Prudential estimates AI-assisted development at six to eight weeks

By James Calloway

Prudential estimates that artificial intelligence could compress insurance product development from four to six months to six to eight weeks.

That estimate came from Priscilla Ng, Prudential plc’s Group Chief AI Business Officer and Group Chief Customer and Marketing Officer, in a fireside chat with Tim Charlton, editor-in-chief, at the Asian Banking & Finance and Insurance Asia summit in Hong Kong on 5 November. Prudential has not achieved the shorter cycle; six to eight weeks remains Ng’s projection.

How much time could AI actually save?

Traditional product development requires actuaries to perform extensive calculations that determine premiums and assess risks, including factors such as smoking status and other customer characteristics. Product pricing and legacy systems can further delay policy launches, contributing to a four-to-six-month process.

Ng said: “I estimate that with this kind of just like an AI product entry, right? It can actually shorten the time for four to six months to actually maybe just six weeks, eight weeks.”

The shorter process would not remove actuarial judgment. “We cannot just rely on the agent and the the AI to actually come up with the pricing model; you have to have a human in the loop to check about it,” Ng said. Human reviewers would remain responsible for actuarial calculations and pricing decisions.

Faster development could also give insurers more scope to tailor coverage to specific customer segments instead of relying on standardized policies that take months to design.

Where does AI enter the insurance workflow?

Beyond product development, Prudential is investing in AI across agency distribution, operations and healthcare services. These programs cover several distinct interfaces and tasks:

• Agency distribution: AI tools analyze customer information, identify protection and wealth-accumulation gaps, and generate reports before client meetings. Less preparation time could leave agents with more capacity for customer discussions rather than administration.
• Agent coaching: AI-powered tools let agents practise sales and service skills at any time, overcoming some limitations of scheduled classroom training. Managers can monitor participation, assess performance and identify employees who need further support.
• Underwriting: Prudential is exploring an agent-facing chatbot that assesses a customer’s medical circumstances, indicates the likelihood of approval and identifies required documents. It could reduce preliminary feedback from about one week to minutes, although final approval would still depend on underwriting requirements.
• Healthcare access: In Indonesia, customers can use WhatsApp-based tools to seek doctor recommendations and arrange appointments.
• Account review: In Hong Kong, AI analyzes photographs of Mandatory Provident Fund statements and consolidates information from multiple accounts, helping customers review them together.
• Feedback analysis: Prudential receives about 11,000 customer comments annually across Asia, making manual review difficult. AI identifies recurring complaints and gives management teams insight into service issues.

A September 2026 report from KPMG International found that 71% of insurance executives primarily used AI for content generation or routine task automation, while only 29% ran end-to-end processes through AI agents or automation. The report also found that 92% of insurers reported productivity improvements or lower operating costs, but only 25% used AI to support growth through new products and services.

What could stop these gains from scaling?

Expansion across markets faces data-infrastructure constraints, regulatory requirements and uneven employee adoption. Ng identified common data standards, reliable system architecture and AI governance as essential foundations for scaling AI across Prudential’s operations in Asia and Africa.

KPMG’s September 2026 report found that only 11% of surveyed insurers considered their data foundations and governance strong enough to scale AI effectively. Just 8% rated their workforce as highly proficient in AI tools.

Ng said technology investments would produce limited benefits unless employees incorporated the tools into daily operations. Her expectation is that insurers will move beyond routine analysis toward more advanced applications supporting product development, customer service and decision-making.