AI-Driven Business

Direction: Dr André Nemeh

NEMEH ESCTransformational opportunities…

AI-driven business is about businesses intelligently knowing their customers on an individual basis. It is about old hefty cost structures being swept away overnight. It is about product and services that are optimally designed to fly off the shelves. It is about HR knowing the best employees to hire and marketers knowing the proven winning ads to write. It is about firm financing and supply chains determined at the lowest cost and the highest benefit. It is about strategy that builds on intelligence that sees beyond human capabilities.

… and existential threats

Firms operating with the best AI-driven advantages will inevitably triumph in their industry. The nature of AI technologies tends to lead to single or few winners in any given industry. That creates obvious existential issues for the firms that fall behind. Witness the upheaval in financial market trading, where start-up hedge fund AI-driven algorithms have, in just a decade, wiped out the trading desks of large investment firms that stood for a century as reliable major profit centres. We are increasingly see this spread across every industry.

AI-driven Business @ Rennes

AI-driven Business at Rennes School of Business focuses on understanding both the advantages of AI applied to business, as well as how firms can transform to implement these new techniques. In this area technical AI and digital expertise is blended with work practices expertise to find solutions that work, and to help businesses to be prepared for this revolution. Researchers within the area publish research on those efforts in leading journals in all business disciplines. We also train students and executives to be able to take advantage of these opportunities.

This research centre includes three interrelated themes: (i) AI Strategy for Competitive Advantage, (ii) AI for Operations, Optimization & Automation, and (iii) Human & Ethical AI.

Research Axes

AI Strategy for Competitive Advantage

(Head: Dr André Nemeh)
This sub-area studies how AI impacts businesses and their strategies and policies. AI is considered as a special resource offering magnified data processing capabilities that advance the acquisition, understanding, and processing of knowledge, as well as resource allocations in companies. This helps shape the decision making and consequent performance of strategic actions. The rise of AI may fundamentally change how firms obtain and sustain their competitive advantages, not just for high-technology industries, but also for more traditional businesses. Researchers of this sub-area develop new theories supported by empirical evidence to explore this novel opportunity.

AI for Operations, Optimisation & Automation

(Head: Dr Oncü Hazir)
This sub-area involves investigating how AI and big data analytics can offer new opportunities to deal with challenges in operations and project management. A key challenge in the new era of AI, which researchers of this sub-area focus on, is the need to make rapid decisions which cope with the uncertainty and inherent complexity of a dynamic business environment. Computational intelligence approaches of AI and big data analytics tools help address this challenge by supporting decision making under uncertainty.

Human & Ethical AI

(Head: Dr Laura Noval)
This sub-area recognises that the development and exploitation of AI technologies in business raises anxiety and excitement surrounding its future implications for businesses, individual workers and societal fairness. For businesses, the developments in AI will clearly offer gains in productivity and reduction of human error by replacing certain non-routine and cognitive tasks currently requiring human intelligence. However, this development poses multiple social and environmental concerns relevant to business managers and policy makers. Researchers working for this sub-area investigate questions pertinent to these issues and business solutions to such problems.


The professors and PhD students in this research center are actively working around the three research clusters, with 19 publications in top-level international academic journals such as the International Journal of Production Economics, European Journal of Operational Research, Human Resource Management Journal, Technological Forecasting and Social Change and Tourism Management among others. They are also engaged in dissemination of their research through teaching on the AI summer school, the MSc in Big Data Analytics, the MSc in Strategic and Digital Marketing and on the PhD programme at Rennes SB.

Research Themes

Full Professors

Associate Professors

Assistant professors



PhD Students

  • Yara Atallah
  • Karen Rankin
  • Mohammad Tarhini
  • Lakshmi Vuddaraj


The professors and PhD students in this research center are actively working around the three research clusters, with 19 publications in top-level international academic journals such as the International Journal of Production Economics, European Journal of Operational Research, Human Resource Management Journal, Technological Forecasting and Social Change and Tourism Management among others.

