Internal communications and AI training — Giraffe Social
A large part of my job has been coaching other people to do interviews. So it does feel a little unusual to be doing one myself.
A large part of my job has been coaching other people to do interviews. So it does feel a little unusual to be doing one myself.
Your firm has the tools. Now what? This article looks at the process I use to help law firms implement AI clearly and safely.
It seems many law firms are struggling to demonstrate returns on their technology investments. Forrester predicts that 25% of planned AI spend will be deferred to 2027 as decision-makers struggle to tie value to growth. However, the issue isn't the tech. It's the culture, as this blog post explains.
Directory submissions take hours to pull together, and most get skimmed in 15 minutes. If the work highlights are too long, too vague, or full of "market-leading" nonsense with no proof, researchers tune out. This kit catches all of that before you upload.
AI is moving from single chatbots to agentic workflows: multi-step systems that plan, draft, check, and iterate. That shift changes how you work, how clients evaluate firms, and how you protect your reputation. The biggest differentiator isn’t the tool, it’s governance, review discipline, and clear client-facing communication.
Two days in trying Google's Workspace Studio and I've already set up some helpful automations to save time every day.
Law firm marketers can secure AI budgets by aligning technology adoption with financial outcomes, such as billable hour reclamation and improved client value. Using structured data to demonstrate ROI transforms AI from a perceived "cost centre" into a strategic business asset.
A strategic approach to AI adoption ensures law firms maintain compliance with GDPR 2025 and the EU AI Act while avoiding technical debt. Strategic implementation prioritises high-value workflows and human-in-the-loop oversight to protect firm reputation and data integrity.
The shift from traditional search engines to AI-driven answer engines requires brands to optimise for machine-readability and citation frequency. Success depends on "entity disambiguation" and providing structured, factual content that AI models can verify as authoritative.