Discover how AI tools, agents, and internal search like Glyph are transforming employee productivity and reshaping work in modern organizations.
Modern organizations are witnessing a paradigm shift in how work gets done. In 2025, artificial intelligence (AI) tools and autonomous agents have moved from experimental novelties to integral parts of everyday workflows. These AI-powered assistants are reshaping knowledge work and operational efficiency, helping employees achieve more in less time. From email and coding to company-wide knowledge search, AI is becoming the ultimate productivity partner. This thought-leadership overview explores how organizations can leverage AI tools, AI agents, and internal AI search systems to supercharge employee efficiency – and why those who do so lead the pack.
AI has quickly evolved into a “co-pilot” for knowledge workers. Unlike past technologies that simply provided information, today’s AI can summarize, code, reason, converse, and even make basic decisions on our behalf. In practice, this means routine cognitive chores can be delegated to smart software, freeing humans to focus on higher-value tasks. The result is a step-change in productivity – in fact, a well-implemented AI system can deliver roughly a 30% productivity increase on average, with some workflows seeing even greater gains through automation. Forward-thinking companies recognize that pairing employees with AI tools is as transformative as past innovations like the internet or smartphones.
Concretely, a new wave of AI productivity tools is already boosting individual efficiency across common work activities:
These real-world tools illustrate a trend: AI is augmenting individual roles – be it an email inbox, IDE, or search bar – and turning tedious tasks into quick conversations with an assistant. The cumulative effect is significant. Employees using AI tools can communicate, code, and find knowledge far more efficiently than before. As a result, knowledge workers can redirect their time to creative and strategic endeavors that truly require human insight.
While most AI tools focus on improving task-specific workflows (like email or coding), Glyph Internal Search solves a deeper problem: surfacing trusted knowledge across the entire organization. It functions as a smart internal search engine designed for modern teams that are overwhelmed by scattered files, siloed documents, and hard-to-find insights.
Glyph allows employees to search across internal sources like PDFs, strategy docs, meeting notes, wikis, and even Slack threads using natural language queries. Instead of asking IT where a document is or pinging a colleague for background info, employees can simply type a question—“What’s our GTM strategy for Q3?” or “Show me the latest sales deck for healthcare clients”—and Glyph delivers the answer, fully grounded in your company’s own data.
By bringing AI-native semantic search into the enterprise stack, Glyph helps teams:
For knowledge-driven teams, this is game-changing. Instead of “where do I find this?”, the mindset becomes “just ask Glyph.” It's like giving every employee their own research assistant that never sleeps, forgets, or misses a file.
Organizations using Glyph Internal Search report saving hours per week per employee and dramatically reducing duplicated work, especially across sales, operations, customer success, and product teams.
While personal productivity tools tackle individual tasks, organizations also waste enormous effort in one area: searching for internal information. Studies show that employees spend a startling amount of time just hunting down knowledge. According to research, the average employee may spend 1.8 hours per day (up to 9 hours a week) searching for and gathering information That’s roughly 20–30% of the workday lost to playing “digital hide-and-seek” with documents and data In practical terms, if you hire five employees, one of them is essentially just searching for answers full-time, not creating value.
AI is changing this equation through AI-enhanced internal knowledge search systems. Instead of manually combing through intranet pages, SharePoint sites, or Slack threads, employees can ask an intelligent search assistant and get the right answer or document in seconds. For example, Glyph Work AI offers an internal search assistant that can summarize meetings, analyze PDFs, perform internal searches, and even draft documents based on company knowledge. By parsing the organization’s collective information – wikis, knowledge bases, past conversations – such an AI can retrieve precise answers that used to require multiple emails or hours of digging.
Imagine a few scenarios of an AI-powered internal search in daily workflow:
These use cases show how an AI knowledge search reduces time spent finding information and improves decision-making. Answers come not just faster, but also richer – often with context or next-step suggestions. Employees can trust that they’re drawing from the latest and most relevant internal knowledge, which leads to better decisions. Moreover, integrating such AI search into daily workflows (e.g. accessible via a chat assistant, intranet portal, or even voice command) means it becomes a natural first resort whenever a question arises.
The impact on efficiency is profound. Atlassian’s recent survey of 12,000 knowledge workers found that teams waste about 25% of their time just searching for answers when information isn’t easily accessible. On the flip side, organizations that make knowledge readily discoverable perform much better. For instance, teams using Atlassian’s integrated knowledge tools (with AI features) spend 50% less time searching for information than others. In other words, a good internal AI search can give back hours of productive time every week, across an entire workforce. Employees can move faster, and projects don’t get stalled waiting for information.
Forward-looking companies are not waiting to embrace these AI-driven efficiencies. They are actively weaving AI into their workflows to automate repetitive tasks, surface insights, and augment human intelligence across the board. In fact, nearly all large companies are investing in AI in some form – yet only about 1% feel they are fully leveraging AI’s capabilities to the fullest. Those few leaders, however, offer a glimpse of what’s possible when AI optimization becomes a strategic priority.
Consider some real-world strategies and wins from organizations already adopting AI:
The common thread among these leaders is a holistic approach to AI adoption. They identify high-impact use cases (like route planning, fraud detection, knowledge sharing), invest in the right tools or partnerships, and prepare their workforce to integrate AI into daily work. Early results show improved efficiency, cost savings, and often better outcomes for customers as well. As one CEO put it, the goal is a “digital workforce” where human teams and AI agents work together seamlessly to achieve outcomes. Companies building such digital workforces today are setting themselves up to outperform peers in the coming years.
