Article | June 2, 2021
Intelligent Automation (IA) is one of the trending buzzwords of our times. What makes automation smart? Is it new? Why the renewed focus? Bill Gates believed automation to be a double-edged sword when he said: “Automation applied to an efficient operation will magnify the efficiency. … Automation applied to an inefficient operation will magnify the inefficiency.”
IA lies at the intersection of robotics, artificial intelligence (AI) and business process management (BPM).
But before you think HAL from 2001: A Space Odyssey, J.A.R.V.I.S. from Iron Man or Terminator 2: Judgment Day scenarios, first, a little context. IA is not new; automated manual processes have been in existence since the dawn of the Industrial Revolution. It enabled speeding up go-to-market, reduced errors and improved efficiencies. Over time, automation made its way into software development, quality assurance processes, manufacturing, finance, health care and all aspects of daily life.
“Intelligent” automation backed by robotics, AI and BPM creates smarter business processes and workflows that can incrementally think, learn and adapt as they go — for instance, processing millions of documents and applications in a day, finding errors and suggesting fixes or recommendations.
What Intelligent Automation Does, Humans Can’t
IA enables the automation of knowledge work by mimicking human workers’ capabilities. It includes four main capabilities: vision, execution, language, and thinking and learning. Each of these capabilities combines different technologies that are used as stand-alone or in combination to complement each other.
One oft-quoted IA example is fraud detection and prevention in the BFSI sector. Robotic process automation (RPA) optimizes the speed and accuracy of the fraud identification process. Since RPA can go through months’ worth of data in a matter of hours and throws up exceptions, teams cannot keep up with the speed and scale needed to resolve the issues flagged. However, speed and efficiency are of the essence where fraud management is concerned.
The answer lies in AI and BPM coupled with RPA. IA can streamline the process end-to-end. Pascal Bornet notes in his book, Intelligent Automation, that IA can help improve the overall automation rate to nearly 80%, and it can help improve the time to solve a fraud incident and obtain clients’ refunds by 50%.
While RPA provides excellent benefits and quick solutions, cognitive technologies offer long-term value for businesses, employees and customers.
IA And Digital Transformation
IA adoption is growing swiftly across the enterprise, being fast adopted by more than 50% of the world’s largest companies. Its benefits are relevant to the majority of business processes. For example:
• Industrial systems that sense and adapt based on rules.
• Chatbots that learn from customer interactions to improve engagement.
• Sales and marketing systems that predict buyer journeys and identify leads
The Future Of Work: Bitter Or Better
There is much speculation when it comes to IA and the future of work. The main contention is that robots will take away jobs from humans. My argument is that, while it will cause role changes, it doesn’t necessarily mean job losses.
The Industrial Revolution helped automate “blue-collar” jobs in manufacturing and agriculture. Similarly, IA will automate many white-collar jobs that are tedious and tiring. A recent IBM report shows that 90% of executives in firms where IA is being used believe it creates higher-value work for employees.
So, no, we will not be living in a dystopic world controlled by bots running amok! IA means better roles, the elimination of laborious tasks and improvements in employee well-being.
The Promise Of The Better Life
In 2018 alone, over $5 trillion (6% of global GDP) was lost due to fraud. Medical errors in the U.S. incur an estimated economic value of almost $1 trillion — and 86% of those mistakes are administrative. A 2017 Medliminal Healthcare Solutions study found that 80% of U.S. medical billings contain at least minor errors — creating unnecessary annual health care spending of $68 billion. The World Economic Forum cited an ILO report that “estimates that the annual cost to the global economy from accidents and work-related diseases alone is a staggering $3 trillion.”
Now, let us imagine we can save $5 trillion globally through the deployment of IA. It means:
• Global budgets allocated to education could more than double.
• Global healthcare budgets could be increased by more than 70%.
• Environmental investments could be multiplied almost twentyfold.
Transitioning To Intelligent Automation
However, adopting IA is not like flipping a switch. There are some key steps an organization must experience in its bid to be automating intelligently.
• Planning. For the successful adoption of IA, business leaders must understand the relationship between people and machines. Enterprises must plan so as not to disrupt other parts of the business and integrate IA seamlessly into the existing programs. Instead of adopting IA across the processes, identify where it delivers the most value. Automating broken processes will not fix the problem. IA will only reap rewards on stable and mature processes
• Change management. IA is not easy to implement. There will be a great deal of resistance to adopting IA in your organizations. Designing a change management strategy, an execution road map, an enterprise operating model and key metrics for ROI will help your cause. Invite key stakeholders from the outset to ensure buy-in and train your employees to work in collaboration with IA.
• Governance framework. Establishing a governance framework helps determine who will watch the watchmen. The bigger the role of IA in your organization, the more critical governance becomes. Designing a framework will help you monitor performance as well as define exceptions and errors. It is a recipe for disaster if you don’t have a command and control center to ensure IA is making the right choices. Even more reason for humans with industry expertise to still “have their jobs” and excel at them.
Future Of Intelligent Automation
The future of IA will direct businesses to a more adaptive model that is beneficial for business leaders to uncover higher value and employees to do more satisfactory and creative roles. Preparing for an intelligent future means adapting our technology, skills and education to fit the future of the workforce.
What are we waiting for?
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Article | August 6, 2020
Learning how to monitor etcd is of vital importance when running Kubernetes in production. Monitoring etcd will let you validate that the service performs as expected, while detecting and troubleshooting issues that could take your entire infrastructure down. Keep reading to learn how you can collect the most important metrics from etcd and use them to monitor this service. etcd is a foundational component of the Kubernetes control plane. It stores your cluster desired state (pods, secrets, deployments, etc.), among other things. If this service isn’t running, you won’t be able to deploy anything and the cluster can’t self-heal.
