A Synergistic Alliance Between Human and AI Capabilities

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AI has demonstrated remarkable proficiency across various tasks traditionally performed by humans, such as diagnosing diseases, language translation, and customer service, with rapid advancements continually underway. While concerns persist about AI potentially displacing human workers across industries, this outcome is neither inevitable nor the most probable. Never before have digital tools been so attuned to human needs, nor have humans been so adaptable to utilising these tools. Although AI will significantly reshape work dynamics and roles, its primary impact lies in complementing and enhancing human abilities rather than replacing them. AI for speed, and humans for quality, just like Novable.

Research reveals that the most substantial performance enhancements occur when humans and machines collaborate. Through this collaborative intelligence, humans and AI synergistically leverage each other’s strengths: the leadership, creativity, teamwork, and social skills of humans, alongside the speed, scalability, and analytical capabilities of AI. Successful businesses recognise the necessity of both human and AI capabilities in driving innovation and productivity.

To fully leverage this collaboration, businesses need to grasp the most effective ways humans can complement machines, how machines can amplify human strengths, and how to adjust business procedures to facilitate this partnership.

Implementing five key principles can facilitate the collaboration between AI and humans:

• Rethinking business processes
• Fostering a culture of experimentation and employee engagement
• Taking a proactive approach to AI strategy, ethically managing data collection
• Restructuring work to integrate AI while nurturing relevant employee skills.

A survey conducted across 1,075 companies spanning 12 industries revealed a positive correlation between the adoption of these principles and the success of AI initiatives, measured in terms of efficiency, cost-effectiveness, revenue generation, and other operational metrics.

Human involvement is essential in three key capacities. Firstly, humans are responsible for training machines to execute specific tasks. Secondly, they play a vital role in interpreting and elucidating task outcomes, particularly in cases where results may be unexpected or contentious. Lastly, humans are tasked with ensuring the ethical and responsible utilisation of machines, including measures to prevent harm to individuals, such as ensuring robots do not pose a threat to human safety.

Training. AI assistants are currently undergoing training to exhibit increasingly nuanced and intricate human characteristics, including empathy. For example, the startup Koko has devised technology aimed at enabling AI assistants to express empathy. Instead of employing pre-scripted responses like “I’m sorry to hear that,” the Koko system engages users by seeking further details about their situation and subsequently offering advice to help reframe their perspective.

Explaining. As AI systems increasingly make decisions using complex processes that are not easily understood, there is a growing need for human experts to clarify their actions to users who may lack expertise in the field. These experts play a crucial role, particularly in evidence-based sectors like law and medicine. In these fields, practitioners require insights into how AI algorithms assess various factors in making decisions, such as sentencing or medical recommendations.

The same goes for insurance claims and law enforcement investigations, where understanding why an autonomous vehicle made certain decisions in an accident or failed to prevent one, is essential.

Sustaining. Apart from having individuals capable of elucidating AI outcomes, companies require “sustainers” – employees dedicated to ensuring that AI systems operate effectively, safely, and ethically.

Intelligent machines are assisting humans in broadening their capabilities through three means. Firstly, they amplify our cognitive abilities, enhancing our mental strengths. Secondly, they engage with customers and employees, thereby liberating us to focus on more advanced tasks. Lastly, they embody human-like skills, effectively extending our physical capacities.

Amplifying. AI can enhance both our analytical skills and decision-making processes by delivering pertinent information precisely when needed. Moreover, it can also elevate creativity. Take, for instance, Autodesk‘s Dreamcatcher AI, which serves to augment the imagination of designers, even those already exceptional in their field.

Interacting. Collaboration between humans and machines opens up new and more efficient avenues for companies to engage with both employees and customers. Think about apps that can transcribe meetings and distribute a searchable version to absent participants, enhancing accessibility and productivity. These applications are inherently scalable.

Embodying. Many artificial intelligences primarily exist as digital entities. However, in other scenarios, intelligence manifests within robots that collaborate with human workers. Equipped with advanced sensors, motors, and actuators, AI-enabled machines can now identify people and objects, operating safely alongside humans in various settings like factories, warehouses, and laboratories.

