“Speed is the new currency of Business.”  

— Marc Benioff, CEO of Salesforce  

  

Intelligent Automation is defined as the orchestration of several new and emerging technologies. The coordinated use of these technologies is being used today to drive the next quantum leap in business value. This business value emerges in different areas such as enhanced efficiency, increased worker performance and satisfaction, reduction of operational risks, and improved response times and customer journey experiences. These technologies can be grouped in categories such as:  

 

1. Advanced Analytics: Companies are producing vast amount of data, and over 70% of this data is not used in any way. New systems and algorithms are able to process, identify, structure, and present data that drive actionable insights.   

2. Artificial Intelligence: Applications that use human skills such as visual perception, speech recognition, recognition of printed and hand-written text, decision making, and language translation.  

3. Machine Learning: The application of algorithms, statistical models and logical processes to analyze situations and make decisions without using explicit instructions. This differs from “standard” Artificial Intelligence because ML systems change and improve over time, as opposed to be a static solution.  

4. Robotic Process Automation: Tools that automate tasks that are repetitive, rule-based, and/or manual. They work by replicating the actions of an actual human interacting with one or more software applications to perform tasks such as data entry, process standard transactions, or respond to simple customer service queries. The use of RPA allows humans to concentrate on more value-added components of the value chain, while vastly improving speed, quality, transparency and accountability within existing processes.  

5. Business Process Re-Engineering (BPR): The analysis, design, optimization, and deployment of strategies to support an organization’s objectives by continuously improving process output, flow, satisfaction, cost and effort. Processes in and between organizations are benchmarked throughout several different categories, and then continuously monitored, modified, and optimized. The main difference between this technology and the classical “Hammer” process reengineering is that contemporary BPR can be largely automated.

 

There is tremendous impact being created across enterprises. Some of these examples include:  

 

1. 20-35 percent annual run-rate cost efficiencies in large-scale processes such as Order to Cash, Record to Report, Hire to Retire, Procure to Pay, etc.  
    

2. Reduction in straight-through process time of 50 to 60 percent.  
    

3. ROI most often is in triple-digit percentages.  

 

Paradoxically, many of today’s tactical automation implementations are not successful in delivering the promised business value. In fact, several sources cite a success rate of less than 50%. So how can we reconcile the radical rethinking of the large-scale processes’ companies use today with these new and untested capabilities? 

 

The answer lies in the intelligent, focused application of these next-generation tools. There are three main value clusters for Intelligent Automation being used today:  

 

1. Augmenting human capabilities: “Taking the robot out of the human”, i.e. taking over repetitive, boring, difficult or dangerous tasks that don’t add business value and lower client and employee satisfaction. 
    

2. Orchestrating data fusion and application extensibility: Data and applications are becoming consistently more complex over time. Creating interfaces between systems—some just created this year, other legacy instances that may be over 50 years old—is increasingly complex as well. Intelligent Automation enables fast, inexpensive and powerful interoperability between all data and applications. Moreover, these integrations occur at a fraction of the time and cost as compared to older technologies such as APIs.
    

3. Enabling new business models and tech capabilities: The effective use of Intelligent Automation enables new ways of doing business and achieving business value that weren’t possible even a couple of years ago. This includes the virtually unlimited availability of skilled laborers, computing power, and sophisticated analytics driving actionable insights.

 

The dramatic potential that Intelligent Automation offers has certainly been noticed by the market. AI startups alone have mushroomed from near zero in 2000 to more than 12,000 today. AI investment alone has reached more than USD 50b/year in 2019, with a 20% CAGR expected until 2025. Most AI is developed by governments, academia or small companies, as the state of the art of the technology overwhelmingly enables small, tactical AI solutions (Artificial Narrow Intelligence—“ANI”). Artificial General Intelligence (“AGI”) is much more difficult, so there are comparatively few large corporations that are working on these—mostly Alphabet (Google), Microsoft, Facebook, and the like. In these, though, the R&D investment is enormous—Microsoft alone is spending USD 8b in 2019 on ANI, not counting acquisitions such as the USD 1b Open AI deal. AI lends itself to consolidated financial plays, so AI-centric funds are growing over 35%/year (such as Softbank’s Vision and Vision 2 Funds—this last, an AI-focused second $108 billion Vision Fund with LPs including Microsoft, Apple and Foxconn.  

