Collaboration and cooperation are not synonyms. Collaborative teams have a common goal that requires the strengths of all involved parties. This goal is more specific than a company-wide objective. Cooperation is much simpler. If you’re pleasantly supporting other individuals’ or teams’ goals, you’re cooperating with them. Collaboration generally requires cooperation; cooperation does not necessarily mean a project is collaborative. However, some corporate cultures fail to acknowledge the key differences between these two practices.
One practice is not necessarily better than the other. Different goals, circumstances, and scenarios call for collaboration or cooperation—or both. Yet, in order to be truly innovative and bring products to market quickly, collaboration is key. It’s essential for engineering, IP, marketing, and leadership teams to understand the difference between collaboration and cooperation so they can implement them effectively.
If your organization owns or is developing intellectual property and seeks to promote collaboration, consider how AI can improve communication and simply workflows within and between teams.
Encouraging cross-team collaboration can unlock innovative ideas more quickly, giving your organization a competitive advantage. This sounds like a no-brainer, but putting collaboration into practice may be more difficult than it appears. The first step to a more collaborative culture is examining what roadblocks have inhibited teamwork in the past. Removing these barriers to collaboration allows teams to work toward a common goal without institutional obstacles.
In addition to culture, communication can be a hurdle to collaboration. For IP-holding companies innovating, this can lead to distorted priorities and ideas languishing in the initial innovation lifecycle stages. AI-enhanced communication is the key to collaboration that’s beyond simple cooperation because it is more streamlined, overcomes bottlenecks, and allocates resources more effectively.
Build a Team
Collaboration does not necessarily mean all hands on deck. You’re not preparing dinner, but the danger of “too many cooks in the kitchen” should be considered. Depending on the project at hand, you may need multiple engineers with different specialties, or someone from marketing as well as sales. However, not everyone who will be working on the project needs to be involved in cross-team communication or decision-making.
Multidisciplinary teams applying AI tools for more orchestrated and systematic communications are the mode of the future. This puts a lot of pressure on innovators but it is a tremendous opportunity to put those who truly drive your organization firmly at the steering wheel. With better inventions and more accurate disclosures, everyone within the organization can be on the same page early in the innovation lifecycle, with innovators guiding processes.
Perhaps you have a single project manager or another point of contact to ensure everyone involved is aware of all the moving parts that collaboration requires. This individual should not be the only person other team members are communicating with. Meetings, whether in person or virtual, should include representatives from each department involved in the project. This way, problem solving can happen in real time (rather than through a single point of contact) as project elements impact one another.
Collaboration is very difficult—if not impossible—without a common goal. Clearly defining project goals, as well as individual tasks and overall timeline, gives everyone involved an unambiguous idea of what is required and expected. Without this structure, it’s hard to complete the project at hand effectively or measure progress toward an end goal, such as product improvement, patented innovation, or another competitive advantage.
Follow a Process
In order to reach defined end goals, there must be a process that considers ongoing communication, incremental milestones, and other elements essential to collaboration. Having a template for these steps makes them repeatable for collaboration across disciplines. These templates can be produced with process mining, helping differentiate and establish the human and technology based tasks within broader workflows to create an objective picture of your operations.
Optimized processes should require minimal organizational disruption to implement because automation or technological implementation should avoid radical IT or job description changes. Cloud based software can help achieve just that by allowing technology to intersect at the point in workflows that makes the most sense for the organization and individual employees.
Manage with AI
Collaboration takes time. There are additional communications and processes that aren’t necessary when working within your own team. There may also be barriers to these elements of collaboration, such as departmental jargon or organizational structure. Introducing an AI-based tool designed to eliminate these challenges, such as IP.com’s IQ Ideas Plus™, streamlines the communication and processes required to bring an innovation to market.
Implementing AI optimally goes beyond automating workflows and leveraging analytics. It’s about using it in a way that reflects your organization’s character and goals. At a time when organizations are questioning the dangers of ‘rouge AI’, IP.com supports maximum utility and transparency. This ensure teams are on the same page and operating with core organizational values in mind.
IP.com understands the ethical and practical considerations IP-holding organizations have about AI generally. Here are some we often hear when customers consider how AI can enhance collaboration:
- Will AI tools work consistently and predictably across use cases?
- Does augmenting human workflows with automated ones earn better results?
- Does AI augment rather than replace human judgment?
- Do search tools access only publicly available data?
These are fair questions as organizations look to enhance collaboration for better innovation and ROI. Our innovation suite has always incorporated customer feedback for products that are trusted and transparent. Tools like semantic search and idea novelty scoring are only useful if they are free of bias and understood by those using them.