Service
Artificial Intelligence
Building a Smarter, Connected Tomorrow with AI-first and Agentic AI
HIGHLIGHTS
- Supply chains are still operating in silos, reacting to disruptions instead of predicting them. Manual processes and fragmented systems continue to limit performance and resilience.
- Inefficiencies in sourcing and procurement continue to limit speed and scalability, even as cost and agility pressures intensify.
- Rising ESG expectations demand real-time accountability, yet most organizations still lack the data cohesion to respond effectively.
- Many Gen AI initiatives stall after promising pilots due to lack of clarity on where and how to scale. A domain-first phased approach helps companies realize value from Gen AI investments.
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Strategic sourcing and procurement
Rethinking how supply chain operations are managed has become essential for businesses striving to stay competitive, especially as they face growing pressure to boost efficiency, cut costs, and keep up with faster product cycles. Generative AI (Gen AI) has the potential to transform supply chain management within the enterprise. Companies that adopt Gen AI in their operations can build greater resilience and gain a lasting competitive edge. Gen AI is particularly effective in enterprise functions with high concentration of knowledge, where many employees handle repetitive, narrow tasks like searching, aggregating data, entering information, and switching between applications. This makes procurement and supply chain operations ideal candidates.
Businesses generate large volumes of supply chain data, but much of it remains siloed. AI-based optimization models can bring this data together, offering a unified view that supports better planning, analysis, and decision-making. This integrated approach can optimize the supply chain from upstream vendors to direct customers, making operations more efficient and data driven.
Gen AI can revolutionize sourcing by automating tasks such as identifying and evaluating suppliers, gathering supplier intelligence, and managing supplier risks. With its advanced capabilities, Gen AI delivers insights and recommendations that help businesses make smarter decisions. Whether it's direct or indirect sourcing, Gen AI can streamline the entire process, leading to improved efficiency, and cost savings.
Contract management
Gen AI can simplify contract management by automating key tasks like identifying templates, drafting documents, comparing terms and conditions, summarizing bidder proposals, and tracking supplier performance.
By reducing manual effort, Gen AI helps save time and minimize errors. It supports the full contract lifecycle, from creating drafts and negotiating terms to securing approvals from authorized stakeholders.
Gen AI can enable dynamic scenario planning, real-time risk mitigation, and predictive demand sensing by integrating external signals, such as geopolitical events, weather patterns, and market shifts, into supply chain models.
The goal is to ensure performance meets expectations while reducing liabilities and protecting the rights of all parties involved. Gen AI also assists in generating and publishing RFI/RFP/RFQ documents, comparing contract terms, evaluating proposals, and managing contracts through generation, summarization, querying, and text analysis.
Governance, risk, and compliance
Gen AI can support companies in achieving their ESG (Environmental, Social, and Governance) goals for supply chains. By providing accurate and timely data on ESG issues, Gen AI enables better decision-making and helps organizations meet their sustainability objectives.
Using Gen AI tools, businesses can standardize management practices, reduce uncertainty, and comply with regulations. This includes mapping sustainability goals to specific milestones and following industry standards for reporting and compliance.
The jury is out on the art of the possible for applications of Gen AI in supply chain and procurement, but a preliminary view is as follows:
How GenAI transforms manual operations in procurement with intelligent automation.
Strategic outlook: Unlocking Gen AI’s full potential
To truly unlock Gen AI’s transformative power in supply chain operations, enterprises must go beyond automation and embrace strategic orchestration. This elevates decision-making from reactive to proactive. Moreover, Gen AI can foster supplier collaboration by generating multilingual insights and facilitating transparent negotiations across global networks. As supply chains become more decentralized and digitally connected, Gen AI will serve as a cognitive layer that harmonizes data, people, and processes. The future lies in building adaptive supply chains that learn and evolve continuously. To achieve this, organizations must invest in AI literacy, ethical governance, and scalable infrastructure. Gen AI is not just a tool, it’s a strategic capability that redefines how supply chains think, respond, and grow.
The full potential of Gen AI in supply chain and procurement is still unfolding, but early insights suggest promising applications across multiple areas.
So, how big do you want your game in Gen AI to Be?
It all depends on the power of your imagination and how clearly you define the problem you're trying to solve.
Key gen AI terminologies to explore
Gen AI refers to algorithms that generate new text and images, provide contextual search results, and convert complex inputs into complex outputs, unlike predictive AI. The Gen AI model lifecycle includes the following stages:
Stage one: Grounding and model selection
The Gen AI lifecycle begins with the grounding phase, where facts and policies are established, including the principles of Responsible AI. The next step involves selecting foundational models such as Titan (AWS), Claude 2 (Anthropic), Llama2 (Meta), PaLM, HuggingFace, and Gemini (GCP). These models support various Natural Language Processing (NLP) tasks like text generation, summarization, information extraction, open-ended Q&A, embeddings, and search.
Stage two: Retrieval-augmented generation (RAG)
In this stage, enterprise-specific data and policies are used to fine-tune models for delivering contextual and relevant outputs.
Stage three: Fine-tuning and scaling
The final stage involves fine-tuning LLMs and scaling them across the enterprise through prompt engineering. This process can be continuously refined by developing new use cases and selecting the right functional models.
To sustain Gen AI initiatives, organizations need cross-functional collaboration between IT and business teams. Alternatively, they can partner with Gen
AI service providers to establish an AI Office. This team can help with ROI analysis, business case development, and roadmap planning.
