Generative AI is changing how we develop code and now it’s turning the tables against low-code automation.

For years, enterprise automation has been spearheaded by tools such as UiPath, with their rise to prominence being shaped around the concept of "a bot for every employee". The growth of Robotic Process Automation (RPA) started with the promise of Citizen Developers and the idea that anyone could create a bot to automate monotonous work. However, half a decade after the initial buzz, it's clear that citizen development on low-code automation tools didn't quite hit the mark.

The Promises and Pitfalls of Low-Code Tools

This shortfall is primarily because building bots is an order of magnitude more complex than using API-based no-code tools like Zapier and Workato. Bots are capable of performing the same actions as human users on almost any application and can even go beyond human abilities by incorporating AI-based skills into their workflows. However, crafting robust bots that can manage enterprise workloads is a task better suited to professional automation developers.

So why do we continue to build bots with costly and constrained low-code tools? We know that many automation developers supplement their low-code with coded solutions. Even UiPath recognizes this trend by introducing coded automation to its product line-up.

This year, Generative AI has made a substantial impact on the scene. Every low-code tool incorporates some form of Generative AI, but we can see that the future of bots is set to deviate from the current path. Bots were never a perfect match for low-code tools. This association was mainly established for marketing purposes and to make the process seem simpler than it truly is. While low-code bot studios can create impressive demos with their UI recorders, they struggle with operations and scaling in the real world.

Low-code was essentially a response to a shortage of automation talent. Now, Generative AI is addressing that need within the Python world. Some low-code advocates might argue that Python doesn't offer the same capabilities as low-code, but this claim is fundamentally incorrect.

The Python-Based Future: Introducing Robocorp

Robocorp started as a code-native bot ecosystem, leveraging Python, the language of AI and automation. Robocorp offers developer tools and operational platforms that elevate Python bots well beyond what traditional low-code RPA can achieve. Additionally, they do this far more quickly regarding bot development and performance, with only a fraction of the licensing and IT infrastructure cost.

Robocorp's Control Room is an enterprise-grade orchestration platform for managing bots, and their data flows, complete with logging, troubleshooting, and auditing capabilities. It also has a full API that can be integrated with any iPaaS and BPM tool. And with consumption-based pricing, the days of bot licenses are numbered.

The Efficacy of Python in Bot Development

But what about the skills required to build bots? Isn't Python more complex and slower to work with? Generative AI has flipped this narrative, positioning Python as the preferable choice. Large language models like OpenAI’s GPT-4 and Meta’s recently announced Llama 2 can write Python code fluently. And with the latest addition of a Code Interpreter, users can write the code they need and run it in a single prompt.

On top of general-purpose AI tools, Robocorp has announced ReMark💬, an AI-powered developer assistant that’s free for all its users. It can answer specific automation questions and generate code in seconds so that no one has to start building from scratch anymore.

ReMark tool has been great and has made me really efficient at my coding. Something that used to take me 1 hour to do now takes me 10-15 minutes. - Robocorp Partner

The Python ecosystem is extensive and continuously expanding. Almost every function conceivable has a Python library dedicated to it, and every large language model can write Python code. It's time to acknowledge that bots were never meant to be built on low-code, and to embrace Python as the superior automation ecosystem it has proven itself to be.

Embracing the Change: The Future of Automation

In conclusion, Python-based automation is here, and it's set to change the landscape. The future of automation is not confined to low-code tools; it's heading towards a more efficient and scalable solution, with Python at its core and powered by Generative AI. It's time to embrace this change!

Check out the video below to learn more about the latest in Generative AI and Robocorp.

Tommi Talks Episode 8: Generating Bots with OpenAI Code Interpreter

Python is Taking Over Low-Code in Automation

The landscape of Robotic Process Automation (RPA) has evolved over the past five years from low impact desktop automation to mission critical back-office integration and business process platform. Now we are seeing a new wave of evolution in RPA as Generative AI is entering the playing field.

RPA is becoming the access route for deploying AI into enterprise IT systems. We have to rethink if “Robotic Process Automation” is anymore even the right term for this technology or should we talk about automation through bots / digital assistants instead.

This isn’t about a wild future vision either, but something that’s happening right now. Here are the emerging trends that Generative AI brings to the world of enterprise automation today.

Low-code has been a big hit in enterprise for a good reason. It removes what we describe as the “blank canvas problem”. When your world is limited to a finite set of actions, composing an initial solution is easier and you don’t have to be an expert with the tool to get started.

