A New AI Series on CPO Rising
The power of artificial intelligence (AI) is undeniable. Its impact and influence are revolutionizing the Procurement automation market. According to research from Ardent Partners, AI will soon be ubiquitous in this sector, offering unparalleled efficiencies and capabilities. The transformative possibilities of AI will drive innovations in source to pay, eSourcing, risk management, and overall supply chain management, streamlining enterprise operations and improving business decision-making.
In view of these advancements, Ardent Partners is excited to announce a special “AI in Action” interview series. We invite your company to participate in this series and contribute to the thought leadership around AI by having a product or strategy executive respond to high-level questions from Andrew Bartolini, our Founder and Chief Research Officer for Ardent Partners.
Today’s AI in Action profile features a conversation with Santosh Nair, Chief Product Officer for GEP.
AB: Good day Santosh, tell our readers a little bit about yourself.
SN: My name is Santosh Nair and I am the Chief Product Officer for GEP. I am responsible for GEP’s customer growth strategy across procurement and supply chain platforms. Previously, I’ve led GEP’s professional services organization, deploying the company’s platforms to clients globally and enabling value from ongoing customer success. I have 20 years of experience in driving digital transformation for global organizations, shaping strategy, and driving execution across cloud ecosystems, best practice S2P processes, data management, platform deployment, change management, and ongoing digital innovation.
AB: Thank you. I think I know your answer but let me ask anyway … what are the biggest opportunities for AI to improve procurement efficiencies and productivity?
SN: Deep, Sophisticated, and End-to-End Category Management
AI can consolidate data from internal systems (e.g., spend analysis, ERP, etc.), external market intelligence (e.g., pricing trends, industry benchmarks, etc.), and strategic business goals to craft unique, data-driven strategies for each procurement category. It provides a central hub with real-time decision-making around commodity movements, risk metric changes, compliance improvement opportunities, and much more. For example, in a vertical like healthcare, AI can optimize procurement of critical supplies by analyzing supplier capacity, regulatory constraints, and price volatility. AI further orchestrates execution by providing actionable insights and real-time adjustments, enabling continuous improvement in sourcing, cost, and supplier performance aligned with specific business conditions. For example, AI can enable autonomous negotiations and supplier performance improvement plans to recommend actions and drive execution, with the human playing the role of “approver” as a co-pilot. These capabilities help elevate category management to top quartile performance.
Truly Integrated Risk Management
AI enables a unified approach to risk by integrating supplier risk, performance management, contract management, supply chain risks, geopolitical events, and disruptions into a single, real-time dashboard. These metrics have historically been challenging to track and measure holistically but AI helps centralize the information for decision-making. It provides a deep, holographic digital identity of suppliers, incorporating risk factors such as financial stability, ESG metrics, and operational reliability. For instance, during a global supply chain disruption, AI helped to identify alternative suppliers with lower risk profiles and simulated potential impacts on cost and timelines, helping businesses proactively mitigate risks. In another case, a Fortune 50 banking institution shifted from zero to 85% automated risk controls in 30+ countries globally.
Across-Boundary Total Orchestration
AI brings together procurement and supply chain aspects — including processes, policies, strategies, and operations — into a single framework. It offers persona-based interfaces tailored to roles such as category managers, procurement leaders, and suppliers. By providing intuitive dashboards with real-time visibility, predictive recommendations, and collaboration tools, AI helps users achieve their specific objectives. For example, a category manager might receive alerts about price trends and suggested supplier negotiations, while a supplier receives automated updates on order schedules and requirements, ensuring streamlined execution. In one example, a leading industrial manufacturing company improved compliance by 45% through end-to-end process orchestration.
Supplier Engagement and Collaboration
AI revolutionizes supplier management by automating and enhancing discovery, selection, negotiation, onboarding, and collaboration processes. Beyond cost optimization, it maximizes value by evaluating suppliers on risk, quality, sustainability, innovation potential, and relationship strength. For instance, AI could identify innovative suppliers in emerging markets that align with a company’s sustainability goals, facilitate seamless onboarding with automated compliance checks, and foster collaboration to co-develop products that drive competitive advantage for the enterprise.
Stakeholder Communication and Alignment
AI transforms how procurement communicates and aligns with business stakeholders, addressing a critical gap in today’s organizations. Real-time insights and predictive analytics enable procurement to act as a strategic co-pilot for functions such as marketing, sales, and profit centers. For example, AI can provide a marketing team with instant updates on the availability of promotional materials or sales teams with cost-efficient solutions for product launches. This dynamic alignment ensures procurement is seen not as a cost-cutting silo but as a growth enabler that directly supports revenue-driving initiatives.
