Aerium Team
Practical AI for Finance
Note: All working titles for sections, subject to change.
What we're seeing
How AI is evolving in finance
As a function, finance has been at the forefront of AI transformation because it is directly tied to the business' P&L. Over the past 24 months, the scope of work for AI has broadened from being a glorified Google search to automating reports, managing expense reports, and handling quoting across multiple business units.
Tokens are the fastest growing spend category
With the explosion of AI usage, tokens have become the fastest growing spend category1. Managing tokens can be challenging at the organizational level, but at the individual level, one can route tokens.
Where things are going
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Where AI can help you
Getting started
The best way to start using, experimenting with, and benefitting from AI is to download Claude Cowork.
How it's being deployed at Hammond
AI is being deployed within Hammond to accelerate quoting and deliver 100% visibility across the backlog. Custom models were trained on historical BOMs and cost details to predict unit costs for any configuration without requiring engineering team input.
Additionally, the FP&A team is leveraging claude cowork and long-running agents to automate reporting.
Identifying finance workflows where AI can affect margin, forecast quality, or decision speed
Slide content to be pulled in
Working session
Picking HPS's next finance opportunities
In groups of 4-5, discuss how you think AI could be used at 3 levels:
- Within your day-to-day role
- Within your team
- Across Hammond as a whole
Some examples to start the conversation:
- Plant performance dashboards: Automated daily reports on performance on the plant level by automating analysis
- Inventory, capacity, and working capital optimization: Analyzing historical data and incorporating signals across news, commodity markets, and other sources to improve working capital position
- Pricing and sensitivity analysis: Conduct price sensitivity analysis by analyzing historical and current market sentiments
Making the adoption stick
Once there is a list of 2-4 ideas per team, discuss:
- The feedback loop: what users flag, who reviews it, and how the model/report improves over time
- What cadence is this needed: Is this a daily need, monthly need, or annual need
- What problem annoys you most / would make you happiest if it was solved tomorrow
Appendix
What is a token?
Outstanding questions
- What is the makeup of the group?
- How many people will be in the session?
- What are they hoping to get out of this?