However, going beyond simple demos becomes increasingly difficult as you have to layer in error handling and more complex logic. Soon you will find yourself coding in a limited restrictive framework and thinking “why are we doing this in low-code in the first place”. Not to even mention scaling your solution out to hundreds of complex mission critical automations.

Generative AI is the perfect solution for getting over the blank canvas problem with actual code. You can prompt AI to create your script and collaboratively work together on fine-tuning it. No expert software engineering skills needed and you will have the full power and flexibility of a scripting language such as Python at your disposal. We have already seen people with little to no programming skills building complete web applications with the help of Generative AI. Now this is coming to change RPA and automation as well.

Low-code / no-code is great for problems with very limited scope, but in the world of automation and bots, we are calling an end to these beautiful handcuffs that trade off too much against an initial wow effect. Let’s have AI handle that for us instead.

Generative AI is not just about simplifying coding. It brings with it new skills and capabilities to automation itself.

With zero-shot learning, Generative AI eliminates the need for large training datasets. Its ability to perform tasks such as classification and natural language processing (NLP) extraction without any learning data aligns perfectly with the RPA ethos, facilitating much faster deployment with less effort.

Moreover, bots are no longer limited to back office work – with text generation they can now engage in outbound communication with stakeholders. This will require a human in the loop to make sure that messages are aligned but it’s still very powerful addition to bot capabilities.

AI isn’t limited to public information or the training cutoff date either. Retrieval-augmented Generation (RAG) can be used to ground AI generated content to your own documents and business context. Securely available LLMs, such as Azure OpenAI, combined with easy to deploy vector databases is lowering the entry barrier here significantly.

The Ideal Tech Stack for Delivering AI + Automation

When it comes to the technology stack, Python reigns supreme in the world of AI and automation. LLMs are fluent in Python, and every application and technology has a Python library that allows connecting and interacting. In browser automation, the Playwright project stands out as the most modern and powerful tool.

Robocorp has developed extensive open-source tooling to make it as simple as possible to build and package Python automations in a way that they can repeatably run in any new environment without installation and portability hassles. All this is free to use for everyone.

On top of the bot itself, when we talk about enterprise usage, security and governance is on top of everyone’s mind. Orchestrating bots and data is also a challenge that needs to be solved. This is where Robocorp Control Room comes into play. Its aim is to make deploying Python automation easy, secure, and seamless while providing great data connectivity across systems.

Generative AI is transforming the RPA landscape, opening new possibilities for automation and redefining our approach to building bots. Its integration with existing RPA practices leads to faster, more efficient, and more intelligent automation processes.

As we have entered this new era, now is the time to re-evaluate how companies of the future automate their business processes. This is also the perfect opportunity to move your automation program to a modern architecture and leverage the fastest growing open software ecosystem that ensures you will have access to the latest innovations in the years to come.

How Generative AI Changes RPA

Legacy RPA providers have dropped the ball and failed to deliver on their promises, causing a noticeable disruption in the market for implementation and reseller partners.

The demand for automation services is reaching new peaks as organizations aim to work smarter and save costs. Unfortunately, legacy RPA providers have failed to deliver on their promises, causing a noticeable disruption in the market for implementation and reseller partners.

Most notably, one of the current legacy RPA market leaders, a company that defined how to work with partners to grow its market share has taken a complete 180 turn and started to actively compete with some of its partners. This hasn’t gone unnoticed in the industry.

At the same time, Robocorp is seeing its managed service provider (MSP) partners grow faster than ever before. The winning formula in automation services for MSPs includes:

Understanding use cases that repeat across organizations.

Packaging your service offering to deliver clear outcomes.

The opportunity is vast and extends across insurance, healthcare, legal services, and beyond.

Robocorp's Managed Service Provider (MSP) model offers predictable pricing, fast deployment, and industry-leading capabilities. It's designed to provide the perfect platform for automation as a service provider from a technological and commercial point of view – Our commercial model aligns with your business, not the other way around.

What's best is that the platform currently empowering these repeatable mid-market use cases can seamlessly extend to cater to high enterprises, making our solution scalable and versatile.

Now is the best time to adopt a fresh platform and capitalize on the market opportunity. With the incorporation of Generative AI, Robocorp is unlocking new use cases and making development even faster and easier. The first sign of things to come is ReMark💬, Robocorp’s AI developer assistant; you can expect more to come.

The future of automation is here, filled with endless possibilities. At Robocorp, we invite you to seize this opportunity and join us in taking the potential of automation to market. Let's work together to close the gap between the promise and the reality of automation, and let's do it now because the future of work waits for no one.