AB: How are you ensuring the accuracy and reliability of customers’ AI models (AI data output)?
SN: Master Data Management (MDM)
GEP’s expertise in master data management ensures that the foundational data for AI models is structured, standardized, and harmonized across systems. By creating a single source of truth for enterprise data, MDM enhances data integrity and eliminates inconsistencies, enabling more accurate and reliable AI-driven insights.
Advanced Data Quality Services
GEP provides cutting-edge data quality services to prepare data for AI models. These services include data cleansing, enrichment, de-duplication, and validation to ensure datasets are complete, consistent, and error-free. This preparation lays the groundwork for high-performance AI models that can generate actionable and reliable outputs.
Robust Model Training and Validation
Models are trained using diverse and representative datasets, applying cross-validation techniques to prevent overfitting and improve generalizability. Testing is conducted on unseen data to validate model performance across different scenarios. This ensures that the model performs consistently and reliably in real-world applications.
Continuous Monitoring and Feedback Loops
After deployment, models are continuously monitored to detect issues such as data drift or performance degradation. Feedback loops are implemented to collect user input and real-world outcomes, which are then used to retrain and refine the model over time, ensuring sustained accuracy and relevance.
Explainability and Transparency
AI outputs are accompanied by clear explanations of how predictions or decisions were made. Techniques such as SHAP or LIME are used to provide insights into the contributing factors, helping customers understand and trust the model’s outputs. Transparent reporting on model assumptions and limitations builds confidence in the AI system.
Comprehensive Risk Management
Potential risks, such as bias or inaccuracies, are proactively identified and mitigated through rigorous testing and ethical AI practices. Real-time monitoring ensures that anomalies are addressed promptly, and contingency plans are in place to handle unexpected model behavior. This safeguards reliability and customer trust in the AI system.
AB: What are the leading AI subsets (e.g., machine learning, natural language processing, deep learning, etc.) with the biggest impact on procurement solutions? And why?
SN: Generative AI
Generative AI is revolutionizing procurement by enabling seamless integration across enterprises, supply chains, and supplier networks. Its ability to synthesize diverse data sources creates innovative strategies that go beyond traditional cost cutting. It drives value in areas like new product design, supplier collaboration, and sustainability, helping procurement align operational goals with broader strategic objectives.
Machine Learning (ML)
Machine learning serves as the backbone of procurement AI, analyzing large volumes of historical and real-time data. ML enhances spend analysis, demand forecasting, supplier risk assessment, and cost modeling, continuously adapting to market trends to deliver both immediate efficiencies and long-term strategic alignment.
Digital Twin Technology
Digital twins allow organizations to simulate procurement scenarios and supply chain dynamics, providing a risk-free environment for testing strategies. By modeling disruptions such as supplier shutdowns, digital twins help teams evaluate mitigation plans, improve resilience, and make informed decisions in real time.
Natural Language Processing (NLP)
NLP transforms unstructured data — like contracts and supplier communications — into actionable insights. Applications range from real-time sentiment analysis during negotiations to AI-powered chatbots that streamline procurement workflows, allowing teams to focus on high-value initiatives.
Deep Learning (DL)
Deep learning powers advanced capabilities such as anomaly detection, image recognition, and predictive analytics. Its ability to uncover patterns in complex datasets helps manage multi-tier supply chain visibility and proactively mitigate risks by analyzing external factors like shipping routes or geopolitical events.
Reinforcement Learning (RL)
Reinforcement learning is an emerging approach that enables autonomous systems to optimize procurement strategies through trial and error. From dynamic contract negotiations to real-time inventory adjustments, RL is set to unlock new levels of efficiency and adaptability in procurement processes.
AB: How can you support procurement clients when an AI skills gap deficiency exists?
SN: Simplified, User-Friendly Tools
AI solutions with intuitive interfaces eliminate the need for advanced technical skills. Persona-based dashboards and integrated workflows empower users to access actionable insights effortlessly, allowing non-technical teams to benefit fully from AI capabilities.
Low-Code/No-Code Platforms with Prebuilt Modules
Low-code and no-code platforms simplify AI adoption, enabling teams to customize workflows and deploy AI-driven analytics without programming expertise. These solutions reduce reliance on technical teams, making AI accessible to a wider audience.
Comprehensive Training Programs
Tailored training sessions cover AI fundamentals, tool-specific capabilities, and practical use cases. These programs equip procurement teams — both technical and non-technical — with the skills to integrate AI into their daily workflows confidently.
