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The Performance Of Employees Of Banks With Work Motivation as Moderator

This study aims to influence the effectiveness of accounting information systems and work motivation on employee performance. Furthermore, work motivation is a moderator between the effectiveness of accounting information systems and employee performance. This research was conducted at the Rural Bank (BPR) in Gianyar Regency. Retrieval of samples with Purposive Sampling technique. Technical analysis of the data used is using the approach Partial Least Square (PLS). Results of the study show that the effectiveness of the accounting information system and motivation to work affect the performance of employees. Furthermore, work motivation can moderate the effect of the effectiveness of accounting information systems on employee performance.

Increasing Literature And Sharia Financial Management Through The One BMT Housing Area Program In Banguntapan District Bantul In Yogyakarta

The target of this PKM program is economically productive communities, namely people in housing in Banguntapan sub-district who have just established BMT covering aspects of marketing/socialization and management and accounting information systems (small businesses). The aim of the program is to develop an economically independent community with high literacy on syar'i transactions. Business partners in this program are BMTs that have just been established with a residential mosque base, namely BMT Sakinah, BMT Al-Anbiya and BMT Al-Huda. The problems faced by the three partners are: 1) Lack of competence and management in managing BMT, 2) Lack of legality, 3) Lack of Islamic financial literacy, so that BMT members still do not understand the function of BMT. As for the method of devotionare general lectures, short courses, tutorials and simulations. Materials that are focused on include the management and members of BMT: 1) Socialization of Transaction Contract applications legalized by the National Sharia Board, 2) Consolidating the understanding and application of Management Information Systems and Accounting Information Systems for BMT, for approximately eight months. The result of this dedicationis special assistance and provides BMT financial system software so that the BMT, BMT Sakinah (Puri Sakinah 2 Housing), BMT Al-Anbiya (Pesona Banguntapan Kotagede Housing) and BMT Al-Huda (Graha Banguntapan Housing) can manage accountably, professional and transparent. This program is expected to be able to facilitate the development of partner businesses so that they are able to provide a multiplier effect for housing-based BMTs and other newly established mosques.

Digital Accounting and Gender Discrimination

Digital technologies can process data much faster and more reliably than humans. Therefore, businesses must ensure a radical transformation in their infrastructure in order to keep up with the competitive environment. When establishing an accounting information system in enterprises, it should be ensured that the system can produce complete and uninterrupted information. However, one consequence of the digitalization process in accounting information systems is that the need for accounting personnel tends to decrease day by day. The aim of this study is to examine the digital developments in accounting and financial consultancy services and to emphasize the need for accounting engineering, which can contribute the most to these developments In this context, the structure and consequences of gender differences of individuals working in the accounting profession were examined.

The role of accounting information systems in firms’ performance during the COVID-19 pandemic

This paper investigates the influence of accounting information systems (AIS) on firms’ performance during the COVID-19 pandemic and how they help enhance employees’ performance and the external auditing process. This paper is qualitative in nature using the inductive approach. In-depth primary data were gathered through semi-structured interviews conducted in the year 2020. Due to the pandemic, the interviews with ten auditors were done online through the Zoom software application. The empirical findings of this paper show a positive impact of AIS on firms’ performance and a more significant influence on employees’ performance and the auditing process. AIS reduces costs and human errors, eases operations, speeds up work tasks, and increases employees’ productivity during the COVID-19 pandemic. The findings also show that there is no direct impact on firms’ overall cash flow/revenues. This paper increases our understanding of how AIS can influence and improve firms’ performance and the significance of implementation factors such as training. It provides practical guidelines for regulators and managers to utilize accounting information systems to perform better.

The implementation of AIS and financial performance: empirical study in selected Jordanians banking institutions

This article presents a reflection on the role of current Accounting Information Systems on financial performance in Jordanian banking sector. The study is based on primary data collected through a structured questionnaire. The data were analyzed using Multipleregression analysis. The results indicated a significant impact of (SVQ) Service Quality, (IFQ) Information Quality, and (SYQ) System Quality on financial performance. Whilst showed no significant effect of (DQ) Data Quality on financial performance. The effectiveness of the accounting information system also improves the financial performance of banks. In light of the findings, this study suggests the management to establish better strategies that can ensure the effectiveness and efficacy of AIS. The findings would be valuable for academic researchers, managers and professional accounting to acquire a better vision of the AIS by testing the phenomenon in Jordan as a developing country This study is only conducted in Jordan as a developing country. Future research can be orientated to other national and cultural settings and compared with the results of this study.