Contact us to learn more about Robocorp’s Managed Service Provider offering.

Legacy RPA Providers Have Dropped the Ball with Partners - Robocorp's MSP model offers predictable pricing, fast deployment, and industry-leading capabilities.

Legacy RPA Providers Have Dropped the Ball with Partners

RPA has been going through a huge boom in the last six years and now we are about to see a shift in how enterprises adopt RPA. Now I’ve been following the RPA space since 2016 and this is what I’ve seen.

2016-2018: 1st gen winners appear to the big audience... There are talks about “a bot for every employee” and RPA is framed as a magical AI driven tech that simply watches what you do and automates that. A ton of focus was placed on attended and desktop bot use-cases.

2019-2020: I counted at least 100 RPA tech vendors on the market. It seemed that there was a new tool coming out every week. A lot of consolidation started happening as well when big platforms started to acquire RPA capabilities.

There was little to no differentiation between the upstarts. Many vendors were touting intelligent automation, hyperautomation, cognitive automation and really saying just about anything they could come up with to differentiate their brand in the crowd. Industry pundits were big on fantasies about autonomous intelligent bots but the reality in the IT trenches couldn’t be further from that.

At the same time there were a lot of skeptics asking if RPA is a band-aid technology and questioning if it would ever become a real market category.

Citizen developers were still the focus of the analyst industry but customers began seeing more value from headless to unattended automation - an example of this was bots that crunch big back office workloads. It’s clear that RPA isn’t a tech where citizen developers create magical assistants on their desktop and instead every company is standing up teams to operate RPA internally.

2021-2022: RPA has been validated as a “must have” for every enterprise. We’ve seen teams running hundreds of bots and felt the pain of scaling. 1st gen winners start expanding TAM by adding features in areas like document processing, process discovery, API connectivity.

Today I’m seeing a lot of software vendors looking to add RPA capabilities into their products. My bet is that every single integration platform will have RPA-like capabilities within two years. There’s also a push by RPA service providers into the mid-market – more on that some other time.

I think we are at a point in the RPA hype cycle that we are coming out from a slump in expectations.

So where does the enterprise go with RPA from here? We know what’s working, how it's working, what’s real and what’s fantasy. 1st gen platforms have gotten so big and complex, not to mention expensive and heavy to operate. They might be good at the basic RPA tasks but mediocre at best in everything around that.

I know it’s just one stock but UiPath hasn’t performed well against the S&P tech index for a variety of reasons. Looking back on UiPath’s move to becoming a publicly traded company, it is clear it was built with a significant amount of hype revolving around the promise of a one-stop “fully automated enterprise” and being the next SAP. Sure the market is pulling the rug under many other tech stocks as well but $PATH is right up there with Peloton and Zoom. Another 1st gen leader, Blue Prism, was just purchased by a private equity firm.

According to Gartner, by 2024, 40% of RPA buyers will utilize a multi-vendor strategy. This points to a different future than the vision of single enterprise wide automation platforms taking over. We have dedicated technologies, such as Instabase for dealing with unstructured documents, and Celonis for understanding processes.

We started working on Robocorp in 2018 and incorporated in 2019 with a vision to build an open-source technology stack and community for RPA developers. It was a counter move to the early hype about magic and AI in business process automation. We knew that citizen developers cannot ship big and powerful headless automations and that’s where a lot of the value is going to be generated.

We also missed some things with our initial idea. The shift to cloud has been slower than expected, although enterprise loves our cloud-native pricing that’s based on consumption. We also underestimated the power of low-code when done right and that’s why we’ve course corrected and built Automation Studio.

An important side note, low-code in RPA isn’t about enabling everyone to build bots. Just look at low-code game development platform Unity that started with a vision to enable everyone to make games, but it’s still a complex professional tool. This is how we see low-code and RPA, and we’ve created a tool that’s easy to pick up and powerful to use for the long term.

Now, observing the shift in RPA narratives, I think we are at a point where a dual aka multi vendor strategy will be key for success in enterprise RPA. Looking at Wikipedia definitions of the hype cycle phases, it’s safe to say that we’re entering phase 4.

The future of RPA is bright in the sense that it’s getting used by more and more companies every day, and those companies are seeing real value. Some of our enterprise customers have seen massive improvements in TCO by replacing their old bots with Robocorp. Many others are looking to add a second RPA vendor because they don’t want to be locked in and get stuffed with more features they don’t use.

