Supreet is a data and AI product evangelist and the product owner of various data-driven products. Before this role, she was a Data Science Consultant, where she worked on impactful data science use cases and made launch strategies for various pharmaceutical clients. She completed her (MBA+MS) in Data Science from Rutgers University. She holds a Bachelors in Mathematics. She is an ardent writer and international speaker on data science and AI topics. She is also on the advisory board for Rutgers University’s MBS analytics program and other organizations. She advocated for women in technology and was selected as one of the Google WomenTech Makers ambassadors. She was recently named the LinkedIn Top Community Voice for Machine Learning and Data Analytics.
In today’s fast-paced world, automation has become a buzzword often associated with increased efficiency and productivity. With the advent of technology, automation has made inroads into almost every industry, from manufacturing to healthcare.
However, as machines continue to replace human workers in many areas, questions are being raised about the role of humans in this automated landscape. The debate between human autonomy vs. automation is not new; it has existed since the industrial revolution. But with recent advancements in artificial intelligence and machine learning algorithms, this debate has taken on a whole new dimension.
According to a study conducted by McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030. However, proponents of automation argue that it can enhance human autonomy by taking over mundane tasks and allowing individuals to focus on more creative and strategic work.
Let’s discuss some mundane tasks that could use a touch of AI for better and faster results:
- Data Quality Frameworks: Historically, Data Quality Checks were conducted in Excel, which made them susceptible to human error. However, companies now have the opportunity to enhance their data quality processes by developing automated data quality pipelines driven by predefined rules. This approach helps identify and flag instances of poor data quality. Instead of analysts having to review every single record manually, they can focus their efforts on analyzing the flagged records, significantly improving the efficiency of the process.
- Image Recognition: In the past, patients would typically undergo basic scans requiring doctors to analyze each scan manually. However, there is now an opportunity to streamline this process by leveraging image recognition models for initial analysis. By employing these models, initial findings can be derived automatically, and healthcare professionals (HCPs) can subsequently validate and add any necessary information. It’s important to note that this approach doesn’t eliminate the crucial role of HCPs. Instead, it reduces the effort required for initial analysis, allowing them to focus their expertise on validation and providing additional insights when needed.
- Personalized HealthCare: As individuals, we often experience minor health issues such as headaches, stomach aches, etc. Typically, our initial response is to seek urgent care. However, by leveraging a customized chatbot model trained on patient vitals and comprehensive disease data, we can offer an AI-powered chatbot accessible to everyone. This chatbot can provide initial care recommendations by asking questions and analyzing the responses. It enables individuals to receive prompt guidance based on their specific symptoms and conditions, allowing them to make informed decisions about their health.
All these examples aim to augment and empower employees rather than eliminate their roles. By involving them in the process and keeping them informed, we can ensure that specific manual tasks are reduced, freeing up their time to focus on more strategic initiatives, research work, or areas they are passionate about. This approach allows employees to leverage their skills and expertise to bring more excellent value to the organization, fostering growth and fulfilment.
AI can change the way you operate, so here are some best practices that organizations can follow for successful AI implementation:
- Define a clear strategy: Define a clear plan for AI and automation within your organization. Determine the goals and objectives you want to achieve through these technologies. Align the strategy with your overall business objectives to ensure that AI and automation initiatives contribute to the organization’s success.
- Identify suitable use cases: Identify the areas within your organization where AI and automation can bring the most value. Look for tasks or processes that are repetitive, time-consuming, or prone to errors. Prioritize use cases that significantly impact productivity, efficiency, or customer experience.
- Data readiness and quality: AI and automation rely heavily on data. Ensure your organization has a robust data infrastructure, including data collection, storage, and processing capabilities. Assess the quality and availability of your data to determine if any improvements are necessary. Clean, relevant, well-structured data is crucial for training accurate AI models and achieving reliable automation outcomes.
- Build interdisciplinary teams: Establish teams of data scientists, software engineers, domain experts, and business stakeholders. Collaboration among these different roles is essential for successful AI and automation projects. Encourage a culture of open communication, knowledge sharing, and collaboration to foster innovation and practical problem-solving.
- Ethical considerations: Prioritize ethical considerations when developing and deploying AI and automation systems. Ensure your organization adheres to legal and ethical frameworks, privacy regulations, and data protection standards. Establish guidelines for the responsible use of AI, including transparency, fairness, and accountability.
- Test and iterate: Implement a robust testing and iteration process for AI and automation projects. Start with small-scale pilots or proofs of concept to validate the technology and gather feedback. Continuously monitor and evaluate the performance of AI models and automation processes, making improvements based on the insights gained.
- Continuous learning and adaptation: AI and automation technologies evolve rapidly, so staying updated with the latest advancements and industry trends is crucial. Encourage a culture of constant learning within your organization, invest in research and development, and foster partnerships with external experts and organizations. Regularly evaluate the performance and ROI of your AI and automation initiatives and adapt your strategy accordingly.
In conclusion, I firmly believe that “AI won’t take your job, but a person using AI will.” We need to embrace technological advancements and harness the power of AI while ensuring that humans remain an integral part of the process. We can create robust and impactful products by combining human expertise with AI capabilities. Embracing this synergy allows us to leverage the strengths of both humans and AI, fostering innovation, productivity, and meaningful outcomes.
This fascinating topic of automation vs autonomy, along with others will be discussed at the annual Ai-Med Global Summit, scheduled for May 29-31 2024 in Orlando. Book your place now!
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