The Arcade Token Strategy
Imagine you’re at an arcade. Each game you want to play requires a coin, but you don’t know how many games you’ll play or the total cost by the day’s end. This uncertainty is similar to running GenAI queries without a clear understanding of the costs.
To address this, I see automation as a strategic tool, much like knowing how many games you can play with a fixed number of coins. By identifying and automating repetitive GenAI tasks, we can significantly reduce the need for continual manual queries, which often lead to increased costs.
Why Focus on Automation?
1. Reduced Costs: Like putting a string on a coin to retrieve and reuse it, automating repetitive queries means we spend less while doing more.
2. Increased Efficiency: Automation speeds up routine tasks, freeing our team to focus on more complex and value-driven activities.
3. Predictability: Just as you’d plan your arcade visits better if you knew the game costs upfront, automating GenAI tasks provides a clearer view of operational expenses, helping avoid budget surprises.
4. Strategic Partnership with COE: Working with the Center of Excellence (COE), we can identify which GenAI tasks are routine and set up automation. This collaboration ensures that we are not just reacting to costs but proactively managing them.
The key challenge here is not just the expense but the lack of visibility into potential cost overruns. Without clear insight, planning and budgeting for GenAI can feel like a game of chance.
Integrating automation into our GenAI strategy isn’t just about cutting costs—it’s about making our operations more predictable and efficient, much like having an infinite number of plays at the arcade with a known budget. It’s about transforming our approach from reactive spending to strategic planning.
We all should be looking into, optimizing our GenAI usage to get the most out of our resources while keeping costs transparent and under control.
𝗡𝗼𝘁𝗶𝗰𝗲: The views expressed in this post are my own. The views within any of my posts or articles are not those of my employer or the employers of any contributing experts.
