Survey: 80% of professionals say AI will transform work within a year

The workplace revolution promised by artificial intelligence isn’t coming—it’s already here, and accelerating faster than most organizations realize. While 80% of professionals believe AI will significantly impact their work within the next year, according to new research from Thomson Reuters, the business information services company, most haven’t experienced the full transformation yet.

This presents both an opportunity and a risk. Companies that position their workforce strategically now will gain competitive advantages, while those that lag behind may find their employees unable to keep pace with AI-enabled competitors. The key lies in understanding that AI adoption differs fundamentally from previous technology waves like e-commerce or cloud computing—it’s happening faster, touching more job functions, and creating more immediate productivity gains.

Kirsty Roth, chief operations and technology officer at Thomson Reuters, puts it bluntly: “The impact will be huge. What’s more, we haven’t seen the full effect yet. These are early days for AI.” Her company’s Future of Professionals Survey, which polled 2,275 professionals and C-level executives from more than 50 countries, reveals that 55% of workers have either experienced significant changes in their work over the past year or anticipate major shifts in the coming year.

However, the research also uncovers a concerning trend: almost one-third of organizations are moving too slowly with AI adoption. This creates a widening gap between AI-enabled teams and those still working with traditional tools. To avoid being left behind, business leaders need to focus on three critical areas.

1. Make AI accessible across your organization

The most fundamental step involves ensuring every employee has access to AI tools appropriate for their specific role. This goes beyond simply purchasing software licenses—it requires thoughtful matching of AI capabilities to job functions and providing the training necessary for effective adoption.

Thomson Reuters’ research shows that 46% of organizations have invested in new AI-powered technology within the past year, yet only 30% of professionals regularly use AI to start or edit their work. This gap suggests that many companies are buying AI tools without ensuring widespread adoption.

“Ensure everyone in your company has access to AI and is using it,” Roth emphasizes. “Many people talk about the revenue or efficiency opportunities from emerging technology. I think the key thing to recognize is that professionals who aren’t using AI will quickly find themselves unable to do their work.”

The accessibility challenge extends beyond technical access to include cultural adoption. Organizations where leaders demonstrate AI usage themselves see dramatically better results—survey respondents who said their leaders were leading by example were 1.7 times more likely to see benefits from AI compared to those whose leaders weren’t actively using these tools.

Consider practical implementation: A marketing team might use AI for content creation and campaign optimization, while finance professionals could leverage AI for data analysis and forecasting. Legal teams might employ AI for document review and contract analysis, while customer service representatives could use AI-powered chatbots and response suggestions. The key is matching AI capabilities to specific job requirements rather than implementing generic solutions.

2. Share best practices systematically

While more than half of survey respondents said their organization already experiences some benefits from AI, the value often remains concentrated in small pockets rather than spreading organization-wide. This represents a significant missed opportunity, as the most innovative AI applications often emerge from individual employees experimenting with creative solutions.

“Find a good way to share best practices,” Roth advises. “You typically find there are good, creative people in pockets of the company that come up with clever ways to use AI across more advanced use cases.” However, she warns that these innovations become worthless if they remain isolated: “Get those things shared and ensure people understand what’s working and what’s not.”

Effective knowledge sharing requires structured approaches. Some organizations create internal AI communities of practice where employees can demonstrate successful use cases and troubleshoot challenges together. Others establish regular “AI showcase” sessions where different departments present their most effective implementations.

The collaborative aspect proves crucial because AI adoption success depends heavily on understanding context and workflow integration. A sales team’s AI-powered lead scoring system might inspire similar approaches in marketing, while a customer service team’s AI chatbot implementation could inform internal help desk improvements.

Organizations should also document failed experiments alongside successes. Understanding what doesn’t work—and why—prevents other teams from repeating costly mistakes and helps refine AI strategies over time.

3. Connect with industry peers

The third critical step involves looking beyond your organization’s walls to understand how AI is transforming your industry. Companies with visible AI strategies are almost four times more likely to benefit from AI than firms without significant adoption plans, according to the Thomson Reuters research.

This external perspective provides crucial context for internal AI initiatives. “I talk to my peers all the time in different types of companies as to what they’re doing and what’s working for them,” Roth explains. “You’ll get an awful lot of intelligence like that.”

Industry peer networks offer several advantages. They provide benchmarking opportunities to understand whether your AI adoption pace matches competitive standards. They also reveal emerging use cases that might not be obvious within your organization’s current operations. Perhaps most importantly, they offer reality checks on AI investment priorities and implementation timelines.

Professional associations, industry conferences, and informal peer networks all serve as valuable sources for this external intelligence. The key is approaching these conversations with specific questions about AI implementation challenges, measurable outcomes, and lessons learned rather than seeking generic advice.

Measuring AI’s real impact

Understanding whether your AI initiatives are actually delivering value requires more sophisticated measurement than many organizations currently employ. Survey respondents predicted that AI will save them five hours weekly—approximately 240 hours annually—for an average value of $19,000 per professional. However, proving these benefits requires careful tracking.

“What we’re discovering is that if you ask people, they aren’t very good at guessing how AI saves them time,” Roth notes. Many organizations start with high-level questions like “Are people using the tools?” but need to evolve toward more specific metrics: “Are people using tools every day? Are they a core part of the way they work? Are they using them for a certain amount of time in the day?”

The measurement challenge becomes particularly complex when AI enables what experts call “higher-value tasks”—work that requires more strategic thinking, creativity, or relationship building. For example, Roth describes how engineers using AI to accelerate coding have more time to focus on developing new ideas and business models: “What we’re starting to see with our best engineers is that some of the simple things, like coding and testing, are being reduced significantly. Therefore, they’ve got more time to think about, ‘OK, what’s next? What do I want to build after this? And what does my customer want me to put in the product that’s coming next?'”

The competitive imperative

The window for strategic AI adoption is narrowing rapidly. Organizations that delay implementation risk finding themselves at a permanent disadvantage as AI-enabled competitors pull ahead in productivity, innovation, and market responsiveness.

The research suggests that successful AI adoption requires more than technology investment—it demands cultural change, systematic knowledge sharing, and external perspective. Companies that master these elements will position their workforce for the AI-driven future, while those that don’t may find their employees increasingly unable to compete in an AI-enhanced marketplace.

As Roth concludes: “We all have a duty to stay on top of these changes, or we won’t be employed in the future.” The choice facing business leaders isn’t whether to adopt AI, but how quickly and effectively they can transform their organizations to harness its potential.