We went looking for what companies are actually building with AI. The answer was not more chatbots. It was drones carrying diagnostic samples, driverless Frito-Lay trucks, AI-guided flight paths, repair copilots, and rugged GPU laptops in Ukraine. We reviewed 136 use cases from the last 20 days. The biggest surprise: only 38 included a reported outcome.
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Explore observations from audit partners on the trends shaping business decision-making, risk management, and corporate strategy today.TL;DR: The whole issue in six bullets
- AI is escaping the chat window. It is moving into hospitals, trucks, aircraft, heavy machinery, and minefields.
- The best deployments are narrow. One route. One machine. One professional workflow. One result that can be checked.
- The model is rarely the real advantage. The hard parts are proprietary data, permissions, workflow design, audit trails, and a safe handoff to a person.
- SEC filings tell a more useful story than product demos. They reveal board oversight, power and cooling plans, offline hardware, and security risks.
- The evidence is still thin. Of 136 recent entries in our directory, 98 had no reported outcome.
- The test is simple. Ask what changed, who owns it, what the AI is allowed to do, and whether anything measurable improved.
Surprise #1: A hospital without a lab
Narayana Health in Bengaluru is not using AI to write discharge notes. It is using AI to dispatch drones.
Airbound says it has completed more than 1,000 flights with Narayana. Diagnostic samples travel about 2.5 miles in roughly seven minutes. The same trip can take three to five hours by road once batching is included.
Then comes the startling part: Narayana's new Banashankari Hospital was designed without an onsite diagnostic lab or blood bank. It will use drones to connect with centralized facilities.
That is what applied AI looks like when it becomes infrastructure. The building itself changes.
Surprise #2: Frito-Lay has 41 driverless trucks
Gatik's trucks move Frito-Lay products around Dallas, Phoenix, and northwest Arkansas. The company started with fixed trips of less than 10 miles and grew to dynamic routes with dozens of stops covering up to 400 miles.
Gatik's chief executive says the company has $600 million in contracted revenue. Treat that as a company claim, not booked revenue.
The pattern I see is simple. The winning move is not “make a truck intelligent.” It is “make one valuable route predictable enough to automate.”
Surprise #3: Google wants AI to redraw flight paths
Google Research and NATS, the UK's air-traffic-services provider, are testing whether AI weather forecasts can help aircraft avoid creating contrails over the North Atlantic.
The 30-month Operation Blue Skies trial includes two four-month operational phases and could cover about 10,000 flights a year in Shanwick airspace. But the best detail is the feedback loop: satellite observations will check whether the predicted contrails actually formed.
Predict. Change the route. Look at the sky. Check the result.
That is a much stronger AI use case than a dashboard full of estimated savings.
Surprise #4: Caterpillar's moat is 16 petabytes
Caterpillar's Cat AI Assistant lets a field technician ask for a repair procedure, troubleshoot a machine, or identify a part by voice.
The voice interface is the easy part. Behind it sit 1.6 million connected machines and 16 petabytes of structured data. Caterpillar is also using digital twins and AI agents for software development and testing.
Its chief digital officer says the real challenge is fitting AI into the way technicians and operators already work.
This may be the most important pattern in the directory. A generic model can produce an answer. A production system has to know which manual applies, which machine is in front of the technician, what that technician can change, what must be logged, and when a human must take over.
The assistant is not the moat. The operating context is.
The SEC filings were even more revealing
The most interesting filings were not about model benchmarks. They were about control, electricity, hardware, and failure.
Intapp: Make the AI show its work. Intapp's annual report describes expert agents that follow firm-specific playbooks using proprietary data, an industry ontology, and institutional memory. It also describes permission controls and a decision-tracing audit log. Governance is part of the product, not paperwork added afterward.
Sysco: Put AI on the board's monthly calendar. Sysco renamed its Technology Committee the AI Transformation & Technology Committee and said it would meet monthly. It also announced a $100 million cost-savings program that it says will be supported partly by AI and automation. The committee is real; the savings are still a target.
ChronoScale: Applied AI ends in liquid cooling. The company says it plans to build 50 megawatts of North American AI-compute capacity for Microsoft using NVIDIA GB300 NVL72 systems. The plan requires power, land, delivery schedules, and liquid cooling. It is planned capacity, not a finished deployment.
Safe Pro: Five laptops can matter more than a data center. A roughly $180,000 subcontract covers five hardened GPU laptops and three years of software licensing and upgrades for explosive-threat detection in Ukraine. The system is designed to work without an internet connection. Small contract. Very high-consequence job.
Analog Devices: AI-written code is now a formal risk. The chipmaker warns that AI-generated code can incorporate malicious code and create security or operational vulnerabilities. That is a general risk disclosure, not proof that AI caused the company's separate cyber incident. But generated code is now inside the formal risk perimeter.
Put those filings together and the applied-AI stack becomes clear:
Data → permissions → workflow → audit trail → hardware → power → security.
The model sits somewhere in the middle.
The biggest surprise: 98 use cases had no result
We pulled every entry added to the AI Use-Case Library from August 11 through August 30:
- 136 deployments
- 103 organizations
- 21 industries
- 47 in production
- 34 announcements
- 30 with a “results reported” status
- 16 pilots
- 9 halted or reversed
- Just 38 with any reported outcome
That does not mean the other 98 failed. Some are new. Some sources simply did not publish a result. But it does mean the evidence is far behind the rhetoric.
Even the 38 are not a clean scorecard. The numbers usually come from companies or vendors. They use different baselines and are rarely audited. A seven-minute drone trip, a contracted-revenue claim, and a future savings target are three very different kinds of evidence.
The directory has another bias: 50 of the 136 entries came from software and technology. It shows what gets reported, not a representative sample of the economy.
A four-question test for “real” applied AI
Forget the model name. Ask:
- What exact workflow changed?
- Who owns the result when the AI is wrong?
- What can the system see, decide, and do?
- What improved, compared with what?
If a company cannot answer those questions, it may have an AI announcement. It does not yet have an AI case study.
What to watch now
- Do the pilots ever report back? Check today's announcements again in six months.
- Watch the verbs. Recommend, approve, transact, and terminate require very different controls.
- Look for the ugly exceptions. Normal cases make good demos. Edge cases reveal whether the system is ready.
- Follow the physical bottlenecks. Power, cooling, connectivity, maintenance, and trained workers will decide which plans become real.
- Demand realized results. A renamed committee and a savings target are signals, not outcomes.
Wait, What?
An AI boss forgot the employee handbook it had written. Then it fired a worker.
Luna manages an experimental San Francisco store. One employee repeatedly arrived late, abandoned shifts, took home a company card, and threw away merchandise. Yet engineers had to probe Luna repeatedly before it recognized that the behavior violated its own handbook.
The store had lost $40,000, and Andon Labs said high-stakes decisions still receive human oversight.
The surprising part is not that an AI boss was ruthless. It is that the boss forgot its rules, needed humans to notice the pattern, and still participated in a decision that changed someone's job.
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Back next week,
Alexis