Page 55 - CMA Journal (Jul-Aug 2026)

F oc us S ec t ion

Predicting Demand Before it Becomes a The important consideration is determining where Data silos, inconsistent formats and poor-quality Responsible AI Requires Human However, greater automation also increases the • Ilamurugan, P. K. (2026). Future Trends in Procurement Automation:
Problem automation adds value and where human approval information can signi cantly a ect the performance of Judgement importance of governance. Future procurement Leveraging Machine Learning in Spend Analysis. Zenodo (CERN European
must remain mandatory. AI systems. systems will need to balance agility and e ciency with Organization for Nuclear Research). https://doi.org/10.5281/
One of the strongest applications of AI in procurement Turning Spend Data into Strategic AI should strengthen professional judgement rather sustainability, transparency, fairness and resilience. zenodo.18369864
is predictive analytics. By analyzing historical This makes data governance a procurement priority. than replace it. Procurement decisions often involve • Lakhal, N., Aazami, A., & Kummer, S. (2025). The role of arti cial intelligence
purchasing data, operational information and market Insight Organizations need integrated systems, clear data factors that cannot be reduced to historical data alone, Conclusion: Smarter Procurement, Not across the source-to-pay framework: Theoretical and practical aspects.
100160–100160.
https://doi.org/10.1016/
5(2),
Digital
Business,
trends, ML models can help forecast future demand. Procurement teams often hold signi cant amounts of standards and appropriate controls over data quality including ethics, strategic priorities, supplier Just Automated Procurement j.digbus.2025.100160
and access.
Procurement is Moving from what happened to Improved demand forecasting can help procurement data on suppliers, prices, purchasing patterns and For public-sector organizations, the challenge is even relationships and exceptional circumstances. AI can AI and ML have the potential to fundamentally • Obinna, A. J., & Kess-Momoh, A. J. (2024). Developing a conceptual
handle repetitive tasks, identify patterns and provide
market conditions. AI-based spend analytics can bring
technical framework for ethical AI in procurement with emphasis on legal
Transactions to Strategy understanding why it teams avoid stock-outs, manage inventory more this information together to identify savings greater because data access must be balanced with recommendations, while procurement professionals strengthen procurement by improving e ciency, oversight. GSC Advanced Research and Reviews, 19(1), 146–160.
happened, what is likely e ectively and improve supply-chain resilience. privacy, security and stewardship responsibilities. retain responsibility for interpreting those forecasting demand, identifying risks, supporting https://doi.org/10.30574/gscarr.2024.19.1.0149
The digital economy is changing how organizations to happen next and Research has highlighted the potential of intelligent opportunities, supplier risks and sourcing trends recommendations and making consequential supplier decisions and turning procurement data into • Özcan, B., & Taş, B. K. O. (2026). FinTech and green public procurement
operate, compete and create value. Procurement is no what action should be procurement systems and big-data analytics to (Ilamurugan, 2026). The Risk of Automating Bias decisions (Tushar & Jaiswal, 2026). strategic insight. (GPP) in Europe: driving sustainability forward. Financial Innovation, 12(1).
longer simply an administrative function focused on taken. improve forecasting accuracy, while deep-learning Predictive analytics can help procurement AI does not automatically eliminate human bias. If Human-in-the-loop models are particularly relevant But technology alone is not the answer. Poor-quality https://doi.org/10.1186/s40854-026-00937-6
processing purchase orders and controlling costs. It is technologies can support more detailed analysis of professionals move from analyzing historical historical procurement data contains biased decisions because they allow AI to provide recommendations data, algorithmic bias, regulatory uncertainty, privacy • Patrucco, P. P., Dimand, A. M., & Smith, B. (2026). Innovators and
increasingly a strategic function that in uences Where AI Can material demand patterns (Sun et al., 2025). expenditure to anticipating future risks and or supplier preferences, AI systems may reproduce while keeping decision-making responsibility with concerns and organizational resistance can limit the Transformers: From policy to public value: how procurement governs AI
e ciency, resilience, supplier relationships, Make the Biggest The practical bene t is a shift from reactive purchasing opportunities. AI-based scenario planning can also those patterns. Research has raised concerns that authorized professionals. This approach is especially bene ts of intelligent procurement (Ayobami et al., adoption in US State government. International Journal of Physical
sustainability and organizational performance. Di erence to proactive planning. allow organizations to test di erent sourcing strategies AI-driven supplier scouting may result in increasingly important where procurement decisions can a ect 2024). Distribution & Logistics Management, 1–21. https://doi.org/10.1108/
ijpdlm-02-2025-0084
before implementing them. This is where procurement
Arti cial Intelligence (AI) and Machine Learning (ML) AI has applications Detecting Fraud and Managing Risk can move beyond being a cost centre and become a similar supplier pro les, potentially reducing supplier supply networks and wider stakeholder interests The future of procurement should therefore not be • Paul, P. O., Ogugua, J. O., & Eyo-Udo, N. L. (2024). The role of data analysis
are accelerating this shift by enabling procurement across almost every source of strategic intelligence. diversity and making it harder for new suppliers to (Ribeiro et al., 2025). de ned by how much human involvement can be and reporting in modern procurement: Enhancing decision-making and
teams to process large volumes of data, identify stage of procurement, Procurement is particularly vulnerable to fraud, The Business Case: What Organizations enter the market (Ribeiro et al., 2025). Preparing the Procurement Workforce removed. It should be de ned by how e ectively supplier management. International Journal of Management &
Entrepreneurship Research, 6(7), 2139–2152. https://doi.org/10.51594
patterns and make faster, more informed decisions. including supplier irregularities and con icts of interest. AI and ML can This creates what researchers describe as “bias Technology alone will not deliver digital procurement technology and professional expertise can work /ijmer.v6i7.1262
These technologies can support smarter and more selection, demand forecasting, fraud detection, strengthen controls by identifying unusual transaction Can Gain co-evolution”, where historical patterns can in uence transformation. Organizations also need people who together. • Ribeiro, J. L., Plangger, K., & Montecchi, M. (2025). Decoding Responsible
resilient procurement processes, although their contract management, spend analysis and strategic patterns, scoring supplier risks and monitoring The most immediate bene t of AI in procurement is future decisions and reinforce existing biases. understand how to use, question and govern AI For organizations operating in the digital economy, the Procurement: Conceptualizing Bias Co evolution in AI aided
procurement activities in real time (Ezeji, 2024).
