Gen AI Tools by Use Cases

In December 2023, the conversation was dominated by familiar use cases: chatbots that could handle customer queries, tools that could generate content at scale, image generation, and systems capable of accelerating code development. Soon after, new categories for transcription, translation and localization, capabilities that quickly proved their utility in everyday workflows made sense. 

But now, a pivotal new category is necessary: Agentic AI. This addition signals that business and technology leaders now see autonomous AI agents as a distinct productivity paradigm: a transformative shift in how we think about AI in the workplace. Before turning to this new frontier, it’s worth pausing to look at the AI applications that have already proven their value. These tools have moved well beyond novelty and are now woven into daily workflows.

Continuity in core capabilities: AI has changed how we work, and the tools we use are here to stay. What began as experiments are now indispensable parts of our routines.

Generative content creation: AI tools that create and edit text, images, presentations, and videos have become a huge part of our work lives. A Microsoft survey found 75% of global knowledge workers already use generative AI at work. Most users say AI saves them time (90%), helps them focus on more important tasks (85%), and makes them more creative (84%). We’ve come a long way from worrying that AI would stifle our creativity. In fact, a 2024 marketing report found that 15% of marketing teams “couldn’t live without AI” because it made everyday tasks like creating presentations and transcribing audio so much faster and smoother.

Knowledge work and communication: AI is also transforming how we handle information and communication. Companies now use advanced chatbots for customer service and internal helpdesks, dramatically improving response times. AI-powered research assistants can sift through documents and summarize key findings in seconds, tasks that once took humans hours of painstaking work. The impact on software development has been equally transformative. A study by MIT Sloan, Microsoft Research, and GitHub found that generative AI coding tools can reduce programming time by 56%, allowing developers to focus on higher-level problem-solving rather than routine implementation.

Task automation: Daily tasks like managing email and scheduling meetings are now so much easier thanks to AI. Modern email assistants can sort your inbox and draft responses, and AI schedulers can find optimal meeting times without endless back-and-forth among participants. These tools have begun to seriously alleviate our digital overload. A McKinsey report estimated work automation with GenAI and other technologies could boost productivity growth 0.5 to 3.4% annually.

The foundational uses of AI for productivity have proven resilient and enduring. However, the competitive landscape continues to evolve rapidly, with some companies rebranding (you’ll notice the fresh logos in our graph) or pivoting their focus as the market matures. Take Tome: once a specialized presentation tool, it has shifted toward building enterprise solutions, reflecting shifting market dynamics and heightened competition in an increasingly crowded field.

The new frontier: Agentic AI: If generative AI is about extending human capability, Agentic AI is about autonomous execution. Agentic AI refers to systems that can delegate, act, and learn on their own to accomplish a goal. Instead of waiting for precise, step-by-step prompts, an AI agent can take a high-level request like, “Organize a one-day offsite for my team next month,” and then navigate multiple tasks and tools to fulfil it. Agentic AI tools today can check calendars, research options, book venues, schedule travel, and draft agenda.

Over a quarter of business leaders: say their organizations are already exploring agentic AI to a significant degree. The vision is for these agents to process different types of data (text, images, voice), coordinate with other AI services, and learn from experience to reliably execute complex tasks. Emerging platforms show the breadth of possibilities: Manus is a general AI agent that bridges thoughts into actions, designed to independently carry out complex real-world tasks without direct or continuous human guidance. Similarly, Beam focuses on simplifying data workflows and Sana‘s AI agents are revolutionizing enterprise search and knowledge management. Stack AI allows teams to stitch together modular agents that orchestrate multiple AI services and traditional software tools. Devin positions itself as an “AI software engineer” that can independently write, test, and ship code. While not included in the agentic category, agentic modes of the frontier models, like ChatGPT and Claude are also starting to make waves.

This doesn’t mean companies are relinquishing full control. Early trials often keep a human “in the loop” to supervise. But the momentum is undeniable. Tech strategists now talk about an “AI workforce”: a concept that emphasizes augmentation, not replacement: AI agents as teammates rather than substitutes. For forward-looking firms, this means new opportunities to streamline operations, from an AI agent that triages IT support tickets overnight to one that summarizes market intelligence for a strategy team.

The bigger picture

AI has quickly moved from a novelty to a necessity in workplaces worldwide. The same Microsoft survey mentioned earlier found that 79% of business leaders say their company must adopt AI to stay competitive. This represents a generational consensus that AI has transitioned from “nice-to-have” to “must-have” status. 

The goal across all these domains is not simply to do more work. It’s to free up our time and mental energy for what truly matters: the strategic, creative, and interpersonal aspects of work and life that AI cannot as easily replace. In just one year, AI’s role in productivity has grown from a set of practical tools to a broader ecosystem that includes emerging “colleague” agents. Yet, the mission remains consistent: to reduce friction and enable people to focus on what they do best.

The AI landscape will undoubtedly keep evolving, but the lesson is already clear: the toolbox is expanding, and those who adopt thoughtfully will move not just with the tide, but ahead of it.