Find out how the reliance on human-produced knowledge demands a robust strategy and FEAT to avoid pitfalls in AI adoption

Joon-Seong (JS): Businesses can consider the following principles to integrate GenAI and large language models (LLMs) seamlessly throughout their value chain.

    • Lead with value: Shift the focus from siloed use cases to prioritizing business capabilities across the entire value chain based on the return on investment. Organizations need to be value-led in every business capability that they choose to reinvent with GenAI. It is recommended to pursue investments in two categories: firstly, “table stakes” investments that offer radical efficiency; and secondly, strategic bets that stand to bring truly novel advantages.
Lee Joon-Seong, Senior Managing Director, Center for Advanced AI Lead, Growth Markets, Accenture
    • Understand and develop an AI-enabled secure digital core: Enabling a modern data platform is very important for success. Re-architecting applications to be AI-ready is also critical, with a flexible architecture that allows you to access a range of models in partnership with the ecosystem. This security is a foundational element for protecting personal or proprietary data.
    • Reinvent talent and ways of working: Adopt people-centric approaches to GenAI adoption and innovation. To prepare workers for further integration of AI, organizations must adapt existing operating models for new ways of working. For example, adopt new techniques and high-performance equipment; investing in modernizing technology for a secure digital core; emphasizing people in all reinvention strategies, such as new leadership training, involving people in design, and upskilling them on GenAI.
    • Close the gap on responsible AI: Another imperative is to design, deploy and use AI to drive value while mitigating risks. This includes the strategy and development of responsible AI monitoring and compliance, as well as employing AI security into the entire value chain. For example, ensure all stakeholders can assess their AI and data analytics solutions based on fairness, ethics, accountability, and transparency (FEAT) principles. Demystify GenAI complexities and draft a risk framework for its responsible integration.
    • Drive continuous reinvention: Given that GenAI-led reinvention journeys are complex and multi-year in nature, firms need to be prepared to allocate capital, time and talent for the long term. To do that, embrace a modular, step-by-step approach to innovation that spans multiple years. In addition, cultivate a corporate culture that views continuous reinvention not just as a strategy but as a core competency.