From Wow to Value: Getting the Job Done for Credit and Procurement Analysts

Generative AI has delivered no shortage of impressive demos. But for credit and procurement analysts, value is not measured by fluency or speed — it’s measured by whether a solution gets the job done, saves time, and can be trusted in high‑stakes decisions.

Moving from wow to value requires a shift in focus: away from the model, and toward the user problem, the data foundation, and the workflow where decisions are made.

Value Means Solving the Problem — and Saving Time

A solution is only useful if it removes work, not if it creates a new layer of review.

Analysts are already skilled at validating sources and applying judgement. If AI forces them to re‑check every number and narrative, the efficiency gains disappear. Speed without confidence is not value.

Starting With the User and the Task

Effective AI solutions start with a clear understanding of what the analyst is trying to produce — often a structured output such as a credit memo or vendor assessment.

Too often, what is presented as a solution is merely an open-ended chatbot; instead, start with a simple input — like a company or commodity — and generate a structured draft aligned with the task. Set up a canvas that brings together data, unstructured content, and insight. This immediately creates context for both the user and the AI, guiding how data is retrieved, prioritized, and refined – reducing the risk of hallucination.

Refine with a Conversational Interface

Now that there is context, a conversational interface that allows users to edit and add more data to a section of the output enables more targeted prompting and generation from the AI.

AI Is Powerful — but Not Always the Right Tool

Not everything should be AI-generated

Financial tables, ratios, and standardized calculations are often better handled through deterministic APIs or Excel models. Gen AI adds the most value where synthesis and context matter — bringing together research, filings, news, and risk signals into a coherent narrative.

The strongest solutions emerge from combining deeply specialized analyst expertise with the scalable power of AI — brought together through an agentic framework and a set of skills that unify both approaches into a single, cohesive system.

Gen AI Changes the Economics of Application Development

Gen AI has dramatically reduced the time required to build enterprise applications — but only when built on the right foundation.

Strong Data Foundations Are Non‑Negotiable

AI amplifies whatever data it sits on, whether that data is contextually relevant or even factual. Without governed, linked, and well‑curated data, Gen AI scales inconsistency and risk. When metadata, lineage, and entitlements are already in place, agentic solutions can be deployed quickly and safely across domains.

From Prompts to Skills — and a Conversational Architecture

High‑impact solutions don’t rely on one‑off prompts. They define reusable skills — retrieving data, prioritizing sources, assembling sections—that can be orchestrated into structured outputs and surfaced through a conversational interface.

The result is a dynamic information architecture that allows users to navigate, interrogate, and refine insight naturally.

Connecting Data Across Sources — AI‑Ready or Not

Agentic frameworks are especially powerful because they can orchestrate structured and unstructured data, internal and external, AI‑ready or not. Financials, trade flows, risk indicators, and research can be combined seamlessly within a single workflow.

Increasing the Utility of Content — With No Extra Effort

One of Gen AI’s most powerful effects is how it increases the marginal value of existing content.

Research, reports, and risk assessments that analysts may not have time to read can now be summarized and embedded directly into workflows. Content that was once “nice to have” becomes actionable — without additional effort from the user.

Transparency and Quality Metrics Make AI Usable

Still, you will never get a 100% perfect answer from AI. What matters is knowing how strong the answer is and where human judgment should focus.

Two elements are critical:

• Process transparency, using safe reasoning summaries that explain what steps were taken and why - the agents' chain of thoughts and business rules.
• Quality metrics, scoring outputs on dimensions like data integrity, faithfulness, completeness, and structure, with clear guidance on how to improve weak sections.

These signals transform AI from a black box into a tool analysts can trust.

From Credit Memos to Smart Vendor Selection

At S&P Global, we can rely on a strong foundation of linked Fundamental Data, Transcript, Consensus Estimates, Ratings data and Research, Macroeconomic analysis, credit scores and benchmarks, trade data, commodity pricing, and supply chain links.  We have invested over the years in making our data AI-Ready, building a Data Marketplace and a Metadata Marketplace that are also client-facing.

We have worked with clients to streamline the preparation of Credit memos. What once took 20–40 hours — much of it spent gathering existing information — can now be drafted in minutes within a customizable, repeatable structure, which follows the canvas designed with the clients, and that can be refreshed on demand, and reviewed with clear quality signals.

We extended the same approach to procurement. Agentic solutions can guide vendor selection by combining trade data, pricing, credit risk, and country risk—served in context and aligned with internal policies—without forcing users to jump between systems.

From Wow to Value

The path to impact is clear: start with the user problem, build on strong data foundations, use AI where it adds leverage, and design for transparency rather than perfection.

That is how Gen AI moves from wow to value — and earns a lasting place in credit and procurement decision‑making.

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