Paper Titles
Duty Cycle-Aware Energy-Efficient MAC Protocol for Single-Layer Hierarchal UAV-WSN
p.81
Advancing Circular Economy: Utilizing Shredded Scrap Tyres in Gabion Wall Construction
p.93
Flammability Characteristics of Waste-Based Fibre-Reinforced Composite Using Palmyra Fibre and Waste Polythene
p.101
Experimental Study to Investigate the Compression Responses of Concrete with Waste Tire Aggregates under Varying Loading Conditions
p.115
Spatially Integrated Water and Wastewater Management: A Model Design for Fish Markets in Sri Lanka
p.129
Performance Verification of the Leachate Treatment Plant: A Dilution-Based Engineering Approach
p.141
AI Adoption in the Research Component of Business Processes: Enhancing Effectiveness in Client Acquisition for IT Services Organizations
p.151
Investigating the Deep Learning Performance for Deforestation Mapping Using Landsat Multispectral Data
p.163
Applying the Multifaceted Potential of Seaweed in Bioplastics to Advance Sustainable Development in Sri Lanka – A Review
p.175

AI Adoption in the Research Component of Business Processes: Enhancing Effectiveness in Client Acquisition for IT Services Organizations

Article Preview

Abstract:

This study explores the adoption of Artificial Intelligence (AI) within the research component of business processes and its potential to enhance client acquisition effectiveness in IT services companies. In an environment where traditional research methods remain dominant yet inefficient, AI offers opportunities to streamline data collection, improve insight generation, and reduce time-to-strategy. However, AI adoption across IT research teams remains inconsistent due to barriers such as data privacy concerns, skill gaps, and limited organizational readiness. The research applies a qualitative approach using semi-structured interviews with nine professionals from a global IT services organization. Thematic analysis reveals that while AI tools such as ChatGPT and Microsoft Copilot are increasingly used for summarization and trend analysis, their integration remains exploratory rather than systematic. Barriers for AI adoption include limited AI literacy, lack of structured training, leadership hesitation, and concerns about trust and compliance. Conversely, enablers such as pilot testing, internal AI platforms, peer learning, and strong leadership support are identified as critical facilitators of successful adoption. A five-phase AI Adoption Framework is then proposed, offering a practical roadmap for organizations to transition from ad-hoc AI use to structured, outcome-driven integration. The findings contribute to academic literature by addressing gaps in B2B AI adoption research and offering practical implications for IT service organizations seeking to enhance research efficiency and client acquisition performance through AI.

You might also be interested in these eBooks
The 4th SLIIT International Conference on Engineering and Technology (SICET) View Preview

Info:

Periodical:

Advances in Science and Technology (Volume 177)

Pages:

151-162

DOI:

https://doi.org/10.4028/p-jDf5vl

Citation:

Cite this paper

Online since:

May 2026

Authors:

Abdullah Al Bahey*, Chalani Ruchira Oruthotaarachchi

Keywords:

Acquisition, AI Adoption Framework, Artificial Intelligence Adoption, Business Process Research, Client, IT Services

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2026 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] C. L. Citroen, "The role of information in strategic decision-making," Int J Inf Manage, vol. 31, no. 6, p.493–501, Dec. 2011.

DOI: 10.1016/J.IJINFOMGT.2011.02.005

Google Scholar

[2] S. A. Fazal, S. A. Wahab, A. S. Bin Yaacob, and N. F. Mohd Zawawi, "Host-Country Market Environment, Intra-Firm Technology Transfer Performance and Corporate Sustainability: A Conceptual Study," International Journal of Business and Management, vol. 11, no. 4, p.91, Mar. 2016.

DOI: 10.5539/IJBM.V11N4P91

Google Scholar

[3] A. Meester, "Why Customer Centricity Is A Key To Long-Term Success." Accessed: Mar. 08, 2025. [Online]. Available: https://www.forbes.com/councils/forbesbusinesscouncil/ /07/18/why-customer-centricity-is-a-key-to-long-term-success/

DOI: 10.2307/jj.14335363.7

Google Scholar

[4] A. N. Abuzaid, "AI-Driven Business Processes: Efficiency Gains, Cost Reductions, and Implications for Economic Performance," 2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024, vol. 45, p.1–5, Apr. 2024.

DOI: 10.1109/ICKECS61492.2024.10617370

Google Scholar

[5] W. Brown, O. Johnson, and W. Brown, "The Impact of Artificial Intelligence on Customer Relationship Management," Aug. 2024.

DOI: 10.20944/PREPRINTS202408.0766.V1

Google Scholar

[6] C. Jung, "The new politics of AI: Why fast technological change requires bold policy targets | IPPR," Institute for Public Policy Research, London, Feb. 2025. Accessed: Mar. 09, 2025. [Online]. Available: https://www.ippr.org/articles/new-politics-of-ai

Google Scholar

[7] H. Mayer, L. Yee, M. Chui, and R. Roberts, "Superagency in the workplace: Empowering people to unlock AI's full potential | McKinsey," McKinsey & Company. Accessed: Mar. 09, 2025. [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

Google Scholar

[8] Adecco, "Global Workforce of the Future: What's Working? Navigating the AI Revolution and the Shifting Future of Work," Apr. 2024. Accessed: Mar. 02, 2025. [Online]. Available: https://adeccogroup.it/wp-content/uploads/2024/09/GWoF_2023_Research_Deck_Adecco_Group_Launch.pdf

Google Scholar

[9] L. S. Zavodna, M. Überwimmer, and E. Frankus, "Barriers to the implementation of artificial intelligence in small and medium-sized enterprises: Pilot study," Journal of Economics and Management (Poland), vol. 46, no. 1, p.331–352, Jan. 2024.