One of the most exciting developments in 2025 is the rise of custom AI agents in the workplace. These are AI systems (powered by large language models and connected to various enterprise tools) that can not only find information but also take actions on behalf of users. We are witnessing a convergence of AI-driven search and automation: the AI can understand a goal, fetch the necessary data or context, and then execute tasks across applications to fulfill that goal. In essence, these agents behave like ultra-capable virtual team members who can handle the drudgery of multi-step processes.
For example, consider a sales operations agent built for a company: when a new sales lead comes in, the agent could automatically research the lead’s company (via an AI search), draft a personalized intro email (using a writing AI tuned to the company’s style), log the lead and the email into the CRM system, and schedule a follow-up reminder for the sales rep. All of those steps – which might normally take an employee an hour of switching between apps – are handled end-to-end by the AI agent in seconds. The human salesperson only needs to review the email and press send (or even have the agent send it if fully trusted). This is not a futuristic fantasy; it’s increasingly feasible with today’s technology.
AI agents work by chaining together capabilities: they can integrate with calendars, email, databases, and third-party apps through APIs. Notably, companies like Zapier are enabling non-engineers to create such agents easily. Zapier’s Agents platform allows organizations to “build your custom AI agent in minutes… equip your agents with company knowledge and have them do work across 8,000+ apps”. In other words, one can quickly spin up an AI that knows your business data and can perform tasks in SaaS tools ranging from Google Sheets to Salesforce. Zapier reports that over 50,000+ businesses are already experimenting with AI automation like this. Users have praised that “we're all waiting for agents, but Zapier already has them”, highlighting how these tools can process leads, answer emails, manage calendars, and more with minimal human intervention.
Similarly, enterprise software giants are embedding agentic AI into their products. Salesforce recently introduced an “Agentforce” layer for its platform, enabling customers to deploy autonomous agents for complex tasks like simulating a full product launch or orchestrating a marketing campaign. Microsoft 365’s Copilot is adding features called Copilot Actions that can automate repetitive tasks across Outlook, Teams, and other apps – for instance, interpreting an email and then scheduling a meeting and preparing a draft response, all from a single prompt. These advances point to a future where routine workflows can be initiated by a simple request to an AI agent. You might say, “AI, prepare the monthly sales report and send it to the team,” and the agent will fetch data from the analytics system, generate a summary report, and email it out – acting as both researcher and executive assistant.
The convergence of AI search + action is a game-changer. Instead of just providing an answer, the AI can directly do the next step. For knowledge workers, this means less time on menial coordination and more on strategy and creativity. It’s also a remedy for the common “app overload” in offices – the AI agent navigates the complexity of multiple systems so the employee doesn’t have to. As one early adopter noted, the best AI agents “keep trying different approaches until they get results”, behaving like proactive problem-solvers rather than static programs.
Of course, with great power comes great responsibility – organizations must set boundaries and oversight for what agents can do, ensure data privacy, and maintain a human-in-the-loop for critical decisions. But when thoughtfully implemented, custom AI agents can become reliable teammates that handle the busywork and let human colleagues focus on what truly requires human judgment.
The year 2025 marks an inflection point in how work is done. AI tools, internal knowledge assistants, and autonomous agents are no longer experimental – they are here and now, actively boosting productivity and transforming workflows in leading organizations. By leveraging these technologies, companies can reduce drudgery, speed up information flow, and support employees to achieve more each day. The end goal is not to replace humans, but to liberate them: AI handles the repetitive tasks and information overload, while humans apply creativity, empathy, and critical thinking where it matters most.
Organizations that embrace AI as a collaborative partner are already reaping benefits – from saved hours and lower costs to more innovation and agility. Teams that use AI regularly are 2.2× more likely to easily find the knowledge they need and significantly more effective in their work. Leaders who leverage AI report having more time to focus on priorities and guide their teams. In essence, AI is becoming the force multiplier for human talent.
However, capturing this value requires a proactive strategy. Companies should invest in the right tools (be it off-the-shelf AI apps like Superhuman or custom in-house solutions), enable their internal data for AI search, and train their people to work alongside AI agents comfortably. It’s also wise to start with clear use cases – whether it’s automating a workflow or improving access to knowledge – and then scale up successes across the organization. As many have learned, deploying AI is as much about change management as it is about technology.
In conclusion, the message for organizations in 2025 is clear: AI isn’t just an add-on, it’s an efficiency revolution. Those who integrate AI deeply into how their employees work will enjoy faster decisions, happier teams, and a sharper competitive edge. The future of work is a partnership between human expertise and AI capabilities – and in that partnership, the sum is far greater than the parts. By turning AI tools and agents into trusted co-workers, companies can unlock a new level of productivity and innovation, setting the stage for success in the years to come.
Sources: The insights and examples above were informed by industry reports and real-world cases, including McKinsey’s analysis of AI’s transformative potential, Atlassian’s surveys on teamwork and AI adoption, and numerous reports of organizations implementing AI for significant gains. Platforms like Superhuman, Cursor, Perplexity, Glyph AI, Salesforce, and Zapier provided concrete illustrations of how AI is boosting efficiency in communication, coding, search, and complex workflows. These examples underscore a common theme – when humans and AI work together, productivity soars.
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