A deeper look into BPO, through an exploration of better data capture and discovery AI tools that take advantage of the digital wave.
As the demand for offshoring continues to grow, more companies are choosing Business Process Outsourcing (BPO) services as an alternative way to reduce costs and improve efficiencies. This outsourcing model is an ideal solution for businesses looking to grow with minimal upfront capital expenditures.
When organisations outsource their business processes, they’re able to focus more on what they do best and outsource less-critical tasks to a service provider. Additionally, they’re also able to tap on the expertise of third-party experts who are able to perform specialised operations at scale.
Business Process Outsourcing (BPO) is a business practice where a company’s business processes or operations are outsourced to a third-party service provider or subcontractor.
BPO is not a new concept. In fact, many of the processes that can be outsourced with BPO range across the back to the front office. Companies have been outsourcing BPO tasks such as IT, tech support, accounting, HR and sales for decades. Most sectors remain resilient, but a few observations were noted in the IT-BPO sector which this article will take a deeper look at.
How is the market for BPO performing?
The market size for the global BPO industry stands at USD 232.32 billion as of 2020. And the sector is only becoming more financially robust with a compound annual growth rate of 8.5%.
The Indian IT-BPM industry, for example, has been a pioneer in the global IT landscape for over two decades. In 2021, the industry is predicted to register an annual growth of 2.3% to reach USD 194 billion in revenue.
The Philippines’ BPO industry is also one of the most dynamic and fastest growing sectors in the country. With more than 1.3 million professionals employed in the sector today, it continues to be one of the country’s most significant contributors to economic growth. The industry has an estimated total contribution of $26 billion or 9% to GDP.
Why BPO businesses are turning to technology for increased scalability and adaptability
Outsourcing to lower-cost countries has been a popular business strategy in recent times. However, the cost of running business operations in these countries is still increasing. There is a mounting pressure for organisations to look at both cost reduction and cost optimisation in BPO solutions. This includes reviewing labour costs, geographical location, the number of service providers, and the type of services that are outsourced.
In fact, IBPAP reports that 71% of organisations have initiated cost cutting measures. But how far can they go before hitting the ceiling in scalability?
This is why companies need to gear towards technology to drive scalability and sustainability in their digital transformations over the long term. One of the key factors that makes technology a highly sustainable option for BPO companies is that it optimises human resources. This means that they don’t have to have a large workforce on their payroll achieving less returns than their full potential.
With AI technology, employees can transition into higher-level positions that require less routine work and more analytical thinking. They can also be assigned to take on supervisory work that goes alongside AI applications. These allow BPOs to free up resources for skilled employees who would otherwise be stuck in an endless cycle of routine tasks.
Artificial intelligence is one such technology revolutionising the world of business processes outsourcing. AI-powered platforms and tools can take care of a lot of mundane tasks and administrative work for a fraction of the time and cost it would take a human. With AI, you can automate your data-entry work, customer service, order fulfillment and more.
But applying AI in BPO has challenges too, particularly with data
Unfortunately, AI’s applications are limited because of the complexity of data faced by BPO companies and their clients. The performance of AI predominantly hinges on data input–its quality, volume and format. Probabilistic AI models such as Machine Learning are widely used in businesses today, but they’re mostly good at making sense of a massive amount of structured data.
These probabilistic AI models are also built with specificity in mind. So they can accurately make sense of data only if the model was built using the same kind of data in the first place. For instance, a shipping invoice model can only read shipping invoices, not commercial invoices.
However, BPO companies may receive a multitude of processes to execute from various clients. Each process deals with specific sets of data that could be very different from each other. If BPO companies were to build or procure multiple models to deal with each unique set of data they have, the process would take lots of customisation work and IT spends.
Most of the data faced in the real world is unstructured and variable. Because this is hard to tackle with cost-efficient AI, many enterprises are forced to limit the scope of AI’s applications.
What is the real potential of AI in BPO?The BPO industry is becoming more aware of the importance of data. And because data is the foundation of any successful business process, AI applications in BPO must effectively deal with data. More specifically, the unstructured and variable nature of data. Are there AI solutions that can effectively unlock value from unstructured and variable data? Fortunately, the answer is yes. Earlier this year, TAIGER spoke about various AI tactics designed specifically to unlock value in data faced in the BPO industry. The sharing took place as part of the second episode of IBPAP’s event, Beyond Boundaries: A Digital Transformation Journey in Data Capture and Discovery, Applications and Tools.
IBPAP is the IT & Business Process Association of the Philippines, an enabling association for the information technology and business process management (IT-BPM) industry in the Philippines. The organisation advocates providing niche business process services by digitally enabling the workforce, to allow them to perform more high value and complex services.
