Mission Senior Vice President Phil Tomlinson oversees the company’s global products, including trust and security, AI services, digital customer experience, and risk and response. With over 20 years of experience, he has developed digital solutions and advocates to improve mental health in the workplace.
Taskus is a global outsourcing company that provides next-generation digital services and customer experience solutions for high-growth businesses. Focusing on trust and security, AI services, digital customer experience, and risk and response, Taskus helps companies scale effectively while maintaining top-notch support and security. Focusing on innovation, agility and employee well-being, Taskus works with some of the world’s leading brands to achieve outstanding results.
The task emphasizes the balance between technological innovation and human-centered AI. How does this philosophy affect the development and implementation of AI solutions in your industry?
At Taskus, we believe that AI should enhance and enhance human expertise, not replace it. Therefore, we design and deploy AI solutions for this interaction. While designing automation, we do not seek to automate the entire workflow. We understand the most valuable processes in human expertise and then integrate and optimize AI to benefit from that expertise. This helps improve the final result, thereby reducing the number of false positives in fraud detection, for example, compared to fully automated methods.
We found that this integrated approach predicts the entire workflow to enhance human decision-making, resulting in smarter, more effective solutions, both in customer support, content, and risk assessment.
Can you elaborate on how Taskus leverages its proprietary platform, TaskGPT, to improve productivity and customer experience?
Taskgpt is an AI platform that uses Genai to increase productivity and improve customer experience. It can automate routine tasks so that human agents can handle more complex problems. The platform speeds up workflows and helps agents deliver faster and more accurate responses.
TaskGPT can help agents retrieve information and make smart suggestions in real time, making customer interactions smoother and more effective. The demo on our website shows how the platform handles queries instantly, provides useful insights and personalizes customer service faster and more efficiently. By combining AI-driven automation with human supervision, TaskGPT can help scale operations without sacrificing quality, resulting in increased customer satisfaction and faster solutions.
What role does missions play in responsibly deploying AI while helping clients deal with transparency and ethical considerations?
We have developed AI governance frameworks that focus on equity, accountability and transparency in decision-making. Our approach includes the use of multiple training data to help mitigate bias and ensure that AI models are aligned with social expectations. We also make sure that the AI system is interpretable and that its decisions can be easily understood to provide full transparency. As AI continues to evolve, we will continue to integrate human surveillance and risk management methods to ensure that AI deployments not only comply with regulatory standards, but are both safe and ethical.
Agesic AI is expected to revolutionize the industry with its action-oriented capabilities. What are the most promising applications of Taskic AI, which is currently being explored or supported?
Proxy AI enables AI to transform from passive responsive to active, decision-making agents by combining Genai’s content creation capabilities with certain principles in more traditional programming, allowing us to empower AI, but in specific situations or guidelines. We are exploring ways in which proxy AI can expect customer needs and then act. For example, by scheduling a meeting or issuing a refund without manual intervention. In fraud detection, proxy AI can analyze real-time data and behavior patterns to predict and prevent fraudulent activity. In healthcare, it can handle all aspects of claim processing and appointment planning, thereby increasing operational efficiency. These action-oriented features help optimize workflows and deliver an excellent customer experience.
How does the task address the potential risks associated with proxy AI, especially in industries such as fintech and social media?
We mitigate potential risks by taking a multi-pronged approach. First, we build and deploy AI solutions that align with ethical standards, especially in high-risk industries such as fintech and social media. Furthermore, we can often take advantage of the existing corpus of existing directives, guidelines and regulations for organizing government decisions. Agent AI can click on these stores to inform them of their ability to move.
A large part of our risk reduction is not designing our AI solutions to automate the human element – we incorporate strong human supervision into all AI-driven processes. This ensures that any AI-driven decisions are monitored and adjusted or corrected if necessary.
Finally, we test and verify AI solutions to ensure they run in safe and effective parameters. We work closely with our clients to build an AI model that is both efficient and ethical. Our approach ensures AI-driven actions comply with regulatory standards and helps mitigate risks such as bias and misinformation.
Artificial intelligence security is an increasingly concerned issue for enterprises. What safeguards or best practices have implemented tasks to ensure that AI is used in critical applications?
We ensure that our AI model is fair by conducting thorough bias audits and fairness checks, and we prioritize fairness and transparency. We protect sensitive data through strong cybersecurity measures to reduce the risk of violations. Interpretation is a key part of our approach, ensuring AI decisions are understandable for both businesses and consumers. Human surveillance at high risk situations ensures that AI systems do not make major mistakes. By continuously monitoring AI models and complying with industry standards, we can ensure responsible AI deployments while maintaining trust and regulatory compliance.
With your extensive experience in trust and security, how do you see AI evolving to address misinformation, content auditing, and other social challenges?
The task is to use AI to help solve misinformation and harmful content. We work with companies that develop the world’s most advanced Genai models and have an experienced and dedicated team to protect the platform and company from emerging threats and bad actors. Our AI-driven content auditing solutions detect and tag hate speech and harmful content in real time, allowing platforms to remove problematic material before spreading. Our AI model takes into account both context and intent, helps prevent over-censorship and ensures legitimate content is still accessible. Furthermore, we support global platforms by using AI for scalable multilingual moderation and ensure content strategies are maintained across languages and cultures.
How does TaskUS work with customers to ensure that its AI deployment is fair, accountable, and transparent with emerging standards?
We work closely with our clients to develop a tailored AI governance framework that prioritizes equity, accountability and transparency. We develop responsible AI policies, conduct fair audits to identify and address potential biases, and provide compliance training to ensure AI stakeholders are equipped with the knowledge they need. In addition, we assist customers in compliance with global regulations and ethical AI standards, providing the tools needed for transparency and auditability. Our approach ensures that AI systems are not only effective, but also responsible, supporting ethical deployments that align with business goals and social expectations.
The use of synthetic and hybrid data is gaining momentum. How can tasks help customers leverage these data types to enhance model accuracy and reduce bias?
Synthesizing and mixing data can improve model accuracy and reduce bias. We use synthetic data to augment real-world datasets to ensure that AI models have a more diverse and representative basis for making predictions. By combining synthetic data with select real-world data, we can improve the quality of training data sets while addressing possible biases in certain data sources. These approaches also ensure privacy compliance, as synthetic data does not involve personally identifiable information. By integrating these data types into our training workflow, we are creating more inclusive, accurate and secure AI models.
What are your biggest challenges when introducing synthetic and hybrid data solutions? How do you overcome them?
Because it is “synthetic” or fabricated, making the synthetic data “real” becomes a challenge. Keeping privacy regulations comply with and preventing the replication of bias in synthetic data can also be challenging. We use real-world solutions and advanced generation techniques to create synthetic data to make it as realistic as possible. We also work with the legal and compliance teams to ensure that our synthetic data is consistent with privacy regulations such as GDPR and CCPA, thereby minimizing privacy risks. Finally, we don’t automatically “trust” our comprehensive data without bias – we verify it strictly to make sure it is not biased, and we refine the model based on what we learn from the verification.
Multilingual AI has the potential to break the language barrier. How does the mission enable businesses to create more inclusive and culturally conscious AI solutions?
We train models on various language datasets to improve the accuracy of multiple languages. This helps us improve voice-to-text and natural language processing (NLP) capabilities, so we can provide real-time multilingual customer support and engage with customers around the world. We also integrate cultural adaptability into our AI solutions to align content with local specifications and sensitivity. This allows our AI solutions to resonate with a wider range of users and effectively break down language barriers.
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