Deploying a Model for Assessing Cognitive Automation Use Cases: Insights from Action Research with a Leading European Manufacturing Company

Engel, Christian; Elshan, Edona; Ebel, Philipp

doi:10.24251/hicss.2021.755

Cited by 4 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to rule-based front- and back-office automation with RPA, CA is characterized by its experimental character (Amigoni and Schiaffonati, 2018), learning requirements (Jordan and Mitchell, 2015), context sensitivity (Lieberman and Selker, 2000), and black box characteristics (Castelvecchi, 2016) . These properties of CA should help us account for a representative share of CA’s distinguishable characteristics (Engel et al, 2021).…”

Section: Conceptual Foundations and Related Workmentioning

confidence: 99%

Stairway to heaven or highway to hell: A model for assessing cognitive automation use cases

Engel

Elshan

Ebel

et al. 2023

Journal of Information Technology

Self Cite

View full text Add to dashboard Cite

Cognitive automation (CA) moves beyond rule-based business process automation to target cognitive knowledge and service work. This allows the automation of tasks and processes, for which automation seemed unimaginable a decade ago. To organizations, these CA use cases offer vast opportunities to gain a significant competitive advantage. However, CA imposes novel challenges on organizations’ decisions regarding the automation potential of use cases, resulting in low adoption and high project failure rates. To counteract this, we draw on an action research study with a leading European manufacturing company to develop and test a model for assessing use cases’ amenability to CA. The proposed model comprises four dimensions: cognition, data, relationship, and transparency requirements. The model proposes that a use case is less (more) amenable to CA if these requirements are high (low). To account for the model’s industry-agnostic generalizability, we draw on an internal evaluation within the action research company and three additional external evaluations undertaken by independent project teams in three distinct industries. From a practice perspective, the model will help organizations make more informed decisions in selecting use cases for CA and planning their respective initiatives. From a research perspective, the identified determinants affecting use cases’ amenability to CA will enhance our understanding of CA in particular and artificial intelligence as the driving force behind CA in general.

show abstract

Section: Conceptual Foundations and Related Workmentioning

confidence: 99%

Stairway to heaven or highway to hell: A model for assessing cognitive automation use cases

Engel

Elshan

Ebel

et al. 2023

Journal of Information Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…To do this, apply a top-down approach to gather the most relevant processes, offerings and data aligned to them. If possible, define a key performance indicator that the use case should improve (Engel, Elshan et al, 2021). The use of a strategy map (Kaplan & Norton, 2000) proved very useful in the case study.…”

Section: Find Short-term Feasible Ai Ideas Top-downmentioning

confidence: 99%

How Top-Down AI Introduction Leads to Incremental Business Improvement

Brunnbauer

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Artificial intelligence offers the opportunity for radical improvements such as completely new business solutions. It also enables the improvement of existing business. This paper reports on a case study that tests two strategies to identify AI use cases: top-down and bottom-up. The use cases are differentiated according to whether they promise incremental or radical business improvements and whether they are realizable in the short or long term.The top-down strategy identifies use cases that promise short-term but incremental improvements. They relate to existing business, but no disruptive ideas emerge. The bottom-up strategy allows for a broader understanding of AI's potentials to improve business. Completely new and disruptive ideas emerge, but require huge upfront effort. Organizations best start with AI pilot projects that are feasible in the short term: Either by first applying a bottom-up strategy that is supplemented and evaluated with the top-down strategy, or top-down only.

show abstract

Section: Work System-oriented Research Opportunitiesmentioning

confidence: 99%

“…Besides IS ecosystem-related challenges, organizations face a variety of socio-technical challenges to leverage cognitive automation for competitive advantage (see also Engel et al, 2021b). Here, organizations are still struggling to gather and process the large amounts of data that are needed to train software robots for cognitive automation and face challenges in the realm of data quality, which is viewed to be one of the major challenges of cognitive automation (Bruckner et al, 2012;Engel et al, 2021b;Lacity & Willcocks, 2018b;Poosapati et al, 2018). Furthermore, organizations 1 3 are challenged when approaching to transfer algorithmic insights, i.e., ML outcomes, between domains, and face issues in explaining what happens between data input and ML outputs, which leads to the black box character of AI (Lacity & Willcocks, 2018b).…”

Section: Work System-oriented Research Opportunitiesmentioning

confidence: 99%

“…Here, research has emphasized the need for making decisions of cognitive automation tools explainable to business users, which may be approached by developing novel key performance indicators and respective visualizations (Herm et al, 2021). Moreover, there exist challenges in embedding cognitive automation technology in organizations, which refers to the required changes that need to be induced into socio-technical systems (Engel et al, 2021b;Lacity & Willcocks, 2018b). This opens up various research opportunities:…”

Section: Work System-oriented Research Opportunitiesmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive automation

2022

Self Cite

View full text Add to dashboard Cite

Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization. We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research.

show abstract

Deploying a Model for Assessing Cognitive Automation Use Cases: Insights from Action Research with a Leading European Manufacturing Company

Cited by 4 publications

References 21 publications

Stairway to heaven or highway to hell: A model for assessing cognitive automation use cases

Stairway to heaven or highway to hell: A model for assessing cognitive automation use cases

How Top-Down AI Introduction Leads to Incremental Business Improvement

Cognitive automation

Contact Info

Product

Resources

About