Day 3 of My 15-Part Series: Enhancing Decision-Making with Multi-Expert AI Prompting for Product Leaders

I must admit , it took me some time to research for the part 3 and all i can say its worth a read it. In today's post, we'll explore how product leaders can leverage multi-expert AI prompting to navigate complex decisions, like the classic “Build vs. Buy” dilemma. Using simulated expert personas, AI can provide a balanced, synthesized perspective on financial, technical, customer, compliance, and market factors—empowering you to make more informed choices in product strategy. Let's dive into how this innovative approach can add clarity and confidence to your decision-making process.

Recently i got an opportunity to read this super interesting research study done very recently on Multi-Expert Prompting from the National University of Singapore underscores how combining multiple expert perspectives in AI responses can significantly improve the accuracy, safety, and usefulness of insights for high-stakes decisions.

This approach enhances Ethan Mollick’s idea of calling on the “best available human.” Rather than relying on one AI-generated “expert,” multi-expert prompting has the AI simulate and integrate input from several distinct experts. By capturing diverse viewpoints—for example, from an economist, user researcher, technical architect, and compliance officer—the AI then synthesizes these perspectives to provide product leaders with a balanced, holistic answer.

Why This Matters for Product Leaders

Product leaders frequently balance conflicting priorities: maximizing user experience, adhering to regulations, ensuring technical feasibility, and supporting financial goals. Each of these areas requires specialized knowledge, and an AI that inhabits multiple expert personas offers critical advantages:

1. Enhanced Insight and Reliability: Multi-expert prompting leverages what the researchers call “Nominal Group Technique,” a structured decision-making process that enriches AI responses by synthesizing consensus and highlighting divergent viewpoints.
2. Improved Safety and Alignment: With experts representing different domains, this approach flags potential risks and ensures considerations from all essential areas are surfaced, reducing the risk of oversight in areas like security or user privacy.
3. Better Decision Confidence: According to the study, responses generated through multi-expert prompting are perceived as more useful and trustworthy, helping product leaders make well-rounded decisions with a greater degree of confidence.

Practical Example for Enterprise Products- Using Multi-Expert AI to Decide on “Build vs. Buy” for an AI Tool

Let us understand the above concept with an classic example. Imagine you're a product leader at a mobility company evaluating whether to build or buy an AI tool that can drive revenue by enhancing customer experience. However, you lack easy access to a panel of experts to navigate the complexities. By leveraging multi-expert AI, you can simulate five expert perspectives to guide your decision.

Key Expert Personas (defined by you in any LLM) :

1. Financial Analyst: Evaluates cost-benefit, long-term ROI, and impact on margins.
2. AI Engineer: Assesses development complexity, resource needs, and technical scalability.
3. Customer Experience (CX) Specialist: Considers user impact, feature usability, and potential for driving engagement.
4. Compliance Officer: Examines legal implications, data privacy requirements, and regulatory risks.
5. Market Strategist: Analyzes competitive landscape, market positioning, and future scalability.

Insights from Each Expert (directly from AI) :

Financial Analyst

• Pros (Buy): Lower upfront costs, predictable ROI if vendor tools have a proven track record.
• Cons (Build): High initial development and ongoing maintenance costs; ROI uncertain if adoption lags.

AI Engineer

• Pros (Build): Customization control over features tailored to car rental operations.
• Cons (Buy): Vendor dependency for updates; possible limitations with integration into existing systems.

Customer Experience Specialist

• Pros (Buy): Faster implementation; aligns with customer expectations if similar tools are in market.
• Cons (Build): Slower delivery, potential for trial-and-error phases affecting early user experience.

Compliance Officer

• Pros (Build): Greater control over data governance, enabling strict adherence to data policies.
• Cons (Buy): Privacy and data-sharing risks with third-party vendors, adding compliance complexity.

Market Strategist

• Pros (Buy): Can deploy quickly to stay competitive with other car rental companies offering AI-driven services.
• Cons (Build): Slower time-to-market could risk losing out on early customer interest in AI features.

Synthesized Decision (directly from AI) :

After analyzing these perspectives, a multi-expert AI might synthesize a recommendation based on the consensus and unique points of each expert:

• Recommendation: Lean towards buying an AI tool with flexibility for customization. Given the high upfront costs and longer timeline associated with building from scratch, buying aligns better with immediate market needs and ROI goals. However, prioritize vendors with strong compliance frameworks and adaptable APIs to ensure data security and easy integration into the existing platform.

This approach lets product leaders explore multidimensional challenges efficiently, with insights that would typically require a panel of experts to achieve. For product leaders working in enterprise contexts, multi-expert AI prompting is a game-changer. By accessing expert-driven, multi-angle analyses, leaders can navigate complexity with insights that are more informed, safer, and strategically aligned.

Super excited to have this already a possibility in the coming months.

For a deeper dive, see the full research paper.