DOI: 10.22367/JEM.2024.46.13

Google Scholar

[10] M. Hlatshwayo, "The Integration of Artificial Intelligence (AI) Into Business Processes".

Google Scholar

[11] M. Rana et al., "Obstacles to the Full Realization and Adoption of Artificial Intelligence (AI)," South East Eur J Public Health, vol. ISSN, p.1003–1016, Nov. 2024.

DOI: 10.70135/SEEJPH.VI.2251

Google Scholar

[12] P. Bedué and A. Fritzsche, "Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption," Journal of Enterprise Information Management, vol. 35, no. 2, p.530–549, Mar. 2022.

DOI: 10.1108/jeim-06-2020-0233

Google Scholar

[13] N. P. Rana, S. Chatterjee, Y. K. Dwivedi, and S. Akter, "Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm's operational inefficiency and competitiveness," European Journal of Information Systems, vol. 31, no. 3, 2022.

DOI: 10.1080/0960085X.2021.1955628

Google Scholar

[14] P. Bhojraj and V. Bhaiswar, "MARKET RESEARCH IN MARKETING ON EOT CRANE INDUSTRY," vol. 3, no. 6, 2017, Accessed: Mar. 27, 2025. [Online]. Available: www.ijariie.com

Google Scholar

[15] L. Chen, M. Jiang, F. Jia, and G. Liu, "Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework," Journal of Business and Industrial Marketing, vol. 37, no. 5, p.1025–1044, Apr. 2022.

DOI: 10.1108/jbim-09-2020-0448

Google Scholar

[16] M. Cherian and K. Grace Manoja, "Leveraging AI for Predicting Marketing and Customer Insights-An Overview," Journal of Informatics Education and Research, vol. 5, no. 1, p.1526–4726, Jan. 2025.

DOI: 10.52783/JIER.V5I1.2050

Google Scholar

[17] S. Kotha, "The Transformative Impact of Artificial Intelligence and Machine Learning on Marketing Operations," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, no. 6, p.176–182, Nov. 2024.

DOI: 10.32628/CSEIT24106166

Google Scholar

[18] P. Kazienko, N. Szozda, T. Filipowski, and W. Blysz, "New business client acquisition using social networking sites," Electronic Markets, vol. 23, no. 2, p.93–103, Jun. 2013.

DOI: 10.1007/s12525-013-0123-9

Google Scholar

[19] S. L. Wamba-Taguimdje, S. Fosso Wamba, J. R. Kala Kamdjoug, and C. E. Tchatchouang Wanko, "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects," Business Process Management Journal, vol. 26, no. 7, p.1893–1924, Nov. 2020.

DOI: 10.1108/bpmj-10-2019-0411

Google Scholar

[20] S. K. Pattanayak, "Generative AI in Business Consulting: Analyzing its Impact on Client Engagement and Service Delivery Models," Article in International Journal of Enhanced Research in Management & Computer Applications, vol. 9, p.2319–7471, 2020, Accessed: Mar. 15, 2025. [Online]. Available: https://www.researchgate.net/publication/385818283

Google Scholar

[21] V. Uren and J. S. Edwards, "Technology readiness and the organizational journey towards AI adoption: An empirical study," Int J Inf Manage, vol. 68, p.102588, Feb. 2023.

DOI: 10.1016/J.IJINFOMGT.2022.102588

Google Scholar

[22] P. Ajibade, "Technology Acceptance Model Limitations and Criticisms: Exploring the Practical Applications and Use in Technology-related Studies, Mixed-method, and Qualitative Researches," 2018, Accessed: Apr. 01, 2025. [Online]. Available: http://digitalcommons.unl.edu/libphilprac/(1941)

Google Scholar

[23] Abdullah Al Rian et al., "Development and Empirical Validation of an Information Technology Acceptance Framework," Journal of Business and Management Studies, vol. 6, no. 6, p.36–47, Nov. 2024.

DOI: 10.32996/JBMS.2024.6.6.3

Google Scholar

[24] Pooja, S. H. Krishna, G. M. P. Kumar, Y. M. Reddy, S. Ayarekar, and M. Lourens, "Generative AI in Business Analytics by Digital Transformation of Artificial Intelligence Techniques," Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, p.1532–1536, 2024.

DOI: 10.1109/IC3SE62002.2024.10593404

Google Scholar

[25] K. Ekuma, "Rethinking Upskilling and Reskilling in the Age of AI and Automation: A fsQCA Approach," Sep. 2023.

DOI: 10.20944/PREPRINTS202309.0055.V1

Google Scholar

[26] D. F. Channon and A. A. Caldart, "McKinsey 7S model," Wiley Encyclopedia of Management, p.1–1, Jan. 2015.

DOI: 10.1002/9781118785317.WEOM120005

Google Scholar

[27] P. Malik, "The McKinsey 7-S Model Framework, Explained (2025)," whatfix.com. Accessed: Apr. 07, 2025. [Online]. Available: https://whatfix.com/blog/mckinsey-7s-model/

Google Scholar

[28] V. Braun and V. Clarke, "Using thematic analysis in psychology," Qual Res Psychol, vol. 3, no. 2, p.77–101, 2006.

DOI: 10.1191/1478088706QP063OA

Google Scholar