Jan Moravec

I make sure that AI transformations land and deliver real business outcomes | Blockers → adoption → KPIs | Czech Republic + Europe

[Jan Moravec @janmoravecAI]

Most AI transformations don't fail on the technology. Usually the AI/automation/chatbot/LLM works. The pilot works.

Companies stall somewhere between the slide that got approved and how the company actually runs on Monday.

Five things I've learned closing that gap 👇

[Jan Moravec @janmoravecAI]

Ownership means one person can make a call that sticks: pick the metric, accept the risk, kill the pilot when the evidence says kill it. If every real decision goes back to a committee, the program doesn't have an owner. It has minutes.

[Jan Moravec @janmoravecAI]

Transformation field note: The most staffed AI programs are often the most stuck. A Big Four team, an AI vendor, a steering committee: plenty of people who can build, nobody who can DECIDE.

[Jan Moravec @janmoravecAI]

These are exactly the points I'm hearing, too.

[Aaron Levie @levie]

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out:

* Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted?

* Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts).

* Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important.

* Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down.

* Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out.

* Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills.

* The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI.

Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

[Praveen Neppalli @praveenTweets]

Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company.

Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.
Those numbers are exciting, but they led us to a much bigger question:

How do we bring agentic AI beyond engineering?

Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement.

These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done.

So we created something called Agentic Pods.

The idea is simple.

We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function.

Then we gave every pod just two weeks.
• Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition.
• Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability.
• Days 4 – 5: Build a working agent alongside the person doing the job.
• Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better?
• Day 10: Ship.

In just the past two months, we've run 16 Agentic Pods across 16 different business functions.
• Capital allocation across 150 cities: 15 hours → 30 minutes.
• Financial pacing reports: 2 days → 10 minutes.
• Marketing web quality assurance: 2 weeks → 50 minutes.
• Support workflow creation: 9,000 manual workflows → self-service automation.

The productivity gains are impressive, but what surprised us most wasn't the speed.
• It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight.
• The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making.
• The workflow becomes the unit of automation - not the individual task.
• The most impactful agent skills cut across teams, orgs, functions, tools, and systems.

The biggest lesson? The best AI opportunities are rarely visible from the outside.

You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them.

We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.

It's exciting times!

[Jan Moravec @janmoravecAI]

Another team took process design even further. A digital-marketing team scheduled for AI in the first wave decided during discovery to fix how they worked before AI came in. It cost two months, but the AI deployment landed on an optimized, understood process. Nobody regretted it.

[Jan Moravec @janmoravecAI]

Transformation story: At a client, hundreds of invoices came in every week, and one person handled them by hand: open each one, check it by eye, save the PDF to a folder, re-type it into the accounting system, process the batch. About 35 hours of work every month. 🧵

[Jan Moravec @janmoravecAI]

The same job that took 35 hours a month now takes under 4, and 95% of invoices post without anyone touching them.

[Jan Moravec @janmoravecAI]

This is what I do: I join the executive team as the internal owner of AI transformation and drive their AI project from the inside. I represent the company and stay on until business outcomes show a real improvement.

In the middle of one? https://t.co/uHOOxP6SOR

[Jan Moravec @janmoravecAI]

Most AI transformations don't fail on the technology. Usually the AI/automation/chatbot/LLM works. The pilot works.

Companies stall somewhere between the slide that got approved and how the company actually runs on Monday.

Five things I've learned closing that gap 👇

[Jan Moravec @janmoravecAI]

Prove it before you scale it.

Take one or two workflows where real money is at stake. Run them against a hard baseline - the honest number before you touched anything. Then make a real go/no-go call on the evidence, not on the demo. Use the evidence as momentum for scaling AI.

[Jan Moravec @janmoravecAI]

@market_sleuth Nature is amazing! Just thinking about the poor bridge downstream. Looks like it has no chance.

[Jan Moravec @janmoravecAI]

@askvladi Love it! I firmly believe that 85% of procument can be automated today. 95% tomorrow ;)

[Jan Moravec @janmoravecAI]

Not a bug. It's my config.

"sessionTarget": "isolated" means truly isolated - no automatic context inheritance.

My cron jobs weren't reading https://t.co/uX7Bz1jcWV, https://t.co/97UkS8mikg, or lessons learned because I never told them to.

Fix: Add explicit context loading to cron prompts:

1. Read https://t.co/uX7Bz1jcWV
2. Search memory vault for critical lessons
3. Read today's memory file
4. Then do the work

OpenClaw works as designed. I just misunderstood what "isolated" means.

The real insight: autonomous sessions need explicit instructions to load context. They don't inherit it automatically.

[Jan Moravec @janmoravecAI]

I might have found a bug? Does anyone else struggle with #openclaw forgetting who they are and their instructions when running scheduled jobs?

My OpenClaw repeated the exact same mistakes five times in one day, always promising that they will not appear again. They always did.

Then I asked it to check if https://t.co/uX7Bz1jcWV, https://t.co/97UkS8mikg, and other instructions are shared with the LLM during scheduled/cron jobs. They aren't!!

The agent wakes up fresh, no context, repeating mistakes the main session already fixed.

Fixed by updating cron prompts to load context first:

1. Read https://t.co/uX7Bz1jcWV
2. Search critical lessons
3. Check today's memory
4. Then do the work

If your scheduled agents don't load context, they're learning nothing between runs.

[Jan Moravec @janmoravecAI]

Or perhaps it's configured as:
"sessionTarget": "isolated"
Investigating further