Executive Q&A with Juan de Castro, COO at Cytora

Building confidence in AI: four top tips for employers to prepare staff for the use of AI

With over twenty years of experience in growth strategies and digital transformations, Juan de Castro is the CCO/COO at Cytora, where he leads product strategy, sales, distribution, partnerships, and operations. Having previously served as the UK COO at Hiscox and a senior manager at McKinsey’s Silicon Valley office, Juan brings a wealth of expertise in leveraging technology in corporate settings.

Juan de Castro, CCO/COO at Cytora

What is AI to you? What does AI mean for you?

AI to me is an essential tool that reshapes how we understand and mitigate risks in the insurance industry. It enables us to process vast amounts of data efficiently, improving accuracy in risk assessment and pricing. Ultimately, AI means innovation and the ability to drive forward the modernisation of traditional insurance practices, aligning them more closely with today's technological advancements. Critically, it enables the insurance industry to provide better service and products to clients. By making risk analysis more precise and efficient, we can offer more affordable, tailored products to clients. As AI continues to develop, it will become a greater enabler of human-led innovation, allowing those in the insurance industry to do more—quicker and with more accuracy.

In your opinion, what is the most exciting application for AI in the Insurtech world?

Generative AI (GenAI) presents a transformative opportunity for technological advancement, which I believe is truly a once-in-a-generation event. Notably, the insurance sector has been highlighted by the World Economic Forum as one of the industries with the most to gain from GenAI implementation. I’d be amiss if I didn’t say that what we are doing at Cytora right now is one of the few real production use cases of AI that is delivering genuine business impact in insurance. We're at the forefront of applying AI within the insurtech landscape, moving beyond theoretical proofs of concept to actual production. Our use of Large Language Models (LLMs) is not just experimental; it's a reality that's adding tangible business value for our clients. By leveraging AI, we enable insurers to automate their underwriting workflows, deploying human capacity only where human judgment is required. With Cytora's digital risk flows, insurers double their underwriting productivity, boost their responsiveness, and drive more granular and consistent risk selection.

What are the major hurdles in promoting AI software, and how do you overcome them?

Adopting AI software within the insurance industry comes with significant challenges, notably concerns around data privacy and the accuracy of the models. These issues are critical but can be effectively managed to ensure that data remains secure and model outputs are both accurate and transparent. As an insurer, it is essential to collaborate closely with IT teams and external partners to ensure that data-handling processes adhere to stringent security protocols. Establishing robust security measures is key to maintaining the integrity and privacy of data. Another critical aspect is the provenance of the models' outputs. Insurers need to be able to trace the origins of any data generated by AI models. This capability ensures transparency and aids in maintaining accountability, which is crucial for building trust and compliance in the use of AI technologies. By addressing these areas diligently, we can overcome the major hurdles in AI adoption and leverage these advanced tools to their fullest potential in the insurance industry.

How would you encourage an InsurTech team to take up AI?

The insurance industry has shown a remarkable openness to the advantages that Generative AI (GenAI) offers. In discussions with various insurers, there's a clear interest in leveraging this technology as a competitive advantage. However, the critical focus remains on choosing the appropriate application. Unlike other sectors that employ GenAI for creating new content like images, write-ups, or videos, the insurance industry uses Large Language Models (LLMs) primarily for risk and claims digitisation. This involves converting unstructured data into structured data quickly and efficiently. These models are not only adaptable and trainable but also deliver more precise outcomes. Since we are not tasking these models with generating new content, we largely avoid issues with data inaccuracies often referred to as "hallucinations." Importantly, GenAI does not eliminate the human element in insurance; rather, it significantly reduces manual tasks, allowing professionals to dedicate more time to decision-making, creative thinking, and high-value initiatives. This shift not only enhances productivity but also fosters innovation within the industry.

What are your four top tips for employers to prepare their staff for the use of AI?

  1. Focus on user adoption: It’s critical to explain to underwriters that AI is a tool designed to strengthen their capabilities, not replace them. They need to understand how AI can enhance their roles and help them succeed by taking over repetitive tasks, providing insights, or supporting decision-making, allowing them to focus on more strategic and creative aspects of their job.
  2. Avoid ‘big-bangs’: Companies must avoid disrupting the workplace with sudden, comprehensive changes to established workflows, as employees are unlikely to react as effectively to dramatic change. Introduce AI systems in a planned and staggered way, allowing employees to adjust to new processes over time. This phased approach will make the transition smoother and less intimidating.
  3. Communicate the vision: Naturally, employees will worry about the threat of AI making their job roles obsolete. To help reassure employees, it’s important to share the company’s vision for AI integration, focusing on the positive outcomes. Make sure to communicate how AI will contribute to the company’s success and, by extension, to the security and growth of employees’ careers. This can help in aligning the team’s efforts
    with the organisational goals and reduce anxiety around AI adoption.
  4. Practise inclusive change management: Involve employees in the change process from the start. Gather their input, address their concerns and make them feel a part of the AI implementation journey. This inclusivity can build ownership, ease the transition and foster a culture of continuous learning and adaptation.


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