adoption remains at an early stage. A review of 46 sourcing. A review by Spreitzenbarth et al. (2024) e ciency. By automating repetitive activities, Organizations therefore need mechanisms to monitor systems. Successful AI adoption requires appropriate goal should be procurement that is faster, more Organizational Decision making. British Journal of Management, 37(1).
studies on AI and ML in Purchasing and Supply identi ed eleven major AI use-case clusters in Research into high-value public procurement has accelerating decision-making and reducing manual AI systems, identify problematic feedback loops and organizational infrastructure, cultural change and new transparent, more resilient and more sustainable. https://doi.org/10.1111/1467-8551.70027
Management found that research has paid relatively procurement, including contract analysis, supplier and identi ed indicators such as single-bid tenders as e ort, AI can improve the speed and e ciency of intervene when necessary. workforce capabilities (Balkan & Akyüz, 2025). Many Achieving that goal requires more than AI and ML. It • Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024). Arti cial
limited attention to operational implementation, cost spend management, risk management, demand potential signals of corruption risk. Machine-learning procurement processes (Lakhal et al., 2025). The issue is particularly important in public organizations are still developing the expertise requires reliable data, capable people, clear intelligence and machine learning in purchasing and supply
management: A mixed-methods review of the state-of-the-art in literature
considerations and supplier sustainability forecasting and negotiation support. techniques, including Random Forest models, have AI can also improve decision quality by analyzing large procurement, where fairness, equal opportunity and required to design, implement and manage AI systems governance and responsible use of technology. and practice. Journal of Purchasing and Supply Management, 30(1),
(Spreitzenbarth et al., 2024). also demonstrated potential in identifying patterns datasets and identifying patterns that may be di cult accountability are as important as e ciency. AI-driven e ectively. 100896–100896. https://doi.org/10.1016/j.pursup.2024.100896
Smarter Supplier Selection associated with procurement risks. for individuals to process manually. Decision-support When these elements come together, AI can help
The opportunity, therefore, is not simply to automate systems can provide procurement professionals with decisions must therefore be subject to appropriate This is particularly important for the public sector, procurement evolve from a transaction-focused • Sun, Q., Wu, C., Ren, T., Liu, Y., Zhao, P., & Guo, Y. (2025). Research on the
Design and Implementation of Intelligent Procurement System Based on
procurement. It is to use technology to turn Choosing the right supplier directly a ects cost, For public-sector procurement, this capability can be timely information, predictive insights and scenario human oversight. where capacity constraints, complex procurement function into a strategic driver of value, risk Big Data Analysis. 1–6. https://doi.org/10.1109/
procurement data into practical intelligence and quality, delivery and long-term operational particularly valuable. Instead of relying solely on assessments (Paul et al., 2024). Governance Cannot Be An regulations and the need to balance nancial management, sustainability and organizational icec2nt65402.2025.11380060
strengthen its contribution to organizational value. performance. Traditional supplier assessment often periodic reviews, organizations can use AI to identify The bene ts extend beyond e ciency. AI can improve Afterthought e ciency with social and environmental objectives performance. • Tushar, & Jaiswal, P. (2026). Human-AI Decision Architectures in Green
potential anomalies earlier and direct human attention
create additional challenges (Andersson et al., 2025).
