Before joining JDX, I remember seeing a demo of the original JDX platform and asking a blunt question: “Why would I buy this when I already have ChatGPT?”
It wasn’t meant to be provocative. It was an honest question. If AI can generate job descriptions instantly, then the value of any job-related system has to live beyond authoring.
When generation becomes easy, everything else becomes harder to ignore: how those jobs get governed, standardized, connected to pay, architecture, skills, and compliance, and whether they hold up as the organization changes.
That mindset shift is why we built JDX+.
Over the past 15 years, we noticed the same issue among enterprise organizations: Job architecture and management exists as a project, not a capability.
And because it’s this big, bad, resource-sucking project, most companies:
But then M&A, reorganizations, new business lines, and technology shifts ultimately lead to role and responsibility changes. And, unfortunately for many teams, that also means that their expensive, static job documents begin decaying almost immediately.
While you can control mergers and organization changes, you can’t halt technological advancements.
Smart leaders are therefore pivoting to keep up with the latter. In fact, the World Economic Forum estimates that by 2030, 60% of employers expect broadening digital access to transform their business, and 86% expect AI and information processing advances to be transformative.
The problem though, is that adopting AI won’t make things “better.” Instead, AI will expose how fragile the systems around your jobs really are.
Additionally, 39% of workers’ existing skills are expected to be transformed or become outdated between 2025 and 2030 (which is probably why 93% of high-coverage organizations are looking to overhaul their job information within the year).
This isn’t groundbreaking news.
What is shocking is how many organizations know they have outdated data that poses a real threat yet continue to tackle the problem like they always have.
Our mission became clear. We needed to build a new system to help HR teams create a job architecture and management system once, then use it to standardize and manage job information across the full lifecycle.
We built JDX+ around three principles that make job information durable, usable, and insight-ready.
Job architecture is universally acknowledged as strategic, and almost universally dreaded to implement.
The work is predictable:
Most of that work is manual, repetitive, and slow. That’s where software—and AI, specifically—belongs.
Because JDX+ already manages structured job data, our Job Architecture Builder can recommend families, subfamilies, streams, and levels, surface patterns across roles, and use confidence scoring to flag exceptions for human review.
Instead of reading 3,000 job descriptions, teams focus on the minority of cases that actually require judgment.
Note that this isn’t “push a button and your architecture is done.” Real organizations still need debate, approvals, and alignment. But compressing manual work changes the economics and the timeline.
It turns “we can’t do this” into “we can do this now.”
Establish a consistent framework, make relationships explicit, and review what needs attention with JDX+.
Job templates are the foundation behind JDX+’s job description governance and approval workflow capabilities.
JDX+ gives you:
That balance is what makes your processes scalable, repeatable, and built for insight.
JDX+ also uses AI as an assistive and governed tool within its templates (no ChatGPT slop, here). Because the template fields carry clear definitions and content, you can reliably draft and refine compliant job descriptions.
Input the job information yourself or use AI to determine high-quality information for your roles.
Most HR systems generate reports, but very few make job information visible in ways leaders can act on.
When job information is inconsistent, analytics becomes a static dashboard exercise.
HBR has put a hard number on this pattern, estimating that bad data costs the U.S. economy $3 trillion per year. Whether that cost shows up as wasted time, wrong decisions, or audit exposure, the root cause is the same: poor structure multiplies downstream cost.
That’s why analytics in JDX+ is built directly on top of governed templates, version control, and job architecture. Insight only works when the foundation is structured.
Because the job data is standardized, JDX+ surfaces what most organizations struggle to see:
Importantly, the analytics don’t make decisions for you. They bring forward structured, trustworthy data so you can make better ones, with less debate about what’s “right.”
With AI-assisted insights, JDX+ allows you to ask a question and save the answers for sharable reporting.
We built JDX+ so job information stops being a static document and starts becoming a living system.
Job descriptions will always matter, but the real asset is job information: structured, standardized, and useable data across your entire organization.
JDX+ will help you reduce the messiness that comes from speed, cut the cost of manual processes, and turn job data into something you can operate on.
If you’re evaluating your current approach, map your pain to three areas: standardization, job architecture, and insight. Gaps in any one of them show up downstream.
If job content lives in multiple systems, start by consolidating, then standardize. Governance only works once you trust the structure.
If you’re facing change—M&A, reorganization, or AI-driven role evolution—treat job architecture as an ongoing capability, not a one-time project.