ANALISIS HAMBATAN KUALITAS SISTEM INFORMASI AKUNTANSI PERBANKAN DI ERA INDUSTRI 4.0

The fourth Industrial Revolution significantly changed banking products and services. The bank needs relevant and quality accounting information to support them in providing products and services to customers. The required accounting information is generated from an accounting information system owned by each bank. The purpose of this research is to determine how much the independent variables can influence the successful implementation of the accounting information system in banking. There are three independent variables analyzed, namely personality characteristics, organizational structure and quality of business processes. The results of this study are expected to be useful for banks in implementing strategies when managing the quality of accounting information systems that will produce quality accounting information. The type of data used in this study is primary data obtained through distributing questionnaires to 46 banking companies in Indonesia. Verification analysis was performed using the SEM-PLS test tool. The results showed that problems in the quality of accounting information were caused by the application of accounting information systems that were not maximally integrated. Furthermore, it was found that the application of accounting information systems in banking organizations must be able to accommodate personality characteristics, organizational structure and quality of business processes.

Fakor-Faktor Yang Memengaruhi Kinerja Sistem Informasi Akuntansi Dengan Pendidikan Dan Pelatihan Sebagai Variabel Moderasi Pada PT. Bukit Megagriya Makmur

This study was conducted to determine the effect of user participation, user ability, and top management support on the performance of accounting information systems through education and training as moderating variables at PT. Megagriya Makmur Hill. This research uses classical assumption test, data quality test, descriptive statistical test, multiple linear regression, Moderated Regression Analysis test, and hypothesis testing. The results of the study show that user participation, user capabilities, top management support, have an effect on the performance of accounting information systems. When moderation occurs, education and training are not able to moderate user participation and top management support on accounting information system performance, while education and training are able to moderate users' ability to accounting information system performance.

FAKTOR DETERMINAN KOMPETENSI SUMBER DAYA MANUSIA DAN SISTEM INFORMASI AKUNTANSI TERHADAP KUALITAS LAPORAN KEUANGAN DENGAN DIMODERASI SISTEM PENGENDALIAN INTERNAL

AbstrakKualitas laporan keuangan dapat dilihat melalui hasil audit. Laporan keuangan menjadi alat bagi investor untuk mengukur kinerja perusahaan yang dilakukan oleh jajaran manajemen, sehingga laporan keuangan yang disajikan harus berkualitas. Faktor-faktor yang mempengaruhi kualitas laporan keuangan adalah dengan penerapan sistem informasi akuntansi (SIA), kompetensi sumber daya manusia (SDM), dan penerapan sistem pengendalian internal (SPI) dilingkungan perusahaan atau organisasi. Tujuan penelitian ini adalah untuk mengetahui pengaruh penerapan SIA terhadap kualitas laporan keuangan, pengaruh kompetensi SDM terhadap kualitas laporan keuangan di mana SPI sebagai variabel moderasi. Objek penelitian adalah suatu perusahaan X yang 3 tahun terakhir belum memiliki hasil opini audit Wajar Tanpa Pengecualian (WTP) dan yang dijadikan responden adalah karyawan bagian keuangan atau akuntansi sebanyak 450 orang. Hasil penelitian ini menunjukkan bahwa penerapan SIA dan kompetensi SDM berpengaruh terhadap kualitas laporan keuangan serta SPI mempengaruhi hubungan kompetensi SDM terhadap kualitas laporan keuangan. Namun SPI tidak mempengaruhi hubungan penerapan SIA terhadap kualitas laporan keuangan.Kata Kunci: Kualitas Laporan Keuangan, Kompetensi SDM, Sistem Informasi Akuntansi, Sistem Pengendalian Internal AbstractThe quality of financial reports can be seen through audit results. Financial statements are a tool for investors to measure the company's performance by management, so that the financial statements presented must be of high quality. Factors that affect the quality of financial reports are the application of accounting information systems (AIS), human resource competencies (HR), and the application of internal control systems (SPI) within the company or organization. The purpose of this study was to determine the effect of the application of AIS on the quality of financial reports, the influence of HR competence on the quality of financial reports in which SPI is the moderating variable. The object of this research is a company X which in the last 3 years has not had an unqualified audit opinion (WTP) and the respondents are employees of the finance or accounting department as many as 450 people. The results of this study indicate that the application of AIS and HR competencies affect the quality of financial reports and SPI affects the relationship between HR competencies and the quality of financial reports. However, SPI does not affect the relationship between the application of SIA to the quality of financial statements.Keywords: Quality of Financial Reports, HR Competence, Accounting Information Systems, Internal Control Systems

