An accessibility overlay is one line of JavaScript that promises ADA compliance while you do nothing. The FTC fined accessiBe a million dollars over that promise. Here is why a widget cannot fix a problem that lives in your code, and how real AI auditing does the reverse by finding the broken line so a person can change it.
Automated accessibility tools catch maybe a third of WCAG problems. I pointed Claude Code at Tallyfy, my own product, and let it run a real WCAG 2.2 audit with a live screen reader across four codebases. It found bugs that axe-core cannot see, and it showed clearly where the work still needs a person.
The hard part of a big AI job is not the work. It is making the agent run for many sessions without drifting or claiming it is done when it is not. I used an accessibility audit across four codebases as the test. The setup that kept Claude Code on track was a git ledger, atomic parallel claims, and two verification passes.
A VPAT is the report that states how accessible your product is, measured against WCAG. People ask what it costs and price the document, but the document is the cheap part. The real cost is re-auditing every release, and that is the number an AI agent actually moves. Here is the ADA, WCAG, Section 508 and EN 301 549 stack underneath it.
Axe-core catches about a third of WCAG failures and skips anything that needs judgment. Here are the thirteen criteria a scanner cannot decide, how an AI agent drives a real VoiceOver session to cover them, and the save button that passed every automated check and was silent to a blind user.
An AI context layer feeds every model one governed source of company truth, and DataHub and Atlan will sell you that read half today. The half that notices when a person did not get what they wanted, the re-ask nobody logged, is what turns a knowledge store into a brain.
Some advisors resist letting a company connect AI to its own systems, dressed up as too risky. The Everlaw survey found 90% of legal professionals expect AI to change billing within two years. The real driver is an AI consultant protecting the gatekeeper role.
A dashboard is a decision you have stopped making. Goodhart law corrupts the metric the moment it becomes a target, and watching a number feels like managing it. Name the decision each dashboard should trigger and the one person who owns it, or delete the dashboard.
Good-enough AI is driving commoditization from below. Stanford HAI clocked a 280-fold drop in the cost of running a GPT-3.5-level model. Once a cheaper model clears the bar for a job, the frontier model stops earning its premium for that job.
Most operating metrics get noisy or gamed once AI absorbs the task work. Revenue per employee stays hard to fake. When Facebook bought WhatsApp for about 19 billion dollars, the company had 55 people. That ratio, output per head, is the acid test of whether AI bought you a real gain in output.
Companies build AI agents shaped like their org chart: an ERP agent, an HR agent, a finance agent. Each one is a silo with a chat box. The real payoff shows up when skills compose across functions, because data exists to tell a story or trigger an action, not to sit in one department.
Business intelligence was always the quantitative side: rows, numbers, things that fit in a column. The qualitative half, the calls and emails and tickets where the why actually lives, was invisible to it. That half is most of your data, and it is where AI adds value BI never could.