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Role of Machine Intelligence in
Accelerating Automation
Businesses have always tried to “do
more with less”. This is particularly true in
today’s business environment of increasing
competition, changing customer expectations
and a global economic slowdown.
Companies are under immense pressure
to increase productivity, reduce costs and
improve quality, while, at the same time,
working with lesser resources.
Historically, this relentless movement towards
achieving more with less has followed a
typical S-shaped pattern, as displayed in the
image below:
The lower and upper parts of the curve
represent steady, incremental improvements
that keep happening at multiple levels in an
organization. However, the steep central
portion represents periods of rapid change,
often aided by a disruptive process or
technology-enabler.
As can be seen from the figure, the IT service
industry has seen a steady increase in
productivity due to various incremental and
disruptive forces. This process has been
going on slowly over the last two decades.
Today however, the industry stands on the
cusp of another period of rapid change.
Several business analysts have predicted
that automation is going to bring in disruptive
transformation to the IT services industry.
Two factors are expected to drive this trend.
The first is the maturity and convergence of
Social, Mobile, Analytics (including Big Data)
and Cloud technologies, which are driving
Digital Transformation across sectors. The
second is the advent of Cognitive Automation
technologies (that rely on machine learning or
Artificial Intelligence technologies), which are
expected to bring about a dramatic change in
the way traditional IT services are delivered.
This newsletter will offer insights into some of
the latest trends in business and technology
which are driving the move towards greater
automation. We will also present Capgemini’s
views on this developing trend and discuss
the benefits and opportunities provided by
it. Finally, we will also outline Capgemini’s
approach—Automation Drive—designed
to offer services based on such automation
technologies to clients and provide samplings
of some of the tools/assets that enable our
clients’ automation journeys.
We hope that this information will
be helpful. Do write in to us at groupindustrializationautomationcoe.
[email protected] for any additional
information.
Foreword
Ashwin YardiCOOCapgemini India Pvt Limited
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Prior to the development of robots, machines or algorithms, people did pretty much everything in an organization; from setting the strategy and vision, to management and execution and all the activities that come along with it. However, we no longer have the human capacity to sustain this approach.
Today, on the one hand, data volumes have exploded, and market competition has intensified, while on the other, cognitive computing based on Artificial Intelligence (AI) and Big Data technologies has evolved massively. Such cognitive technologies can now be applied to real-world data to derive actionable insights almost in real-time. A tremendous amount of computing power is also available “on-tap” due to the maturity of cloud-based services. It is these technological advances that are driving the adoption of Automation: technologies that can monitor systems analyze datasets and carry out tasks on behalf of humans - faster, accurate and more efficient manner.
In order to better support the speed of business and adapt to constantly evolving business environments, IT organizations must automate their application lifecycle management and develop sound cognitive abilities to predict and resolve application issues before they impact end-users. This paper examines the use of autonomics, and elaborates on Capgemini’s Automation Drive suite that helps organizations to automate their application lifecycle operations.
Technology Enablers for Smart Machines:
As human beings will not be able to effectively manage such large volumes, the next
logical step is going to be automation with the rise of smart machines, such as the
following which are already being used today:
• Analytics to help human operators sift though and find information for decision-
making in unstructured and semi-structured historical data such as log files
• IT Process Automation (ITPA) or Run Book Automation (RBA) and Robotic Process
Automation (RPA) to automate repetitive tasks, using software robots
• Digital or Virtual assistants (i.e. chatbots) to automate human operators and systems
interactions, thanks to natural language processing
• Cognitive intelligence and deep learning to discover patterns in data and convert them
into insightful information, using self-learning systems
Introduction
Automation | the way we see it
3
Automation transfers repetitive functional and cognitive processes to machines – solving
complex business problems at a speed and power far beyond the capability of human
beings. It enhances productivity, improves quality, decreases time-to-market and reduces
cost.
Capgemini enables organizations to reap these benefits by integrating powerful
automation tools into every process and application to drive competitiveness.
Our Automation Drive suite can help your organization automate your processes to drive
new paths to growth, automate your technology to drive greater efficiency and resilience,
and automate your decision making to drive greater agility and responsiveness. When we
combine machine power with business vision, we will find new ways to innovate and turn
automation into an engine for business growth.