Pengaruh Sistem Informasi Akuntansi Dan Integritas Terhadap Kinerja Karyawan Pada RSUD SMC Tasikmalaya

This study aims to examine the effect of accounting information systems and integrity on employee performance at SMC Tasikmalaya Hospital. The populations in this study were employees of the SMC Tasikmalaya Hospital as many as 491 people. The sampling technique used stratified random sampling. The data used is primary data obtained directly from respondents using a questionnaire. The data analysis technique uses multiple linear regressions. The results of this study indicate that the accounting information system partially affects employee performance. Integrity partially affects employee performance. Meanwhile, simultaneously accounting information system and integrity affect employee performance.

Ketidakpastian Lingkungan dan Lingkup Sistem Informasi Akuntansi Manajemen Terhadap Kinerja Manajerial Pada Usaha Mikro, Kecil dan Menengah (UMKM) di Surabaya

ABSTRACT Micro, small and medium enterprises are one of the economic sectors that play an important role in Indonesia. The diversity of the scope of management accounting information systems, environmental uncertainty and performance achievements of micro, small and medium enterprises (MSMEs), is a picture that reflects the business of this sector. This is what encourages researchers to analyze more deeply the effect of environmental uncertainty and the scope of management accounting information systems on managerial performance in SMEs in Surabaya. This study uses multiple linear regression analysis, with a population of all MSMEs in Surabaya. The results of the research together reveal that the research model is empirically proven to be fit to explain the dependent variable. The partial test results reveal that only the scope of the management accounting system has a significant negative effect on managerial performance.  ABSTRAK Usaha mikro, kecil dan menengah menjadi salah satu sektor perekonomian yang berperan penting di Indonesia. Keberagaman lingkup sistem informasi akuntansi manajemen, ketidakpastian lingkungan dan capaian kinerja usaha mikro, kecil dan menengah (UMKM), merupakan gambaran yang merefleksikan usaha sektor ini.  Hal inilah yang mendorong peneliti untuk menganalisis lebih mendalam pengaruh ketidakpastian lingkungan dan lingkup sistem informasi akuntansi manajemen terhadap kinerja manajerial pada UMKM di Surabaya. Penelitian ini menggunakan analisa regresi linier berganda, dengan populasi seluruh UMKM di Surabaya. Hasil penelitian   secara bersama-sama mengungkapkan bahwa model penelitian terbukti secara empiris fit dapat menjelaskan variable dependen. Hasil uji parsial mengungkapkan hanya lingkup sistem akuntansi manajemen yang secara signifikan berpengaruh negatif terhadap kinerja manajerial.

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Analysis Effect Quality of Accounting Information Systems to Support Company Performance

D Rosa 1 and A P Purfini 2

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 662 , Issue 3 Citation D Rosa and A P Purfini 2019 IOP Conf. Ser.: Mater. Sci. Eng. 662 032015 DOI 10.1088/1757-899X/662/3/032015

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1 Departemen Akuntansi, Universitas Komputer Indonesia, Indonesia

2 Departemen Komputerisasi Akuntansi, Universitas Komputer Indonesia, Indonesia

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The purpose of this study is to explain the Accounting Application system quality qualitatively in facilitating the financial sector performance in a company. The method used in this study is semi-structured interviews with informants of the quality of the accounting information as budget accountants in a company. The results showed that a quality accounting application system is from the analysis existing accounting information systems quality such as security, accessibility, reliability use, data integration, and availability of correct and timely information that is able to support company performance. Companies must have these four elements in the accounting system they use. The result of this study is that the company has used a quality accounting information system because it has fulfilled all four elements and is able to support the employees' performance in the company. The conclusion from this study is this company has been able improving the company's performance quality ofr the employees by using good accounting information system.

