities.

AI's ability to automate complex processes while enhancing decision-making positions it as a transformative force in outsourcing. As adoption grows, businesses will increasingly integrate these technologies to replace traditional BPO functions, creating opportunities for innovation across industries such as healthcare, legal services, and creative work.

In conclusion, as the disruption of the BPO industry by AI accelerates, companies that proactively embrace this transformation will lead in an era defined by intelligent automation—delivering value-driven solutions that redefine customer experience and operational excellence.

If you’re embarking on a journey to leverage Generative AI to transform your business, please get in touch!