AI-as-a-Service (AIaaS)
Managed AI services allow organizations to focus on strategic objectives while GEP handles technical complexities, such as data integration and model management. This ensures value generation even in the presence of a skills gap.
AI Digital Garage and Bootcamps
Hands-on programs such as digital garages and bootcamps provide opportunities for teams to explore AI tools in real-world scenarios. These initiatives build confidence and skills, ensuring a smooth transition to AI-driven processes.
AB: How do you view your customers’ (and prospective customers’) AI attitudes?
SN: Enthusiastic Innovators
These early adopters see AI as a strategic enabler, actively pursuing advanced use cases like generative AI for supplier collaboration or predictive analytics for demand forecasting. They prioritize innovation and seek cutting-edge solutions to gain a competitive edge.
Cautious Explorers
Balancing enthusiasm with caution, these customers pilot AI solutions in specific areas, such as supplier risk assessment, to assess feasibility. They seek clear ROI and ease of implementation before committing to broader adoption.
ROI-Focused Pragmatists
This group evaluates AI through the lens of measurable outcomes, such as cost reduction and process automation. They prefer proven technologies with quantifiable benefits, ensuring scalability and reliability before investing.
Skeptical Observers
Skeptics remain cautious about AI adoption, often due to concerns over data privacy or workforce disruption. They require strong proof of value and incremental demonstrations of success to build trust in AI solutions.
Transformation Seekers
Viewing AI as essential for organizational transformation, these customers focus on its ability to integrate across boundaries and drive strategic goals. They value AI’s potential for fostering innovation, enhancing collaboration, and aligning procurement with broader business objectives.
AB: Where do you see AI solution offerings within procurement in five years?
SN: AI is set to redefine procurement and supply chain management, shaping the future through several key characteristics and strategic advancements:
Key Characteristics of Future AI Solutions
End-to-End Integration
AI will seamlessly unify procurement, supply chain, and enterprise operations into integrated platforms. This holistic approach will eliminate silos, enabling more efficient, agile, and collaborative processes.
Proactive Intelligence
Future AI will anticipate organizational needs, mitigate risks, and identify opportunities autonomously. By predicting trends and disruptions, AI will help businesses maintain a competitive edge and adapt dynamically to changing environments.
Human-AI Collaboration
AI will function as a strategic co-pilot, augmenting human decision-making with actionable insights while independently managing routine tasks. This partnership will empower professionals to focus on innovation and long-term strategy.
Cross-Boundary Innovation
Generative AI will drive collaboration across departments, supply chains, and supplier networks, aligning operational realities with strategic objectives. This cross-boundary capability will unlock innovative solutions and foster closer alignment between functions.
Sustainability and ESG Focus
AI will embed environmental, social, and governance (ESG) principles into procurement workflows. It will prioritize sustainability in supplier selection, risk management, and operational decisions, helping organizations meet regulatory and ethical standards.
Strategic Vision of Future AI-Based Systems
Holistic and Integrated Ecosystems
AI systems will evolve into unified ecosystems that bridge procurement, supply chain, and enterprise functions. This integration will enable seamless collaboration and innovation, aligning strategy with execution at every level.
Dynamic and Real-Time Decision-Making
Through advanced simulations and predictive analytics, AI will support real-time decision-making. Digital twins and reinforcement learning will allow teams to anticipate and adapt to risks and opportunities dynamically.
Supplier-Centric Innovation Platforms
AI will enhance supplier collaboration by emphasizing innovation, sustainability, and strategic alignment. Co-innovation will extend beyond cost metrics, driving long-term value creation through shared goals and projects.
Personalized and Autonomous User Experiences
AI platforms will deliver tailored experiences for users across roles, leveraging natural language processing (NLP) and low-code interfaces. Autonomous systems will optimize routine processes, freeing up teams to focus on strategic priorities.
Value Well Beyond Cost Savings
The focus of AI will extend beyond cost reduction to prioritize innovation, sustainability, and business growth. AI will align procurement processes with organizational goals, creating value across multiple dimensions, including compliance and community impact.
In the next five years, AI will transform procurement from a functional necessity to a strategic powerhouse. Through integration, prediction, and collaboration, AI will enable organizations to adapt dynamically, innovate boldly, and thrive in an increasingly complex and interconnected world. This evolution represents not just an improvement in processes but a fundamental redefinition of procurement’s role in achieving business excellence.
AB: I appreciate your time today!
SN: Thank you.
