Category: Business

Artificial intelligence is supposed to make your work easier. But figuring out how to use it effectively can be a challenge.

Over the past several years, AI models have continued to evolve, with plenty of tools for specific tasks such as note-taking, coding, and writing. Many workers spent last year experimenting with AI, applying various tools to see what actually worked. And as employers increasingly emphasize AI in their business, they’re also expecting workers to know how to use it.

“I think 2025 was just a taste of what’s to come. Folks were figuring out how to deploy AI for productivity,” said Wade Foster, CEO of workflow automation platform Zapier.

The number of people using AI for work is growing, according to a recent poll by Gallup. The percentage of U.S. employees who used AI for their jobs at least a few times a year hit 45% in the third quarter of last year, up five percentage points from the previous quarter. The top use cases for AI, according to the poll, was to consolidate information, generate ideas, and learn new things.

The Washington Post spoke to workers to learn how they’re getting the best use out of AI. Here are five of their best tips. A caveat: AI may not be suitable for all workers, so be sure to follow your company’s policy.

Automate your inbox

Managing your email is a pain. And while email providers offer tools to help, AI can do even more, Foster said.

Create an AI agent or use an AI app that can sort, organize, and prioritize your inbox based on simple commands. Think of it as creating a complex set of rules that automate which folders emails go to, how they’re labeled, and whether they’re marked as urgent. Instead of creating a rigid list of keywords or contacts, use natural language to identify topics or issues you need to track.

AI can also automatically draft responses to specific types of emails you regularly get. For example, AI can draft a response directing people to the career website anytime someone asks about job openings, Foster said.

“You can get pretty darn close to an automated inbox,” he said.

To automate, you’ll need tools by services like Zapier, which offers limited free versions and premium options, or SaneBox and Superhuman, both of which have tiered pay options.

Create a personal assistant

AI can be particularly useful in getting you up to speed, prioritizing tasks, and tracking progress, several workers said.

Helen Lee Kupp, cofounder and CEO of virtual community and nonprofit Women Defining AI, said she built an “AI chief of staff” at the beginning of the year to prioritize tasks. She speaks to Claude voice mode in the mornings, which then structures her day. To build it, she asked the bot to create an AI assistant and provided a list of parameters and attached work documents. She then edited the instructions and pasted it into a Claude Project, generating a customized bot she can reuse.

“It’s really nice in the morning to be able to dump whatever’s on my brain and have a first draft of here’s how we think of priorities,” she said.

Another option: Build a daily briefing agent that sends an email with a to-do list and important updates from your email and calendar, Foster said. To do this, make a custom GPT (you’ll need ChatGPT Plus, which costs $20 a month) by clicking “explore GPTs” on the sidebar and then “create.” To automate your briefing, connect ChatGPT to your email and calendar, but beware of security and privacy risks. Prompt it to email you every morning with specific details around the information you want, Foster said. You can also create a daily to-do app. You may need additional tools or a hosting service to do this, but ChatGPT can provide instructions.

To avoid giving ChatGPT access to your accounts, manually upload your calendar, task list, or select communications and prompt it to prioritize from there.

Build what you need

To solve specific problems, several workers said they built custom apps and tools using chatbots and simple commands to generate code, a concept known as vibe coding. Michael Frank, co-founder and CEO of agentic AI risk platform Radiant Intel, said he’s used Claude Code and app builders like Google Antigravity to build an app that aggregates local news for him. But people can build apps to help them learn a new skill or provide feedback on their work to improve, he said. Think about your mistakes or time-intensive tasks and build something for that, he said.

“These are not going to radically transform anyone’s life, but can it make you 5, 10, or 15% more productive? Absolutely,” he said.

Lee Kupp said she’s used AI platform Gumloop, which has a limited free option and doesn’t require coding knowledge, to build an AI agent that monitors a Slack channel for website feedback and logs problems into a tracker. For Alexander K. Moore, assistant professor of marketing at the University of Illinois at Chicago, Replit and Claude Code helped him quickly build customized webpages to more efficiently conduct surveys.