Capgemini’s View on Automation: Combining Machine Power with Business Vision
Automation | the way we see it
The Automation Drive Suite enables the integration of powerful automation tools into every process and application to drive increased competitiveness and efficiency across our clients’ business and operations. It is a unified, open and dynamic collection of automation tools, services and expertise bringing the best of Capgemini under one Automation umbrella. The figure provided below illustrates the three major components of the Automation Drive Suite: (a) Framework (b) Tools & IP and (c) Services. These are explained in greater detail in the next few sub-sections.
Figure 1: Components of Capgemini’s Automation Drive Suite
Figure 2: Automation Drive Framework and Areas of Impact
The Automation Drive Framework encompasses the full scope of what automation has to offer; from monitoring, robotics and orchestration services to advanced artificial intelligence and cognition, along with fully autonomous services. They provide unique insights and guidance that ultimately translates into real business impact: improved productivity, increased quality, heightened agility and faster time-to-market all of this delivered using some of the latest technology solutions and tools within automation. The figure provided below illustrates the reference framework for classifying different types of automation tools into categories of: (a) Monitor (b) Industrialize & Orchestrate (c) Cognitive Services.
4
Capemini’s Automation Drive Suite
Automation Drive Framework
Industrialize &Orchestrate
CognitiveServices
Monitor CapgeminiTools
PartnerTools
The Automation Drive Suite
Design Deploy Support
FRAMEWORK TOOLS & IP SERVICES
Robotic Process
Automation
IT Process
Automation
Continuous Delivery
Test
Aut
om
atio
n
Ser
vice
Orc
hest
rati
on
BusinessArti�cial Intelligence,
Intelligent Agent,Predictive Analytics,Machine Learning,Natural Language
Processing
TechnicalPredictive Analytics,Machine Learning,Natural Language
Processing, Machine Intelligence
Increased quality and compliance, simplified processes,
facilitated 24/7 coverage
New sources of revenue, new insights, reduced time to market
FRAMEWORK
Monitor Industrialize & Orchestrate Cognitive Services
End User Experience Monitoring
Business Process Monitoring
Application Performance Monitoring
Technical Monitoring
Eve
nt C
orr
elat
ion
Automation of IT Input
Automation of IT Output
Business Outcomes
Less human resources, more robots
Automation | the way we see it
5
Capgemini’s Automation Drive Suite consists of an entire array of automation tools and Intellectual Property, including cutting-edge Capgemini automation tools as well as best-in-class partner technology. Figure 3 provides representative samples of some of these tools that clients can choose from. Capgemini also works with customers to help them select the right tools for their business process automation requirements, thus enabling them to choose from best-of-breed products for automation. This arrangement also prevents any lock-in with the platforms provided by IT service providers.
These are the services that bring speed and scalability to our clients’ value chain with analysis and strategies that are designed to fuse automation into their business and IT processes. These services are continuously enriched with new innovative automation services from innovation & strategy, business services, application services to infrastructure and cloud services. Figure 4 has a sampling of such services that Capgemini provides to its clients, many of which are based on modern automation tools such as Run Book Automation or IT Process Automation, Robotic Process Automation, Natural Language Processing, Digital Assistant, Cloud Service Automation, etc.
