The other week, I sat down with Shiv Trisal, who leads global GTM at Databricks across manufacturing, automotive, transportation and logistics, energy, and utilities.
Early into our conversation, he told me about a recent conversation he had with the CEO of a large industrial company. The company had invested heavily in digital infrastructure, and Shiv asked how long it takes them to understand the factors driving the company’s margins.
“Shiv, it can take me three weeks.”
Then the CEO paused.
“If at all.”
The CEO explained to Shiv that someone was still having to pull information from different systems and then figure out what had happened. This “human middleware,” as Shiv put it, is still needed in most production deployments across industrial enterprises.
Shiv’s career has been focused on making technology work in legacy industrial settings. Before Databricks, Shiv led product strategy and commercialization for all of Raytheon Technologies’ Connected Aviation division. In this role, he built a predictive aircraft-health platform that’s now deployed at over 50 airlines. He also built the commercial engine to sell those products.
“AI has to change the fundamental economics of the work being performed,” Shiv explained when I asked how he defines success for AI inside large industrial companies. In our conversation, we discussed what this means in practice, why it’s still breaking down in places, and where the biggest opportunities are.
Redefining the Unit of Value
When Shiv was building predictive maintenance products, his team initially thought that if they predicted a component was going to fail and could send an alert with a recommendation before it happened, that alone would provide a tremendous amount of value.
In part, it did. But it also meant that for every alert, someone still had to review and validate it, and then decide what to do next. So instead of alleviating workloads, the alerts actually created more work to manage, especially since many of the alerts were false alarms.
“Did we make that human’s life harder or easier by overloading that person with more alerts?” he asked. “We called it ping and ding. Ping, meaning alert, and ding, meaning showing up in an inbox. If there were a thousand pings and ten thousand dings, that’s not helpful for anybody.”
Today, Shiv defines value in terms of resolution. A system has to usher a problem all the way through to a completed action before it can be considered valuable. “I can research faster, but it doesn’t mean I did work faster,” he said.
As I wrote about here, this applies to anyone building AI solutions that touch the physical world. Speeding up one step usually means the bottleneck moves somewhere else. So which step you accelerate matters more than how much you accelerate it.
John Deere as a Case Study
One of John Deere’s early AI applications was parts forecasting. Across its dealer network, different locations each had different approaches to deciding which parts to stock. Deere wanted to better connect equipment needs with inventory decisions.
“Inventory management is actually the moneymaker. Parts are the profitability lifeline for equipment manufacturers,” Shiv said.
Shiv described Deere’s investments in equipment telemetry, parts information, and service data as a way of making the business understandable to software. “They built their own language, and I use the word language freely here, of how they run farming.” This is all context about the relationships between a machine, its customer, its service history, and the relevant policies. “We basically know what’s happening at any farm, and we basically know where parts are,” he added.
More importantly, however, this work built a foundation for subsequent applications. He pointed to repair and warranty quoting as an example of what this enables. Because Deere built this live machine-readable context layer, agents can handle qualifying requests without waiting for a human to be at their desk. “After all, what’s the point of a human in the loop if the human is sleeping?” Shiv joked, adding that quotes are won or lost based on a matter of hours or less sometimes so if you’re waiting for a human to manually approve a quote, that can cost you the deal.
In other words, Deere did the hard work of making its business understandable to software upfront, so deploying agents was actually the easy part. Today, building the context layer is where most companies are getting stuck.
Making Industrial Knowledge Usable
“The reason AI works is the data. It’s not the models,” Shiv said. “The models became too much of the spotlight. They’re important. They make data more valuable. But without data, the models can’t actually do any work reliably inside of a company.”
And the data that matters most isn’t public. “Nobody tells you how to do defect detection in the semiconductor industry on Reddit,” he said.
The most valuable data is bespoke to each company. A general-purpose model needs access to that information and an understanding of how it fits together.
AI can help with this. Shiv gave the example of field engineers writing reports of what happened to an asset, each report with a different format. “That’s a problem that would have taken a data scientist to go train a machine learning model on a very specific corpus,” he said. “Something that would’ve been a six-month project is down to hours.”
He also sees opportunities to automate the work of connecting new data to an existing representation of the business. “Why does it have to take a human every time to go physically model every aspect for your use case? You should always have a connected knowledge graph that gets automatically refreshed as you add more data.”
Shiv breaks the path from data to action into three layers.
Context: machine-readable understanding of equipment, people, parts, policies, and relationships
Knowledge work: analysis, forecasting, retrieval, and reasoning needed to decide what should happen next
Action: permitted changes a system can make to carry a decision through (this layer needs very explicit boundaries)
Each layer should use different tools. For example, mathematical and optimization tools for calculations, machine learning for forecasting and patterns, and language models for tasks that need interpreting and summarizing information. “Where I have certainty, use math. Where I have uncertainty, use machine learning because you can quantify uncertainty,” he said.
The Business Case Is Part of the Product
Companies that start by asking for “agentic use cases” are choosing an approach before defining the problem. “What you’re telling me is you’ve presupposed a solution even before you’ve defined a problem. Why would you do that?”
Instead, Shiv’s starting point is competitive advantage. What does the company do particularly well? Where could AI strengthen that advantage, and where might it threaten it?
These questions require leadership involvement. “Without top-down, bottom-up doesn’t succeed,” Shiv said.
Executives set priorities and budget, but the broader organization adopts the tools. “You need both perspectives and buy-ins to build something useful,” Shiv told me.
People spend time learning tools and changing their routines. “Your productivity actually drops before it goes back up,” Shiv said. “The question is how many places in the organization can you afford that to happen?” Companies need to be selective about where they choose to implement AI, and how many changes they take on at once.
“All ROI analysis is wrong, but it needs to be done,” Shiv said, quoting a line he says he’ll repeat for the rest of his life. It’s the stake in the ground, a starting estimate that forces priorities, and that you measure reality against and revise as you learn.
The 50% Flat Tax on Engineering & The Opportunity
Toward the end of our conversation, I asked Shiv what he would build if he were starting a company today.
“Past waves of digital transformation have left engineering and R&D mostly untouched,” he said.
He sees a substantial amount of engineering work happening between and around existing tools. “My experience of working with engineers in the physical world is that they pay a 50% flat tax on their productivity. Even if there were software paradigms they were trained in, the work they do outside the system is where the gap is.”
He gave the example of information generated by simulations. “I don’t need to recreate a simulation engine, that’s not the point,” he said. “But the artifacts from those simulations: which ones were successful, which ones weren’t, what were the parameters? If I have enough of that, I can create a faster model to help people reduce their reliance exclusively on simulations,” he said. Simulations are expensive and slow, so the accumulated record of what worked and why is a shortcut and narrows down what engineers need to investigate.
Engineers build on prior designs, combining known approaches and investing in the candidates most likely to succeed. “Everybody gets obsessed with breakthrough innovations, whether it’s in material science or drug discovery,” Shiv said. “But the reality of most engineering work is it’s derivative. You integrate, validate, test, and pick the designs with the highest chance of success.” There is an opportunity to support this whole sequence, from finding relevant prior work through evaluating options and coordinating next steps.
“AI will create a whole new class of software that we don’t even know yet,” Shiv said. “There is 99% of software that comes in the next five years where we don’t even know what it looks like today.”
Author’s note: An LLM was used for light copy editing only (spelling, grammar, and clarity). Content, meaning, tone, and structure remain unchanged.