Hybrid AI as an alternative AI tactic that works better with ‘messy’ data
In IBPAP’s sharing, TAIGER riveted on a different take to AI—hybrid AI. It recognises the challenges with each discipline of AI, and their unique strengths. So, instead of using a singular AI discipline such as Machine Learning in isolation, hybrid AI blends both statistical AI and symbolic AI together.
Statistical AI like Machine Learning and Deep Learning is great at using large volumes of data to identify patterns and make predictions.
Symbolic AI, on the other hand, tends to better mimic human intelligence. By better understanding logic, symbolic AI the intent, context and meaning of textual data like a human. Examples include Natural Language Processing, Knowledge Representation and Reasoning.
This hybrid approach recognises the challenge with each unique discipline. By including Machine Learning amongst Natural Language Processing and other symbolic AI disciplines, the approach captures the strengths of each while compensating each of their shortfalls.
Models powered with hybrid AI require less lengthy customisation than pure Machine Learning models, meaning that they can be deployed quicker. They are also more suited for ‘messy’ data that’s variable and unstructured. When applied to information faced by BPO agencies and their clients, hybrid AI can bring about greater levels of automation. This invites higher returns on investment, while freeing up blue collar workers for more value added tasks.
Two AI tools and use cases to apply in IT-BPO
Hybrid AI’s strengths lie in optimising digital processes by effectively understanding all kinds of data. Most importantly, it does so with more human intelligence with scalability at its core. Here, we break two ways to take advantage of AI in the IT-BPO industry:
1. Intelligent document processing to turn documents into structured data
Documents are a core part of every business process, and particularly those that are outsourced. Think claims forms for payroll and expense reconciliation, medical invoices to execute medical services. Or resumes and other employee records for HR operations.
Such documents come in all formats and vastly different contents. Some are neatly labelled, some have tables, and some are free flowing text. The variability of these files means that humans are often left to process them manually–by closely reading each document, categorising and storing them accordingly, or extracting the required data to be used in follow up processes. This is where intelligent document processing can expedite the process by automating each cognitive task.
What is Intelligent document processing?
Intelligent document processing (IDP) consists of a host of business solutions that process data from all types of documents by capturing, organising and extracting the data therein using a range of AI technologies.
In this example on a power of attorney legal document below, we can easily conceptualise how a scanned document is easily transformed into clean, structured data.
Limitations of intelligent document processingHowever, there’s a mixed bag of capabilities among IDP tools in the industry. One critical difference is the technology behind the solution which defines how it is used thereafter. Machine Learning based models which are common in the industry tend to have rather high accuracy rates, but only because they’re trained over thousands and thousands of document samples to read a specific document subtype. These models tend to take up to a few months to be customised by IT experts, and will need continual maintenance to stay accurate. Additionally, these models are not transferable to other document types. Each document type would need its own baseline extraction model to complete the extraction process. A model to read legal powers of attorney will never be able to effectively process a bank cheque. And so, IDP tools for the IT-BPO industry need to be able to develop models with speed and ease. It’s a fundamental criteria considering how BPO companies have a wide range of documents to accommodate a broad range of clients and their respective data sources. Presently, tools that can develop effective models with speed and cost-efficiency are few and rare.
Hybrid AI approach to intelligent document processing for BPOIn the session, TAIGER shared how its hybrid AI approach caters better to the document processing needs of the IT-BPO industry. TAIGER’s IDP tool, Omnitive, applies advanced natural and semantic language processing algorithms, combined with Machine Learning. These different AI technologies are used together to automatically identify, extract, clean, validate and store key information from unstructured and semi-structured documents. Unlike statistical Machine Learning approaches which ‘learn’ based on samples, language-based AI algorithms understand the semantics of words. This reduces the amount of document samples required to train baseline models, reducing the time spent to develop models to a matter of hours to days. Users can even build these models themselves using a no-code interface, allowing them to automate document processing for an endless variety of use cases.
IDP use cases in BPOIDP can be applied to an extensive range of business processes that are document-centric. With an effective IDP tool that is easy to implement, quick to develop models and high-performing in data processing, the possibilities of applications are endless. Here are some use case examples and their document types to illustrate the versatility of IDP.
|Outsourced business process||Document types|
|Customer experience management||Purchase orders, customer inquiry, application forms, account opening forms|
|HR and payroll||Payslips, claims forms, travel invoices, medical invoices, medical prescription forms, resumes,|
|Legal and compliance||Contracts, identification documents, credit reports, income verification documents|
|Finance and accounts management||Invoices and receipts, reconciliation statements, letters of credit, cheques, financial statements|
|Real estate||Operating statements, mortgage payments, purchase agreements|
|Logistics||Bills of lading, supplier invoices, packing lists, labels|
|Travel and tourism||Hotel booking, flight booking, passports, vaccination certificates, passenger locator forms|
With one of our banking clients, Banco Santander, Omnitive successfully automated the onboarding process for small-medium businesses. Highly complex documents such as articles of association and powers of attorney were automatically processed at 90% accuracy, helping to reduce the total onboarding process from 3 days to 15 minutes. The solution translated to an annual cost reduction of millions of euros.