You can also build a customized dashboard on Claude to track sales, customer satisfaction, the performance of a product or feature, or other key metrics, said Jhalak Rawat, chief operating officer at manufacturing AI start-up Soff. Tell it to provide action items to improve them. Use the dashboard to help get your next promotion, she added.

“It’s a good way to show the work you do,” she said. And “it takes one prompt, which takes 10 minutes to write.”

Warm your cold intro

Before pitching a new client or connecting with a new colleague or other professional contacts, use AI to find commonalities to break the ice, Rawat said.

She uses Comet, Perplexity’s AI browser, to find commonalities between her and another person based on their LinkedIn profiles and information available on Google. She once was able to connect to the CEO of a company she wanted to target because Comet told her that he was a pizza lover who once led a pizza company — a detail buried in a podcast. That tasty tidbit provided a way to warm her cold intro. This can also help when people are trying to meet new contacts for a career switch.

Enhance your meeting notes

Meeting notes and transcriptions from video meeting providers usually fall by the wayside, several workers said. But they’re more likely to refer to their notes if they actually take them.

So they use Granola, an AI-powered notepad that enriches meeting notes without an AI bot showing up as a participant (there’s an option to notify others it’s in use). It transcribes the meeting and follows the structure of your notes, adding detail and action items. You can even write notes before the meeting or ask questions about a meeting or explore trends within your meetings. Foster said he’s used it to identify topics for social media posts within his conversations. Granola can also coach people, he said.

“People take it as face value,” he said. “With AI, it has a neutrality to it.”

One bonus tip: Make your AI chatbots less sycophantic, which Moore says results in straightforward feedback. In ChatGPT, go to personalization options by clicking your name in the lower left corner of the screen. In the “custom instructions” and “more about you” sections, emphasize accuracy, clear reasoning, and explanation over flattery, praise, and agreeing with you. Tell it to push back and be blunt. Moore offers a sample prompt on his Substack post, “Tell me the truth! or How to get your AI to stop telling you what you want to hear.”

The toughest part about learning how to effectively use AI at work is starting, workers said. But once you get going, it gets easier.

“Don’t overthink it,” Lee Kupp said. “Pick one [large language model] and get started.”

Dean Baker has earned a reputation for predicting economic catastrophe, and he tries to follow his own advice.

After the economist warned of a stock bubble in the late 1990s, he rebalanced his investments to reduce exposure to the market. Several years later, he became concerned that soaring home values would fall to earth, so he and his wife sold their condo in Washington.

He was right both times: The dot-com bubble burst in March 2000, and D.C.-area home prices crested in 2006 before slumping toward the depths of the Great Recession in 2009.

Now Baker, who’s a distinguished senior fellow at the Center for Economic and Policy Research, has that foreboding feeling again.

Investment in artificial intelligence has propelled the stock market to record highs, but he’s shifting his investments to be less exposed to what he considers to be an AI bubble edging closer to popping. “I don’t make a point of coming up with a negative forecast,” he said. “I just try to have open eyes on the economy, and sometimes I see something that other people don’t.”

Baker is among a select group of people with track records of foreseeing major economic train wrecks. These proven prophets of doom are winning attention in online posts and media interviews, as more people begin to wonder if the AI boom is too good to be true. That’s giving economic groundhogs like Baker a chance to spread their market wisdom more widely or actively cultivate big new audiences.

Michael Burry, whose mid-2000s bet against the housing market inspired Michael Lewis’ 2010 book, The Big Short, triggered headlines across financial news outlets in November when his hedge fund Scion Asset Management disclosed it was betting that the stock prices of AI darlings Nvidia and Palantir will fall significantly over the next few years.

The same month, Burry, who didn’t respond to a request for an interview, started a Substack newsletter that often predicts an AI-catalyzed market implosion. It has more than 195,000 subscribers and is called Cassandra Unchained, after the princess of Greek myth cursed to foresee the future but to always be ignored.

“OpenAI is the next Netscape, doomed and hemorrhaging cash,” Burry wrote in a post on X last month that was viewed more than 2 million times, likening the maker of ChatGPT to a casualty of the dot-com bubble. (The Washington Post has a content partnership with OpenAI.)

