Intelligent Automation: Designing the Intelligent Organization

The Driver Behind Enterprise Situational Awareness

Intelligent automation is an emerging solution to enable intelligent decision-making at the strategic level, but it doesn’t necessarily have to be complex or require cumbersome new infrastructure. In fact, since overly complicated approaches to intelligent automation tend to result in project failure, simplicity may be the order of the day.

“Intelligent Automation” is such a new term that you won’t find it in Wikipedia, Merriam-Webster or any of the other dictionaries that serve as an official reference source.  However, the emergence of this term exemplifies the speed at which technology moves.  We are clearly in the early stages of a transformation no less dramatic than the invention of the internet. The dawn of a new age in quantitative and empirical methods will change how businesses operate as well as the role of traditional finance professionals.[i]  To compete in this new digital environment, finance professionals must be willing to adopt new operating models that reduce costs and improve performance through better data.  In short, a new framework is needed for designing an intelligent organization.

Advanced analytics and automation will play increasingly bigger roles as tactical solutions to drive efficiency or to assist executives with solving complex problems.  This article takes a step back from tactical examples of automation to consider how the role of a strategic cognitive framework informs the design of an intelligent corporation.

The convergence of technology and cognitive science provides finance professionals with powerful new tools to tackle complex problems with more certainty.  However, the real opportunities lie in reimaging the enterprise as an intelligent organization.  An intelligent organization is designed to create situational awareness with tools capable of analyzing disparate data in real or near-real time.

Automation of redundant processes is only the first step.  An intelligent organization strategically designs automation to connect disparate systems (e.g., data sources) by enabling users with the tools to quickly respond or adjust to threats and opportunities in the business, proactively.  Situational awareness is the product of this design.  In order to push decision-making deeper into the organization, line staff need the tools and information to respond to change in the business, as well as the flexibility to adjust and mitigate problems within prescribed limits.  Likewise, senior executives need near-real time data that provides the means to query performance across different lines of business with confidence and anticipate impacts to singular or enterprise events in order to avoid costly mistakes.

The future of financial reporting is becoming increasingly complex at the same time that finance professionals are being challenged to manage emerging risks, reduce costs and add value to strategic objectives.  These competing mandates require new support tools that deliver intelligence and confidence in the numbers.  So how can this be done as budgets are being squeezed in the face of rising competition?  Thankfully, a range of new automation tools is now available to assist finance professionals in achieving better outcomes against these dual mandates and more.  However, to be successful, finance executives need a new framework that anticipates the needs of staff and provides access to the right data in a resilient manner.

Intelligent automation also means getting a good return on your technology investment!

A cognitive framework provides finance with a design roadmap that includes human elements focused on how staff uses technology, as well simplifying the rollout and implementation of advanced analytical tools.  The cognitive framework is comprised of five pillars, each designed to complement the development of an intelligent organization.  Kevin Legere, Director of Product Design at software provider ACL, says, “much of the challenge in delivering clear, measurable business value through enterprise governance, financial controls and risk programs today can be attributed to the severe lack of thoughtful design-thinking on the part of finance and risk professionals in part, but indeed even more so among the industry bodies, standard-setters and technology providers that support those professionals.”

A cognitive framework is based on the following pillars:

1. Cognitive Governance
2. Intentional Control Design
3. Business Intelligence
4. Performance Management
5. Situational Awareness

Each of these pillars is designed to complement the other when implementing intelligent automation most effectively.  Cognitive governance is the driver of intelligent automation as a strategic tool in guiding organizational outcomes.  The goal of cognitive governance, as the name implies, is to facilitate the design of intelligent automation to create actionable business intelligence, improve decision-making and reduce manual processes (risks) that lead to poor or uncertain outcomes.  In other words, cognitive governance systematically identifies the “blind-spots” across the firm, then directs intelligent automation to reduce or eliminate the blind spots.

Cognitive governance is the process of shedding light on uncertainty.

The end game is to create situational awareness at multiple levels of the firm with better tools to facilitate and understand risks and errors in judgment and to streamline inefficient processes.  Human error or decision-making under uncertainty is increasingly recognized as the greatest risk to organizational success; therefore, it is critical that senior management create a systemic framework for reducing blind spots in a timely manner.  Cognitive governance sets the tone and direction for the next four pillars.

Intentional control design, business intelligence and performance management are the tools an organization uses to create situational awareness in response to cognitive governance mandates.  A cognitive framework does not require huge investments in the latest big data “shiny objects.”  In fact, one need not spend millions on machine learning or other forms of artificial intelligence.  Alternative automation tools are readily available today and used by firms to simplify operations.  In other words, intelligent automation does not have to be complex or require cumbersome new infrastructure.  Access to advanced analytics is now available in a variety of cloud services and formatted to support organizations large and small.  However, for firms who want to use machine learning/AI, the cognitive framework easily integrates any tool or regulatory risk framework widely in use today.  A cognitive framework is focused on the one factor that others ignore: how humans interact and use technology to get their work done most effectively.

Network complexity has been identified as a strategic bottleneck in response times for dealing with cybersecurity risks, cost of technology and the inflexibility to change in fast-paced business environments.[ii]  Improperly designed automation processes may simply add to infrastructure complexity without a proper framework.  There is also a dark side to machine learning/AI that organizations must understand in order to anticipate best use cases and avoid the inevitable missteps that will come with autonomous systems.  Microsoft learned a hard lesson with “Clippy,” its chatbot project, which was shelved when users taught the bot racist remarks.  While there are many uses for AI this technology, is still in an experimental stage of growth.[iii]

Overly complicated approaches to intelligent automation are the leading cause of failed big data projects. However, “leveraging design thinking when considering how people, process and technology intersect is centered on an organization’s vision and how desired outcomes are aligned to the organizations key objectives.  Long-term success is a product of creative design to respond to opportunities and manage risks,” according to Kevin Legere, ACL.

Simplicity is the new value proposition that should be expected from the implementation of technology solutions.  Intelligent automation is one tool to accomplish this goal, but execution requires a framework that understands how people use new technology effectively.  Simplicity must be a strategic design imperative based on a framework for creating situational awareness across the enterprise.

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[i] https://www.questia.com/library/journal/1P3-54858491/the-dawning-of-the-age-of-quantitative-empirical-methods

[ii] https://www.ipswitch.com/Ipswitch/media/Ipswitch/Documents/Resources/Whitepapers%20and%20eBooks/The-Challenges-of-Controlling-IT-Complexity-Report.pdf?ext=.pdf

[iii] https://www.bloomberg.com/features/2016-microsoft-future-ai-chatbots/