2. Knowledge management and information discovery for data-driven processes
Going beyond document processing, imagine the possibilities once you’re able to turn unstructured documents into actionable data. What companies can get is a bird’s eye view of your organisation and customers. Because better data capture leads to better data discovery in turn.
Knowledge powers business processes. But BPO companies are experiencing an influx of data, multiplied across different clients from various industries. Data management is a core business function for these companies, and it can be overwhelming with the amount of data that needs to be managed. Being unable to find information to conduct day-to-day operations can majorly compromise efficiency and output. This leaves the company in a reactive state, which is costly both financially and operationally.
A knowledge management AI tool provides enterprises with a solution to this problem by using a more language-led approach to AI. It helps with categorising enterprise data using AI, and making them easy to access across the whole organisation.
Internal knowledge management tools for the enterprise are otherwise known as enterprise search engines.
What is enterprise search?
Enterprise search is an organisation’s internal information management system that retrieves and locates information from multiple enterprise-type sources.
In a nutshell, enterprise search engines do three main tasks. At the backend, they crawl the relevant sources of information to ingest data, then organise the data by storing it as an index. Finally, enterprise search presents all the information on a user-facing search interface for users to easily access the information they need. The example here shows how enterprise search can provide organisations with a 360 degree view of their corporate clients based on multiple data sources and using different ways to access insights.
Semantically powered enterprise search engines for BPO
There are a few considerations to look out for when investing in an effective enterprise search engine. The key question is how good are they in dealing with data?
From the data ingestion and indexing point of view, search engines for BPO need to have a governed method to receive information from all sorts of repositories to accommodate their large clientele. And unlike public search which typically deals with TXT or HTML sources, enterprise search needs to also crawl, understand and organise documents, images, emails and so on. Their compatibility with information sources and the human language within them provides the critical foundation for a one-stop location to retrieve knowledge.
How easy information is accessed is the next important consideration. Do they have semantic AI capabilities to understand a host of complex human search queries? Are they able to match these queries to the right answer by also effectively understanding the information they’ve indexed?
Tools like Omnitive Search, a semantic enterprise search engine, fill an important gap by checking these boxes. A wide range of AI-based features such as Natural Language PRocessing allow it to utilise the meaning of information. This helps the engine provide more accurate search results and understand user queries more efficiently and precisely.
Knowledge management use cases in BPO
With ever-expanding client data and employee turnover, knowledge management through effective enterprise search has a host of advantages in various processes. Here are some examples of how BPO companies can become more insights-driven in their business processes.
Outsourced business process
Finance and accounts management
Conduct KYC processes and safeguard account security by monitoring clients and their relationships with different entities.
Ecommerce and retail
Streamline product management for the back to mid office, glean insights into customer segments and purchase behaviours and conduct market research.
Contact centre processes
Provide agents with a full view of each customer to facilitate client servicing, and a central location to update customer records.
Training and development
Onboard and train BPO employees by allowing them to easily search for training materials, company records and customer data on their demand, bringing them up to speed faster and with less resources.
The bottom line: BPO can unlock quantifiable value with AI to achieve scale
The BPO industry has long been reliant on manual processes. Adoption of AI has constantly been perceived as a threat to human jobs. However, the reality is that AI can encourage digital enablement and employee reskilling, to develop a company-wide competitive edge for the booming BPO industry.
With performance as a key metric where BPO services are assessed, BPO companies must start exploring intelligent yet cost-efficient technologies to open the window to long-term scalability. Emerging technologies like hybrid AI are as relevant to large multinational clients as they are to BPO agencies. In this era of digital transformation, AI is a golden opportunity for many BPO companies to grow their clientele, optimise operations, and deliver a competitive edge.
About TAIGER’s Omnitive suite
Omnitive is TAIGER’s solutions suite hosted on an AI platform with a range of tools specialising in managing all kinds of data. The tools cover information extraction, search engines and virtual assistants. Deployed with a modular approach, they lay the critical foundations in knowledge-intensive industries like corporate legal and compliance.
Today, Omnitive is deployed across sectors verticals such as legal tech institutions, global financial institutions, the public sector. The platform successfully copes with highly unstructured information to increase productivity and returns on investment.
Speak with one of our solutions managers today on how the Omnitive solution suite can unlock better data management, extraction, discoverability and connectivity within your organisation.