Operand Bets That AI Can Run Your Business Strategy

The Macro: Business Intelligence Is Swimming in Dashboards

The BI and analytics market is enormous. Somewhere north of $30 billion, depending on whose research you trust. Tableau, Power BI, Looker, and Sisense have spent the last decade making it easier to visualize data. They succeeded. Every mid-size company now has dashboards. The problem is that dashboards don’t make decisions. They present data and hope someone with the right context interprets it correctly.

This is the gap that a wave of AI-native analytics companies are trying to fill. Hex and Mode are adding AI layers to notebook-style analytics. ThoughtSpot lets you ask questions in natural language. Palantir’s AIP platform targets large enterprises with AI-powered operational decision-making. But most of these tools still end at “here’s the insight.” The human still has to decide what to do about it.

For retailers and manufacturers specifically, the decision surface is enormous. What should this product cost in this store this week? How many units should we order for next quarter? Which SKUs should get shelf space in which locations? These are optimization problems with real financial consequences, and most companies solve them with spreadsheets, gut feel, and a quarterly planning meeting that takes two weeks.

The Micro: From Insight to Action, Without the Middleman

Operand builds AI systems that don’t just analyze business data. They execute on it. The company works with retailers, manufacturers, distributors, and capital allocators on day-to-day decisions around pricing, forecasting, allocation, and diligence. The pitch is that their AI doesn’t hand you a report and walk away. It integrates into your operational workflow and drives the strategy directly.

The founding team is three college dropouts who clearly decided startups were more interesting than lectures. Ram Gorthi dropped out of Dartmouth, where he was studying CS and Math. Akhil Iyengar left Cornell, where he was doing CS and Econ. Arjun Sahney rounds out the trio. They came through YC’s Winter 2025 batch and have grown to a five-person team in San Francisco. Tom Blomfield, the Monzo founder and YC partner, is listed as their primary partner, which says something about the caliber of mentorship they’re getting.

The “private-markets intelligence” angle visible on their website suggests they’re also targeting capital allocators, not just retailers. That’s a smart expansion of the addressable market. A hedge fund deciding which retail company to invest in and a retailer deciding how to price their products both need the same underlying capability: turning messy real-world data into actionable strategy.

What I find interesting is the ambition of the positioning. “AI to run business strategy” is a big claim. Most companies in this space are careful to say “AI to assist” or “AI to augment.” Operand is saying they want to run it. That takes either serious confidence in the product or serious confidence in the sales pitch.

The Verdict

The market opportunity here is real and underserved. Plenty of tools help you understand your data. Very few actually tell you what to do next, and even fewer integrate deeply enough into operational workflows to execute on those recommendations automatically.

My main concern is the go-to-market challenge. Selling AI-driven decision-making to retailers and manufacturers means selling to organizations that are structurally conservative. These are companies where the VP of Merchandising has been setting prices based on experience for twenty years. Telling them an AI should do it instead is a hard conversation. Operand needs case studies with hard numbers. “We increased margin by X% for retailer Y” is the only thing that moves the needle in enterprise sales.

The team is young and technically sharp, but enterprise sales cycles are long. Six to twelve months is normal for this kind of deal. At 30 days, I’d want to see a few paying pilot customers and real data on outcomes. At 60 days, expansion within those accounts matters more than new logos. Can they go from one department to three within the same company? At 90 days, the question is whether Operand is building a product or a consulting practice. If every customer requires heavy customization, the margins won’t support a venture-scale business. If the AI systems are genuinely generalizable across retailers, this has real legs.