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FROM THE AI DESK

Welcome to our August issue.

This month, we are looking at the difference between having access to AI and getting useful work out of it.

OpenAI's new ChatGPT Work experience is built around longer assignments that can involve files, connected tools, and finished deliverables. Microsoft Copilot is also becoming more useful beyond the chat box, with features that can help inside spreadsheets, presentations, meetings, research, and recurring tasks. At the same time, new AI transparency requirements are beginning to affect how organizations think about customer-facing AI and automated decisions.

Our AI in Action feature looks at a harder question: why has AI become common inside companies while enterprise-wide scale remains rare?

The answer has less to do with finding a smarter model than it does with the work surrounding it.

What Changed in AI

ChatGPT Work Explained: A Guide to the New Workspace and GPT-5.6 Models

ChatGPT Work is designed for assignments that require more than a single response. It can work across connected files and tools, break larger projects into steps, create finished materials, and support recurring or scheduled tasks.

This article walks through the new Work interface, including Projects, Scheduled Tasks, Plugins, and Chat, while also explaining the differences between the GPT-5.6 Sol, Terra, and Luna models. It also includes practical first tasks, such as preparing a meeting briefing, creating a report from several documents, or building a repeatable weekly update.

Explore ChatGPT Work →

 

Copilot Use Cases You May Not Be Aware Of

Copilot can do more than answer questions in a chat window. When it is given a specific task and access to the right information, it can help organize documents, analyze spreadsheets, improve presentations, prioritize emails, research a topic, summarize meetings, and run recurring prompts. This article shares practical examples of how Copilot can support everyday work inside Word, Excel, PowerPoint, Outlook, and Teams, while explaining where features such as Researcher, Facilitator, and scheduled prompts may fit.

Discover more ways to use Copilot →

 

What the August AI Regulation Changes Mean for Your Business

August brought several practical AI policy updates. On August 2, new EU AI Act transparency rules took effect, including requirements to disclose when users interact with certain AI systems and to label specific AI-generated or manipulated content.

In the U.S., regulation remains fragmented. Colorado updated its AI law to cover automated decision-making in areas like employment, housing, lending, insurance, healthcare, and education, with requirements starting January 1, 2027.

Read the August AI regulation update →

 

AI in Action

The AI Adoption Paradox: Why 88% of Companies Use AI, but Only 7% Have Fully Scaled It

AI adoption is widespread, but scaling it remains rare. McKinsey reports that 88% of organizations use AI in at least one business function, while only 7% have fully scaled it across the enterprise. The challenge is often not the AI itself, but the workflows, data, ownership, and processes around it. This article looks at why promising pilots stall and what companies need to turn experimentation into measurable business value.

Explore what it takes to scale AI → 

Human-in-the-Loop

Human-in-the-Loop

Human-in-the-loop means an AI system does part of the work, but a person remains involved at an important decision or review point.

For example, AI might draft a customer response, flag an unusual transaction, or summarize a contract, while an employee reviews the result before anything is sent or acted on.

The goal is not to have a person check every AI output forever. It is to decide where human judgment still matters, especially when mistakes could affect customers, employees, finances, or important business decisions. 

AI is saving us time. How do we know whether that is actually creating value?

AI is saving us time. How do we know whether that is actually creating value?

Start by asking what happens to the saved time. Suppose an AI tool reduces a task from three hours to one. That sounds good, but a two-hour saving does not automatically appear on an income statement.

Maybe the employee can now complete twice as many customer requests. Maybe proposals go out a day earlier. Maybe the team finally has time to review work that used to be rushed. Or perhaps nothing changes because the saved time is absorbed by other tasks.

That is why "hours saved" works better as an intermediate measure than a final result.

Pair it with something the business already cares about: turnaround time, throughput, error rates, backlog, customer response time, conversion, revenue, or another operational measure.
 
 

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