Salvatore Scuderi

EIM doctoral candidate and researcher

introduces his current research topic:

 

Unleashing the Power of Hybrid Intelligence:

The Marriage of AI and Human Capabilities in Strategic Decision-Making

 

Introduction

In the rapidly evolving landscape of artificial intelligence (AI), a ground-breaking concept has emerged – Hybrid Intelligence (HI). This paradigm shift recognizes the symbiotic relationship between AI systems and human intelligence, harnessing the strengths of both to achieve superior outcomes and better decisions. In this article, we delve into the theories of cognitive complementarity and distributed cognition, explore the concept of explainable AI, and examine real-world examples in strategic decision-making within the insurance and re-insurance industry.

The Marriage of Minds: Cognitive Complementarity and Distributed Cognition

Cognitive complementarity posits that humans and AI possess distinct cognitive abilities, and their collaboration can lead to enhanced problem-solving and decision-making. This theory acknowledges that while AI systems excel at processing vast amounts of data quickly, they may lack the nuanced understanding, intuition, and emotional intelligence inherent in human cognition.

Distributed cognition extends this idea by emphasising the distribution of cognitive processes and tasks across individuals or networks and their tools. In the context of hybrid intelligence, it means that decision-making is not confined to a single entity but is distributed across a network of human and AI partners. This interconnected strategy allows for a holistic and efficient utilisation of cognitive resources.

Explainable AI: The Key to Trust and Collaboration

One significant challenge in the integration of AI into decision-making processes is the lack of transparency in AI models and algorithms. Enter explainable AI, a crucial concept that seeks to demystify the decision-making processes of AI systems, making them more accessible and understandable for human collaborators.

Explainable AI is vital for fostering trust between humans and AI. Understanding how an AI system arrives at a decision empowers human decision-makers to evaluate and refine the model, ensuring alignment with organisational goals and ethical considerations. Furthermore, the presence of explainability facilitates productive cooperation by allowing humans to verify, evaluate, or even question the insights provided by AI.

Real-world Applications in Strategic Decision-Making

The insurance and reinsurance industry serves as an exemplary arena for the application of hybrid intelligence. The complexity of risk assessment, pricing and underwriting, regulatory compliance, and market dynamics necessitates a nuanced and adaptive approach to decision-making. Let’s explore how hybrid intelligence is transforming this sector.

Risk Assessment and Underwriting

Hybrid intelligence has revolutionised risk assessment and underwriting processes by combining AI’s data processing capabilities with human intuition and expertise. AI algorithms can analyse vast datasets to identify patterns and assess risk probabilities swiftly. This helps assess risks more accurately which is fundamental to the insurance industry. Meanwhile, human underwriters bring contextual knowledge, empathy, and a deeper understanding of unique cases that may not be apparent to the AI. Market based risk assessment, in the insurance industry involves a method of analysing and handling operational risks associated with market fluctuations. It incorporates both data, such as market patterns and qualitative insights from professionals to anticipate potential risks and guide strategic decision making. With the integration of Hybrid Intelligence (HI) into their decision making procedures businesses harness the speed and data processing capabilities of AI alongside the thinking and contextual understanding of experts to enhance these risk assessment models. This hybrid approach can improve precision in evaluating risks, setting pricing strategies, managing claims and overseeing portfolios thereby ensuring a robust insurance sector in response to market uncertainties and emerging issues, like climate change.

For instance, a leading insurance company has implemented a hybrid intelligence system where AI algorithms analyse historical claims data and market trends to identify potential risks. Human underwriters then validate and supplement these findings with industry-specific insights, ensuring a more comprehensive risk assessment.

Claims Processing and Fraud Detection

In the field of claims processing, the joint work of humans and AI is at the top of both the fast settlement process as well as the detection and prevention of fraudulent claims. AI systems can efficiently evaluate claims data to detect certain inconsistencies and mark possible cases of fraud when necessary. Following that, both experience and contextual knowledge of human claims adjuster can be exerted to verify these AI-generated flags and make informed decisions. By applying AI and ML, together with HI, a quick analysis of First Notice of Loss (FNOL) reports to locate fraud through irregularities and patterns is allowed. Through comparing data, the AI entity identifies claims that are doubtful for subsequent close examination by fraud officers. Additionally, natural language processing is integrated as to processing and handling handwritten texts, which leads to quick solution of claims.

A prominent re-insurance company has successfully implemented a hybrid intelligence approach to claims processing. AI algorithms pre-screen claims, highlighting those with suspicious patterns. Claims and Fraud experts thoroughly investigate these flagged cases, leveraging their expertise to differentiate between legitimate claims and potential fraud.