The Automation Drive Tools & Intellectual Property
Figure 3: Sampling of Key Automation Tools
Figure 4: Sampling of Automation Drive Services
Par
tner
to
ols
:C
apg
emin
i To
ols
:
Code Auto-Remediation Tool for SAP/Oracle based on
non-compliance report from CAST
Cloud-based Contact Engagement Platform
Incident - Knowledge Object based Nanobot
Proactive Predictive Monitoring to preempt incident occurrence,
Automated Failure Mode Analysis, Automated Resource
Allocation
Ticket Data Analysis Dashboard for Analyzing
Application Health
TOOLS & IP
Monitor Industrialize & Orchestrate Cognitive Services
Design Deploy Support
SERVICES
Robotic Process AutomationMachine Learning
Natural Language ProcessingDigital Assistant
Data ScientistsPredictive Analytics
Application Dev & Maintenance
• Application/Device/Log monitoring
• Automation of Routine App Maintenance & Regression Testing
• Runbook Automation & Orchestration
• Application & DevOps Advisory Services
• Capgemini Agile & Lean Delivery
• Continuous Integration/Continuous Delivery Production Line & Toolkits
• My3D Visual Management
• Digital Engineers for Functional & Technical Aspects
Infrastructure and Cloud
• Automation Drive: Cloud Infrastructure Services
• “Automation Drive: Insights Managed Services –
Optimize IT Operations
• Automation-Enabled P&C Services – Transform to Digital
• “Infra Inside” Platform Services – Enable the Digital
Enterprise
• Infrastructure Digital Engineer for Capacity Management,
Cluster Mgt, Compliance, Operations, Monitoring
Business Services
• Capgemini’s Global Enterprise Model
• Digitized Knowledge Worker
• Intelligent Automation Solutions for transactions, web
scraping, uploading & exporting, downloading &
importing, work�ow acceleration, reconciliations
• Automation Capability Center
• Capgemini Accelerators for Process Automation,
Checklists, ROI calculators, Reference Solutions
Automation Drive Services
Automation | the way we see it
6
Cognitive Computing Focus and the Rise of Machine Intelligence
Figure 5: IT Services Automation: From Automated to Autonomous
Automation tools and technologies in the IT services industry today exist as a continuum, ranging from Level 1 through 4, with each higher level incorporating a higher level of machine learning/artificial intelligence techniques than the previous layer. It must be noted that the techniques that fall under machine intelligence are ephemeral in nature, as what is considered machine intelligence today may become part of commodity computing in the near future.
Rising Influence of Machine Learning
Level 1
Level 2
Level 3
Level 4
If error number xxyy appears in the log file, it points to a slow
response from the Database server. Display a message to
the administrator indicating that DB server may require to be
re-started.
If error number xxyy appears in the log file, display a message
to the administrator requesting permission to re-start DB
server. Once permission is received, initiate the Robotic
Process Automation script that carries out the re-start
operation in an orderly manner.
A programmatic “agent” watches the administrator’s actions
and learns the goals. It recognizes that the administrator
re-starts the DB server whenever error number xxyy is seen in
the logs. In order to re-start the DB server, the administrator
follows an orderly step-by-step process, which the “agent”
learns on its own to autonomously create the robotic process
automation script.
Automated Log Analysis discovers that the scenario typically
plays out in the following order:
(a) Error codes aabb and ccdd appear in the logs.
(b) For the next ~20 minutes, there are several end-users who
complain about slow response times from the system.
(c) Within a span of about 20 minutes, error code xxyy
appears in the logs.
(d) After error code xxyy, the admin or the RPA agent re-starts
the DB server following an orderly process.
The agent sets itself a goal of monitoring the business
outcome, i.e. response time to the end-user and also
monitors the log file for error codes aabb and ccdd.
Whenever either of these occur, the system initiates the
process of self-remediation.
Bespoke scripts that provide
assistance to administrator as
he re-starts the DB server in an
orderly manner.
Robotic Process Automation
tools like UiPath have to be
configured / programmed to
carry out the re-start operation.
Cognitive Automation tools
such as IP Soft can “learn”
such actions and convert it into
an automated script, which can
then be refined by a human to
make it better.
A single tool for such level
of intelligent automation that
caters to this entire functionality
(and that too for all variations) is
not yet available in the market.
IT Service Example Example Tool
Automation | the way we see it
7
While Level 1 largely refers to the automation achieved through business rules, Level 4 refers to
technologies and tools that incorporate machine learning and Artificial Intelligence (AI). Particularly
in the past five years, the maturity of technologies in Levels 3 and 4 have been growing.
As mentioned earlier in this paper, the convergence and maturity of Big Data, Cloud and AI
technologies has a strong role to play in this. Consequently, it is natural to expect many more use
cases of cognitive automation applications in the near future.
