Excel’s Enduring Role in Business and the Promise of AI-Enhanced Spreadsheets

Reflecting on over 40 years since Excel's debut, it’s clear that spreadsheets remain foundational to business workflows worldwide. Despite all the advancements in software, Excel’s flexibility, ubiquity, and simplicity keep it indispensable. From my experience automating processes and designing system solutions for SMEs across Norway and the EU, I see how deeply embedded spreadsheets are in everyday operations.

What's particularly interesting now is how startups and fintech innovators are layering AI capabilities on top of this familiar interface. For example, Ramp Labs, a small team within the fintech company Ramp, recently launched Ramp Sheets — a spreadsheet tool infused with AI agents that can search web data, perform targeted edits, answer data-related questions, and even generate formulas automatically. Importantly, Ramp Sheets offers a generous daily quota of AI credits without pushing paid tiers, lowering barriers to experimentation.

From a practical standpoint, integrating AI into spreadsheets could reshape how we handle data normalization, formula creation, and data retrieval — tasks I often automate using APIs and platforms like n8n or Zapier. The potential to build automated workflows that interact with external data sources, monitor key metrics, and iterate based on results is significant.

How I’d approach this in practice:

• Start by gathering and normalizing existing spreadsheet data to ensure consistency.
• Use API integrations to enrich spreadsheets with live data from relevant sources.
• Develop automation scripts or workflows that trigger updates, alerts, or reports based on data changes.
• Establish monitoring systems to track performance and user interactions.
• Iterate regularly, refining AI prompts and workflow efficiency to align with evolving business needs.

Practical takeaways:

1. Spreadsheets remain a core tool, so enhancing them with AI can dramatically boost efficiency without replacing familiar workflows.
2. Democratizing AI access through no-cost or low-cost models encourages wider adoption and experimentation.
3. Seamless integration via APIs is key to unlocking connected, real-time data environments.
4. Automation workflows should always include monitoring and iterative improvement.
5. Human factors, like user trust and ease of use, remain critical when introducing AI-driven features.

Source: Ramp Labs (labs.ramp.com/sheets)