 

RPA has lent itself to much more traditional development, even though the hype cycle is no less strong than that of Artificial Intelligence. In fact, RPA grew at a blistering 63%/y pace in 2018, becoming the fastest-growing technology on the planet. This is remarkable, especially since the total market is only about USD 1b in 2019. This remarkable growth has led to mind-bending results and valuations.  

 

RPA solution providers like Automation Anywhere, Blue Prism and UI Path, have shown dizzying growth trajectories and high valuations that are unlike any other technology companies—already a vertical that lends itself to financial hyperbole.  

 

This revolutionary, new way of reshaping business processes crafts, deploys, and manages new and existing value streams—and continuously improves them over time. These new value streams drive revolutionary operating models that leverage them to drive improved and new ways of doing business. Used correctly, Intelligent Automation is a powerful catalyst for change. It enables much higher speed to value, process quality, business agility, and lower risk and cost, while lowering risk and cost in ways not seen since the inception of Business Process Outsourcing. 

 

Use Case 1  

One example of Intelligent Automation is in rethinking Enterprise Asset Management for a major Oil & Gas multinational. This company has over 40,000 kms of pipeline in many countries. Pipeline Maintenance, Repair and Operations is very complex, expensive, and dangerous; typically, crews are sent out to physically inspect the assets from time to time and perform repairs on them as required. This is a major issue: problems, unlike crews, do not operate on a predictable schedule. Further, when crews are on their maintenance trips, they do not carry every tool and part they may conceivably need. This inevitably leads to even more delay in resolving very serious issues. The most important flaw in the existing model, however, is in Environmental Health and Safety risks. Pipeline go through some nasty places; places that are too wet, too high, too underground, or too war-torn to be safe. In fact, 20–30 crew members die every year performing this maintenance.  

 

This is a critical issue for the company. It costs USD 250 million/year to maintain, yet they have poor visibility into their asset performance, don’t have enough personnel to properly maintain the equipment, and yet are exposed to the nightmare risk of people dying and disastrous spillages that are environmental, economic, and PR nightmares.  

 

But what if? What if this Oil & Gas company could have unlimited eyes, ears and hands watching the pipelines every hour of every day? What if these new employees were trained to identify exactly what issues are occurring and could decide exactly what had to be done, and take the necessary steps to resolve the issue as quickly, effectively and safely as possible?  

 

Of course, the hypothetical above is impossible using humans and the existing, human-centric processes. But, when we use the guiding principles of Intelligent Automation, the problem becomes solvable. The company is using hundreds of RPA Digital Workers (DW) to constantly watch pipelines through a sensor fusion of security cameras, drones, satellite imagery, and even geo-tagged social media. This information is consolidated into a data lake, which then is analyzed by several different kinds of Artificial Intelligence. These AIs can be general or specialized, but each is responsible to detect types of issues. One may be specialized in detecting unauthorized personnel close to the equipment; another may be specialized in types and degrees of corrosion. These AIs, orchestrated by the DW, analyze and discuss the enormous amount of information they receive every moment—and then decide what to do about it. Any issue has a resolution workflow attached to it; the AI decides the appropriate resolution and the DW quickly connect to all the required data, applications and systems to execute on the solution.  

 

Now, what if there’s an issue the AIs have not yet encountered? If the AIs don’t agree on an issue, they can bring in a human in the loop to solve the issue and teach them. An interesting example was when the solution first encountered graffiti on a pipeline. The IA solution could not find a process that covered the issue, so the DW sent an email to the responsible manager asking how to categorize the issue. The manager indicated the correct resolution process and emailed that back to the DW. The DW retrained each AI individually to recognize the new issue, and further retrained each DW how to process it.  

The benefits of the solutions go beyond the detection and disposition of thousands of issues—following best practices, not making mistakes, and dramatically improving response and resolution times. The DW also interface with hundreds of legacy applications and data throughout many of the client’s, third party contractor, and supplier environments. This allows great business agility and transparency at low cost—for example, an analytics system originally quoted at USD 5 million- and six-months’ development time was delivered in three weeks and a cost of USD 200,000 using Intelligent Automation.  