Some examples of solutions/assets which Capgemini have used to enable our clients to realize the
value of cognitive automation technologies are described in the following sections:
Capgemini and IBM® have developed an innovative, end-to-end HR solution to support companies of all sizes in the optimal management of challenges like recruitment, internal mobility and career progression. Based on IBM Watson and BigInsights, the solution applies real-time insights from cutting-edge big data and analytics to a clients’ HR processes. Natural language processing, machine learning, predictive analytics, and data visualization all combine to enable this approach. Using this solution, clients can better match human resources to job requirements, streamline HR processes, save time, and reduce costs. This solution has been shown to be a major boon for Capgemini’s own internal resource supply chain management.
An example of the visualization provided by the People Analytics solution that enables the filtering of the right candidates for a given job description is shown in Figure 6.
Figure 6: People Analytics Solution from Capgemini
People Analytics and Human Resource Supply Chain
Automation | the way we see it
8
Incident Knowledge Object based Nanobot (IKON)
Smart Analytics
Figure 7: Discovering the Relevant Knowledge Objects using Capgemini’s IKON
Figure 8: Capgemini’s Smart Analytics Enables Proactive and Predictive Monitoring of Services
Used by IT Service Management desks, IKON acts as an enabler by “understanding” the incident details and selecting the ‘nearest’ right solutions from the Known Error Database, using Natural Language Processing techniques. It provides an analyst with a view of the top three possible solutions, with a relevancy percentage in relation to the incident detail. It also provides a usage percentage for those solutions, based on how often they have been applied to similar incidents in the past history.
Smart Analytics for Application Development and Maintenance services enables pro-active, predictive monitoring to forecast resource requirements and incident volume as well as pre-empt incident occurrence. Furthermore it helps identify performance bottlenecks as well as opportunities for operational improvements. While the traditional analysis
(See Figure 7). Based on the relevancy and usage percentages, the right solution to the incident is to be picked up and implemented by the system analyst. Thus, IKON addresses the challenge faced by system analysts in doing manual and tedious search through the Known Error Database to pick the right historical solution that will help him reduce the turn-around-time for repeat incidents, thereby increasing the efficiency of problem resolution.
of structured data provides helpful insights into operational bottlenecks, the ability to analyze and make sense of semi-structured data has given a significant boost to the predictive analytics capabilities. It is this feature of Smart Analytics that has made a huge difference in its ability to manage operations optimally.
Incident ID Priority Service Type Reported Date Assignee Assigned Group Product Name StatusKO-
RelevancySummary
INC000014936439
Open Incident(S) with KO Relevancy
Low User Service Request
15/08/1405:55:20.000
SubhabrataKundu
WBEI.MIS.SF.GP-NAV
Navision-Copy Center
InProgress
KS397718 88%
KS501762 88%
KS386358 75%
User request information for all the users in the WBSF department
Automation | the way we see it
9
Robotic Process Automation (RPA)
Figure 9: Robotic Process Automation: Typical Business Processes & Their Characteristics
Robotic Process Automation in back office processes refers to the picking up of certain repetitive and template-based tasks and getting them done by robots. Statistics indicate that robots make fewer mistakes and work significantly faster than humans. Robotic automation can also cost as little as one-third of the price of an offshore full-time employee (FTE), and as little as one-fifth of the price of an onshore FTE. Robotic Process Automation also frees employees from tedious tasks, hence enabling them to focus on value-adding initiatives that involve creativity and decision-making. Companies, Therefore, companies choose to implement RPA for a number of reasons, including cost savings, quality improvements, reductions in headcount, assurances that regulatory
requirements will be followed, and increase in the speed of processes.
Capgemini has partnered with different RPA product vendors – e.g. UiPath, Automation Anywhere, Blueprism etc. – to deliver RPA-based automation solutions. While the base robotic technology is provided by the product vendor, Capgemini has created accelerators for different processes carried out in the context of Business Services and Application Maintenance activities. One massive advantage of RPA is that it is a technological transformation and one doesn’t need to switch systems and processes, so there is no downtime associated with a shift towards RPA.
