Editors Note: Today’s blog is a collaborative piece by Ross Rexter, Acorio’s VP of Advisory Services, and Ian Clayton, Senior Business Process Consultant.
Welcome to our new blog series related to the topic of Intelligent Automation (IA), or Artificial Intelligence (AI), or Robotic Process Automation (RPA) as it relates to Acorio’s favorite topic; Service Management and how ServiceNow is leading the way.
While we have been actively speaking on this topic in public and advising clients on IA strategies in relation to their business goals, recently we’ve seen such a significant uptick in the number of organizations seeking advice on these themes that we feel the trend requires some discussion, perspective, and practical advice on how to consider ‘Intelligent Automation’ within the Enterprise Service Management capability roadmap.
Today’s IA/AI Discussion: With all the buzz, what’s relevant?
As in any emerging trend, the amount of hype and early-formed information can easily leave parties who are just casual interested confused.
The themes of AI, RPA, and IA are no different. Adding on to the confusion is that these concepts, in particular, are being discussed in the media in terms of their future impact on society in quite frankly…science fiction terms. Hype and sensationalism including “Hollywood” make this for an interesting topic to put into perspective about how can we, Service Management leaders, sift through it all and come up with a coherent vision of how the emerging technology of Intelligent Automation fits into the business enterprise for
So, if we are not talking “Westworld” type sci-fi, what are the IA topics for Enterprise Service Management?
Organizations that are moving to put an IA vision in place have some common characteristics; they are under pressure of globalization, hyper-competitiveness, and ever-increasing cycles of innovation and change. They are seeking to understand if IA is real in the Service Management realm, and how it might shape, or disrupt workforce practices, skills, and operating models.
Fueling the pressure to compete faster in more markets in different ways, many organizations foresee a skills shortage and possible breaking point their ability to handle future workloads. In ServiceNow’s 2017 State of Work survey (with over 1,800 business leader respondents), the numbers were quite startling.
- About half the respondents believe the level of work has increased by at least 20% over the previous year
- 46% suspect they will risk reaching a breaking point in 2018, and 80% think they will likely reach it in 2020
- 82% find it difficult to hire the skills they need to support the desired business growth
- 93% want to reduce the human effort performing mundane tasks and unleash the creativity within the workforce, and 79% believe automation can lead to job creation and grow the business
- Almost everyone saw an increasing demand for collaborative and problem-solving skills, but hardly anyone knew what level of automation was in use versus what they need
Now, the cost of doing nothing with intelligent automation is significantly higher than the cost of adaptation. There is a big risk of doing nothing.
Breaking Down IA
The first wave of early adopters have shown us the cost of automation outweighs the costs of doing nothing, and the second wave of adopters are quickly gearing to go.
With that backdrop, organizations ask us for our definition of ‘Intelligent Automation’, and how it differs from plain ‘Automation’ of the past. They also want to know if the hype around artificial intelligence and machine learning is real, and how best to incorporate its principles as part of a digital transformation strategy exploiting the ServiceNow platform.
What is Intelligent Automation in the perspective of delivering Enterprise Services?
IA in Enterprise Services is not just putting new lipstick on the same old ITSM pig.
So, while we shouldn’t get caught up in the sci-fi coverage of IA and AI, we also shouldn’t remain in our old perspective of what traditional automation was meant to do and limit our thinking of emerging IA technology as “just a turbo-charged way” to achieve the same old goals of automation.
Intelligent Automation goes beyond traditional automation thinking that only sought to eliminate human touch and activity to mimic the physical work performed by humans with the goals of increased output and quality, and reduced labor costs. The working principle was if a process could be standardized, it could be automated – ‘dumbed down automation’.
With Intelligent Automation, we still seek to dramatically reduce the amount of mundane, repetitive human work, but the new goal is to free up workers to perform more valuable knowledge related work, identifying and solving problems quicker and more accurately, within a culture of continuous improvement. Worker levels of satisfaction are almost on a peer with those of customer satisfaction.
This shift in mindset is very nascent, but one we believe is critical to formulating the company’s vision for IA before trying to solve for “how to do it”.
What is in the IA Toolbox?
The working set of Intelligent Automation solutions is constantly growing. They span a common ‘continuum’ of progressive systematic ‘Listen, Do, Learn, Think’ capabilities and include:
- Smart Workflow and Process Optimization: An automated series of activities within an individual or networked set of processes, with specific routing, notification, and processing rules matched to governance, authorizations, security policies, and operational procedures
- Machine Learning (ML) & Advanced Analytics: Machine Learning is a type of artificial intelligence that enables computers to learn without being explicitly programmed, usually from large and various sources of data, producing an analytical model. The Advanced Analytics component exploits the analytical data model to present, support, or make reliable predictions, decisions and results
- Automated Operations (AO): Traditional rule-based management of infrastructure availability and performance to detect, aggregate, correlate, filter and respond to specific events, including time-sensitive triggers to schedule actions and tasks
- Natural Language Processing (NLP) and Generation (NLG): Another type of artificial intelligence, NLP is the ability to understand human speech as it is spoken. NLG produces language, prose, and a narrative to support computerized speech delivered to humans
- Robotic Process Automation (RPA): A computer software ‘robot’ trained to mimic the action of a human worker – a virtual co-worker. Analogous to the robotic worker used in manufacturing, RPA robots access information and complete tasks as humans do
- Cognitive Computing: Automated (information/insights-as-a service) systems that recommend courses of action to help a human complete a task, or make a decision, they do not perform the action
- Artificial Intelligence (AI): Automated systems that determine and execute a course of action based upon an analysis of information, prevailing conditions, subject matter expertise, and using previous experiences
- Product Intelligence: The amalgamation of one or more forms of automation into a “smart”, hyper-personalized device, such as a smart speaker (Google Home, Amazon Echo), or smartphone (Apple’s Siri, Microsoft’s Cortana, and “OK Google”).
- Cyber Intelligence: Automated systems to determine and respond to emerging or existing threats to services, caused by unauthorized access to computers, networks, organizational assets, including information
The Challenge Ahead
Enterprise IT departments already exploit hardware and software automation tools to help monitor and manage service infrastructures, but weaving in the new IA technologies presents the challenge of considering the much larger spectrum of the Service Management life-cycle and determining where starting points should be. And, it’s not just IT Service Management anymore, it’s Service Management for the business functions too. Whew!
The practical exercise of identifying real business scenarios that are candidates for IA is becoming more urgent especially if the enterprise is already planning, or have embarked on a ‘digital transformation’ journey which may already be out in front of the Service Management function.
While there is no “one size fits all” we increasingly find ourselves helping clients answer several key questions to begin drawing out an IA Vision and Strategy, including:
- What automation is already in place, and working/not working well?
- Where friction lies, and opportunities to improve exist?
- What situations are candidates and favorable, and which should be avoided?
- How can a tangible return be guaranteed and demonstrated early?
- What is the best design using a blended approach for human and automated touch?
Intelligent Automation is a powerful set of thinking, technologies, and capabilities. It combines human skills, knowledge, activities, and decision-making, with the common types of automation. It is both a source of competitive advantage and a must-have component of any digital transformation strategy.
The level of potential business value with the emerging value of intelligent automation presents opportunities to enterprises that have never been seen before.
Join us again for our next installment where we’ll look further into creating a roadmap to start down your own IA path in Service Management.