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A Guide to Identifying Artificial Intelligence in ERP Systems in Accounting Functions

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• Célia Rocha Santos   ORCID: orcid.org/0000-0001-9928-6456 14 ,
• Graça Azevedo   ORCID: orcid.org/0000-0002-6346-4035 15 , 16 &
• Rui Pedro Marques   ORCID: orcid.org/0000-0001-8449-0526 15  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 990))

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Artificial Intelligence (AI) is a topic that has been heard by many and fantasized about for decades. This study proposes a guide for semi-structured interviews to identifying AI in ERP systems in accounting functions. The guide presented in this study was tested by three different techniques, (1) internal testing, (ii) expert assessment and (iii) field-testing. With this guide we can evaluate the existence, the challenges and opportunities and the future trends of AI in the accounting functions of ERP systems.

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Célia Rocha Santos

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Institute of Information Technology, Lodz University of Technology, Łódz, Poland

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Santos, C.R., Azevedo, G., Marques, R.P. (2024). A Guide to Identifying Artificial Intelligence in ERP Systems in Accounting Functions. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-031-60328-0_29

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Academic Journal of Computing & Information Science , 2021, 4(4); doi: 10.25236/AJCIS.2021.040411 .

Research on the Security of Accounting Information System in the Big Data Era

Boris Zhenhan Hu

Bishop Amat Memorial High School, US

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Since the 21st century, information technology has made tremendous progress. Especially in recent years, the concept of big data has attracted more and more attention. Accounting information security and the construction and maintenance of accounting information systems are also facing huge opportunities and challenges. At present, in the era of big data, the importance of accounting information security and accounting work is increasing day by day. If the accounting information system is not properly analyzed, it will lead to the disclosure of financial information, and in severe cases, it will even harm the economic interests of the enterprise. This article analyzes the development of accounting information system security with the theme of strengthening information security.

Information system, Accounting, Big data era, Security

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Boris Zhenhan Hu. Research on the Security of Accounting Information System in the Big Data Era. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 4: 60-63. https://doi.org/10.25236/AJCIS.2021.040411.

[1] Li Wei. Analysis of the security of accounting information systems in the era of big data[J]. Computer Products and Circulation, 2018(07): 94.

[2] Qi Shaobo, Teng Haoyu. Research on the Security of Accounting Information System in the Big Data Era [J]. Economic and Trade Practice, 2017(10): 83-84.

[3] Tang Changsheng. Research on the improvement of accounting informatization in the era of big data [J]. Journal of Huainan Normal University, 2014, 16(02): 12-15.

[4] Kwarteng A, Aveh F. Empirical examination of organizational culture on accounting information system and corporate performance: Evidence from a developing country perspective[J]. Meditari Accountancy Research, 2018.

[5] Harini AS, Kurniawan A, Umiyati I. The influence of accounting information system implementation and internal control effectiveness on the performance of employees (Case study on micro, small, medium enterprises subang regency)[J]. Journal of Accounting for Sustainable Society, 2019, 1(01): 88-88.

‘Accounting for Hydroclimatic Properties in Flood Frequency Analysis Procedures’

“Flood hazard is typically evaluated by computing extreme flood probabilities from a flood frequency distribution following nationally defined procedures. … These procedures, also known as flood frequency analysis, typically recommend only one probability distribution family for all watersheds within a country or region. However, large uncertainties associated with extreme flood probability estimates (>50-year flood or Q50) can be further biased when fit to an inappropriate distribution model. … Here, we demonstrate that hydroclimatic parameters can aid in the selection of a parametric flood frequency distribution.”

Find the paper and full list of authors at Hydrology and Earth System Sciences.

