Generative AI

Generative AI refers to a class of artificial intelligence technologies designed to produce original content by learning from massive stores of data and emulating large-scale data patterns. Its foundations trace back to the 2017 breakthrough introduction of the transformer architecture by Google, which enabled machines to understand and generate language with contextual depth, scalability, and precision. This technology laid the groundwork for the development of large language models (LLMs) trained on large bodies of text to mimic human communication, summarize information, and respond with relevance across a wide range of topics.

While underlying research progressed steadily, the global market awareness and demand for generative AI shifted dramatically with the release of ChatGPT in late 2022. ChatGPT was the first widely adopted interface where both consumers and enterprises could experience the power of generative AI. It reframed generative AI from an experimental concept to a mainstream productivity and decision-support tool. Organizations quickly recognized that generative AI could do far more than autocomplete sentences, it could answer math, legal, procurement, and strategic logic-based questions with a level of fluency and pattern recognition that rivaled human capability. 

Since then, generative AI has evolved at breakneck speed. Generative AI systems are now being trained not only for conversational tasks but for complex reasoning, multimodal inputs, and visual generation. The competition has moved beyond text into AI-generated images, video, code, simulations, and decision support, ushering in a new generation of intelligent digital systems and groundbreaking platforms that can engage across nearly every format of enterprise communication and knowledge work.

Operationally, generative AI is no longer about working alone. It is about simulating language-based logic and human decision structures at scale. This allows companies to redefine workflows across procurement, IT, finance, legal, HR, and beyond. Organizations that master how to adopt, integrate, and govern these systems will not just gain efficiency, but will shape how decisions, creativity, and strategy are executed to support generative AI in procurement.

Generative AI is deceptively simple: a user types in a question and receives an immediate response. The immediacy, context, and confidence associated with the standard generative AI interaction makes it look fairly simple. However, organizations trying to build enterprise-grade generative AI solutions in procurement must take a few technological components into consideration.

1. The Initial Prompt

The initial prompt is the user’s question, instruction, or context-providing content that tells the model what to generate or do. It can be simple (“Say Hello”) or complex (“Summarize this contract, extract key dates, and evaluate compliance risks based on the attached policy”). Prompts define intent, structure, tone, and desired output format which means that prompt quality will often affect the quality of response one receives. Prompt engineering helps shape predictable and high-quality responses.

2. Grounded or Contextualized Prompts

Grounded prompts inject real-time, external, or enterprise-specific data into the prompt to guide the model toward factual and relevant output. This grounding often includes structured context like customer profiles, inventory records, contracts, policies, role descriptions, and instructions to regulate tone or outputs. These grounded prompts may use retrieval-augmented generation (RAG) or other context fusion techniques.

3. The Models (Large Language Model or Multimodal Model)

The model is the core generative engine for generative AI and it is typically a neural network (often transformer-based) trained to predict the next token or output based on the input sequence. These models can be text-only or support multi-modal text, image, code, or speech. The models are trained on large reams of data and responsible for generating coherent, relevant, and logical content.

4. Training Data

Training data is the foundational dataset used to pretrain the model and it defines the model’s initial knowledge and capabilities. The sources for this data may include books, web content, social media platforms, documentation, code repositories, public domain data, and other available text. Training data may also include supervised fine-tuning (RLHF or instruction-tuning) to align the model to human intent or expectation.

5. Output (Generated Content)

The output is the text, image, code, or logic generated by the model in response to a prompt. This is the “answer” that users receive when they ask generative AI for an answer. This output can range from natural language text, to structured formats (JSON, tables), to executable code.

6. APIs and Integration Layer

APIs allow a model to be embedded into business applications, workflows, or user interfaces. These APIs may call external systems or invoke tools, databases, and CRMs as part of the response process. When an application or external data is referenced by generative AI and this action is used to affect future responses and prompts, this combination often leads to an agentic workflow.

7. Agentic Workflows

Agentic workflows are multi-step processes where autonomous or semi-autonomous agents use models, tools, and memory to perform tasks over time. For example, a procurement agent may read an RFP response, pull in contract templates, and draft a response. These workflows may use planning, decision-making, tool-use, memory, and reasoning loops to get work done. 

Planning: The system determines a multi-step path to accomplish a task.

Decision-making: The system evaluates conditions and chooses between options.

Tool use: The model uses one or more external tools strategically.

Memory: Context is retained across steps or interactions.

Autonomy: It operates semi-independently based on a task objective.

8. Retrieval-Augmented Generation (RAG)

RAG improves accuracy by retrieving relevant external documents or data and injecting them into the prompt. This approach combines a vector store for semantic search with the model’s generation capability. RAG is often used for helping with enterprise search, contract search, technical documentation, and policy compliance.