 

It’s clear that the use of any one of these technologies by itself would not have delivered a fraction of the value that of their harmonized use. It is a testament to the power of this new way to deliver business value that Intelligent Automation is saving the company USD 80 million/year in lower maintenance costs and better outcomes. More importantly, it’s saving lives as well.  

 

Use Case 2  

A major Manufacturing company used Intelligent Automation to lower its client and supplier relationship costs by over 90%. This particular company has an enormous number of SKUs and serves over 22,000 clients all over the world. Their products are complex, so they have over 50,000 suppliers. Clearly, the requirement to have a fast, reliable, and cost-effective supply chain is critical for this company, its suppliers, and its clients.  

 

Typical supply chains are still heavily dependent on people. They need people communicating with shippers, preparing paperwork, ensuring compliance and accuracy. Further, companies have different application stacks and inefficient analytics that make it very difficult to get a picture of the company’s supply chain performance—much less keep it running at peak efficiency.  

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But what if? What if you could have DW watching every single shipment—its products, paperwork, transportation, and client needs—every hour of every day? Further, what if you could create a super-smart DW that knew everything about your company—its applications, data, policies, best practices, etc.—and could make smart decisions based on the best data? Further, what if this DW could go to each supplier and client and connect to their applications and data to transact, process, and negotiate in the company’s behalf?  

 

In fact, the developed solution uses a Digital Worker that is given all the tools it requires to know how to operate on behalf of the company. Further, the DW has an AI component that understands how to connect with the supplier or client systems—and does so without human help. Machine Learning is used to find the best way to serve the company’s interests; for example, it can detect the inventory for a certain supply is low and reach out to every supplier to ask for a quote, then negotiate and close without human interference. From there, the solution learns how to build cost-effective routing considering many variables (cost, time of travel, temperature, shipper quality, etc.) at the same time. DW keeps a constant watch on the shipment, and can auto-remediate a potential issue (for example, mismatching shipment documentation) without human intervention as well.  

 

The results are obvious and dramatic. The company significantly reduced its reliance on humans to maintain its supply chain; it also improved the quality, speed, and agility, while gaining complete transparency and visibility of its operations. Another interesting result was that the company reduced the time and effort for its clients and suppliers to do business with them, providing opportunities for the company to get discounts and other advantages.

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These use cases illustrate the power of strategically-placed Intelligent Automation. This ability to change the way companies do business has not been ignored by large corporations, which are scrambling to build the integrated capabilities necessary to build the next quantum leap in business value. For example, SAP has significantly augmented its automation capabilities in the last two years: not only has it improved its native Leonardo automation solution, but it also bought an RPA firm called Contextor in 201822. SAP is offering Intelligent Automation skills to augment its own product suite, as well as extend processing and integration beyond its own products.  

 

Other companies have followed suit, notably Microsoft with its October 2019 announcement of an RPA suite called Power Automate23. Growing out of Microsoft’s Business Process Management solution called Power Platform (formerly known as Flow), Power Automate offers native BI functionality, low code and workflow management skills. It also comes with 275 prebuilt connectors for apps and services. As would be expected, it also has deep integrations with Office 365, Dynamics 365 and Azure.  

 

Other tech companies are using Intelligent Automation to grow share of mind (and pocket). Appian, for example, uses RPA to take actions and coordinate the performance of its BPM software. 24/7 AI, a major Natural Language Processing and Decisioning solution, uses RPA and ML to create human-like “chatbots” that offer human-like interaction to call center clients. These “chatbots” come at a fraction of the cost of classical call center operations, making call center agents more than three times as effective.  

 

Market analysts confidently expect other technology giants such as IBM and Oracle to join the fray very quickly. All major players recognize that they are playing catch up in the Intelligent Automation market—and that using IA strategically is critical for survival for both clients and themselves.  

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Intelligent Automation has the potential of forever changing business as we know it. Its potential to replace large parts of human labor is as game changing as the steam engine was for the Industrial Revolution. This change will not occur by itself, though; it requires a concerted effort by academia, corporations, clients, and thinkers that will define how to put these new components together to transform processes that haven’t changed materially for a hundred years. The prize, though, is worth it: a world where humans can concentrate on being human—and let the machines do the drudge work.  

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