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Artificial Intelligence

Is AI the Answer to Better Teamwork?

Innovative companies around the world are working on big problems, like treating cancer with mRNA vaccines and making autonomous cars safer. Addressing these complicated issues takes more than just an inventor or two with good ideas and intentions. In fact, “90% of companies are confronting problems so complex that teams are essential to solving them.” Yet, collaboration is often limited by institutional inefficiencies, such as “poorly designed tasks, ineffective collaborative work practices, and inadequate information systems.”

As solving complex problems, from COVID-19 to climate change, becomes increasingly urgent, companies must find ways to overcome these inefficiencies. Is an AI teammate part of the answer?

Benefits of an AI Teammate

Artificial intelligence is good at tasks that humans don’t always do well; missteps, especially in data collection, are called “human error” for a reason. When teams delegate detail-oriented, time-consuming tasks to AI, human team members have more time to focus on tasks that AI can’t do well.

When AI is the one gathering data and searching for insights, humans have more—and more accurate—information available to make decisions. While AI can make autonomous decisions based on available data, human judgment is difficult, if not impossible, to replicate with current technology. This symbiotic relationship allows for faster, better decisions at all levels of business.

AI can also serve as a go-between. The right tools help teams store, share, and communicate essential details.’s AI-powered IQ Ideas Plus™ provides feedback on novelty to engineers and streamlines the innovation disclosure process, enhancing collaboration between an organization’s R&D and IP teams.

Barriers to Working with AI

Adding an AI partner to your current workflows is not enough to realize the benefits of artificial intelligence. The first barrier to working seamlessly with AI is a failure to integrate it completely into your business. This requires big changes—organizational, procedural, and cultural—that are not completely risk-free. Adding to this challenge is the unavoidable fact that AI is not always a perfect solution from day one. Machine learning is an evolving process that takes human input and oversight. Over time, AI and human teammates will learn how best to work together—not unlike adding a new human employee to a team.

Relying on AI as a member of the team is also challenging for the same reason organizations hesitate to rely on AI for any task: the “black box.” If AI can’t explain how it arrived at a specific solution, whether it be a decision on who to hire or what to patent, it may be a less valuable teammate.

If companies are, in fact, dedicated to solving the problems our society faces, they must explore all avenues for novel solutions. This likely includes AI, which likely offers substantial benefits if teams (and their employer as a whole) are willing to work through initial barriers to adoption.

The Friendly Face of AI

At any point in the digital revolution, computers have needed time to become more integrated with people. They’ve needed to become more user-friendly and intuitive so that when they are introduced to work environments, they can be seamlessly inserted into taskflows and workflows. recommends a few general perspectives for enabling smooth AI integration.’s innovation suite adds natural language processing tools and data visualization to your internal repertoire without some of the concerns that plague other AI systems.

Avoid bias. A key question for those using AI is whether or not data is free of bias and independent of outside influence. Therefore, innovators must consider where data comes from and any biases it might reflect. All AI rests on certain assumptions but those assumptions should be data-driven.’s natural language processing tools incorporate how humans search and analyze data–naturally. As we build our proprietary algorithms, considers whether data genuinely reflects human characteristics and remains independent of entities that benefit from prompting search results and analytics.

Consider AI ethics. The implications of ethics and AI are massive. Racial discrimination, multiple types of empirical and social bias, plus issues of improper application all concern AI creators and observers.’s innovation suite employs natural language tools to reflect the ways humans search for and incorporate information while avoiding biases in search results.

Improve human workflows–not eliminate them. There are fears that AI will replace humans. Although this concern is likely overwrought, those implementing AI should be cautious about how AI is applied in an organization and how it’s received by employees. Organizations with a culture that embraces change are likely to realize sooner that AI enhances workflows and eliminates unnecessary steps. In the IP industry, classic Boolean search and more rudimentary data management dominated. Increased costs and lost opportunities were the norm. Now, semantic search captures more data, faster and at a lower cost.

Ensure operational security. One question facing enterprise-level businesses seeking to apply AI tools are questions of data security. This is particularly true as cloud-based software makes platforms available to anyone, anywhere. Bad actors are able to infiltrate or create software backdoors into both local and cloud-based databases to steal trade secrets. If you are an organization in North America, a domestic AI provider may be more likely to take these risks into account to provide better security and reflect your organization’s values.