  • Agi, M. A., & Jha, A. K. (2022). Blockchain technology for supply chain management: An integrated theoretical perspective of organizational adoption. International Journal of Production Economics, 108458.
  • Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646.
  • Aziz, S., Dowling, M., Hammami, H., & Piepenbrink, A. (2022). Machine learning in finance: A topic modeling approach. European Financial Management, 28(3), 744-770.
  • Baek, C. H., Kim, S. Y., Lim, S. U., & Xiong, J. (Forthcoming). Quality evaluation model of artificial intelligence service for startups. International Journal of Entrepreneurial Behavior & Research.
  • Bakici, T., Nemeh, A., & Hazir, Ö. (Forthcoming). Big data adoption in project management: Insights from French organizations. IEEE Transactions on Engineering Management. 10.1109/TEM.2021.3091661
  • Farahani, R. Z., Ruiz, R., & Van Wassenhove, L. N. (Forthcoming). Introduction to the Special Issue on the role of Operational Research in future epidemics/pandemics. European Journal of Operational Research, 304(1).
  • Jalan, A., Matkovskyy, R., & Potì, V. (2022). Shall the winning last? A study of recent bubbles and persistence. Finance Research Letters, 45, 102162.
  • Michelotti, M., McColl, R., Puncheva‐Michelotti, P., Clarke, R., & McNamara, T. (Forthcoming). The effects of medium and sequence on personality trait assessments in face‐to‐face and videoconference selection interviews: Implications for HR analytics. Human Resource Management Journal.
  • Nagula, P. K., & Alexakis, C. (2022). A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price. Journal of Behavioral and Experimental Finance, 36, 100741.
  • Schwob, A., de Kervenoael, R., Kirova, V. and Vo-Thanh, T. (Forthcoming), “Casual selling practice: a qualitative study of non-professional sellers’ involvement on C2C social commerce platforms”, Information Technology & People,
  • Atwal, G., & Bryson, D. (2021). Antecedents of intention to adopt artificial intelligence services by consumers in personal financial investing. Strategic Change, 30(3), 293-298.
  • Jalan, A., Matkovskyy, R., & Urquhart, A. (2021). What effect did the introduction of Bitcoin futures have on the Bitcoin spot market?. The European Journal of Finance, 27(13), 1251-1281.
  • Matkovskyy, R., Jalan, A., Dowling, M., & Bouraoui, T. (2021). From bottom ten to top ten: the role of cryptocurrencies in enhancing portfolio return of poorly performing stocks. Finance Research Letters, 38, 101405.
  • Yarovaya, L., Matkovskyy, R., & Jalan, A. (2021). The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets. Journal of International Financial Markets, Institutions and Money, 75, 101321.
  • Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S., & Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological forecasting and social change, 161, 120261.
  • Bouraoui, T. (2020). The drivers of Bitcoin trading volume in selected emerging countries. The Quarterly Review of Economics and Finance, 76, 218-229.
  • de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. Tourism Management, 78, 104042.
  • Hammami, H., Hammami, M., Coulibaly, S., & Marzouk, M. (2020). Determinants of FDI attractiveness: A MCI model approach. Economics Bulletin, 40(2), 1033-1048.
  • Hasan, R., Lowe, B., & Petrovici, D. (2020). Consumer adoption of pro-poor service innovations in subsistence marketplaces. Journal of Business Research, 121, 461-475.
  • Hasan, M. R., Shams, S. R., Rahman, M., & Haque, S. E. (2020). Analysing pro-poor innovation acceptance by income segments. Management Decision, 58(8), 1663-1674.
  • Hazır, Ö., & Ulusoy, G. (2020). A classification and review of approaches and methods for modeling uncertainty in projects. International Journal of Production Economics, 223, 107522.
  • Jha, A. K., Agi, M. A., & Ngai, E. W. (2020). A note on big data analytics capability development in supply chain. Decision Support Systems, 138, 113382.
  • Matkovskyy, R., & Jalan, A. (2020). Can Bitcoin Be an Inflation Hedge? Evidence from a Quantile-on-Quantile Model. Revue économique, 7, 1021-1041.
  • Matkovskyy, R., Jalan, A., & Dowling, M. (2020). Effects of economic policy uncertainty shocks on the interdependence between Bitcoin and traditional financial markets. The Quarterly Review of Economics and Finance, 77, 150-155.
  • Tóth, Z., Nieroda, M. E., & Koles, B. (2020). Becoming a more attractive supplier by managing references–The case of small and medium-sized enterprises in a digitally enhanced business environment. Industrial Marketing Management, 84, 312-327.


The AI-driven Business area of excellence has different types of collaborations:

  • Collaborations with other higher education institutions and organisations to enhance cooperation and create new synergies,
  • Collaborations with businesses through consultancy, research and innovation development, data analysis etc.,
  • Cross-disciplinary collaborations with other areas of excellence within Rennes School of Business to make each area stronger.

Information on our current key research collaborations are detailed below. We are always interested in new research collaboration opportunities. Please contact either the AI Business Director, Andre Nemeh, or any Principal Investigator to discuss the possibility of working together.

