Human Element is Still the Key in Generative AI

Human Element is Still the Key in Generative AI

Without human intervention, especially subject matter experts from the particular domain, a Generative AI model will simply be unable to create anything worthwhile


Very often people want to know the tool or chatbot used to generate a photograph or a painting. The underlying objective is that once the tool is known, they too could produce a similar work. Nevertheless, this is like asking a good chef, the cooking range he or she uses, or a photographer their preferred camera.

What we need to understand that describing a painting or a photograph to a Generative AI tool like DALL-E effectively requires a thoughtful combination of detail and creativity. The goal is to communicate your vision as clearly as possible to guide the AI in generating an image that aligns with your expectations. The description or the prompt could end up being a lengthy essay that includes everything from the golden rules of composition, to lighting, mood, setting, environment, colour schemes, etc. In other words, without human intervention, especially an expert or a subject matter expert from the domain, the AI model will simply be unable to create anything worthwhile.

And, when crafting your prompt, balance specificity with creativity. Too much detail can constrain the AI’s creative process, while too little might result in an image that doesn’t meet your expectations. It’s about finding the right mix of guidance and flexibility to inspire a result that closely aligns with your vision. We find that across industry sectors, human element continues to play a vital role in extracting the optimum outcome from Generative AI.

The Human Element Key to Harnessing GenAI in Insurance

The insurance industry stands to benefit greatly from generative AI (gen AI) technologies, according to Barbara Karuth-Zelle, COO and board member of German insurance giant Allianz. In a recent interview, published in McKinsey insight,Karuth-Zelle outlined three key strategies for insurance companies to leverage gen AI:

  1. Keep solutions human-centric. Allianz supports the EU’s proposed AI Act aimed at regulating the technology while ensuring ethical standards, data privacy, prevention of bias and discrimination, and transparency.
  2. Involve employees in AI development through reskilling and retraining. Allianz provides internal ChatGPT access, encourages experimentation on use cases, and offers intensive AI training. Its responsible AI framework ensures data confidentiality and accuracy.
  3. Scale key use cases across the business, including claims coverage, catastrophe prediction, marketing personalisation, underwriting, document management, and software development.

Karuth-Zelle emphasised that successful digital and AI transformations depend more on mindset, people and organisation than just the technology itself. A foundation of sharing, curiosity, creativity, agility, diversity and simplified products and processes allows innovation to be quickly scaled.

Some key data points reinforce Karuth-Zelle’s points:

  • The global market for AI in insurance is projected to grow from $1.2 billion in 2021 to $11.9 billion by 2027, at a compound annual growth rate of 45.7%. (Source: MarketsandMarkets)
  • 75% of insurance carriers say they are investing in data science, machine learning and AI, more than any other industry. (Source: SMA Research)
  • Over 80% of insurance carriers expect AI technologies to have a significant impact on the industry’s economics and competitive dynamics over the coming decade. (Source: Deloitte)

As a leader driving Allianz’s tech transformation over the past decade, Karuth-Zelle has seen firsthand the importance of resilience, creativity, clear scope, diverse teams and speedy delivery. While the technology is powerful, she believes the human element is still the key ingredient for success. With the right strategies and leadership, insurance companies can harness AI to sustainably transform their operations for the future.

The convergence of Human Talent & AI

While artificial intelligence (AI) is transforming various industries by enhancing productivity and efficiency, the human element remains crucial in many sectors. Here are some instances from other industries where the combination of AI and human expertise is important:

  1. Healthcare: AI assists in diagnosing diseases, analysing medical images, and developing personalised treatment plans. However, the human element, such as empathy, emotional support, and critical decision-making, is essential in-patient care. Doctors and nurses use their experience and intuition alongside AI-generated insights to provide the best possible care.

Example: IBM Watson Health uses AI to analyse vast amounts of medical data, helping doctors make more informed decisions. However, the final treatment decisions are made by healthcare professionals who consider the patient’s unique needs and preferences.

  1. Education: AI can personalise learning experiences, provide adaptive assessments, and automate grading. However, human teachers are crucial for engaging students, fostering creativity, and providing emotional support. Teachers use AI-powered tools to enhance their teaching methods and offer personalised attention to students.

Example: Duolingo, a language learning app, uses AI to adapt lessons to each user’s learning style and pace. However, the app also connects learners with human tutors for conversation practice and feedback.

  1. Customer Service: AI-powered chatbots and virtual assistants can handle routine customer inquiries and provide 24/7 support. However, human agents are essential for handling complex issues, providing empathy, and building customer relationships. Companies use AI to streamline operations while relying on human agents for critical interactions.

Example: Zendesk, a customer service platform, uses AI to automate ticket routing and provide agents with relevant information. However, human agents handle the actual customer conversations and use their emotional intelligence to resolve issues.

  1. Creative Industries (e.g., design, music, art): AI can assist in generating ideas, creating prototypes, and automating repetitive tasks. However, human creativity, intuition, and emotional expression are essential for producing meaningful and impactful work. Artists and designers use AI as a tool to enhance their creative process, not replace it.

Example: Adobe Sensei, an AI platform, helps designers automate tasks like image resising and colour matching. However, the creative direction and final design decisions are made by human designers who bring their unique perspectives and experiences to the project.

  1. Human Resources: AI can streamline resume screening, candidate matching, and employee performance analysis. However, human HR professionals are crucial for conducting interviews, making final hiring decisions, and fostering a positive company culture. HR teams use AI to make data-driven decisions while relying on human intuition and emotional intelligence to build strong teams.

Example: HireVue, an AI-powered hiring platform, analyses candidate video interviews and provides insights to employers. However, the final hiring decisions are made by human HR professionals who consider the candidate’s fit with the company culture and team dynamics.

In each of these industries, the combination of AI and human expertise leads to the best outcomes. AI enhances efficiency and provides valuable insights, while human judgment, creativity, and empathy ensure that the final products or services are tailored to the unique needs of each situation.


Know more about our Top Ranked PGDM in Management, among the Best Management Diploma in Kolkata and West Bengal, with Digital-Ready PGDM with Super-specialization in Business AnalyticsPGDM with Super-specialization in Banking and Finance, and PGDM with Super-specialization in Marketing.

Leave a comment

Your email address will not be published. Required fields are marked *

© 2023 Praxis. All rights reserved. | Privacy Policy
   Contact Us
Praxis Tech School
PGP in Data Science