Recently, I had the privilege of addressing 90+ top-level executives, delving into the various aspects of generative AI. Our discussions covered a wide range of topics, including the practical applications of generative AI across different industries. We delved into subjects such as establishing a center of excellence (COE), setting up safeguards and guidelines, formulating policies and procedures, mitigating risks, selecting suitable technologies, optimizing data quality, identifying and prioritizing relevant use cases, promoting responsible AI practices, and contemplating the future impact of generative AI on the workforce.
Currently, I dedicate my time to collaborating with Fortune 500 companies, evaluating the influence of AI. My EY role involves aiding clients in devising a strategic plan, comprehending the optimal data architecture, establishing an approach for governance and responsible AI, as well as charting a course to prioritize use cases and execute essential pilot projects. These initiatives span multiple areas such as consumer engagement, supply chain, product innovation, IT, and employee enablement, among others.
During our discussion we talked about why now? This ties into proprietary research I have been speaking on over the years tied to AI. I have researched across generational cohorts, and what is consistent is the primary behavioral driver for why individuals will adopt and engage with intelligent systems it’s tied to ease & convenience.
This leads to four key pillars that are critical to understand the potential impact of Gen AI beyond efficiency gains that also enable ease and convenience for employees and consumers.
1 – Accessible – Generative AI is a major step towards ease and convenience for generation of images and various forms of text-based outputs but also how we work with data and most importantly how we can democratize access to insights like never before.
2 – Enabler – I see Generative AI enabling us similar to how the calculator revolutionized mathematics by simplifying complex calculations and reducing human errors. Generative AI has the potential to impact a much broader range of tasks such as creativity, problem-solving, and decision making.
3 – Knowledge Strategy – Most organizations talk about data and how they drive data driven decisioning. The reality is there are still limitations in how our clients store, action and derive business decision from insights, it’s based on diagnostics vs truly allowing for real-time decisioning and advanced scenario modeling. That is where AI and generative AI can enhance data-driven decisioning in real time that doesn’t require an individual to understand advanced analytics.
4 – Intelligence Augmentation – I am big believer in intelligence augmentation. That this technology will empower versus replace. Roles like data science will evolve from focusing primarily on processing of information and model building, to fine tuning data with a focus on storytelling based on output. The types of data we work with will shift as well.
As we shift from enablement to autonomous actions, Ai will become more capable of generating content, designs, and ideas, the role of human workers will shift from being primary creators to acting as editors, curators, or supervisors of AI-generated content.
In the near-future work outputs will be about combining our creative instincts with AI-generated content to enhance our decision making. One final thing of note, humans still possess superior emotional intelligence and understanding of cultural nuances. The combination of machine-based output + human understanding that will be key.
Follow Tom Edwards @BlackFin360