GenAI tools enhance employee productivity by saving time and enabling focus on higher-value tasks
Generative AI is already here. Tools like OpenAI’s ChatGPT, Google Gemini, and Microsoft Copilot are shifting how work gets done. Instead of just automating rote tasks, they fundamentally change how people across organizations manage their time. According to a study led by the London School of Economics and consulting firm Protiviti, the average employee using GenAI saves about 7.5 hours per week. That’s nearly a full workday back, every single week. Over a year, that’s roughly $18,000 in productivity gains per employee.
This is transformation. When employees automate repetitive content drafting, inbox management, and basic data tasks, they unlock time and cognitive focus for strategic thinking, the type of work that actually moves the business forward. And this isn’t limited to highly technical roles. Whether it’s creating internal documentation, summarizing meetings, or pulling structured insights from raw data, GenAI applications are proving useful across departments.
The important part is that most of these tools are publicly accessible. If your teams aren’t seeing these efficiency gains yet, it usually means one of two things: they haven’t been encouraged to try them, or they lack structured guidance to use them effectively. Give them both, and you’ll see real impact.
As Daniel Jolles, Research Officer at LSE’s Inclusion Initiative, put it, the use cases and results vary by role. But once you give people training, structure, and autonomy, the productivity gains are clear and significant.
Younger generations are at the forefront of adopting generative AI in the workplace
Gen Z and Millennials are leading AI adoption, and that’s not a surprise. These generations grew up with technology that evolves fast. They’re comfortable trying new tools without waiting for formal approval. That’s useful behavior for a company operating in a fast-moving environment. According to the same study, 73% of Millennials and 71% of Gen Z employees are already using generative AI regularly in their work. Compare that to 60% of Gen X and 52% of Baby Boomers.
This isn’t about who’s better, it’s just a signal. Younger teams are often your early adopters. If you’re serious about digital transformation, use that to your advantage. Let them test early use cases, gather feedback, and scale what works. But don’t stop there. Relying only on early adopters creates silos, and that slows momentum.
For real traction, the entire organization needs to understand the tools and their impact, not just the tech-savvy ones. Business leaders should ensure that AI is accessible and appropriate across age groups. That might mean onboarding resources designed for different levels of digital readiness. Encourage reverse mentoring. Create a structure where the knowledge diffuses in all directions across your teams.
The message is simple: the more people you bring into the fold, the faster your organization levels up. Technology doesn’t discriminate. Adoption shouldn’t either.
Generative AI is predominantly used for content creation, communication, and data analysis
Generative AI is being adopted fast, but it’s not being used randomly. Employees are focusing their AI usage on three core areas: writing and content creation (43%), communication and collaboration (34%), and data analysis and visualization (27%). These tasks cover a wide portion of the knowledge work in most companies. When people automate these functions, they free up significantly more time and improve quality at the same time.
The scale of impact in content-focused tasks is already obvious. Employees are using AI to write internal reports, client emails, marketing drafts, and even summarize video or meeting transcripts. It’s not eliminating these functions, it’s optimizing them. Across the board, workers are moving faster and reducing cognitive load. It’s not just about speed; accuracy is increasing as well, especially in data-heavy tasks.
The communication layer is an interesting one. Many C-suite executives still rely on traditional workflows to cascade information. But if your teams are using GenAI for response drafting, summary creation, and collaborative messaging, it changes the pace of operations. Meetings run shorter. Alignment happens faster. Execution becomes more fluid.
On the technical side, data analysis with GenAI is opening up possibilities. People with limited coding experience can generate insights, build visualizations, and create summary reports without needing entire teams of data analysts. For global teams with limited resources, this shift in access to data interpretation is a serious competitive edge.
Daniel Jolles, Research Officer at the London School of Economics’ Inclusion Initiative, pointed out these core usage trends, reinforcing where companies are currently seeing the greatest returns. Spotting these patterns early helps leadership make smarter investments in tools and training.
