My plan for making AI simple, not complicated, for the millions of business software users that learn to co-exist with thinking machines and software robots.

Business software has looked pretty much the same since I started working in tech 21 years ago. Fields crammed next to each other, and some tables with all the data. And buttons that presumably do some things.

Admittedly, there has been a transition from heavy client-based apps to web — but the real impact on user experience remains mostly cosmetic. Just look below, and I’m sure you know what I am talking about. Everyone has seen these over and over again.

A screenshot of the new invoice view from my own company’s accounting system called Netvisor looks like all the other enterprise apps. (Screenshot by the author.)

We live through a more drastic change than I have experienced in my career before. …

A practical case study exploring ticket triage implementation options and the actual machine learning implementation using the platform.

Photo by Kristine Wook on Unsplash

Service centres are ripe ground for automation, with demonstrated results to learn from and plenty of resources available to help get started. Automation benefits are obvious in customer service teams, IT supports, and anything that works based on incoming tickets or service requests.

First and foremost, the customers' happiness and loyalty increase with faster responses. Simultaneously, by removing repetitive and machine-like tasks, the work can be made more meaningful and less dull for the team's staff. Motivated team, better results across the board. …

My list of the coolest ML-related use cases the enterprise RPA teams are working on at the moment.

I am a Chief Product Officer at, a machine learning tool for RPA developers. I view the world through “aito-lenses” so the list contains use cases where our tool can be used, and what I encounter when working with the customers. However, I hope the list serves its purpose for sparking your interest regardless of the tools you use for ML.

2020 was the year of RPA for us at We saw an increasing adoption of our tool as the “brains” of intelligent workflows. We tailored our product to work even better with popular tools like UiPath, Robot…

Small AI is the next big thing in RPA. See my talk on-demand, check out the slides with some further reading links from this collection post.

The stage is ready! Photo by me.

Hel Tech is the top tech meetup in the town, but now for obvious reasons gone online mode. Organized on the first Monday of each month the event covers a variety of top-notch tech topics.

October 2020 event was all about Robotic Process Automation (RPA), and I was in the lineup to talk about our approach to adding intelligence to the automation workflows.

In very short, we advocate for high-efficiency ML solutions that an RPA developer can deploy without engaging the data science team and ending up with a long sciency project and high effort maintenance. …

Machine learning becomes vital in more complex RPA workflows. Follow the simple checklist to see if helps with your next RPA use case.

Robotic Process Automation helps to automate repetitive tasks without extensive development effort, even with systems that provide no API based access to functionalities. Commonly these workflows are developed with a “studio” that describes the flow of events in a visual way.

On a general level, the marriage of AI and RPA is discussed a lot, with Intelligent Automation being the promised land of next-level business processes. But when we talk with RPA teams about the potential use cases for machine learning in RPA, it is apparent that things are more complicated.

It is not always obvious where to spot the…

The way a data scientist do things is radically different from RPA developers, creating a 10–100x gap in “AI to RPA fit”.

Photo by 🇨🇭 Claudio Schwarz | @purzlbaum on Unsplash

Robotic Process Automation (RPA) is the fastest-growing enterprise software category, with large companies scrambling to automate manual tasks while avoiding costly projects to renew their legacy systems.

Mainstream RPA today still mostly covers simple tasks, not processes. There is nothing wrong with the simplicity that gets the job done and brings savings without massive development costs. That is absolutely where the first opportunities of RPA lie for many companies. At the same time, RPA teams I’ve spoken with are pondering the question “what next”.

In real life, processes have judgment-based decision points, they deal with unstructured data, have exceptions, and…

One of the key elements of any machine learning solution is to figure out how to run it in production. It might be easy to run the initial PoC, but in the operational use there will be new data coming in, and the model needs to take this new training data into account in predictions.

A lot of our work revolves around robotic process automation (RPA). In this context every time a bot executes a workflow, there might be a new entry used to improve the predictions.

Using traditional machine learning libraries that produce a static model, the workflow requires…

One of Aito’s users posted a question in the Community Slack that sparked my interest. He was asking how Aito works with Parabola. I had not heard about Parabola before, but looking at it I got excited.

I firmly believe there is so much more to automation than the attention grabbing RPA platforms like UiPath. For example, at the time of writing we are looking into introducing Aito as a library for Robot Framework. There are also UI based tools that make automating workflows super simple. And in this category Parabola seems to be doing really well. …

This post was first published in the blog, dated December 27, 2019.

Quiet(er) holiday season days are perfect for reflecting on the previous year, and making plans for the upcoming 2020. What kind of an impact are you going to make next year, and what are the tools and solutions available in the market today to help you get there as fast and efficient as possible?

When those goals involve the use of AI, I often hear the first objective is to build a data science team. I disagree. Read why!

I was inspired by a year old writing…

Tommi Holmgren

20+ years of SW and tech leadership. Two startup exits. Now building next-generation machine learning tools for RPA developers at Used to travel a lot.

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