There is not going to be a single platform that powers hyper-automation for the enterprise. Instead we are going to see that in 2022-2023 more enterprise RPA users will add complementary technologies into their operations and by doing that, they will have access to the best of each category.

Multi Vendor Strategy is the New RPA Normal

We all know that Gen1 RPA licensing is complicated and messed up. They all use the same licensing model because they were built before the cloud was invented and are fundamentally based on an on-prem world.

What’s broken in RPA licensing and pricing?

To highlight the key challenge with RPA licenses, here's how Gen1 works.

Every runtime environment (this is the bot license)

Developer tools for everyone building RPA bots

Add-ons like intelligent document processing or process discovery

Software licenses for business applications used by bots


Ok, so it has many components but what's really the problem? It's very rigid.

People are measuring the utilization rate of bots, and the industry average hovers around 30% or less. The bot license is treated like an industrial asset with fixed capex and you'll want to squeeze as much value out of that asset as possible.

Large RPA operations struggle with balancing license utilization while trying to meet the business user’s service level expectation. If you stack up too many bot licenses, then you'll have excess capacity and wasted resources. If you have too little capacity, you might have a good utilization rate on paper but you'll need to turn down use-cases that don't fit with your available resources. 

I talked with one large company that had a 90% utilization rate but they ended up hiring a ton of people to fill gaps in some end-of-the month use-cases that would have demanded a few thousand bot hours of capacity. Talk about wasted opportunity, and it's all due to licensing.

I've also seen that many times Gen1 RPA would start with attended use-cases first because those licenses are much cheaper and easier to deploy than a full-blown orchestrator with unattended bot environments. But that’s really missing many of the larger benefits of RPA.

And remember, it’s just software at the end of the day. These are made up of constraints.

When we started Robocorp, we thought about licensing a lot. My intuition was that there needs to be a better way so we started from first principles and thought about the fundamental unit of value that RPA provides. 

An idle bot doesn’t really do anything for you, so we can’t use that as the basis. It’s just an option to run automation but that’s a step removed from the actual automation. 

A process is the actual automation that the bot will execute, but processes can be so wildly different that it’s really difficult to say that each of them are equally valuable.

A digital worker’s output is the work that they perform so we concluded that the working time of the bot is the best proxy for value generated by RPA. When you start thinking from this perspective a lot of things change. This requires a shift in thinking to consumption-based RPA. We are the first in the industry to build an RPA company, from the ground up, based on consumption rather than expensive and complicated licensing.

Robocorp’s platform consists of an open-source automation stack, developer tools, and a cloud-based Control Room. We wanted to give anyone the ability to build bots with open-source tools that are simple to use and universally available. It was a natural choice to adapt a model where Control Room is a paid offering where we can focus on solving the organization’s needs while RPA developers can freely operate without having to talk to sales or think about licenses.

Control Room is the most powerful and capable orchestration platform in the RPA industry, and it’s delivered as a highly secure and available hosted SaaS environment. It measures the runtime on a process level per minute and pricing from self-service to enterprise is built around that concept.

A process can have multiple steps and they can run on as many environments as you want, and even run concurrently. The only thing that matters is the execution time across all environments. Since there’s no limitation on runtime environments, we can easily do things like provision dynamic containers and VMs to run bots without having to think about running out of bot licenses.

Hosting and maintaining such a platform requires a dedicated team and having Control Room as a self-managed platform offers zero benefits for the user. More importantly, this decision also enabled us to fully embrace consumption pricing from the beginning and design it to be an integral part of the Robocorp product.

It’s important to realize that we have built everything around this model from the ground up. It’s nearly impossible to replicate a pure consumption based model when you’re starting from a legacy background and try to bolt it on. This is a classic example of an innovation stack, described in this great book; 

“[The] innovation stack … comprises a series of value-based decisions that level the playing field and disrupt markets. … A competitor could not copy just one part of the innovation stack and hope to win.”

Unlike traditional RPA licensing that’s rigid, consumption pricing is flexible. It allows you to build automations in an optimal way running on as many machines as you want. That’s the nature of cloud elasticity and in general why this paradigm is winning over the market.

Why bother thinking about utilization rates since it’s just compute time? Got a use-case that has unpredictable demand or periodic workloads? No problem. These are the typical arguments for consumption pricing but more importantly, it allows you to ramp consumption over time rather than having to decide on a big up-front commitment. Consumption pricing is derisking RPA projects.

From a vendor perspective, consumption pricing aligns us perfectly with customer interests. We only get paid when the customer is successful. We don’t have the luxury of selling a stack of licenses and waiting for 11 months to check back with the customer to renew.

