Maximize The Value of Your IP with AI
One position that I have been advocating for years is to treat everything we do on our company’s owned or managed tools—whether it’s working on a company-issued laptop, attending meetings, creating documentation, or even making phone calls on a company-managed phone—as the company’s intellectual property. I’m not trying to argue the inclusion of non-work-related activities and personal conversations into work assets, but the idea that every work activity we create, iterate on, or participate in should be, generally speaking, managed, classified, and made searchable within the archives of your company intellectual property (IP). From there, all of these artifacts can then be ingested by artificial intelligence (AI) to be utilized by others within the company—with the right permissions, of course. By treating our work as digital assets, we can unlock new levels of collaboration and efficiency.
Flexible work and digital meetings have reshaped the way we collaborate, allowing us to meet across locations and time zones and include people who might otherwise have been left out. The problem, however, is that we often create even more information silos across our various tools and systems. According to Jared Spataro, Corporate Vice President of Modern Work & Business Applications at Microsoft, “a meeting has become less a point in time and almost a knowledge object that I can query, that I can ask questions of.” This shift is crucial as people are now in three times more Microsoft Teams meetings and calls per week, with inefficient meetings being a top productivity disruptor (Microsoft Worklab article: “With Copilot, Every Meeting Is a ‘Digital Artifact“).
This shift in the nature of meetings—from ephemeral events to digital artifacts—sets the stage for a broader transformation in how we think about all work-related activities and outputs. These outputs, whether from meetings, emails, documents, or phone calls, should be managed as valuable digital assets.
Intellectual Property and the Workplace
Intellectual property in the workplace generally refers to creations of the mind, such as inventions, literary and artistic works, designs, and symbols, names, and images used in commerce. Under the “Work for Hire” doctrine, IP created by an employee within the scope of their employment typically belongs to the employer. However, there are exceptions, such as employment agreements and work created outside of company time. The goal here is not to delve into legal specifics but to understand the broader implication: everything we produce in the scope of our jobs can be considered valuable company assets.
This perspective encourages us to view our everyday work not just as tasks to be completed but as contributions to a collective pool of knowledge and resources that can be leveraged by the entire organization.
Transforming Work into Digital Assets
Generative AI tools like Microsoft Copilot are driving a fundamental shift in how we manage and utilize work. Copilot enables meetings to be transformed into digital artifacts that can be queried and interacted with even after the meeting has ended. This capability allows employees to reclaim time for creative and focused work, helping them thrive in their roles. “With Copilot in Teams, people can consult a meeting and interact with it after the fact—even if they couldn’t attend—allowing everyone to reclaim time for creative and focused work so they can truly thrive” (Microsoft Worklab).
AI’s ability to transform work into digital assets goes beyond meetings. It encompasses all forms of work outputs, turning them into searchable, queryable, and analyzable resources. This transformation is critical for maximizing the value of our collective efforts and ensuring that important knowledge is preserved and accessible.
Practical Applications of AI in Managing Work
Every company on the planet is currently piloting some kind of AI tools, which is a great thing. At the same time, companies are looking to AI leaders like Microsoft to help them better understand the potential return on investment (ROI) of this new technology. AI can significantly enhance ongoing meetings by organizing and synthesizing discussions in real-time. For example, during a meeting, Copilot can help you rephrase a statement you didn’t understand, suggest questions to ask, or generate a list of action items from the discussion. If you missed something while multitasking, you can ask Copilot to repeat it. Moreover, as a meeting host, you can ensure everyone is included by asking Copilot who hasn’t had a chance to speak or who was cut off.
Post-meeting, AI allows employees to interact dynamically with the digital record of the conversation. Copilot can provide a full recap, answer questions about the topics discussed, decisions made, or analyze the mood and sentiment of the meeting. This functionality offers a fine-grained analysis of human interactions, making meetings more than just a one-time event but a resource that can be revisited and leveraged for future work—IF your meetings are properly recorded, transcripts create, and users have the right permissions to consume this information (Microsoft Worklab).
While the improvements to the work meeting culture is impressive, think about the value that could be generated by ingesting and understanding your emails, text and chat conversations, and even your work-related phone calls. These capabilities can be extended to all work outputs, allowing AI to organize, classify, and make these outputs easily retrievable. According to Grant Ingersoll and Daniel Tunkelang, “Search engines enable us to find the right information at the right time from a vast collection of documents or data. The foundations of search are indexing, retrieval, and ranking; and all of these can benefit tremendously from machine learning” (Grant Ingersoll and Daniel Tunkelang, Uplimit: “Better Search through Content Understanding“).
This means that our documents, emails, and other outputs can be effectively indexed and retrieved, ensuring that valuable information is not only never lost, but incorporated into the contextual patterns of our work.
Benefits of Treating Work as Digital Assets
Enhanced Collaboration
By treating work as digital assets, we enhance collaboration within our organizations. When work is searchable and findable, employees can easily access the information they need, leading to better decision-making and innovation. According to Eden Priela, “Data classification and prioritization build better data management practices. Understanding your data’s value and sensitivity allows you to establish retention policies, streamline data storage, and optimize access controls aligned with your organization” (Eden Priela, Converge Technology Solutions: “Why Data Classification Matters & How to Inject It Into Your Security Strategy” ).
Enhanced search capabilities, driven by content understanding, further facilitate collaboration. As Ingersoll and Tunkelang note, “Content understanding is what makes content findable” (Uplimit). By improving our ability to classify and retrieve information, we ensure that employees can quickly find the resources they need to collaborate effectively.
Leveraging the Social Unconscious
The concept of leveraging the collective knowledge within an organization, often referred to as the social unconscious, is vital. The more we share and make our work accessible, the more we can tap into the collective insights and experiences of our colleagues. This can lead to improved problem-solving, creativity, and overall organizational performance. We’ve seen the benefits of tapping into the collective knowledge our organizations through social collaboration platforms, such as Viva Engage (formerly known as Yammer). However, organizing and classifying these various information assets can be cumbersome. The challenge lies in automating this process, which is where AI comes into play.
Automation and Efficiency
Automation through AI plays a crucial role in making the management and utilization of work as digital assets efficient and scalable. Automated tools can classify and tag large volumes of data accurately, minimizing human error and improving the overall effectiveness of data management. “An automated classification tool makes it possible to classify and tag large volumes of data efficiently and accurately. These tools use machine learning algorithms and natural language processing to identify patterns and categorize data, minimizing human error and improving the overall effectiveness of your data management” (Converge).
Improving content understanding at index time enhances the overall findability of content. “By using these approaches to increase content understanding at index time, you’ll increase the overall findability of the content that your users are trying to find and access” (Uplimit). This ensures that our digital assets are not only created but are also accessible and usable when needed.
As we continue to navigate the evolving landscape of work, it is essential to rethink how we manage and utilize our work. By treating everything we do as digital assets and leveraging AI to automate and enhance this process, we can unlock new levels of collaboration and efficiency within our organizations. So once again let me put on my “Chief Advocacy Officer” hat and say: Let’s embrace this shift, capture and classify our digital work activities, and empower our teams to make the most of the transformative technologies at our disposal.
