Council Post: ​​How to get ready for Generative AI journey
Generative AI is a game-changer in the world of artificial intelligence, allowing for the replication of human speech and decision-making. As a result, generative AI is set to transform the way we work, particularly in areas such as production design, design research, visual identity, naming, copy generation, and real-time personalization. However, the world may not be ready for the advent of generative AI, as there are concerns about job loss and striking a balance between AI and human work. In areas like production design, design research, visual identity, naming, copy generation and testing, and real-time personalization, generative AI will become an indispensable creative partner for people, revealing new ways to reach and appeal to audiences. While it comes with its set of advantages, the world is unable to keep up with AI. Either the fear of losing your job to AI or not being able to find a balance between generative AI and the work is an issue. Is the world ready for Generative AI?? By introducing a new level of human and AI collaboration in which most workers will have a “copilot,” generative AI will fundamentally alter both the nature and scope of work as we currently understand it. Nearly every job will be impacted — some will be terminated, most will be transformed, and many new employment will be created. Companies who start breaking down occupations into tasks today and invest in retraining employees to work differently alongside machines will set new performance frontiers and have a significant advantage over less creative rivals. How can you embrace Generative AI:- 1- Jump in with a Business driven attitude Even though new innovations have clear benefits, implementing them across an organisation can be difficult, especially if the innovation is disruptive to the way things are done now. Organisations must approach experimenting from two angles. One, which concentrates on possibilities with low-hanging fruit and employs consumable models and apps to obtain immediate returns. The other is centered on business innovation, customer engagement, predictions, and services utilising models that were made specifically for the organisation using its data. The business case can only be defined and implemented successfully with a business-driven attitude. They will discover which types of AI are most suited for various use cases as they experiment and look into potential for reinvention. This is because the amount of investment and sophistication needed will vary depending on the use case. Additionally, they will be able to test and refine their methods for protecting the privacy of data, carefully model accuracy, bias, and fairness, and discover when “human in the loop” measures are required. 2- People first approach For generative AI to succeed, it requires attention to humans and training must be like technology. In order to tackle the two unique difficulties of generating and employing AI, businesses should significantly increase their investment in personnel. This entails developing expertise in technical areas like enterprise architecture and AI engineering as well as teaching people throughout the organisation how to collaborate successfully with AI-infused processes. In fact, independent economic research shows that businesses are underinvesting significantly in assisting individuals to stay up with AI advancements, which necessitate more cognitively challenging and judgment-based activities. There will also be completely new positions to fill, such as linguistics specialists, AI editors, AI quality controllers, and prompt engineers. 3- Proprietary Data First Access to domain-specific organisational data, semantics, expertise, and procedures will be necessary for customising foundation models. By adopting a use-case focused approach to AI in the pre-generative AI era, businesses could still benefit from AI without having modernised their data architecture and estate. That’s not the situation anymore. Since foundation models require enormous quantities of carefully curated data to learn, every organisation must prioritise addressing the data crisis immediately. Companies must take a deliberate and methodical approach to gathering, developing, enhancing, protecting, and delivering data. They want a modern, cloud-based enterprise data platform with a reliable, reusable set of data products. These platforms’ cross-functionality, use of enterprise-grade analytics, and storage of data in cloud-based warehouses or data lakes allow for the decentralisation of data from organisational silos and its democratisation for usage by the entire organisation. Then, all corporate data can be analysed collectively in one location or via a distributed computing approach, like a data mesh. 4- Responsible AI needs to step up The requirement for every organisation to have a strong responsible AI compliance policy in place is becoming even more urgent given the fast deployment of generative AI. This includes safeguards for evaluating the possible risk of generating AI use cases at the design stage and a way to implement ethical AI practices across the entire organisation. The top-down definition and leadership of an organization’s responsible AI principles should be converted into an efficient governance framework for risk management and compliance with both organisational principles and policies and relevant laws and regulations. Organisations must transition from a reactive compliance strategy to the proactive development of mature Responsible AI capabilities using a framework that combines principles and governance, risk, policy, and control, and technology in order to be responsible by design. This is a crucial time. The way we think about artificial intelligence has quietly undergone a change in recent years thanks to generative AI and foundation models. The world has now become aware of the potential this presents thanks to ChatGPT. Although artificial general intelligence (AGI) is still a long way off, technology is developing at an astonishing rate. The way information is accessible, content is produced, consumer demands are met, and businesses are all about to enter an enormously exciting new phase. Businesses must devote as much money to staff development and operational improvement as they do to technology. Realising the full potential of this stepchange in AI technology will depend on a number of variables, including fundamentally rethinking how work is done and assisting people in adapting to technology-driven change. It’s time for businesses to redefine themselves and the sectors in which they compete by utilising revolutionary developments in AI to push the boundaries of performance. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
Realising the full potential of this stepchange in AI technology will depend on a number of variables, including fundamentally rethinking how work is done and assisting people in adapting to technology-driven change. It’s time for businesses to redefine themselves and the sectors in which they compete by utilising revolutionary developments in AI to push the boundaries of performance.
Realising the full potential of this stepchange in AI technology will depend on a number of variables, including fundamentally rethinking how work is done and assisting people in adapting to technology-driven change. It's time for businesses to redefine themselves and the sectors in which they compete by utilising revolutionary developments in AI to push the boundaries of performance.
["Generative AI"]
["data science", "ChatGPT", "artificial intelligence", "AWS", "AI", "RAG", "Aim", "analytics", "generative AI", "Generative AI", "foundation models"]
["AI", "artificial intelligence", "data science", "analytics", "generative AI", "foundation models", "ChatGPT", "Aim", "RAG", "AWS"]
Muthu has two decades of experience in Data &amp; AI with extensive experience in implementing AI and ML solutions for customers in the Banking, Insurance, Healthcare, Retail, and Manufacturing industry. His research interests include AGI, Edge AI, AI &amp; ML Operations, Quantum ML, and human-computer interaction. Currently Muthu is the Chief Data Scientist at Ascendion. He proactively works with business executives and various departmental heads across the business in order to provide advanced analytic data modeling systems.