Inadequate AI training among employees hinders optimal productivity gains
AI tools only matter if people know what to do with them. And right now, most employees don’t. The research shows that roughly 60% of workers currently using AI have received zero formal training. So you can imagine the delta between current performance and potential performance.
If you want scale, role-specific AI training is foundational. Most tools today are designed to work across functions, but that doesn’t mean people automatically know how to match the technology to their work. They need training based on what they do every day. Finance staff don’t need the same tools as marketing, and product teams need a different workflow than operations teams.
Interactive workshops work. Conceptual overviews don’t. Bring people into live sessions, help them test live tools, and show them how AI improves the specific parts of their work that drain time or yield low return. It doesn’t have to be a massive investment in LMS platforms or external consultants. It just needs to be direct, relevant, and immediately usable.
Daniel Jolles, from LSE’s Inclusion Initiative, said it clearly: “Interactive hands-on workshops tailored to their roles are the most effective for AI adoption.” And the logic really doesn’t get simpler than that. People learn better when the learning applies directly to their own priorities.
Executives who are serious about operational efficiency should view role-based AI enablement as core infrastructure, not a side project. The businesses that provide it will move faster. The ones that don’t will lag behind.
Generative AI is increasingly viewed as a strategic asset
Generative AI doesn’t just speed up work, it reshapes workforce strategy. Enterprises are now treating genAI not only as a productivity tool but as a strategic asset. When roles are stretched or left unfilled, AI can close those operational gaps. It automates repetitive functions and takes over tasks that don’t require creative or complex thinking. That gives human teams more time and space to focus on outcomes that drive differentiation and growth.
This is becoming particularly important in areas impacted by labor availability. From high-turnover business support roles to physically demanding manufacturing environments, organizations are applying genAI and robotics to stabilize output. That’s not theory. It’s already happening. Businesses are comfortably integrating AI into workflows where margin pressure, safety risk, or shortage of qualified talent have made traditional models unsustainable.
Melanie Freeze, Research Director at Gartner, noted that genAI is being prioritized specifically to generate time savings and allow employees to shift their attention to higher-value assignments. The underlying message is that AI is not removing people from the business, it’s allowing them to do more meaningful work.
Kari Briski, Vice President for Generative AI Software for Enterprise at Nvidia, added that AI-driven machines are taking over routine admin work and hazardous tasks in environments like factories. That shift doesn’t reduce headcount, it reallocates it. The result is a more agile workforce focused on tasks that require strategy, oversight, and human judgment.
What this means for leadership is straightforward. If your operating model still relies on human labor for tasks that are standardized or high-risk, you’re behind. AI adoption should now be part of core workforce planning. It’s not just a cost-saving mechanism, it’s a method for unlocking more productive talent across the organization. When used right, genAI amplifies human value. Businesses that move now will set the pace.
Main highlights
- GenAI boosts productivity with measurable ROI: Employees using GenAI save an average of 7.5 hours per week, delivering approximately $18,000 in annual productivity value per person. Leaders should invest in AI tools that streamline workflows and track efficiency gains across roles.
- Younger workers drive faster AI adoption: Gen Z and Millennials are leading the charge in AI usage, while older generations lag behind. Executives should leverage early adopters to drive momentum and implement cross-generational training to accelerate full-scale integration.
- Content, communication, and data are top AI use cases: Employees rely most on GenAI for content creation (43%), communication (34%), and data analysis (27%). Prioritize AI adoption in these common workflows to see faster returns and broader team impact.
- Skills gap limits GenAI’s effectiveness: 60% of employees currently using AI tools report having no formal training. Leaders should implement role-specific, hands-on AI training programs to unlock deeper capabilities and drive consistent value from AI investments.
- AI fills staffing gaps and elevates human work: GenAI and robotics help offset labor shortages and free employees to focus on higher-value tasks. Companies should integrate AI into workforce planning to reduce operational strain and increase strategic capacity.