What Procurement 4.0 Means in depends heavily on human judgement and may towards transactions that require further investigation. visibility and traceability across procurement The rapid development of AI has moved faster than Investment in training and professional development References Procurement and Planning. In Advances in computational intelligence and
robotics book series (pp. 251–278). IGI Global. https://doi.org/10.4018
struggle to respond quickly to changing market
Practice conditions. Automating Routine Procurement processes and support activities such as contract many regulatory frameworks. Organizations therefore should therefore be treated as an essential component • Andersson, P., Arbin, K., & Rosenqvist, C. (2025). Assessing the value of /979-8-3373-6645-6.ch008
discovery, supplier matching, anomaly detection and
The concept of Procurement 4.0 brings Industry 4.0 AI can analyze both structured and unstructured Automation is another major opportunity. AI-powered tender evaluation (Patrucco et al., 2026; Hyppänen & face the challenge of adopting new technologies while of AI adoption rather than an optional activity. arti cial intelligence (AI) in governmental public procurement. Journal of • Waditwar, P. (2025). AI-Driven Smart Negotiation Assistant for
Procurement—An Intelligent Chatbot for Contract Negotiation
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technologies into procurement. AI, ML, the Internet of information to support supplier assessment and procurement systems can reduce repetitive Vilko, 2025). ensuring that procurement remains compliant, The Next Stage: From AI-Assisted to jopp-05-2024-0057 Based on Market Data and AI Algorithms. Journal of Data Analysis and
Things (IoT), blockchain and cloud computing can identify patterns that may not be immediately visible administrative work, accelerate processes and improve Ultimately, the strategic opportunity is to move transparent and accountable (Obinna & Kess-Momoh, AI-Enabled Procurement • Ayobami, A. T., Mike-Olisa, U., Ogeawuchi, J. C., Abayomi, A. A., & Agboola, Information Processing, 13(2), 140–155. https://doi.org/10.4236/
support real-time monitoring, predictive analysis and to procurement teams. Machine-learning approaches, workforce productivity. The emergence of Agentic AI procurement from managing transactions to creating 2024). O. A. (2024). Digital Procurement 4.0: Redesigning Government jdaip.2025.132009
increasingly automated decision-making across the including Decision Trees, Random Forest and Gradient could take this further by enabling systems to perform value. AI can provide insights into supplier markets, Guidance such as IEEE P3119 provides complementary Procurement technology is continuing to evolve. Contracting Systems with AI-Driven Ethics, Compliance, and Performance • Waters, G., & Miller, C. J. (2025). Raising a Global Standard in the
Optimization. International Journal of Scienti c Research in Computer
Procurement of Arti cial Intelligence and Automated Decision Systems
Source-to-Pay (S2P) process. risk exposure, sustainability and innovation process guidance for the procurement of AI and Agentic AI could enable systems to perform Science Engineering and Information Technology, 10(2), 834–865.
Boosting, are increasingly being explored for supplier tasks and make decisions within prede ned automated decision systems (Waters & Miller, 2025). increasingly autonomous tasks, while large language (pp. 1–34). https://doi.org/10.1201/9781003663577-1
This represents a signi cant change from traditional assessment. parameters. Such systems are being explored for opportunities, supporting broader organizational However, organizations need governance models may support conversational negotiations and https://doi.org/10.32628/cseit24102138
objectives.
procurement, where manual processes, fragmented activities including negotiation, compliance and arrangements that cover the entire AI lifecycle from other procurement interactions (Waditwar, 2025). • Balkan, D., & Akyüz, G. A. (2025). Arti cial intelligence (AI) and machine
learning (ML) in procurement and purchasing decision-support (DS): a
data and individual judgement often dominated AI can also support the assessment of supplier nancial supplier management. The Data Problem: AI is Only as Good as selecting and deploying AI tools to monitoring their Digital twins could allow organizations to model taxonomic literature review and research opportunities. Arti cial
decision-making. Digital systems can bring strength, risk pro les and other relevant information AI can also work alongside blockchain-based smart its Information performance. supply-chain scenarios before making procurement Intelligence Review, 58(11). https://doi.org/10.1007/s10462-025-11336-1
information together, identify patterns and provide through the analysis of large datasets. However, these contracts to trigger speci c contractual actions when decisions, while AI combined with blockchain, IoT and • Ezeji, C. L. (2024). Arti cial Intelligence for detecting and preventing
insights across departments (Paul et al., 2024). tools should support not replace professional prede ned conditions are met, reducing The biggest obstacle to intelligent procurement may For procurement leaders, the question should not cloud technologies could create more connected and procurement fraud. International Journal of Business Ecosystem and
judgement, particularly where supplier selection has not be the technology itself, but the quality of the data simply be “Can we automate this?” but also “Should we Strategy (2687-2293), 6(1), 63–73. https://doi.org/10.36096/ijbes.v6i1.477
AI and ML are particularly valuable because they can signi cant nancial or strategic consequences. administrative intervention and improving transaction behind it. AI and ML systems require reliable, complete automate this, under what controls, and who remains responsive procurement ecosystems (Özcan & Taş, • Hyppänen, T., & Vilko, J. (2025). AI in Public Procurement: Potential and
move procurement beyond simply understanding e ciency. and accessible data. accountable?” 2026). Adoption in the Competitive Tendering Process. 1–9. https://doi.org/
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