Day: 18 July 2023

BPO (Business Process Outsourcing) companies are increasingly leveraging the power of artificial intelligence (AI) to enhance their service offerings and provide valuable solutions to their clients. By integrating AI technologies into their operations, BPO companies can streamline processes, increase efficiency, and deliver improved outcomes. Here is an overview of the AI services offered by BPO companies:

1. Intelligent Automation: BPO companies utilize AI-powered robotic process automation (RPA) to automate repetitive and rule-based tasks. This helps in reducing manual errors, increasing speed, and achieving greater operational efficiency. Intelligent automation can be applied to various processes, such as data entry, invoice processing, customer support, and more.

2. Natural Language Processing (NLP): BPO companies leverage NLP, a branch of AI, to enable machines to understand and interpret human language. NLP is used in chatbots and virtual assistants to provide automated customer support, handle inquiries, and assist with information retrieval. NLP also enables sentiment analysis, allowing BPO companies to analyze customer feedback and sentiment across different channels.

3. Machine Learning (ML): BPO companies employ ML algorithms to analyze large volumes of data and extract actionable insights. ML models can be used for predictive analytics, fraud detection, customer segmentation, and personalized marketing. By leveraging ML, BPO companies can make data-driven decisions, improve customer experiences, and optimize business processes.

4. Intelligent Document Processing (IDP): BPO companies leverage IDP, powered by AI, to automate document-intensive processes. IDP utilizes technologies like optical character recognition (OCR), natural language understanding (NLU), and machine learning to extract and analyze data from documents. It helps streamline workflows related to invoice processing, contract management, claims processing, and more.

5. Predictive Analytics: BPO companies utilize predictive analytics models to forecast future trends and outcomes based on historical data. By analyzing patterns and trends, predictive analytics helps in making data-driven decisions, optimizing operations, and improving customer satisfaction. It can be applied to areas like demand forecasting, customer behavior prediction, risk assessment, and supply chain optimization.

6. Virtual Agents and Chatbots: BPO companies develop virtual agents and chatbots that leverage AI technologies to interact with customers and provide personalized assistance. These AI-powered agents can handle customer inquiries, offer product recommendations, provide self-service support, and facilitate smoother interactions. Virtual agents and chatbots help in reducing response times, improving customer satisfaction, and scaling customer support operations.

These are just a few examples of the AI services offered by BPO companies. As AI continues to advance, BPO companies will further explore and innovate in areas such as speech recognition, recommendation systems, intelligent data analytics, and more. By integrating AI into their service offerings, BPO companies can drive operational excellence, deliver enhanced customer experiences, and stay ahead in the ever-evolving business landscape.

ChatGPT has become a topic of conversation in almost every business over the past 6 months.  Since the AI chatbot launched in November 2022, the application has garnered more than 100 million monthly users.  With numbers that impressive it’s difficult to ignore the impact, and potential, of generative AI in daily business operations.

With innovation comes trial and error, and the technology associated with generative AI is no different.  Below, we discuss common snags in seen in the development and implementation of AI programs in business operations, causing them to over-promise and under-deliver over time resulting in lost investment.

1. Lack of Talent/technology Sourcing Strategy: As AI continues to grow in popularity, many leading developers are focusing on research rather than implementation.  While this is great in theory, it may leave many folks with limited access to the technology.  Finding a company that can take technology that is currently available and adapt it to address specific business needs may become a more reasonable option than working directly with AI inventors.

2. Concession to Technology Debt: Generative AI is ever evolving, so it is important to understand that technology work will be required to work out defects, add new features, and continued updates to infrastructure.  To keep up with the latest developments in AI, constant innovation must be available, along with the funding necessary to do so.

3. Neglecting to Plan for the Future: As AI takes off in today’s business landscape, the excitement is real and many have jumped at the chance to be at the forefront of adoption.  However, it’s highly important to plan ahead and look beyond the initial implementation to ensure that continued value is gained through the program.

4. Attempting to Solve Challenges Before Building a Platform: Building a solid AI platform is the best place to start.  Once you have a program launched and in-use, review critical challenges one by one, prioritizing by impact to your operations.  This will allow you to see the ‘big picture’ and invest energy and funding where it’s most impactful.

5. Remembering the Importance of User Experience: ChatGPT has become a household name not because it is brand new technology (the algorithm used to create it has been available since June 2020); AI has been capable of these types of interactions prior to it’s release.  The design and ease of use are what catapulted the success of the platform.  Ensuring a user-friendly interface is a necessity for success.

6. Failing to Restructure Management/operations to Fit a Digital Transformation: ensure that management is in place and ready to make the move to ‘human-in-the-loop’ operations.  Focusing on restructuring roles to fit the service delivery of AI can help to ensure ROI is met.

Is your company an innovator, early adopter, or integrator when it comes in AI technology?  While these labels aren’t mutually exclusive (many companies fall somewhere in between on this spectrum) it’s important to understand your level of AI maturity as you get set to launch a generative AI program.  Understanding the above challenges and acknowledging realistic goals will help to set AI program up for future success.