Market Trends and Portfolio Management

In the ever-changing landscape of the insurance industry, staying abreast of market trends and optimising portfolio management are critical for success. Hybrid intelligence enables organisations to leverage AI’s data analysis capabilities to identify emerging trends and assess portfolio performance. Human decision-makers can then contextualise these insights, considering industry dynamics, regulatory changes, and customer preferences. In the evolving insurance sector staying on top of market trends and effectively managing investment portfolios are crucial. Hybrid Intelligence combines the power of AI with strategic thinking to navigate this dynamic landscape.

While AI excels at analysing amounts of data to identify emerging trends and assess portfolio strength it’s the touch that contextualises these insights, within industry dynamics regulatory changes and evolving customer preferences. By blending machine efficiency, with judgement this collaborative approach leads to a nuanced understanding of the market. It equips insurers with the flexibility to adapt to changes proactively ensuring that portfolio decisions not respond to trends but also anticipate future shifts. This proactive stance helps safeguard. Enhance stakeholder value in a changing environment.

A global insurance conglomerate utilises a hybrid intelligence system to analyse market trends and customer behaviour. AI algorithms predict shifts in consumer preferences and identify potential areas of growth or decline. Human strategists interpret these findings, aligning them with the company’s long-term goals and crafting adaptive strategies for portfolio management.

Product Development & Management

A InsurTech company offers  for renters, homeowners and pet health. They employ AI to process claims promptly and effectively and utilise chatbots, for interacting with customers. The Company approach involves a Giveback initiative directing funds to charities selected by policyholders.

One of the insurance companies has invested  into AI for different reasons, such, as evaluating risks spotting fraud and improving customer service. They’ve come up with a Construction Ecosystem that merges data from technologies like images, wearables and sensors to offer advice and standards for overseeing risks, at construction sites.

Challenges and Ethical Considerations

While the potential benefits of hybrid intelligence in strategic decision-making are undeniable, challenges and ethical considerations must be addressed to ensure responsible and effective implementation.

Bias in AI Models

AI systems are not immune to biases present in the data used for training. When collaborating with human decision-makers, it is crucial to identify and mitigate biases to prevent unfair or discriminatory outcomes. Transparency and ongoing scrutiny of AI models are essential to ensure that they align with ethical standards.

Skill Gaps and Training

The integration of AI into decision-making processes necessitates upskilling and training for human collaborators. Organisations must invest in programs to enhance employees’ understanding of AI, equipping them with the skills to collaborate effectively with intelligent systems. Additionally, fostering a culture of continuous learning is vital for staying ahead in the dynamic landscape of hybrid intelligence. As businesses rely more on intelligence, for decision making it’s crucial to address skill gaps through targeted training. Companies need to design programs that explain AI concepts clearly creating an environment where staff feel comfortable and capable of using these technologies. The goal is to build a workforce that not only understands AI but can also work effectively with it.

Establishing a culture that values and supports learning can position a company to utilise Hybrid Intelligences benefits. Investing in employee education is just as important as investing in technology itself since humans will be the ones interpreting, guiding and applying AI capabilities to achieve objectives.

Data Privacy and Security

The collaborative nature of hybrid intelligence involves the sharing of sensitive information between AI systems and human decision-makers. Ensuring robust data privacy and security measures is imperative to safeguard against unauthorised access and potential breaches. Organisations must implement stringent protocols to protect both customer data and proprietary information. When it comes to HI safeguarding data privacy goes beyond customers to also encompass the privacy of employees. With decision making procedures relying on algorithms and exclusive insights it’s crucial to safeguard property. Additionally in light of GDPR and similar privacy laws the importance of providing explanations becomes paramount. People should have the right to comprehend how decisions are reached using their data whether by a person or an AI system.

Regulations

Regulations play an important role in the use of AI and Hybrid Intelligence in the insurance industry ensuring that the deployment of these technologies is done ethically, securely, and in a way that respects human rights. Governments and global organisations are facing the challenge of keeping up with the paced evolution of AI technology. It is essential to establish regulations that can adapt and foresee developments. These rules should not focus on safeguarding consumers but on steering companies towards the ethical use of AI. Finding a ground between encouraging innovation and upholding societal principles is key.

Decision Makers:

Decision-makers must establish ethical guidelines for AI and HI usage. This involves creating frameworks and standards, developing structures  that prevent decision-made by AI. This includes discriminatory practices, and ensures that AI decisions are fair and transparent.

Automation Bias

Automation bias (or decision delegation bias) occurs when individuals excessively trust and rely on automated systems, such as AI-based large language models (LLMs), without conducting appropriate verification or critical assessment of the results. Automation bias poses a risk in healthcare information systems. Finding the harmony between utilising AI to enhance efficiency and upholding human supervision is crucial. It’s essential for systems to be structured in a way that necessitates validation for choices guaranteeing that AI complements rather than substitutes human discernment.