Although voices of caution are having a moment, that doesn’t mean they’re winning the argument. James Chanos, the founder and managing partner of Kynikos Associates, who bet on the fall of energy giant Enron, said in an interview that market contrarians are often disregarded.

Short-sellers like himself are often viewed “as the village idiots or Dr. Evil,” he said, either wrongheaded or trying to manipulate the market. “There’s kind of no in-between,” said Chanos, who prefers to see himself and others as “financial detectives” hunting for bad actors, fraud or froth that should be cleared away.

A 2025 Harvard and Copenhagen Business School study of the beliefs of market experts during periods of boom and bust suggests that questioning market optimism is a good idea. “Optimism portends crashes: the most bullish forecasts predict the highest crash risk,” the authors found. In most cases, the authors said, “optimism remained unchecked until well after the crash.”

Other economists have identified key factors that indicate a crisis could be around the corner. A 2020 study of postwar financial crashes around the world by economists at Harvard, the National Bureau of Economic Research, and the Copenhagen Business School found that “crises are substantially predictable.” When credit and asset prices grow rapidly in the same sector — conditions the researchers term a “red zone” — there was a probability of about 40% of a financial crisis starting in the next three years, they concluded.

A tech-fueled surge in share prices over recent years has driven the total value of the stock market to far outweigh U.S. economic input, an imbalance that has come before previous downturns. But a report issued Jan. 9 by Goldman Sachs Research said many features of past bubbles are absent.

Corporate debt is relatively low in historical terms, and most of the S&P 500’s 18% returns last year came from increased profits, not investors marking up valuations, the report said. Double-digit earnings growth is “providing the fundamental base for a continued bull market,” wrote Ben Snider, chief U.S. equity strategist. The report forecast that U.S. stocks would continue to grow in value this year.

When Andrew Odlyzko — an emeritus professor of mathematics at the University of Minnesota who has studied economic bubbles and has a history of recognizing warning signs before a crash — started getting calls from journalists asking about a potential AI bubble in 2024, he dismissed the idea. At the time he reasoned it wouldn’t be systemically devastating if a big company like Google, Microsoft, or Meta made an expensive technological bet that flopped.

But things have changed in the past year and a half, Odlyzko said. “Now the investments are exceeding the capacity of these platform companies to finance them out of their cash flow, and they are drawing in other sectors of the economy,” he said.

He pointed to Meta’s recent deal to develop a $30 billion data center project in Louisiana, in which the project’s debt is held in a separate entity off Meta’s books. Such deals remind Odlyzko of the creative financing that led to the Great Recession in 2007.

“If — or more precisely, I’m pretty confident when — things collapse, the spillover effects will be much more substantial, much more deadly,” he said.

Today’s rush to build AI data centers also reminds Odlyzko of the 19th-century railway mania in Britain, a bubble of speculation on new railroad infrastructure. Both frenzies are creating “big infrastructure … that’s actually drawing on other parts of the economy,” he said.

Chanos makes comparisons between today’s AI fever and the 1990s tech boom, as both bull markets have centered on big ideas: AI today and the internet’s beginnings decades ago. In the short term, many early internet businesses cratered, even though the technology worked out in the longer term.

Artificial-intelligence technology “is real and probably will be very important, but lots and lots of companies that claim they’re a great business … are probably not going to be great businesses,” Chanos said.

What’s different is that it’s now much easier for retail investors to jump into the stock market with the rise of stock-trading apps like Robinhood. Chanos said he’s “seeing more and more speculation in terms of retail investors who only know markets that generally go up, and if they go down, they go down for just a short period of time.”

Baker is one of those retail investors who’s preparing for the worst, as he has before — although he hasn’t always had perfect timing. He pulled back his portfolio a couple of years before the dot-com bubble burst in March 2000 and sold his D.C. condo in 2004, about two years before home prices started falling in the region.

Although discussion about predicting market slumps often frames the events as bad, Baker thinks an AI crash could do the U.S. some good.

A slump could lead to a reallocation of resources in the economy, perhaps toward other sectors like manufacturing or healthcare, he said. “There’s all sorts of things you could better use those resources for if the AI really doesn’t make sense,” Baker said.