Introduction
Similar to a runner training for a marathon, AI’s competitive usage also requires training and knowledge. Today, however, organizations that have been successfully utilizing AI are most likely to win. The advantage is staggering, with properly implemented AI solutions ultimately accelerating knowledge work by an average of 40%*. Those left behind must accelerate quickly before their respective industries evolve even further, posting a very unique business risk categorically. Despite this, there are proven methods and techniques for accelerating AI, GenAI’s utility, and more, for organizations looking to accelerate their practical applications of artificial intelligence.
Implementation Challenges Today
In today’s world, every single employee is essentially now a knowledge worker. Thus, applications of GenAI are more or less so relevant to all who are managing even 1 spreadsheet of data, a small group of tasks. Stanford’s Human-Centered AI Institute and MIT’s Sloan School of Management measured the delta of knowledge or data-related work for groups armed with AI, and others without, and found the range of value to be large across nearly all industries.
Acceleration differs mainly across 3 variables:
- The specific tasks, or executions, are being performed.
- The quality of the AI implementation being utilized.
- The user’s AI literacy level.
Some organizations reported productivity gains exceeding 50%, while others saw minimal, or even negative impacts across certain AI product usage internally. While the variables of operations or tasks differ, and the quality of each tool will differ (mostly due to data) we have 1 variable that can be strategically improved: AI literacy.
The gap between people, AI, technology, and practicality can be easily illustrated through the difference of self-paced learning, versus instructor or expert-led development. “One-size-fits-all” training programs typically see only 8-12% productivity improvements, however, personalized programs built around company goals and data can enhance improvements by up to 4x. Translating the specifics of GenAI into our data warehouses can sometimes be difficult, but with the fundamentals of AI literacy, organizations can:
- Prioritize key datasets to be supplemented with AI literacy.
- Prioritize manual processes and operations to be expedited through advanced GenAI.
- Establish a responsible RAI Policy based on organizational ethics.
- Establish a practical AI Playbook for specific teams or business cohorts.
Enabling AI Acceleration
While it may be difficult to envision, orchestrating both data opportunities from your dataset in a bottom-up perspective, and mating them with the top-down AI investments across the organization will enable AI adoption at a remarkable speed. The practical applications of GenAI tend to quite literally procure that “aha” lightbulb moment, and that’s where adoption’s success rate skyrockets. If done carefully across large enterprise organizations, AI use cases can be scaled across thousands of workers.
Having trained over 1000 enterprise professionals around driving internal value from artificial intelligence, AI Prophets is passionate about co-crafting AI solutions to enable human and business growth. Learn how to improve the usage of your AI products with the practical support of a data science expert.
Academic References:
*Brynjolfsson, E., Li, D., Raymond, S., & Yang, E. (2023). Generative AI at work: The impact of AI on productivity and labor markets (SSRN Scholarly Paper No. 4573321). MIT Sloan School of Management. https://doi.org/10.2139/ssrn.4573321