Repetitive tasks carried out 50-60 times a day
Process list and file storage
Periodic reporting, data entry and data analysis
Mass email generation archiving and extracting
Conversion of data format and graphics
ERP and other back office transactions
Freq
uenc
y o
f p
roce
ss
high
high
high
low
low
low
Valu
e o
f w
ork
Typical automation targets
RPA Candidates
RPA Candidates
Complexity of process
Duration of work<5 min >30min
Typical types of business processes in which RPA can be used
Characteristics of processes that can be automated using RPA
Automation | the way we see it
10
Benefits
Incorporating autonomics capabilities within application management can aid in building agility and flexibility by freeing up IT resources to focus on higher value-added initiatives. This provides the IT organization with an increased number of options, as the resources can be applied to a wide variety of initiatives.
Automation technology has been a key enabler for our transformation into an Agile DevOps organization. Release cycle times went from 6-9 months to minutes when required. The zero-maintenance systems management is fully automated. This has not only resulted in a higher availability of key production applications and improved application quality, but also in enhanced end-user experience and satisfaction
Charl Vermeer, IT Manager for architecture and innovation at Kadaster
Incorporating autonomics capabilities within application management can aid with proactively detecting application management issues before they happen. Using predictive methods can help organizations better optimize their resource utilization and avert application outages that could disrupt business continuity.
Incorporating autonomics capabilities within application management can improve productivity and application management ROI. By using autonomics and digital labor instead of manual labor, organizations can reduce the amount of time it takes to execute application management tasks; thereby saving manual resource hours. Applying this to a large scale of applications, organizations can stand to generate significant cost savings over time, which in turn, generate higher cash flows and stimulate ROI.
Incorporating autonomics capabilities within application management can create higher levels of information clarity. Autonomics solutions create systemic logs and offer higher levels of data traceability that can be shared from resource to resource, versus manual fixes which don’t often get catalogued into reporting and are not shared across disparate resource groups.
Increased agility and flexibility
Predictive issue avoidance
Productivity improvement and increased ROI
Secure and consolidated systems knowledge
Automation | the way we see it
Conclusion
11
In every line of business where Capgemini provides IT services, automation today plays an important role in:
• Providing faster, better and cheaper services
• Achieving business outcomes with shorter turn-around time and/or higher consistency of quality
• Enabling the provision of newer services through the adoption of machine learning/artificial intelligence techniques
The progressive adoption of automation and the increased applications of machine intelligence are aided by Capgemini’s Automation Drive Suite. It is designed to help customers orchestrate an integrated application development workflow environment with automated processes. Gartner’s paper, that is included in the next section, provides a detailed analysis of this trend in the automation journey.
Automation | the way we see it
12
Automation | the way we see it
When Smart Things Rule the World – Introducing Autonomous Business
Smart devices will become commonplace in the digital business environment. But with goal-seeking and self-learning capabilities, these “artificial agents” will advance the “sense and respond” paradigm to a new level — autonomous business.
Key Findings• “Autonomous business” is the optimization of
business outcomes through the inclusion of goal-seeking, self-learning artificial agents.
• Advanced capabilities afforded by artificial intelligence (AI) will enhance today’s smart devices to display goal-seeking and self-learning behavior rather than a simple sense and respond mode. These “artificial agents” will work together or on behalf of humans to optimize business outcomes through an ecosystem or digital marketplace.
• Autonomous business is a logical extension of current automated processes and services to increase efficiency and productivity rather than simply replace a human workforce.
• While autonomous business could potentially undermine the need for a human workforce, legal, ethical and practical considerations make this unlikely in the foreseeable future.
RecommendationsCIOs:
• Track developments across the Internet of Things and focus resources on building skills in the areas of analytics and AI to provide the expertise to use information innovation, smart machines and business moments as a source of sustainable competitive advantage within digital business and lay the foundations for autonomous business in the future.
• Encourage the increased use of AI-enabled smart machines to deliver growing levels of autonomous business decision making in relevant areas while educating business colleagues to alleviate fears that such systems may become uncontrollable.
• Raise awareness of the potential social and ethical implications of smart machines and autonomous business.