For instance, in manufacturing, robots are transitioning from conventional, potentially hazardous machines to smart, context-aware “cobots.” A cobot arm might handle repetitive tasks requiring heavy lifting, while a human worker focuses on tasks demanding dexterity and judgment, like assembling intricate components.

To maximise the benefits of AI, organisations must undergo operational redesign. This begins with identifying and defining areas within operations that can be enhanced. This could involve addressing large internal processes, such as slow staff recruitment in HR, or tackling previously unsolvable challenges now within reach with AI, such as rapidly identifying adverse drug reactions across patient populations. Furthermore, a range of new AI and advanced analytical techniques can uncover hidden problems that AI solutions can effectively address.

Subsequently, companies need to co-create solutions by engaging stakeholders to envision collaborative approaches with AI systems for process improvement. Similar to the discovery phase, new AI and analytic techniques can aid co-creation by suggesting innovative methods for process enhancement.

The third step for companies involves scaling and sustaining the proposed solutions.

Flexibility. The teams comprising humans and machines generally demonstrate remarkable adaptability. Cobots can swiftly be reprogrammed using a tablet, enabling them to undertake various tasks in response to shifts in workflow. This agility has facilitated the manufacturer in attaining unparalleled levels of customisation.

Speed. In certain business operations, such as credit card fraud detection, speed is paramount. Companies have mere seconds to assess whether a transaction should be approved, risking financial loss from fraudulent activity or customer dissatisfaction if legitimate transactions are denied.

To address this challenge, major banks have leveraged AI-based solutions to enhance the speed and accuracy of fraud detection. These systems analyse millions of transactions daily, utilising various data points including purchase location, customer behaviour, and IP addresses to detect subtle patterns indicative of potential fraud.

The battle against financial fraud resembles an ongoing arms race: advancements in detection prompt more sophisticated criminal tactics, necessitating continual updates to algorithms and scoring models. Moreover, different regions employ varying detection models, requiring a dedicated workforce of data analysts, IT professionals, and financial fraud experts to maintain vigilance and stay ahead of criminal strategies.

Scale. In many business processes, limited scalability poses a significant challenge to enhancement. This is especially evident in processes heavily reliant on human labour with minimal machine involvement. Take, for example, Unilever’s employee recruitment process. The multinational consumer goods corporation sought to diversify its workforce of 170,000 employees, with a particular focus on entry-level hires and their subsequent advancement into management positions. However, the existing recruitment procedures fell short of evaluating a sufficient number of candidates while providing individualised attention to ensure a diverse pool of exceptional talent.

Decision making. By delivering customised information and guidance, AI empowers employees to make more informed decisions, a particularly valuable asset for frontline workers whose choices can significantly impact business outcomes.

For instance, the field of equipment maintenance has been revolutionised through the utilisation of “digital twins”, virtual replicas of physical machinery. General Electric employs such software models for its turbines and industrial products, continuously updating them with real-time operating data collected from the equipment. Through this process, GE has amassed a wealth of information on both normal and abnormal performance, which its Predix application, employing machine-learning algorithms, utilises to predict potential failures in specific machine components.

Personalisation. Delivering personalised brand experiences to customers represents the pinnacle of marketing achievement. Thanks to AI, such customisation can now be achieved with unprecedented precision and on a massive scale.

The need for new roles and talent. Revamping a business process goes beyond simply integrating AI technology. It necessitates a substantial commitment to nurturing employees with those enabling effective work at the human-machine interface. Initially, individuals must learn to entrust tasks to the new technology. Moreover, employees should understand how to merge their distinct human abilities with those of intelligent machines to achieve superior outcomes, as seen in robot-assisted surgery. Workers need to be capable of teaching new skills and undergo training to function effectively within AI-enhanced processes.

For instance, they need to learn the optimal methods for querying AI agents to obtain the necessary information. Additionally, there should be personnel tasked with ensuring the responsible use of their companies’ AI systems, preventing illegal or unethical practices.

Looking ahead, we anticipate that company roles will be redefined in alignment with the desired outcomes of reimagined processes. Corporations will increasingly structure themselves around various skill sets rather than rigid job titles.

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