Myth: 24/7 use-cases are expensive: The biggest misconception we hear is that a consumption model is expensive for 24/7 use-cases. That’s true if you take our self-service list price of a run minute and multiply it by 40,320 minutes. You get a big number, but luckily that’s just the self-service list price. Remember that bot runtime is only a proxy for value. We know that there are legitimate use-cases that require long processing times. One process can actually run much more than 40k minutes per month since we allow concurrent execution. It could easily do 400k minutes!

This is why we’ve introduced billing caps for high volume workloads. You can run the process as much as you want but the billing is capped on certain maximum consumption on a monthly basis. This gives the customer upside protection and predictability.

Another thing to consider is that quite few 24/7 bot use-cases actually come down to the bot working all the time. Often it’s more about checking if it has work to do. In these cases it’s better to use Control Room’s Work Data Management features to push new work to bots rather than using the bot to actually check for queued work.

Myth: Robocorp only runs in the cloud and I can’t do that with my bots: The need to run on-prem is valid for nearly all enterprise use-cases. Bots need to run inside local networks, sometimes even for mid-market users. Control Room is a cloud-native platform but the bot environments can live where ever needed. We can even provide VM provisioners and container clusters locally that can run dynamic workloads. The fact that we provide cloud elasticity in pricing doesn’t limit your use-cases only to the cloud.

Myth: Consumption pricing will have unpredictable billing each month: While departmental usage can start with a credit card purchase and ramp up over time, typically enterprises prefer to have a fixed annual contract in place. We do this by bundling runtime that can be consumed across any number of automations. This gives the flexibility of consumption pricing with the predictability of an annual contract.

When you are ramping up from zero to a new platform, you don’t really know what the consumption pattern is going to be like. So how much runtime are you actually going to need for the first year? Luckily that’s easily fixed by first year contract terms that allows for experimentation. We’ve seen that Robocorp bots typically run 2x - 8x faster than Gen1 so you might be surprised by how much less runtime is actually consumed.

Myth: License costs are the only thing that bring TCO up: License costs are a big consideration, especially in the enterprise. However, we see that infrastructure can cost much more  than the actual licenses. Luckily, that infrastructure is something we can partly eliminate. We have been able to reduce infrastructure costs by 50% in competitive Gen1 installations. This is achieved by not having to host the Control Room locally and being able to leverage container based runtime, plus using VM resources in a more efficient way. Robocorp can run multiple bots on a single Windows Server and shift workloads to containers.

A huge part of the reduced TCO also comes from reducing bot maintenance. This isn’t directly related to licensing but is still part of the total improvement. Architecting and developing RPA with Robocorp allows you to build it in a more robust and sustainable way. Bots don’t have to be fragile and brittle.

By combining these factors around licensing, infrastructure, and maintenance, we’ve recorded TCO reductions of 70% against well established Gen1 vendors.

Myth: Consumption is the best model for all RPA: I think there’s a category of RPA, that’s mostly driven by ISV and OEM use-cases that fall outside of the consumption pattern. In these instances the end user of the solution isn’t the primary customer and thus the cost model needs to be aligned with the pricing model of the ISV / OEM in question. However, this is a whole separate thread and anyone interested in exploring it further is welcome to contact me directly.

Consumption pricing is a fundamental shift in RPA. It unlocks flexibility in a way that has never been possible before, and it will require some getting used to if you’re used to legacy bot licensing schemes. It’s also a model that’s inherently less risky than considering bot licenses the same way as industrial physical assets that you buy in advance and then try to optimize and measure their utilization. If bots are working, they are producing value. And again, it’s just software – we should treat it as such.

There are not many vendors who are doing consumption pricing. I don’t actually know anyone except Robocorp that has properly implemented it. This is because it needs to be built into the product from ground up and cannot be added as an afterthought. It’s not just a business decision but a long string of product and technology decisions.

Over the long term, we will see a trend towards consumption pricing in all software, not just RPA. Companies like Snowflake are trailblazing the model and they are educating the markets on how it works. We chose the consumption model for Robocorp, not because it’s different, but instead because we think it’s the best model for capturing the value that RPA brings to its users.

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Robocorp offers its clients the ability to build, scale, deploy, and maintain their robot workforce.

All posts by Antti Karjalainen

Python is Taking Over Low-Code in Automation

How Generative AI Changes RPA

Legacy RPA Providers Have Dropped the Ball with Partners

Multi Vendor Strategy is the New RPA Normal

How to change RPA pricing for good

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