Research from Gartner:
AnalysisIn 2014, Gartner published the Digital Business Development Path to summarize the historical stages of Internet-enabled business development that led up to the Nexus of Forces (the confluence of social, mobile, cloud and data) and the emergence of digital business. We also introduced the idea of the next stage in this progression, naming it “autonomous business.” Gartner defines autonomous business in the following way:
“Autonomous business is the optimization of business outcomes through the inclusion of goal-seeking, self-learning artificial agents.”
In other research, we have also highlighted the “digital flip” that surrounds the transition across the nexus to digital business, and the highly disruptive changes that are occurring in this process to the underlying and long accepted “rules” of business. As the extent of this transformation become more understood and visible, at least to those leading-edge organizations that are increasingly embracing digital business, the need to more clearly define what lies beyond becomes more pressing.
Gartner has defined digital business as:
“The creation of new business designs by blurring the digital and physical worlds. Digital business promises to usher in an unprecedented convergence of people, business and things that disrupts existing business models.”
Digital business builds on the technological platform of the Internet of Things to deliver real-time data and connectivity to objects and, in so doing, to create new revenue streams and new value from new capabilities and services that did not previously exist in a viable form. For example, GE’s Industrial Internet connects sensors to jet engines, generators and other machinery in order to provide advanced maintenance and monitoring services not previously offered to thereby realize new revenue streams. At the opposite end of the scale, there are a myriad of devices such as coffee machines, smart clothing
to monitor the wearer’s heart rate and respiration, and many more examples. CIOs across all industries should be tracking such emerging applications across the Internet of Things and bringing them to the attention of their business colleagues as potential new sources of revenue and competitive advantage.
Inherent to digital business is the concept of “digital business moments” — transitory business opportunities that can be exploited to create new value. Broadly speaking, digital business designs are based on a cycle of sense, communicate, analyze and respond, where responses are largely predefined and rule-driven (“If this, then that …”). Business moments arise from the opportunity to analyze the data derived from these sensor networks in order to trigger a value-added response at the most opportune moment. This emphasis on the value of information and the benefits that can arise from advanced analytics is a key advance that lies firmly in the realm of the CIO. The combination of innovation with information and its use is becoming a significant technology-enabled competence.
So what is “autonomous business” and how (and where) does it differ from digital business? In the digital business world, a smart device may detect an event (such as an imminent mechanical failure) and trigger a predetermined sequence of actions (such as calling the maintenance engineer, advising controllers, and so on) to address the issue. As we move forward into the age of autonomous business, the smart device will still detect the event, but the actions it takes will be more complex and less predetermined. It may analyze additional information (the location of various engineers, for example) and issue the work orders or a request for a replacement part from a variety of suppliers, then confirming the order with whoever can supply the item in the appropriate time scale and at the right price. The actions taken are no longer predetermined but much more variable and determined by the “desire” on the part of the smart device to resolve the problem in the most effective manner possible. Autonomous business may seem futuristic, but early examples such as automated (financial) trading are already well-established. With the significant accelerations in advances in areas such as AI, analytics, machine learning and robotics that took place during 2014, we conclude that more complex examples will emerge earlier than previously anticipated. Indeed, noncommercial implementations and all the necessary technology platforms have been around for several years in the R&D labs of major technology players. Recent announcements of self-driving vehicles being granted permission to operate on public roads in the U.S. are a case in point.
Understanding Smart Machines and Their Future EvolutionIf the Internet of Things represents the fundamental technological platform for digital business, then smart machines fulfill that role for autonomous business. Previous Gartner research has explored the capabilities of smart machines in depth and presented the seven properties displayed (to varying degrees) by smart machines, namely:
1 Deals With Complexity
2 Makes Probabilistic Predictions
3 Learns Actively
4 Learns Passively
5 Acts Autonomously
6 Appears to Understand
7 Reflects a Well-Scoped Purpose
In more recent research, these properties were developed to formulate six basic capabilities of smart machines and to project their development in the coming years.
