GenAI workflows for maximum benefit
Generative AI has the potential to revolutionize many industries. Our approach ensures that Generative AI generates maximum benefit for the organization while being aligned with human needs and values.
Overview of Human Pilots AI integration into the workflow process:
Research: Discovery to gain a deeper understanding of potential benefits and risks to understand how Generative AI can be used in your industry or field. This research involves data analysis and human research.
Pharma Case Study: Pharmaceutical companies can use Generative AI to analyze large sets of data to identify potential drug candidates faster than traditional methods. This technology has the potential to reduce the time and cost of developing new drugs. AstraZeneca is using Generative AI to analyze large sets of data to identify potential drug candidates faster than traditional methods. According to AstraZeneca’s Executive Director of Emerging Technologies, Scott Hopkins, “We’ve seen significant value in using Generative AI for drug discovery. It has enabled us to be much more efficient in identifying new drug candidates and bringing them to market faster.”
Identify Use Cases: Once you have a better understanding, identify specific use cases where it can be integrated into your workflow process. This requires analysis of even larger amounts of data to automate or augment certain tasks.
Retail Case Study: Gen AI can be used to analyze customer data to make personalized recommendations to customers or be used to optimize pricing strategies and predict inventory needs. Amazon is using Gen AI to personalize customer recommendations and optimize pricing strategies. “Generative AI is enabling us to provide our customers with a more personalized shopping experience,” says Amazon’s Chief Scientist, Andreas Damianou.
Evaluate Feasibility: For each use case identified, consider data availability, complexity and potential cost and time savings. Start to identify challenges and risks.
Healthcare Case Study: In the healthcare industry, where Generative AI can be used to analyze medical images to identify potential health issues faster than traditional methods. However, this technology may face challenges related to data privacy and security, as medical data is highly sensitive. Medical device manufacturer Philips is using Generative AI to analyze medical images to identify potential health issues faster than traditional methods. However, according to Philips’ Head of Data Science, David Haak, “We’re very aware of the sensitivity of medical data, so we’re taking extra precautions to ensure that data privacy and security are top priorities.”
Prototype: Quickly create prototypes for the first use case identified as a pilot. This prototype should be designed with user needs and values in mind, then tested and refined based on user feedback and data analysis.
Financial Case Study: Goldman Sachs is prototyping a pilot to analyze market data to make trading decisions and found that using Generative AI improved the accuracy of their trading decisions. According to Goldman Sachs’ Chief Data Officer, George Lee, “Using Generative AI has significantly improved the accuracy of our trading decisions. We’re excited to continue exploring how this technology can help us better serve our clients.”
Implement: Implementing the select use case may involve training employees on how to use the technology, integrating it into existing workflows, and monitoring its performance.
Transportation Case Study: Generative AI is being used to optimize routes for delivery trucks. This technology can be integrated into existing GPS systems to improve efficiency and reduce costs. UPS is already a pioneer in using AI to better serve its customers. UPS is incorporating Generative AI to further optimize routes for delivery trucks. According to UPS’ Chief Analytics Officer, Juan Perez, “Using Generative AI has enabled us to optimize our delivery routes and reduce costs…” He’s also said in the past that UPS is "very focused on making sure that we’re using this technology in a responsible way, and that we’re transparent with our customers and with society about how we’re using it. And so we’re investing a lot of time and energy in developing governance and oversight frameworks to make sure that we’re using these technologies in an ethical and responsible way."
Iterate: Evaluating the performance iterate as necessary. The learnings come from user feedback and data to adjust workflows that better meet success metrics of integration and continue to identify new use cases where the learnings can further be applied.
Insurance Case Study: Generative AI can be iterated to improve accuracy and reduce false positives over time. Allstate is using Generative AI to analyze claims data to identify fraudulent claims. According to Allstate’s Chief Data and Analytics Officer, Suren Gupta, “Generative AI has helped us significantly reduce the number of fraudulent claims we process. We’re constantly iterating and improving our use of this technology to better serve our customers.”
Integrating Generative AI into workflows can be a complex process, but it can also yield significant benefits for organizations. Organizations can ensure that Generative AI is integrated in a way that is aligned with human needs and values while maximizing the technology's potential for automation and augmentation.
AI Content Disclaimer: The human author who wrote this augmented the final post with Generative AI tools for transcription (Fireflies.AI), organization (ChatGPT 4), research (Perplexity AI) and image creation (Midjourney 5).
