In today’s rapidly evolving digital landscape, artificial intelligence (AI) has become a game-changer across various industries. From diagnosing diseases to providing customer service, AI is rapidly advancing and proving its potential. However, there’s a common fear that AI will replace human workers entirely. The reality, however, is quite different. The true power lies in the collaboration between humans and AI, where they can complement each other’s strengths and achieve remarkable results. This article explores the concept of collaborative intelligence, the value it brings to businesses, and how organizations can harness its potential.
The Rise of Collaborative Intelligence
AI has undoubtedly transformed how work gets done and who does it. Rather than replacing humans, AI has the potential to augment and enhance human capabilities. While many companies have leveraged AI to automate processes, those that have achieved the most significant performance improvements are the ones that foster collaboration between humans and machines. In a study involving 1,500 companies, it was found that the most successful organizations are those that actively embrace collaborative intelligence.
Collaborative intelligence allows humans and AI to work together, capitalizing on their complementary strengths. Humans bring leadership, teamwork, creativity, and social skills to the table, while AI offers speed, scalability, and quantitative capabilities. For example, what comes naturally to humans, such as making a joke, can be challenging for machines. On the other hand, analyzing gigabytes of data is virtually impossible for humans but straightforward for AI. By combining these capabilities, businesses can achieve remarkable results.
The Value of Collaboration
To fully leverage the power of collaboration between humans and AI, organizations must optimize their approach. Five key principles can help them achieve this:
- Reimagine business processes: Rethinking and redesigning existing processes to incorporate AI and cultivate related employee skills.
- Embrace experimentation and employee involvement: Encouraging employees to experiment and actively contribute to AI strategies.
- Actively direct AI strategy: Taking an active role in shaping AI initiatives and strategies.
- Responsibly collect data: Ensuring data collection practices align with ethical standards and regulatory requirements.
- Redesign work to incorporate AI: Restructuring work processes to integrate AI effectively and develop the necessary skills among employees.
A survey of 1,075 companies across 12 industries found that the more companies adopted these principles, the better their AI initiatives performed in terms of speed, cost savings, revenues, and other operational measures. By embracing collaborative intelligence, organizations can unlock the true potential of AI and achieve significant improvements.
Humans Assisting Machines
In the collaborative intelligence model, humans play crucial roles in assisting machines. There are three primary functions that humans fulfill:
Training
Machine learning algorithms rely on vast amounts of data to perform their designated tasks. Humans play a crucial role in training these algorithms to handle specific jobs effectively. For example, medical apps need extensive training data to detect diseases accurately, while translation apps require data sets to handle idiomatic expressions. Similarly, AI systems need training on how to interact with humans effectively.
Leading technology companies and research groups have already established mature training staff and expertise. For instance, Microsoft’s AI assistant, Cortana, required extensive training to develop a suitable personality. A team of professionals, including a poet, a novelist, and a playwright, dedicated countless hours to create the right blend of confidence, care, and helpfulness for Cortana. Similar efforts were made in developing the personalities of Apple’s Siri and Amazon’s Alexa. This training ensures that AI assistants accurately reflect their respective companies’ brands.
Training has evolved to include even more complex tasks, such as teaching AI assistants to display sympathy. Start-ups like Koko, an offshoot of the MIT Media Lab, have developed technology that allows AI assistants to seem empathetic. Rather than providing canned responses, Koko’s system engages users in conversations, offering advice and reframing their issues. This level of training creates a more human-like and personalized experience for users.
Explaining
As AI systems become more sophisticated, they often reach conclusions through opaque processes, known as the “black-box problem.” In such cases, human experts are required to explain the AI’s behavior to non-expert users. This explaining role is especially critical in evidence-based industries like law and medicine, where practitioners need to understand how AI factors in their decision-making processes.
Explainers are also crucial in helping insurers, law enforcement agencies, and regulated industries comprehend the reasoning behind AI-driven actions. The European Union’s General Data Protection Regulation (GDPR) even grants consumers the right to receive an explanation for algorithm-based decisions. This field of expertise is expected to create approximately 75,000 new jobs to meet the GDPR requirements.
Sustaining
In addition to training and explaining, companies need “sustainers” who ensure that AI systems function properly, safely, and responsibly. These sustainers focus on preventing harm caused by AIs and ensure compliance with ethical norms and consumer-protection regulations.