Embracing the AI Collaboration

In the evolving landscape of industries focusing on a human centred approach it is crucial for organisations to be open, to integrating AI collaborators into their decision making frameworks. This integration requires an understanding of both the strengths and limitations of AI going beyond utilisation. By fostering teamwork, between humans and AI there is potential to redefine roles; humans steering vision and ethical guidance while AI contributes predictive insights and operational effectiveness.

Preparing the Workforce

Transitioning to the era of Human Intelligence (HI) requires a workforce that’s both tech savvy and flexible enough to adapt to the evolving nature of work environments. The conventional boundaries of job roles are fading away paving the way for interactive positions where humans and AI technologies collaborate in a continuous learning process. Therefore getting the workforce ready involves reshaping training frameworks to cultivate a workforce that’s well equipped for tomorrow’s challenges, alongside AI.

The future Landscape on HI

As artificial intelligence systems advance they could extend beyond the insurance industry to fields. For example in healthcare AI might assist in analysing information while doctors offer patient care. Similarly in finance AI could handle data driven trading tasks allowing humans to concentrate on building client connections and strategic endeavours. The possibilities are abundant and extend to sectors that depend on decision making procedures.

Advancing Beyond the Present

In the future the combination of cutting edge technologies such as quantum computing and AI may enhance the potential of intelligence. Quantum computing, known for its abilities could empower AI to tackle increasingly intricate challenges expanding the possibilities of human AI partnerships.

Ethical AI as the Standard

As we progress towards HI systems the demand for ethical AI grows louder. Ethical AI encompasses systems that not make decisions effectively but adhere to human values and societal standards. These systems should uphold fairness, accountability and transparency in every decision they assist with.

 

Conclusion

Hybrid intelligence, grounded in theories of cognitive complementarity and distributed cognition, represents a paradigm shift in decision-making processes. By marrying the strengths of AI and human intelligence, organisations can navigate the complexities of strategic decision-making with unprecedented efficiency and adaptability. The insurance and reinsurance industry serves as a prime example of how hybrid intelligence can revolutionise risk assessment, claims processing, and portfolio management.

As we embrace this transformative era, it is crucial to prioritise explainable AI to foster trust and collaboration between humans and intelligent systems. Realising the full potential of hybrid intelligence requires addressing challenges such as bias in AI models, skill gaps, and data privacy concerns. By doing so, we can unlock the true power of collaboration between humans and AI, ushering in a new era of strategic decision-making that is both innovative and ethically grounded.

Hybrid intelligence combines the strengths of intelligence power and humans nuanced ethical decision making. The insurance and reinsurance sector is paving the way for this concept urging industries to do the same. However the success of intelligence relies on a blend of technological advancement, ethical standards, education and inclusivity.

As we incorporate intelligence into our work environments it becomes crucial to uphold this equilibrium. It is imperative to develop systems that are not only smart but also considerate of human values. Explainable AI plays a role in maintaining this balance by fostering transparency that fosters trust and communication between AI systems and their human counterparts.

Ultimately hybrid intelligence goes beyond technology; it emphasises individuals and how we can use technology to enhance our abilities. It focuses on constructing a future that honours dignity, principles and the collective wisdom of our communities. By tackling challenges and embracing the strengths of both humans and AI we embark on a phase of decision making—one marked by innovation, inclusivity and ethical foundations.

The journey toward intelligence involves growth. Just as we evolve and adjust our AI systems must also adapt. The collaboration between artificial intelligence holds the potential to lead us towards a future where we can tackle the urgent issues of our era.

This article aims to offer an insight into intelligence (AI) discussing its possibilities, obstacles and the ethical factors that should influence its development moving forward.

Questions:

• Are humans prepared for hybrid intelligence?
• How is the concept of Hybrid Intelligence reshaping the importance of knowledge, in industries that have historically relied on expertise?
• How can we make sure that Hybrid Intelligence is implemented in a way that includes and is available, to all levels of the workforce?

 

Profile: Salvatore Scuderi is a doctoral candidate and researcher at EIM, the EIM European Institute of Management. His doctoral research focuses on the perceptions of C-Suite, Business Development and Business Line Managers about the adoption of AI-based hybrid intelligence technologies into the strategic decision-making processes. This article builds on the interplay between theoretical insights and practical applications of Hybrid Intelligence, emphasizing the ethical deployment of AI in conjunction with human intelligence to tackle complex decision-making in various industries. My engagement with Strategy, Decision Making, and Innovation interest groups keeps me abreast of the latest developments and challenges in the field, fuelling my research and practical interventions in the evolving landscape of hybrid intelligence.

 

Keywords: Gen AI, AI and Machine Learning, Deep Learning, Explainable AI (XAI),  Quantum Machine Learning, Edge AI, Decision Making