Ocean Genome Legacy Center receives grant in support of student research

‘local and regional geographic variation in inducible defenses’, ‘top-down control over dissolved organic carbon in the bottom water of the weddell sea and its implication for the continental shelf pump’, ocean genome legacy center receives grant from cell signaling technology, ‘interpretation issues with “genomic vulnerability” arise from conceptual issues in local adaptation and maladaptation’, ‘local adaptation in trait-mediated trophic cascades’, ‘linear extension and calcification rates in a cold-water, crustose coralline alga are modulated by temperature, light and salinity’, garcia to study ‘adaptation in the amazon’ with tnc funding, schultz receives noaa grant to study carbon dioxide removal.

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AI Is Everybody’s Business

This briefing presents three principles to guide business leaders when making AI investments: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects. The principles are supported by the MIT CISR data monetization research, and the briefing illustrates them using examples from the Australia Taxation Office and CarMax. The three principles apply to any kind of AI, defined as technology that performs human-like cognitive tasks; subsequent briefings will present management advice distinct to machine learning and generative tools, respectively.

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Author Barb Wixom reads this research briefing as part of our audio edition of the series. Follow the series on SoundCloud.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

Microsoft Research Blog

Microsoft at chi 2024: innovations in human-centered design.

Published May 15, 2024

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The ways people engage with technology, through its design and functionality, determine its utility and acceptance in everyday use, setting the stage for widespread adoption. When computing tools and services respect the diversity of people’s experiences and abilities, technology is not only functional but also universally accessible. Human-computer interaction (HCI) plays a crucial role in this process, examining how technology integrates into our daily lives and exploring ways digital tools can be shaped to meet individual needs and enhance our interactions with the world.

The ACM CHI Conference on Human Factors in Computing Systems is a premier forum that brings together researchers and experts in the field, and Microsoft is honored to support CHI 2024 as a returning sponsor. We’re pleased to announce that 33 papers by Microsoft researchers and their collaborators have been accepted this year, with four winning the Best Paper Award and seven receiving honorable mentions.

This research aims to redefine how people work, collaborate, and play using technology, with a focus on design innovation to create more personalized, engaging, and effective interactions. Several projects emphasize customizing the user experience to better meet individual needs, such as exploring the potential of large language models (LLMs) to help reduce procrastination. Others investigate ways to boost realism in virtual and mixed reality environments, using touch to create a more immersive experience. There are also studies that address the challenges of understanding how people interact with technology. These include applying psychology and cognitive science to examine the use of generative AI and social media, with the goal of using the insights to guide future research and design directions. This post highlights these projects.

Microsoft Research Podcast

Collaborators: Holoportation™ communication technology with Spencer Fowers and Kwame Darko

Spencer Fowers and Kwame Darko break down how the technology behind Holoportation and the telecommunication device being built around it brings patients and doctors together when being in the same room isn’t an easy option and discuss the potential impact of the work.

Best Paper Award recipients

DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing   Priyan Vaithilingam, Elena L. Glassman, Jeevana Priya Inala , Chenglong Wang   GUIs used for editing visualizations can overwhelm users or limit their interactions. To address this, the authors introduce DynaVis, which combines natural language interfaces with dynamically synthesized UI widgets, enabling people to initiate and refine edits using natural language.  

Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking   Nikhil Sharma, Q. Vera Liao , Ziang Xiao   Conversational search systems powered by LLMs potentially improve on traditional search methods, yet their influence on increasing selective exposure and fostering echo chambers remains underexplored. This research suggests that LLM-driven conversational search may enhance biased information querying, particularly when the LLM’s outputs reinforce user views, emphasizing significant implications for the development and regulation of these technologies.  

Piet: Facilitating Color Authoring for Motion Graphics Video   Xinyu Shi, Yinghou Wang, Yun Wang , Jian Zhao   Motion graphic (MG) videos use animated visuals and color to effectively communicate complex ideas, yet existing color authoring tools are lacking. This work introduces Piet, a tool prototype that offers an interactive palette and support for quick theme changes and controlled focus, significantly streamlining the color design process.

The Metacognitive Demands and Opportunities of Generative AI   Lev Tankelevitch , Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar , Abigail Sellen , Sean Rintel   Generative AI systems offer unprecedented opportunities for transforming professional and personal work, yet they present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. This paper shows that metacognition—the psychological ability to monitor and control one’s thoughts and behavior—offers a valuable lens through which to understand and design for these usability challenges.  

