Practical AI for Finance

Note: All working titles for sections, subject to change.

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.

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.

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The best way to start using, experimenting with, and benefitting from AI is to download Claude Cowork.

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.

Slide content to be pulled in

In groups of 4-5, discuss how you think AI could be used at 3 levels:

1. Within your day-to-day role
2. Within your team
3. 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

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

• What is the makeup of the group?
• How many people will be in the session?
• What are they hoping to get out of this?

Footnotes

1. https://ramp.com/blog/ai-token-spend-launch ↩