We partner with B-Com, the leading digital technology innovation hub in Brittany, on number of research projects on envisaging the long-term impact of AI.

  • ANR-registered research project (Pollen – 2022-2025) on the social acceptability of each stage of an NLP-driven decision-making process (in collaboration with RTO)
  • Metaverse and employee well-being project (2022-2023) to explore creativity and social interactions at work in the metaverse.
  • ANR-registered research project (AI powered, 2018-2021) on prospecting on the future impact and development of fintech


We are a research partner institute with the Irish Institute of Digital Business (dotLAB), one of the largest digital business research groups in Europe. In this partnership we, as dotLAB France, specialise in textual analysis for AI business as part of a global network of research institutes

Zayed University (~100K€)
A funded research project on the application of Latent Dirichlet Allocation topic modelling to predict agricultural commodity pricing (In collaboration with Agribusiness center).

Britanny Innovation and Development agency
Regional Observatory for AI and Data Science for a project to examine: The state of AI adoption by the key sectors in Brittany and the future development of AI; Needs, main obstacles, and constraints in adopting AI; Sectoral disparities/ developments among firms: measuring the impact of AI on firms’ functions; The links between AI users and solution providers.

We collaborate with INSOFE on a number of pedagogic and research projects. INSOFE is one of the largest data science training institutes in India well-known for the excellence of their education. Our projects centre on developing case studies of AI application in business, and research into effective AI business education.

CCI Paris (~20K€)
A funded project with the CCI Paris, Paris School of Business and Università Cattolica del Sacro Cuore, to apply machine learning and deep learning to understand growth patterns of small-to-medium-sized enterprises.

Other Project funding
The AI-driven Business Research Center has received funding for various research projects:
– Banque de France : Fraud detection by using AI
– Région Bretagne (cryptomonnaie, the Economic and Financial Perspectives on Non-Fungible Tokens (NFTs) and Blockchain-Based Investment Strategies conference (May 2022).

Activities and news

Seminars and Workshops

Research seminars


  • Professor Periklis Gogas-Democritus University of Thrace, Greece: “AI & Machine Learning in Finance and Economics”
  • Professor Damien Dupré-Dublin City University: “Urban and socio-economic correlates of property prices in Dublin’s area”
  • Professor Miguel Minutti Meza-Miami Business School: “Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research”
  • Professor Rohit Nishant-Laval University (Canada)- “AI and Sustainability – what does empirical evidence inform us?”
  • Paavo Ritala – Professor of Strategy and Innovation at LUT School of Business and Management (Finland)- “Building and scaling industrial AI capabilities through explorative and exploitative learning: A multiple-case study”
  • Professor Dmitry Ivanov-Department of Business and Economics-Berlin School of Economics and Law (HWR Berlin)- “Industry 4.0 in Supply Chain Networks and Digital Technology for Resilience Management” (Guest Speaker)
  • Volodymyr Babich-McDonough School of Business, Georgetown University. (USA)- “Playing with DISASTER: Behavioral Simulations of Supply Shortages, Competition for Supplier Capacity, Blockchain-enabled Strategic Information Sharing, and Markets for Capacity-Token Trading” (Guest Speaker)


  • Economic and Financial Perspectives on Non-Fungible Tokens (NFTs) and Blockchain-Based Investment Strategies conference ( hosted at Rennes School of Business, April 8th & 9th, 2022 (Dr. André NEMEH & Dr. Roman MATKOVSKYY)
  • The centre is engaged in dissemination of their research through teaching on the AI summer school, the MSc in Big Data Analytics, the MSc in Strategic and Digital Marketing and on the PhD programme at Rennes SB.

Summer School

The AI Business Summer School is a four-week (from 15th May to 9th June) summer school programme providing the students with advanced knowledge in a wide range of aspects of data science applied to business. This includes developing knowledge of Python programming from scratch, network analysis, data visualisation, textual analysis, and application of machine learning and deep learning.

Four courses are available:

  • Data Science for Business
  • AI Business Intelligence
  • Business Textual Learning
  • Business Network Intelligence

The completion of all four courses leads to a Certificate in AI Business and each course is equivalent to 3 ECTS (1.5 US credits).

The AI Business Summer School offers a wide range of benefits:

  • Having the opportunity to learn from a team of experts in AI applied to business
  • Meeting with multicultural and cross-disciplinary professors who are experts in their field
  • Encounter students from all over the world
  • Obtaining ECTS/US credits for students’ curriculum

In the Press


Dr André Nemeh