Article | April 9, 2020
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In 1956, a group of scientists led by John McCarthy, a young assistant-professor of mathematics, gathered at the Dartmouth College, NH, for an ambitious six-week project: Creating computers that could “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
Article | May 17, 2021
Common view is that AI software adoption is 'on its way' and it will soon replace many jobs (example self-driving cars with drivers etc.) and the majority of companies are starting to embrace the efficiencies that AI brings now.
Being a practitioner of AI software development and being involved in many projects in my company AI Technologies, I always found my direct experience in the field in contrast with what the media generally portraits about AI adoption.
In this article I want to give my view on how AI projects affect the work dynamics into clients work processes and compare that with the studies available on the impact of AI and new technologies on work. This should help the reader, especially if he is an executive, to set the right expectations and mentality when he is assessing the potential investment into a new AI project and if his company is ready for it.
To start with, any software development project, including AI, can be summarized into 3 stages: proof of concept (POC) when the prototype has been built, product development when the software is actually engineered at scale, live support/continuous improvements. It occurs often that projects in AI will not go pass the POC stage and this is often due to
1) not right IT/data infrastructure in place
2) not specialist people have been hired to handle the new software or digital transformation process has not been planned yet.
Regarding point 2, the most difficult issue is around hiring data scientists or data/machine learning engineers because many companies struggle with that.
In fact, in a March 2021 O’Reilly survey of enterprise AI adoption, it has been found that “the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring.” And in 2019 software it has been estimated that there were around 144,000 AI- related job openings, but only around 26,000 developers and specialists seeking work.
Of course hiring an internal data scientist, it is not the only problem in restructuring the workforce. Often a corporation has to be able to re-train entire teams to be able to fully benefit from a new AI software.
I can give an example. As many readers know a sales process involves 3 stages: lead generation, q&a call/mails with potential clients and deal closing. Now, a couple of years ago AI Technologies had been engaged to automatize the q&a call stage and we build a ai bot to manage the 'standard questions' a potential client may ask (without getting into the details, using AI and technically word3vec encoding, it is very possible to automate mails/chatbot for 'standardized questions' like 'how much it cost?' 'how long is the warranty for' etc.). Using this new internal solution, it meant the team responsible for the q&a would have been retrained either to increase the number of leads or the number of closing. The company simply decided to not embark into the transformation process required to benefit the new AI adoption.
This example, in various forms, it is actually quite common: companies unless they are really innovative prefer to continue with their corroborated internal procedures unless some competitors threat their profitability.
This bring to the fact that actually AI is not an out of the shelves solution which can be plugged in with no effort. As the moment a POC is under development it should be a good norm to plan a digital transformation process within the company.
Also it is worth mentioning that, it is unlikely that the workforce has to be dismissed or made redundant as many expected following AI adoption. Just following the example above, what the AI bot does actually is to get over the repetitive tasks (q&a) so people can do more creative work engaging more clients (lead generation) or convincing to buy ( deal closing). Of course, it means that some people have to be retrained but also means that with the same people, you can close/generate more sales.
It is a misconception to think that AI solutions will make human work redundant , we just need to adapt to new jobs.
My example resembles a classical example on adoption of ATMs. When ATMs were introduced in 1969, conventional wisdom expected the number of banking locations to shrink, but instead, it actually made it possible to set up many more of them, it became cost-effective. There were under 200,000 bank tellers in 1970, but over 400,000 a decade later.
The other common problem to face when companies want to embrace AI adoption (point 1), it is their current infrastructure: databases, servers, and crm systems have to be already in place. To put it simply, any AI system requires data to work with so it naturally sits on top of data infrastructure in day to day business operations.
In the last two years AI Technologies has been engaged to work with a large public organization (70,000 employees) to build a solution to automatically detect malicious behavior of its employees manipulating their data. To build the AI software we had also designed a system to stream data from each employee terminal into a central database for processing. This infrastructure was not present at the beginning of the project since before the need for malicious detection was arised, the organization never really realized the necessity to gather certain data: a simple login and logout time was all the needed to monitor the activity of their employees (which company folder/file they accessed etc. was not important).
This is a common situation and most of the companies' infrastructure are usually not ready to be used directly with AI solutions: their current infrastructure was simply designed with other objectives in mind.
For sake of completeness, most companies decide to invest their internal resources in other areas of the business rather than crm or expensive data structures. There is no blame on this choice, at the end any business has to be profitable and investing in infrastructure is not always easy to quantify the return of investment.
If anything, this article should have given an idea of the major pitfalls approaching AI projects which can be summarized as follows:
• AI solutions are not out of the shelves , ready made software that can be immediately put in use: they often require new skilled hires within the client organization and potentially a plan how to re-utilized part of the workforce.
• It is often a myth that AI solutions will necessarily replace the employees although it is possible that they have to be retrained.
• Any AI project works on data and infrastructure which are necessary to benefit the new solutions. Before embarking on AI projects an organization has to either budget in a new infrastructure or at the very least an upgrade of the one in use.
In essence, due to the implication on both employees and infrastructure, AI adoption should be considered as a digital transformation process more than a software development project. After the overwhelming hype of attention of the recent years, I would expect that in the next 2-3 years more companies will start to realize what AI projects really are and how to best use them.