Digital Decisioning, at the heart of hyperautomation creating value
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Digital Decisioning, at the heart of hyperautomation creating value

Process technology like workflow management or IoT organizes the data to make decisions efficiently, but organizations make their profit or loss in the day-to-day operational decisions.

In previous blogs, six key technologies of hyperautomation were visited. This blog explores Digital Decisioning and its benefits, risks, and costs when applied to hyperautomation.

Digital decisioning is a process in which automated systems make decisions based on regulations, data, and algorithms. It is a crucial component of hyperautomation, as it involves integrating and coordinating multiple automation technologies to make more informed and efficient decisions. In addition, other hyperautomation technologies gather and organize data and information to make business value-creating decisions.

It is possible to use hyperautomation initially without Digital Decisioning. An organization could use hyperautomation to automate processes and tasks without using advanced technologies to automate operational decision-making. For example, an organization could use robots and other types of automation to perform tasks in a manufacturing process but still rely on human workers to decide how to allocate resources or respond to changes in demand. Or use hyperautomation to automate the workflow and leave the decision-making to specialized case workers. A logical next step would be automating the operational decisions the allocators or case workers make.

Augmented Decisioning could be, for some organizations, a step in between, as it enhances or supplements human decision-making processes. It involves using decision models, data and analytics, artificial intelligence, and other advanced technologies to provide additional information or insights to help humans make more informed and accurate decisions. However, the human makes the final decision and is responsible for guarding ethically and responsible decisioning and the legal implications.

Risks to mitigate

This augmenting step poses an excellent opportunity to learn, elicitate and refine the decision models gradually and to mitigate risks, such as:

  • Bias and Discrimination: Great caution is advised if the algorithms are trained on historically biased data. This bias often leads to discriminatory outcomes, perpetuating social and economic inequalities.
  • Lack of Transparency: Complex algorithms may be difficult to understand and explain. This lack of transparency can make identifying and addressing issues such as bias challenging and quickly erode trust in decision-making.
  • Privacy Concerns: Automated Decisioning often relies on collecting and analyzing large amounts of personal data, which raises privacy concerns, especially if the data is mishandled, shared without consent, or used for purposes beyond what individuals agreed to.
  • Limited Accountability: Assigning responsibility for adverse outcomes can be difficult when automated systems make decisions, creating accountability and legal liability challenges.
  • Loss of critical thinking and human judgment: without oversight and management, this poses a severe risk.

It is vital to mitigate these risks ensuring that Digital Decisioning systems are developed and implemented with careful consideration of ethical and legal principles. This mitigation includes regular monitoring, auditing, and testing of the algorithms and incorporating human oversight and accountability mechanisms into the decision-making process.

In many cases, hyperautomation is used with Digital Decisioning, as advanced technologies such as decision modeling and machine learning can significantly help improve decision-making speed, efficiency, and accuracy.

Best candidates

In the previous blog, Process mining is pointed out in identifying the best candidates for Digital Decisioning:

  • Complex and transparent decision-making: Digital Decisioning can be particularly of value when a large amount of data or complexity is involved in the decision-making process. It can help to analyze and interpret the data more efficiently and effectively and to provide recommendations or insights, providing transparent reasoning.
  • High-volume decision-making: Digital Decisioning can be helpful in situations with a high volume of decisions that need to be made automatically, as it improves efficiency.
  • Repetitive decision-making: Digital Decisioning can be helpful when decisions are being made repeatedly, automating the process and eliminating manual intervention.
  • Time-sensitive decision-making: Digital Decisioning can be helpful when decisions need to be made rapidly and automatically.

Tangible benefits to reap

Overall, Digital Decisioning can be helpful in almost all situations where there is a need to improve the efficiency and accuracy of decision-making. However, it is crucial for organizations to carefully consider the benefits, costs, and risks of adopting this technology and to evaluate whether it is the right fit for their specific needs and goals. Potential benefits include:

  • Improved efficiency: Digital Decisioning can help automate decision-making processes, reducing the need for manual intervention and improving efficiency and can help to free up time and resources that can be better used elsewhere in the organization.
  • Improved accuracy: Digital Decisioning can help improve decision-making accuracy by using insights based on data and analytics, help reduce the risk of errors or omissions and improve the overall decision-making quality.
  • Increased agility: Digital Decisioning can help organizations respond more quickly to market or environment changes, as it can provide real-time recommendations or insights that can inform the decision-making process.
  • Improved customer experience: Digital Decisioning can help organizations make more informed and personalized decisions about interacting with and serving their customers, improving the overall customer experience and leading to increased customer loyalty.

Costs to recon with

These benefits then must be quantified and set against the costs associated with Digital Decisioning, including:

  • Technology costs: Digital Decisioning typically involves adopting new technologies, such as artificial intelligence (AI) and machine learning (ML) tools, which can involve significant upfront costs like purchasing software and hardware, additional licensing fees, hardware maintenance, and technical support.
  • Decision-modeling costs: Digital decisioning typically involves the development of decision models, which can be complex and time-consuming and involve hiring specialized consultants or developing in-house expertise to elicitate, design and maintain the models.
  • Initial implementation costs: Digital Decisioning requires adopting new technologies and processes, which can involve significant upfront costs, including the purchase of software and hardware, the training of employees, and the development of processes and procedures.
  • Ongoing maintenance costs: Digital Decisioning systems typically require continuing oversight, governance, maintenance, and support.
  • Data management costs: Digital Decisioning typically involves the collection, storage, and analysis of data, which can include additional charges such as data storage fees and the hiring of data scientists or analysts.
  • Change management costs: Digital Decisioning typically involves significant changes to how an organization operates, requiring implementation and management effort, involving expenses such as training, communication, and developing new, more effective processes and procedures.

Digital Decisioning can involve both high costs and high benefits. It is crucial for organizations to carefully consider these costs and assess whether the short-term and long-term benefits of adopting this technology are worth the investment. It is also essential to carefully plan and manage the implementation of Digital Decisioning to minimize costs and ensure a successful outcome.

Final thoughts

Overall, the role of Digital Decisioning in hyperautomation will depend on the specific needs and goals of the organization, and it is crucial for organizations to carefully consider the benefits, costs, and risks of adopting this technology as part of their digital transformation strategies.


More articles published in the hyperautomation series


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