Which of the following is the best suited for decision automation

Types of automation

Basic automation

Basic automation takes simple, rudimentary tasks and automates them. This level of automation is about digitizing work by using tools to streamline and centralize routine tasks, such as using a shared messaging system instead of having information in disconnected silos. Business process management (BPM) and robotic process automation (RPA) are types of basic automation.

Process automation

Process automation manages business processes for uniformity and transparency. It is typically handled by dedicated software and business apps. Using process automation can increase productivity and efficiency within your business. It can also deliver new insights into business challenges and suggest solutions. Process mining and workflow automation are types of process automation.

Integration automation

Integration automation is where machines can mimic human tasks and repeat the actions once humans define the machine rules. One example is the “digital worker.” In recent years, people have defined digital workers as software robots that are trained to work with humans to perform specific tasks. They have a specific set of skills, and they can be “hired” to work on teams.

Artificial intelligence (AI) automation

The most complex level of automation is artificial intelligence (AI) automation. The addition of AI means that machines can “learn” and make decisions based on past situations they have encountered and analyzed. For example, in customer service, virtual assistants powered can reduce costs while empowering both customers and human agents, creating an optimal customer service experience.

Overview of IT automation

The use of a repeated set of processes can IT increase IT productivity and efficiency and reduce human errors.

Content management

Content management solutions capture, store, activate, analyze and automate business content

Document processing

Document processing solutions combine artificial intelligence and deep learning to streamline the processing of business documents.

Document management

Document management solutions capture, track and store information from digital documents.

Workflow automation

Workflow automation solutions use rules-based logic to perform tasks with limited to no human interaction.

Decision management

Decision management solutions model, manage and automate business decisions through machine learning.

Process mapping

Process mapping solutions can improve operations by identifying bottlenecks and enabling cross-organizational collaboration.

Automation use cases

The modern era of workflow automation began in 2005 with the introduction of BPM. With the release of Apple’s Siri in 2011, the trend was to move away from physical robots to automation software.

Machine learning is triggering new processes, rerouting running
processes and making action recommendations.

Hyperautomation is the merging of machine learning, software and
automation tools to maximize the number of automation processes.

AI systems will be able to automate robot configurations and use
predictive and probabilistic processing to learn and interact.

Robots will perform multiple tasks, make decisions, and work
autonomously, including self-diagnostics and maintenance.

Workflow software requiring minimal or no coding will be a priority to
make process automation accessible to the organization.

AI and machine learning in automation

Automation encompasses everything activities both mundane and
business-critical. Basic automation is programmed to perform a
repetitive task so humans do not have to.

AI is programmed with logic and rules to mimic human decision making. AI
can be used to detect threats such as changes in user behavior or
increased data transfers.

Machine learning uses data and experiences to learn without additional programming. It offers more sophisticated and informed insights with each new dataset.

Learn more about automation

Magazine Summer 2005

After decades of anticipation, the promise of automated decision-making systems is finally becoming a reality in a variety of industries.

July 15, 2005 Reading Time: 23 min 

For decades, futurists have anticipated the day when computers would relieve managers and professionals of the need to make certain types of decisions.1 Computer programs would analyze data and make sound judgments on such matters as how to configure a complex computer, how to diagnose and treat a patient’s illness or how to know when to stir a big vat of soup with little or no human help. But automated decision making has been slow to materialize. Many early artificial intelligence applications were just solutions looking for problems, contributing little to improved organizational performance.2 In medicine, for example, doctors showed little interest in having machines diagnose their patients’ diseases. In the business sector, even when expert systems were directed at real issues, extracting the right kind of specialized knowledge from seasoned decision makers and maintaining it over time proved to be more difficult than anticipated.

Airlines use automated decision-making applications to set pricing based on seat availability and the hour or day of purchase.

Image courtesy of Flickr user kevin dooley.

Even though the need for automated decision systems was recognized, full-blown decision-making systems were seen as impractical for use in business. So, during the 1970s, managers began to address this need by employing intelligence augmentation tools that provided managers and analysts with “decision support.”3 The idea was for the support system to help managers report, analyze and interpret data as opposed to actually making the business decisions. Although some decision support tools offered the potential for sophisticated statistical insight into business problems, they generally required skilled users to direct their use. The tools were usually not integrated with business applications. As a result, managers used them to help make decisions and then, if computers could help, used separate applications to carry out the decisions. For these and other reasons, such tools didn’t catch on — not nearly to the extent that more transactional software applications, such as enterprise resource-planning systems, did.

The reluctance on the part of executives to embrace decision-support tools during the 1970s and 1980s was not surprising.

About the Authors

Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College and an Accenture Fellow.Jeanne G. Harris is an executive research fellow at the Accenture Institute for High Performance Business.Contact them at and .

References

1. For example, scientist and fiction writer I. Asimov’s “I, Robot” (New York: Gnome Press, 1950) identified the Three Laws of Robotics, an early set of rules to guide automated decision making. R. Kurzweil’s “Age of Intelligent Machines” (Cambridge, Massachusetts: MIT Press, 1990) contains an overview of the history of artificial intelligence.

2. T.G. Gill, “Early Expert Systems: Where Are They Now?” MIS Quarterly 19, no. 1 (March 1995): 51–81.

3. Decision-support systems were defined in A.G. Gorry and M.S. Scott Morton, “A Framework for Management Information Systems,” Sloan Management Review 13, no. 1 (fall 1971): 55–70.

4. For a description of business-rules technology, see B. Von Halle, “Business Rules Applied: Building Better Systems Using the Business Rule Approach” (New York: John Wiley & Sons, 2001).

5. For an overview of yield-management applications in the transportation industry, see A. Ingold, U. McMahon-Beattie and I. Yeoman, “Yield Management” (New York: Continuum, 2001).

6. For more on automated decision making in the consumer credit industry, see T.H. Davenport and J.G. Harris, “Automated Decision Making in Consumer Lending,” research note, Accenture Institute for High Performance Business, New York, June 2004, www.accenture.com.

7. For more on automated decision making at DeepGreen Financial, see J.G. Harris and J.D. Brooks, “In the Mortgage Industry, IT Matters,” Mortgage Banking Magazine, Dec. 4, 2004, 62.

8. For more on automated decision making in the insurance industry, see T.H. Davenport and J.G. Harris, “Lessons For Successful Automated Decision Making From The Insurance Industry,” research note, Accenture Institute for High Performance Business, New York, November 2004, www.accenture.com.

9. “Pickberry Vineyard: Accenture Prototype Helps Improve Crop Management,” 2004, www.accenture.com.

10. T.H. Davenport and J. Glaser, “Just-in-Time Delivery Comes to Knowledge Management,” Harvard Business Review 80, no. 7 (July 2002): 107–111. See also D.W. Bates et al., “Effect of Computerized Physician Order Entry and a Team Intervention on Prevention of Serious Medication Errors,” Journal of the American Medical Association 280 (Oct. 21, 1998): 1311–1316.

11. In a previous study of how organizations build analytical capability, we found that quantitatively oriented experts were almost always present in organizations with high degrees of analytical activity. See T.H. Davenport, J.G. Harris, D.W. DeLong and A. Jacobson, “Data to Knowledge to Results: Building an Analytic Capability,” California Management Review 43 (winter 2001): 2, 117–138.

12. For more on how losing human expertise in a technologically intensive business can undermine organizational performance, see D.W. DeLong, “Lost Knowledge: Confronting the Threat of an Aging Workforce” (New York: Oxford University Press, 2004).

What is an example of automated decision making?

Which decisions can be automated?

Why is making automation decisions important?

Can AI be used for decision