The top 3 are pretty ahead in terms of what they have already built out in terms of assets and clients.
They are all built on top of Microsoft libraries but that does not discount the effort the current vendors have put in to create the market as well as the assets on top of them. It will take some time for anyone to catch up – SAP has also started along the same path.
I think the bigger theme is Microsoft using low code across all the Power Automate features. I think that low code is a big trend. “Learn to low code” is the new “learn to code”.
Microsoft is probably testing the waters. It will take some time to catch up. Overall, It is good to know that RPA is getting attention from top tech companies.
This is a guest post by Vikas Kulhari. Vikas is an Intelligent Automation Consultant at KPMG. He is a Certified Solution Architect helping clients design, create and maintain Intelligent and Robotics Process Automation (RPA) solutions.
Artificial Intelligence (A.I.) is not new technology anymore.
Most of the sectors have already started investing in AI research and implementation. It is ubiquitous now – Autonomous vehicles, Voice controlled bots, Facial Recognition, computer vision, ICR, search recommendations, robots, etc.
However, all of you may be thinking:
Who invented AI?
Who coined this term (AI)?
Where did all this begin?
So, I thought to write a post about the AI journey. Here is a brief Timeline as I see it:
1943 – Turing Machine: Alan Turing invented the Turing test, which set the bar for the intelligent machine; the computer that could fool someone into thinking they were talking to a real person. Grey Walter built some of the first-ever robots.
1950 – I, Robot: It was published a collection of short stories by science fiction writer Isaac Asimov.
1956 – Artificial Intelligence: John McCarthy coined the term “Artificial Intelligence”. A “top-down approach” was dominant at the time: pre-programming a computer with the rules that govern human behavior.
1969 – Shakey The Robot: The first general-propose mobile robot was built. It was able to make decisions about its actions by reasoning about its surroundings.
1968 – 2001: A Space Odyssey: Marvin Minsky, the founder of the AI Lab at MIT, advised Stankey Kubrick on the film 2001: A Space Odyssey, featuring an intelligent computer, HAL 9000.
1973 – AI Winter: The AI Winter began – millions had been spent with little to show for it. As a result, funding for the industry was slashed.
1981 – Narrow AI: Instead of trying to create a general intelligence, research shifted towards creating “expert systems”, which focused on much narrower tasks.
1984 – Bottom-Up Approach: Rodney Brooks spearheaded the “bottom-up approach”. aiming to develop neural networks that simulated brain cells and learned new behaviors.
1998 – Deep Blue: Supercomputer Deep Blue developed by IBM, Faced world chess champion Garry Kasparov.
2002 – Roomba: iRobot created the first commercially successful robot for the home – an autonomous vacuum cleaner called Roomba.
2005 – BigDog: The US military started investing in autonomous robots. BigDog, made by Boston Dynamics, was one of the first.
2010 – Dancing NAO Robots: At Shanghai’s 2010 World Expo, 20 NAO robots danced in perfect harmony for 8 minutes.
2011 – Watson: IBM’s Watson took on the human brain in jeopardy and won against the two best performers of all time on the show.
2014 – Eugene Goostman: 64 years after the test was conceived, a chatbot called Eugene Goostman passed the Turing Test. Additionally, Google invested a billion dollars in driverless cars, and Skype launched real-time voice translation. Amazon launched Alexa, an intelligent virtual assistant with a Voice.
2016 – TAY: Tay was Microsoft’s chatbot. It caused some controversy when the bot began to post inflammatory and offensive tweets through its Twitter account. Microsoft then shut down the service after only 16 hours of launch.
2017 – AlphaGo: Google’s AlphaGo was the first computer program to defeat a professional human Go, player, the first to defeat a Go world champion, and was arguably the strongest Go player in history.
2018 – Google’s fascinating—and creepy—AI: it could make calls on behalf of a user and perform tasks such as booking restaurant tables and hair salon appointments.
2019 – Tesla and Scania’s Autonomous Vehicles: Tesla and Scania have already come up with concept self-driving cars and trucks. Scania trucks don’t have a cab and that may be a game-changer.
As AI grows rapidly, you would see a lot of big-scale AI projects shortly.
In a world of exponential Technologies, it becomes difficult for any single person or vendor or provider to have all the answers. So we have to make sense of these Technologies together – a networked sense-making
Towards this many of the software vendors including the top RPA vendors are inviting all of us to be part of this sense-making. They’re moving towards being platforms where all of us can participate.
In the RPA world, the core platform includes bot(s), a studio and a controller. I like to call this the “operating system” for bots because they provide you a way to build and manage the Bots.
You configure your workflows within the studio and attach them to the Bots which is deployed and managed using a Controller. The terms for these components may change like the Controller may be called Orchestrator or Control room but the core philosophy of the RPA tools remains the same.
This was and remains the core platform. Now there are many components plugging into this provided by the vendors themselves, their partners and people like you and me.
One of the key areas of innovation plugging into this platform is happening upstream to discover the processes. The Process discovery component is being included by the vendor themselves or is being provided by partners. Some vendors are claiming that a major chunk of the automation workflow can be automatically generated using this tool.
Within the platform, many of the RPA vendors are including more and more components which they see as strategic and necessary for the tools to be successful. For example, Blue prism just came out with Decipher which addresses unstructured data. UiPath has a multitude of “Activity packs” and Automation anywhere has many reusable components through their Bot store.
