8 articles
During a webinar on AI-powered client coaching, a colleague shared a 25-question diagnostic survey in Excel. It was ugly, had scoring errors, and looked like what it was — a spreadsheet pretending to be a tool. Thirty seconds of prompting later, it was a polished interactive HTML page that captured, scored, and analysed responses. The quality ceiling on what you hand your clients just disappeared.
I built an AI skill for something completely unrelated to accounting — booking flights — and learned more about context engineering in two hours than months of client work had taught me. The first draft captured my obvious preferences. The corrections surfaced knowledge I didn't know I had. That's the encoding gap, and it's the reason most firms' AI stays mediocre.
The research was valuable. What happened next was more valuable — a 10-minute reflective conversation with the AI that turned a one-off project into a reusable methodology anyone in the firm can follow. That's context engineering. And it's how you stop losing what you learn.
Google Cloud says multi-agent orchestration is the architecture of 2026. Basis — now valued at over a billion dollars — is already running it for Top 25 accounting firms. The question isn't whether this model works. It's whether your practice will build it with your own specifications or let a vendor do it for you.
Xero's updated developer terms now prohibit external AI agents, ban API data training, and impose $99–$895/month access costs — while launching their own proprietary AI. The message is clear: platforms will monetize AI access and restrict yours. The highest-leverage response is to stop waiting for platform access and start building in the gaps where vendors have no gatekeeping power.
A software engineer tried three times to build an AI dashboard before realizing the answer was markdown files and structured knowledge. The same lesson applies to your CAS practice — the competitive moat isn't software, it's encoded practice knowledge that compounds.
Moltbot became the fastest-growing open-source project in GitHub history — 160,000 people giving an AI agent autonomous access to their digital lives. The demand for AI employees is real. The question is whether your practice is ready to build them.
AI output quality crossed the threshold — models now match 14-year subject-matter experts 70% of the time. The bottleneck shifted from prompt engineering to context engineering, and most firms haven't noticed.
