AI at work
How organizations are using AI
Short answer
Organizations mostly use AI for repeated, language-heavy work: answering support questions, writing and reviewing code, summarizing documents and reconciling records. The newest step is AI agents that carry out multi-step tasks with a person approving the result. The projects that pay off start with one narrow task and a clear measure of success.
Where is AI being used?
Customer and employee support
Answering routine questions, drafting replies for staff to approve, and routing requests to the right team. This is one of the most common first uses because the questions repeat and the answers already exist in help articles.
Software development
Writing and reviewing code, explaining unfamiliar systems, writing tests and fixing bugs. Coding agents can now carry a task across many files, with a developer reviewing the result.
Documents and knowledge
Summarizing long reports, pulling figures out of contracts and invoices, searching internal files in plain language, and turning meeting notes into action lists.
Finance and operations
Matching invoices to payments, flagging unusual transactions, drafting variance explanations, and forecasting from past data. A person still signs off on the numbers.
Marketing and sales
Drafting campaigns and product descriptions, tailoring messages to an audience, researching prospects and summarizing customer calls.
Physical work
Inspecting products with cameras, predicting equipment failures, planning routes, and, increasingly, robots that learn tasks from demonstration.
What are recent examples?
These stories come from the daily AI feed. Each links to the original reporting.
OpenAI lets companies claim verified profiles inside ChatGPT
OpenAI launched Businesses in ChatGPT as a beta on November 10. Companies can claim a profile, which carries a blue check once verified, upload product information, and see how often ChatGPT mentions them and how many clicks it sends to their website through a dashboard called Business Hub. Reports say businesses do not see users' conversations or individual prompts. Eligible US ecommerce and online service businesses can also launch a branded Business Agent that answers customer questions. OpenAI says it plans to expand the program to more markets and business types over time.
Why it matters: As more people ask chatbots what to buy, how a business appears inside them is becoming something owners can manage, much like a search listing.
IBM and Microsoft announce Imagine Enterprise, a platform for industry AI agents
IBM and Microsoft announced Imagine Enterprise on November 10, a platform for building and governing AI agents tailored to specific industries. The companies say engineers from both firms will work alongside customers to help move agent projects from experiments into production.
Why it matters: Many organizations are still at the pilot stage with AI agents, and vendors are now selling hands-on help to reach real deployment.
How should an organization start?
- Pick one narrow, repeated task. Good first projects are frequent, low-risk and easy to check: sorting emails, summarizing calls, drafting standard replies.
- Keep a person in the loop. Have the AI draft and a person approve, especially for anything sent to customers or involving money.
- Measure before and after. Time saved, errors caught and cost per task tell you whether it is working. If you cannot measure it, it stays a demo.
- Check where your data goes. Read the provider's data terms, and keep personal or confidential information out of tools that are not approved for it.
Weighing the cost of an AI project against what it should return? The capital budgeting calculator works out its net present value and payback. To understand the technology itself, read how AI works.
Frequently asked questions
What is the difference between a chatbot and an AI agent?
A chatbot answers a question. An agent is given a goal and tools, then works through the steps itself: looking things up, updating records and checking its results, usually with a person approving key actions.
Why do so many AI projects stall at the pilot stage?
A demo is easy; a dependable process is hard. Projects stall when the task was too broad, the data the AI needs is scattered or locked away, nobody owns the result, or there is no measure of success.
Will AI replace jobs in my organization?
So far AI mostly changes tasks within jobs: the routine drafting, searching and summarizing. How that affects roles depends on choices each organization makes. The news feed reports specific cases as they happen.
How do I decide whether an AI project is worth the money?
Treat it like any investment: estimate the cost to set up and run, the savings or revenue it should bring each year, and discount those back to today. SkeenaOne's capital budgeting calculator does that sum.
How this feed is made. The AI feed is researched and written each morning by Claude, an AI model made by Anthropic, and published automatically without human editing. Every story links to the reporting it is based on. AI can make mistakes, so check the source before relying on a detail, and treat nothing here as professional advice.