These components can mostly be included in your workflows through drag-and-drop interfaces. I think the RPA vendors will keep adding these drag-and-drop components as needed. But there is a limit to what each of the vendors can develop.
This is where it gets interesting – the tool vendors are adopting a Platform approach where you and me can contribute to this ever-increasing components for automation. You can contribute components even now though it cannot be monetized. I think we are moving to a future where we would have wider participation and you can monetize what you contribute.
So we are moving to an app store like approach where you have different operating systems and applications for those operating systems. Like you participate in the Google Play Store or IOS app store you have different RPA platforms on which you can automate Enterprises using the respective ecosystems.
Finally, this platform sits on top of a foundation enabled by the RPA vendors. This includes a learning academy where you can learn and try out the tools. You also have a community where you can make sense of the evolving technologies together. The vendors also have an ecosystem of Partners that can help you with your automation.
So this is what I see as the emerging RPA platform. A platform where you can use the rapidly emerging technologies to solve real-world business problems.
RPA is supposed to be the “gateway drug” to Intelligent Automation and beyond.
RPA is low-level stuff – screen scraping and rule-based. Then there’s Intelligent automation (or similar term) which includes pattern recognition and finally, there’s cognitive where you move to human-like self-learning.
So some people have come to think that it’s all neat little boxes where you flow from one to another both in terms of a career as well as how you implement automation.
While the overarching narrative is correct, there is some misunderstanding though.
If you are into RPA thinking that you would first learn RPA and then move into AI, then I think that is a wrong assumption.
RPA is more of a process Improvement track which we have been on for more than 30 years now. It’s a continuation of the Journey of TQM, EAI, BPM and BPA. To me, RPA is a career in process improvement and automation.
Modern AI has a similar but different track which starts with the Dartmouth summer research project Followed by multiple AI Winters finally culminating in deep learning and beyond.
So, AI is a completely different career track which impacts a lot more than automation. If you like to be on AI, you should go pursue that track.
In terms of implementing RPA, some people tend to think that you do RPA first and then you get to intelligent Automation and then Cognitive. I think that’s another misunderstanding. You do not need to wait to incorporate any technologies that suit your automation.
Include Cognitive right from the beginning. In fact, include all available technologies that can help with the use case being automated.
The good news is that you can do that with RPA. You always could – we always used an ecosystem of technologies including internal IP for automation projects.
RPA vendors are making it easier to include emerging technologies as drag-and-drop. They are extending the low code simplicity to include other technologies. So go ahead and include what’s appropriate for your automation now.
RPA is a path to Future of Work
RPA is a way to introduce Digital labor into the Enterprise. RPA is a path to Process Improvement and Automation. So for me, RPA is a path to the future of work where humans work with Bots.
As a career, if you like to stay in automation then RPA is a good bet. If you like to pursue AI, you probably should take up AI separately. You can also do both but you would mostly be better off specializing in one of them.
While you implement RPA, don’t get into the “first RPA then Cognitive” narrative – it is not a sequence. Include Cognitive right from the beginning. Choose fairly decent RPA platform and design your ecosystem including Cognitive now.
Blue Prism is adding an AI-powered document processing directly into the Blue Prism platform. It is called Blue Prism Decipher and is in Beta right now.
Looks like Decipher is OCR software with an AI component. It seems to be similar concept as Automation Anywhere IQ bot but would be free to use skill from the new Blue Prism exchange.
Apparently, Decipher will be pre-trained and optimized to work with invoices. You can have digital workers input invoices into an Accounts Payable (AP) system and then match those invoices to outstanding purchase orders, seamlessly.
There are many free tutorials and certifications available online from various RPA tool academies. You go through online courses, take a few multiple choice questions and boom – you are certified!
I daily see many RPA developers sharing their certificates on Linkedin. I also see a few developers sharing videos of what they have done using the RPA tools. I am more excited about the later and love to see the new use cases that people are using RPA for.
Certification is certainly a good first step. It is not a cakewalk and does encourage hands-on though it is difficult to enforce. The certification by itself is not very useful though. I would follow it up with use cases that you see fit for RPA.
I shared 100 use case ideas some time back and also added a few to this blog. There are many you can find online. There is nothing like doing some real-world use cases that help you or prospective clients in day to day work!
It is also not just Macros or Scripts or Workflows. It is all of these and more. By themselves, some of these may sound old and unattractive technologies. But RPA is NOT primarily about technology.
It is about business value. Not many projects give you 300% ROI in one year or increase productivity by 1500% while doing these much faster and cheaper. Remember that the whole is bigger than the parts – RPA brings many of these technologies together to drive efficiencies that are attractive for business.
I find it easier to think of RPA as new talent – Digital Assistants. RPA tools pick up whatever technology is required to get your process automated to the extent it can. It may be screen scrapping to AI and beyond in the future. Top RPA tools provide an easy way to schedule and manage these digital assistants.
So, if you are looking at RPA purely in terms of technology, think again! It is much more – it is about having the Right strategy & Road-map, Processes selection & prioritization, Stakeholder involvement, Governance, Change management, Security & Compliance, Impact analysis, Bot Administration, Good Design, Agile Build & test, Right partners, Right metrics and surely right Tools.