• Sensing (to detect information about their state and that of their environment)
• Learning (using AI and predictive analytics and the ability to modify their algorithms)
• Acting (increasing capabilities to translate digital intentions into physical actions)
• Creating (physical products from digital designs and even literary and artistic works)
• Interacting (through contextually aware interfaces with both humans and machines)
• Sharing (actively collaborating to share information and augment capabilities)
It is our view that the progression of these properties and the capabilities they support allows for the logical evolution of smart machines to combine their analytical and adaptive learning capabilities to optimize their actions with their ability to create new designs and responses to seek goals and solve problems rather than simply analyze and respond (however intelligently).
From Digital Business to Autonomous BusinessWith the rapid explosion in the number of connected smart devices in the future, it is to be expected that
Automation | the way we see it
Figure 1. The Progression From Automated to Autonomous Operations
ILS = instrument landing system Source: Gartner (May 2015)
machines rather than people dominate the interaction endpoints, suggesting a high preponderance of machine-to-machine interactions rather than person-to-person. Under such circumstances, the concept of the “user” becomes increasingly limiting. When a thing communicates and even negotiates with another thing, who is the user? However, acknowledging that there will still be interactions between people and this new army of smart things leads us to best describe this new emerging relationship between humans and machines, and between machines and machines, in terms of “interactors within an ecosystem.” We define an interactor as “a participant in a technology-enabled exchange, and may be a human or a machine.” Machine interactors will take on the role of “artificial agent,” acting on behalf of a human or another machine within an ecosystem or marketplace order to achieve an outcome.
Thus, we envisage a future scenario in which self-learning, problem-solving and goal-seeking artificial agents will operate alongside humans, increasing both individual and organizational efficiency and undertaking many more routine business processes, leaving the human workforce to drive strategy, set goals and spend more time to interact with each other, customers and prospects to enhance the overall customer experience.
To summarize, we propose that autonomous business be defined as:
“The optimization of business outcomes through the inclusion of goal-seeking, self-learning artificial agents.”
Increasing Levels of Automation Lead to Autonomous OperationsNote that we anticipate that organizations will move to utilize the principles of autonomous business over a period of time, building on the broad existing range of automated solutions already in widespread use (see Figure 1). A business may choose to move from automated service to an autonomous service in limited, but clearly defined areas. Alternatively, it may choose a more broadly based deployment, utilizing goal-seeking artificial agents for significant numbers of interrelated business processes and even entire business services. Such moves are already taking place in the realm of programmatic buying and placement for online advertisement.
As a preliminary step, CIOs should seek to educate their colleagues on the current and near-term advances in AI and the extent to which this is enhancing the functionality of smart machines, and
Automation | the way we see it
encourage their deployment to develop competitive advantage for the enterprise. The idea of self-learning systems and goal seeking is challenging to many individuals who may not fully appreciate either the possibilities or the limitations of these capabilities. Using this framework as a guideline to promote an evolutionary progression in the use of smart machines will help to alleviate concerns from many quarters that increasingly smart machines may wrest control of critical decision-making processes within the enterprise and potentially even go out of control. Moving toward greater use of automated decision making for key processes within the enterprise is a key step in the emergence of a highly effective autonomous business.
Autonomous Business Promotes Community-Based Business ModelsThe progression toward adaptive and independent operations brings with it the specter of fully autonomous companies and organizations. The notion of an independent autonomous organization has been previously explored by Nick V. Flor, who concluded that it can emerge when basic social practices are mediated by technology to allow the instantiation of business processes. He proposes that such a business could operate profitably and grow the customer base without any employees or managers, which limits the practical realization of such a complete entity at this point in time due to issues of ethics, liability and ownership. One consequence of this model is that customers become more than consumers, they are also suppliers and advertisers. In a business dealing predominantly in information, such a progression appears feasible, especially since some of the most successful (albeit controversial) new business launches in recent years (such as Airbnb and Uber) utilize information as a proxy for the physical product — which they do not own, nor do they deliver the product/service directly.
Autonomous Business and EmploymentThe emergence of new business models heavily dependent on autonomous business appears almost inevitable, and will have an impact on employment. However, we do not believe that the elimination of all employees (or their managers or executive board) to be either desirable or plausible in the foreseeable future. The benefit to organizations will be to increase the time available to the human employees to do those things humans do better than machines — provide creativity, empathy, emotion, and so on.
