Why Most AI in Workplace Finance Doesn’t Work—And What We’re Doing About It
By Marc McDonough
CEO, TIFIN @Work
The Problem Isn’t Access to Information. It’s the Lack of Intelligence Around It.
Most AI assistants in the workplace are glorified search bars.
They scrape PDFs, summarize policy documents, and string together generic answers. In high-stakes environments like personal finance, that’s not nearly good enough.
Throwing more data into a bigger context window doesn’t make an assistant smarter. It just makes it more verbose.
If we want AI that actually helps people make better financial decisions, we need structure, specialization, and context. That’s where AI agents and knowledge graphs come in.
The Problem: AI That Can’t Contextualize
AI Agents > Giant Prompts through one LLM
Most AI assistants rely on one giant prompt run through a single, large language model to do everything — search, analyze, decide, explain. But in the real world, we don’t expect one person to do every job. We rely on specialists.
That’s exactly what AI agents are:
Individual components that each focus on a specific task—retrieving data, running calculations, checking eligibility, escalating to a human—then working together to produce clear, actionable guidance.
This is the opposite of a one-size-fits-all prompt. It’s orchestrated reasoning across modular parts.
Knowledge Graphs: The Missing Layer in Most AI Systems
AI is only as smart as the context it can see. Most assistants are blind to what matters—your benefits, accounts, goals, employer-specific rules, and timing.
That’s why knowledge graphs matter.
They connect structured, dynamic information into a map the assistant can reason through—so answers reflect your situation, not some default template.
Why This Architecture Wins
In fast-moving or high-complexity environments, the combination of knowledge graphs and agents unlocks three key advantages:
- Relevance: Only the right context is pulled—no more bloated prompts
- Reasoning: Agents collaborate to construct answers, not just summarize
- Actionability: The outcome can drive alerts, next steps, or escalation
This is what leading AI research calls the evolution from “co-pilot” to “autopilot.”
How We’re Doing It at TIFIN @Work
At TIFIN @Work, we’ve built this architecture into our platform—not because it sounds good in a deck, but because it’s the only way to give people truly useful guidance.
Here’s how it works:
- Our document retrieval provides ground truth for our assistant’s knowledge, meaning it is based on verified, authoritative information from the deep pool of data available on the platform.
- Our knowledge graph augments the document retrieval by drawing the connections between benefits data, user activity, eligibility rules, and advisor services in real-time.
- Our assistant is agent-powered, with specialized modules handling everything from document search to financial calculation to advisor triage.
The output is personalized, prioritized, and actionable.
We’ve deployed proactive agents to deliver:
- Alerts before a match is missed or tax benefit expires
- Goal nudges based on account activity or spending changes
- Advisor escalations when the platform detects complexity
This isn’t financial wellness as it’s usually defined.
We’re not putting more information in front of people.
We’re giving them the tools—and the structure—to take action.
People don’t need more content. They need guidance that is personalized for their situation vs just more information thrown at them. If you’re building AI for real-world decision-making, don’t just scale the model. Scale the intelligence around it.
We’ve built that at AtWork—and we’re just getting started.