9. Memory and Session Context

Persistent memory enables the AI to remember past interactions, user preferences, or previous outputs across a session or multiple sessions. This memory can be short-term and session based or long-term and stored in external memory systems. This is essential for multi-turn conversations, task continuity, and personalization.

10. Governance, Guardrails, and Monitoring

Generative AI in the business world requires governance and guardrails to ensure reliability. Businesses are constrained by a variety of laws, compliance issues, and customer expectations that lead to monitoring for bias, toxicity, data leakage, and output quality. Generative AI governance can include content filters, feedback loops, audit trails, and even human-in-the-loop feedback in some cases. Each of these steps comes with its own set of tradeoffs across performance, trust, and practical utility. But this consideration is crucial for regulated industries and enterprise use cases.

11. Fine-Tuning and Customization

Models can be fine-tuned on domain-specific data such as legal, financial, or procurement data to improve performance and alignment. Customized efforts may also include supervised fine-tuning, prompt tuning, or low-rank adaptation (LoRA) to enable domain fluency and use-case specialization.

12. Enterprise Data Integration and Access Controls

Generative AI strategies for the enterprise must include secure integration with internal systems. Generative AI must respect access permissions, role-based visibility and access, and data residency requirements. As companies align generative AI to their data, they must also consider how identity management systems (SSO, RBAC) and enterprise APIs will be integrated into usage.

Sometimes, generative AI produces responses that sound accurate but are actually incorrect or made up, which are informally called “hallucinations.” This happens because the model is not retrieving facts from a database or the internet. Instead, it is generating responses based on patterns and associations learned during training.

The model has been trained on vast amounts of text, billions and billions of words, to recognize how language is typically used, but lacks an internal understanding of what is inherently true or false. If a question relates to something rare, ambiguous, or missing from the training data, the model draws on statistical patterns from that training to generate the answer that seems most relevant, regardless of whether any real information supports it.  In short, the model builds responses from its internal world view shaped by language patterns, not grounded truth. Business systems using generative AI must account for these challenges and have a good understanding of which models are more likely to have these internal gaps leading to inaccurate answers.

Generative AI should not be seen as a standalone technology or evaluated solely as a potential value driver that cannot be quantified. Generative AI is valuable only when it is aligned to practical business goals and tasks. Levelpath uses Generative AI to target a variety of practical metrics, capabilities, and outcomes.

Faster access to accurate procurement answers

Generative AI enables any user to ask plain-language questions about spend, suppliers, or contracts and receive verified responses in minutes rather than waiting for manual reports. This capability reduces the time needed to procurement-based questions from days to minutes, increases the percentage of queries resolved without analyst supervision, and accelerates sourcing decisions, supplier reviews, and contract actions as procurement can now let the Assistant do it.

Automated progression of procurement tasks

When paired with agent-based AI, Generative AI can detect and execute the next step in processes such as sourcing, supplier onboarding, or contract renewal. This includes drafting RFx documents, routing for approval, and identifying approved suppliers, which shortens average cycle times and improves on-time completion rates for contract renewals and supplier reviews.

More complete supplier, contract, and spend data

Generative AI identifies gaps in supplier, contract, or spend records and enriches them with approved internal and external data sources. This increases the percentage of supplier profiles with complete risk and performance data, improves supplier risk scoring, ensures sourcing events are fully documented, and aligns decision-making with savings forecasts.

Earlier and more consistent stakeholder engagement

Through conversational intake and guided prompts, Generative AI allows stakeholders to engage with procurement before spend is committed. This raises the proportion of spend supported by sourcing policies and approved vendors, improves adherence to procurement guardrails, reduces off-contract purchases, and makes it easier for stakeholders to act within compliant processes.

Continuous adjustment for risk reduction

Generative AI can be applied to historical performance data, market signals, and contract terms to predict risks and recommend mitigation steps before issues arise. Over time, it refines recommendations based on improved models and larger datasets, helping to identify risks earlier, reduce supplier performance issues, and negotiate terms based on risk concerns, service outages, and broader environmental and geopolitical challenges.

Levelpath offers several unique advantages for enterprise Generative AI use cases in procurement. The launch of generative AI coincided with the start of Levelpath, which allowed for the development of our Hyperbridge reasoning engine that powers truly AI-native procurement. The Hyperbridge AI engine dynamically selects the most appropriate large language model for each task, ensuring fit-for-purpose performance. Unlike generic AI tools, Levelpath protects all prompts and business data from being used to train public models, preserving enterprise confidentiality.

The platform is purpose-built for sourcing, procurement, supplier management, and contracts while avoiding the distractions and inaccuracies that plague unmanaged or general-purpose AI. Levelpath’s generative capabilities go beyond summarization and form completion: the AI can enrich supplier records, trigger sourcing events, and route documents for approval. This ability to take action elevates AI from a passive content generator to a valuable procurement Assistant.