Safety engineers play a vital role in anticipating and preventing harm caused by AIs, while ethics managers address issues like discrimination and unfair treatment. Data compliance officers ensure that AI systems handle data responsibly and comply with privacy regulations. By having sustainers in place, organizations can mitigate risks associated with AI implementation and ensure the ethical use of AI technologies.
Machines Assisting Humans
Collaborative intelligence also involves machines assisting humans, amplifying their cognitive strengths, interacting with customers and employees, and embodying human skills to extend physical capabilities.
Amplifying
AI can significantly enhance human analytical and decision-making abilities by providing timely and relevant information. Autodesk’s Dreamcatcher AI, for instance, assists designers by generating thousands of designs based on specific criteria. Designers can then guide the software, iterating and refining designs while the AI handles the calculations. This collaboration allows designers to focus on their professional judgment and aesthetic sensibilities, while AI handles the computational aspects.
Interacting
Human-machine collaboration enables companies to interact with employees and customers in novel and more effective ways. AI agents like Cortana and chatbots facilitate communication between individuals or on behalf of people, such as transcribing meetings and distributing voice-searchable versions to absent participants. Chatbots also excel at providing routine customer service to large numbers of people simultaneously, freeing up human representatives to address more complex issues.
For example, SEB, a major Swedish bank, uses a virtual assistant called Aida to interact with millions of customers. Aida can handle natural-language conversations, access vast amounts of data, and answer frequently asked questions. She can also analyze callers’ tones of voice to provide better service in the future. If Aida cannot resolve an issue, she transfers the call to a human representative, learning from the interaction for future reference.
Embodying
AI-enabled machines, such as robots, can work alongside humans in various industries, recognizing people and objects and ensuring their safety. In manufacturing, robots have evolved from simple industrial machines to smart “cobots” that collaborate with humans. These cobots handle repetitive tasks that require heavy lifting, while humans focus on more complex activities that demand dexterity and judgment.
Companies like Hyundai are taking the concept further by developing exoskeletons that enhance workers’ physical capabilities. These wearable robotic devices adapt to the user’s movements and provide superhuman endurance and strength, enabling industrial workers to perform their jobs more efficiently.
Reimagining Business Processes
To fully harness the potential of collaborative intelligence, organizations must reimagine their business processes. This involves identifying areas that can be improved through the integration of AI and human skills. By leveraging AI and advanced analytic techniques, companies can uncover previously invisible problems and develop innovative solutions.
Unilever, for example, transformed its employee recruitment process by combining human and AI capabilities. The company implemented online games and video submissions to assess candidates’ traits and suitability for specific positions. The AI system analyzes the video responses, considering not only the content but also body language and tone. This approach has significantly broadened the scale of Unilever’s recruiting efforts, resulting in increased diversity and reduced time-to-hire.
General Electric (GE) has revolutionized equipment maintenance by leveraging “digital twins.” These virtual models of physical equipment continuously update based on real-time operating data. By collecting readings from numerous machines, GE’s AI-powered system can predict potential failures and recommend appropriate actions. This collaboration between humans and AI enables faster decision-making and more proactive maintenance, saving GE millions of dollars.
The Need for New Roles and Talent
Successfully implementing collaborative intelligence requires organizations to invest in developing fusion skills among their employees. Fusion skills enable effective collaboration at the human-machine interface and involve delegating tasks to AI, combining human strengths with AI capabilities, teaching intelligent agents new skills, and ensuring the responsible and ethical use of AI systems.
As organizations transition to embrace collaborative intelligence, job roles will be redesigned based on desired outcomes rather than rigid job titles. Companies like AT&T have already begun this transformation by retraining employees for new positions, streamlining job titles, and focusing on skills like data science and machine learning.
Conclusion
Collaborative intelligence represents the future of work, where humans and AI work together to achieve remarkable results. By embracing this collaborative approach, organizations can unlock the true potential of AI and foster significant improvements in productivity, decision-making, and customer experience. Reimagining business processes, optimizing collaboration, and developing fusion skills among employees are essential steps in harnessing the power of humans and AI. As technology continues to evolve, organizations that embrace collaborative intelligence will be the leaders of tomorrow.