Honorable Mentions

B ig or Small, It’s All in Your Head: Visuo-Haptic Illusion of Size-Change Using Finger-Repositioning Myung Jin Kim, Eyal Ofek, Michel Pahud , Mike J. Sinclair, Andrea Bianchi   This research introduces a fixed-sized VR controller that uses finger repositioning to create a visuo-haptic illusion of dynamic size changes in handheld virtual objects, allowing users to perceive virtual objects as significantly smaller or larger than the actual device. 

LLMR: Real-time Prompting of Interactive Worlds Using Large Language Models   Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey, Judith Amores , Jaron Lanier   Large Language Model for Mixed Reality (LLMR) is a framework for the real-time creation and modification of interactive mixed reality experiences using LLMs. It uses novel strategies to tackle difficult cases where ideal training data is scarce or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. 

Observer Effect in Social Media Use   Koustuv Saha, Pranshu Gupta, Gloria Mark, Emre Kiciman , Munmun De Choudhury   This work investigates the observer effect in behavioral assessments on social media use. The observer effect is a phenomenon in which individuals alter their behavior due to awareness of being monitored. Conducted over an average of 82 months (about 7 years) retrospectively and five months prospectively using Facebook data, the study found that deviations in expected behavior and language post-enrollment in the study reflected individual psychological traits. The authors recommend ways to mitigate the observer effect in these scenarios.

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming   Hussein Mozannar, Gagan Bansal , Adam Fourney , Eric Horvitz   By investigating how developers use GitHub Copilot, the authors created CUPS, a taxonomy of programmer activities during system interaction. This approach not only elucidates interaction patterns and inefficiencies but can also drive more effective metrics and UI design for code-recommendation systems with the goal of improving programmer productivity. 

SharedNeRF: Leveraging Photorealistic and View-dependent Rendering for Real-time and Remote Collaboration   Mose Sakashita, Bala Kumaravel, Nicolai Marquardt , Andrew D. Wilson   SharedNeRF, a system for synchronous remote collaboration, utilizes neural radiance field (NeRF) technology to provide photorealistic, viewpoint-specific renderings that are seamlessly integrated with point clouds to capture dynamic movements and changes in a shared space. A preliminary study demonstrated its effectiveness, as participants used this high-fidelity, multi-perspective visualization to successfully complete a flower arrangement task. 

Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination   Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu, Dana Kulzhabayeva, Minyi Ma, Rachel Kornfield, Syed Ishtiaque Ahmed, Alex Mariakakis, Mary P. Czerwinski , Anastasia Kuzminykh, Michael Liut, Joseph Jay Williams   In this study, the authors explore the potential of LLMs for customizing academic procrastination interventions, employing a technology probe to generate personalized advice. Their findings emphasize the need for LLMs to offer structured, deadline-oriented advice and adaptive questioning techniques, providing key design insights for LLM-based tools while highlighting cautions against their use for therapeutic guidance.

Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration   Haotian Li, Yun Wang , Huamin Qu This paper evaluates data storytelling tools using a dual framework to analyze the stages of the storytelling workflow—analysis, planning, implementation, communication—and the roles of humans and AI in each stage, such as creators, assistants, optimizers, and reviewers. The study identifies common collaboration patterns in existing tools, summarizes lessons from these patterns, and highlights future research opportunities for human-AI collaboration in data storytelling.

Learn more about our work and contributions to CHI 2024, including our full list of publications , on our conference webpage .

Related publications

The metacognitive demands and opportunities of generative ai, piet: facilitating color authoring for motion graphics video, dynavis: dynamically synthesized ui widgets for visualization editing, generative echo chamber effects of llm-powered search systems on diverse information seeking, understanding the role of large language models in personalizing and scaffolding strategies to combat academic procrastination, sharednerf: leveraging photorealistic and view-dependent rendering for real-time and remote collaboration, big or small, it’s all in your head: visuo-haptic illusion of size-change using finger-repositioning, llmr: real-time prompting of interactive worlds using large language models, reading between the lines: modeling user behavior and costs in ai-assisted programming, observer effect in social media use, where are we so far understanding data storytelling tools from the perspective of human-ai collaboration, continue reading.

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Advancing human-centered AI: Updates on responsible AI research

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