Siri could browse the web and gather information on this topic for you. ChatGPT could generate this very article for you. Quillbot could paraphrase it for you. That’s Artificial Intelligence, as we know it today. What we all assume to be a new wave of technology taking over the world was already in its primitive form as early as 1951, in a dorm room at the University of Manchester, England- an AI bot developed by Christopher Strachey that could complete a game of checkers within a few minutes. What does this decades-old, yet seemingly novel technology mean for Business Process Outsourcing Companies? This article covers the pros and cons of AI in the outsourcing industry- so you can make an informed choice before denouncing AI completely or joining the AI wave head-on. Benefits of AI In Outsourcing 1. No downtime No coffee breaks. No Christmas holidays. No 3 pm slumps. As soon as you turn the power on, your AI algorithms will run 24 hours a day, 7 days a week, 365 days a year. Using AI algorithms can be used to replace repetitive tasks like customer service operations, data entry, document processing, report generation, invoice processing among many more. Not only can these tasks be automated through AI algorithms to run constantly, but it is also consistent. This means the same task will be repeated at the same standard much faster, with no downtime or human errors leading to both increased turnaround times and overall efficiency. All big pros for outsourcing companies. 2. AI-Driven Decision Making For decades, human judgment has been the central processor of decision-making. But we can now move from experience and gut instinct, subject to cognitive biases ingrained in us from years of evolution to something more objective: AI-driven decision-making. Every single micro and macro-business event is recorded and fed into dashboards, analytics, and spreadsheets. Unfathomable volumes of data are broken down into tiny chunks for us to understand. This is the current approach called “data-driven decision making” where the human mind is still the central processor. AI takes it a step further, completely eliminating the need for human processing. AI algorithms can be programmed to process huge volumes of data, identify patterns, perform calculations, and reach decisions. Outsourcing companies are better off delegating routine decisions that only rely on structured data to AI, freeing up human intellect for more complex, creative, and strategic decisions. 3. Cost Savings and Scalability In the outsourcing industry, onboarding contracts for clients can differ from 6 months, 1 year, 5 years, or indefinitely. Does hiring an entire team specifically for a client of 6 months make sense in terms of labor costs? AI can automate tasks that can significantly reduce such labor costs because AI systems can easily scale up or down to meet fluctuating client demands and offer flexibility to outsourcing companies. 4. Job Reconfiguration There’s a lot of noise of AI replacing jobs due to automation, but there’s also another side to that story. A flood of opportunities opening up for AI-related positions. A 2022 McKinsey report shows a clear shift in market demographics toward AI-supported positions- 39% of companies hired data engineers and 39% hired software engineers for AI-related roles. Challenges of AI in Outsourcing 1. Cost of Implementation The cost of implementing AI solutions requires careful planning and investment. You need to consider the initial setup cost, the potential return on investment, and how long it will take for you to break even. Here are the questions you should be asking: 2. Data Security and Privacy Data security is non-negotiable in outsourcing. AI algorithms may require access to vast amounts of data to function effectively, increasing the risk of exposure if not handled securely. Vulnerabilities in AI systems themselves could be exploited to gain access to sensitive data. Furthermore, when data is transferred for AI processing, it might be stored in different locations with varying data protection regulations. Losing control over data storage and access creates security risks. Here’s how to work around it: Thoroughly vet the AI tool you will be using to identify any security concerns. Establish clear contracts with outsourcing partners outlining data security protocols, access controls, and data destruction procedures after processing. Share only the minimum amount of data necessary for AI tasks to be performed and encrypt data both at rest and in transit to reduce the risk of data breaches. 3. Transparency Outsourcing depends completely on trust and transparency. The inner workings of some AI models can be complex, making it difficult to understand how they arrive at decisions. This lack of transparency raises concerns about accountability and fairness, especially for clients who may be unaware of potential biases within the AI used for their outsourced services. Have regular awareness sessions and consultations with AI experts to enhance client understanding of the use of AI in different processes. Explore using explainable AI models instead, that provide insights into decision-making processes. This transparency helps identify potential biases and data privacy issues. 4. AI Risks Related to Algorithm Bias and Model Accuracy Algorithm Bias: AI algorithms can continue biases present in the data they’re trained on. This can lead to unfair or discriminatory outcomes in hiring decisions or risk assessments. Therefore, the AI training data set should always be diverse and representative. Model Accuracy: The accuracy of AI models also depends on the quality of the data they’re trained on. Inaccurate models used for outsourced tasks can lead to poor decision-making, flawed predictions, and ultimately, dissatisfied clients. Again, ensuring the AI training data set is accurate and reliable solves this issue. AI Hallucination: AI models, particularly large language models, can sometimes generate factual information that looks accurate but is entirely fabricated. This “hallucination” can lead to misleading results or recommendations in tasks like content creation, market research analysis, or legal document reviews. AI hallucination and algorithm bias can be avoided by simply adding a little human touch. A human review process towards the end can easily identify potential inaccuracies, flawed