While it might be argued that machines will be more efficient, the lack of real empathy or inability to make a nonobjective decision will do little to enhance customer experience. The dehumanized organization may be efficient, but it has the potential to be a “cold” organization that may struggle to foster the emotional loyalty that has characterized some of the most successful organizations over the years.
A Simple Example of an Autonomous Business ServiceAs an example, let us consider the concept of a “smart store cupboard” (or if you prefer an industrial setting, a “smart supply chain”).
In an initial (simplified) digital business implementation, the cupboard is installed and maintained by a supplier/operator and is designed to ensure that a variety of (predefined) staple products is available. These are commodity-type products. The user wishes to ensure a base level of stock of these commodity-type products is always available. As items are removed, the cupboard recognizes the number and identity of each product (by image analysis or even something as simple as scanning bar codes, for example) and triggers an order to resupply from the cupboard operator. The operator, by being able to aggregate demand from many active cupboards, is now able to bulk purchase, thereby gaining greater leverage with suppliers and the opportunity to not only guarantee levels of demand but gain additional revenue from small margin improvements. With sufficient volume and efficient purchasing and logistics operations, the supplier can manage a business with predictable revenue and long-term security.
In a more sophisticated autonomous business implementation, the same cupboard is now supplied as a digital service and is tasked — that is, has goals to maintain continuous availability of items at the lowest cost. As items are removed, the cupboard broadcasts a request to the ecosystem requesting bids for the supply of the required item. It compares responses from respondents (other smart devices/agent/interactors within the ecosystem that have the ability to meet the request) and agrees to a “contract” with the successful bidder to deliver the product. Over time, the cupboard will recognize average usage rates (perhaps dependent on external factors such as weather, season or holidays) to “think ahead” and determine required demand in advance of product usage.
Automation | the way we see it
On the bidder side, each smart device/agent would be adjusting price dependent on real-time supply and demand and also their current and desired “reliability rating.” A low rating might encourage the device/agent to broadcast “special offers” to boost volumes and hopefully ratings. A high rating might support a price premium, hence maximizing the commercial outcome for the supplier. At the same time, the bidder may be issuing its own requests to the ecosystem for the logistics elements, thus creating a variety of “service markets” in which numerous collaborating players could operate.
There are a number of significant differences here that characterize the advances made by autonomous business over digital business.
• One-to-many relationship between cupboard and suppliers
• Negotiations with multiple suppliers to optimize price/delivery
• Learning of patterns to influence activities
• Potential multiple markets to support subcontracting and multiple collaborative agents in the chain
• External influences affecting terms and conditions
• Introduction of “social network”-type ratings on performance taken into account
• Disintermediation of supply chain by digital service
Interestingly, it might be argued that the system is doing nothing that a conscientious human might do by comparing prices at different suppliers and being influenced by peer recommendation and special offers. That is the point. The autonomous business smart cupboard is mimicking human behavior, but
is just an autonomous system. It is given a goal of maintaining continuous supplies of a defined set of staple products (at the lowest cost), and via a set of APIs operates independently to optimize its performance against that goal.
Evidence
The Growing Universe of ThingsCurrent Gartner forecasts suggest the Internet of Things will comprise some 25 billion connected devices by 2020, outnumbering the human population by three to four times and increasing by (an average) of some 5 billion new devices per annum. Many other forecasts are more aggressive and (assuming current issues over scalability and infrastructure are resolved) it is not unreasonable to expect upward of 50 billion devices by 2030 (assuming current growth rates remain linear), or even trending toward 100 billion should current growth rates continue to accelerate. This represents around 15 times the human population (and would be roughly in line with historical Organisation for Economic Co-operation and Development (OECD) estimates, made before the Internet of Things started to emerge strongly).
One must also consider the growing number of “virtual sensors,” the systems with which we interact on a daily basis (such as banks, supermarkets and social media sites) and the cameras which identify our location and movements. Every transaction or interaction triggers another data point, adding to growing digital footprint we leave behind us. Visa alone processes more than 200 million transactions per day and almost 900 million people log on to Facebook every day.
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Source: Gartner Research, G00275061, Steve Prentice, 20 May 2015
Automation | the way we see it
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