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Lukasz Pawłowski presents a revolutionary approach to handling data abundance in AI-assisted product research, introducing the concept of 'proto personas'. He emphasizes that in the age of AI, synthetic user personas derived from extensive data research can deliver valuable insight, especially when traditional user research is limited or not feasible.
Rather than getting bogged down by raw data, Lukasz explains how converting information into tangible, 'simulated' user personas brings usability and human-centred perspective back into data analytics. However, he makes it clear that these proto personas are not a substitute for real user engagement, but a supplementary tool for better understanding user behaviour, experiences and needs.
Hear Łukasz break down:
The idea behind proto personas: data-driven, synthetic representations of users created when direct access to real users is limited or unavailable.
Where proto personas add value and where they fall short, clarifying that they are best used to spark hypotheses rather than replace real user feedback.
A more rigorous, repeatable workflow: generating data with proto personas, extracting insights, validating assumptions, documenting learnings, and iterating—showing how AI is influencing research and decision-making cycles.
Why responsibility matters when working with proto personas, including the need to understand data sources, methodologies, and the risk of embedded bias.
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Monterail Team Analysis
Here's how you can reshape your product development using the 'proto personas' concept in AI-assisted workflows:
- Embrace proto personas: Use this innovative, data-driven tool to complement real user research and cut through data overload. Remember, it's not a replacement for human-centred research.
- Develop a discipline: Amplify the efficiency of AI by adhering to a structured workflow. Generate data from proto personas, curate insights, validate them, document your findings, and repeat.
- Balance real user interaction: Understand that while proto personas provide substantial insights, the human touch is invaluable. Don't stop direct interaction with real users. These synthetic personas should just be a supplementary tool.
- Recognize and address bias: Ensure precautions against potential bias in AI systems. Remember to critically consider how the data for proto personas is gathered and synthesized.
- Share and collaborate: After generating insights, document them in a shareable format for the team. Establish a process for systematically incorporating these findings into your project plans.
- Decide carefully: With an overflow of actionable insights from proto personas, choose wisely. Identify what matters most to your product and user experience and prioritise accordingly.
- Reconsider research methodologies: Lukasz's approach challenges traditional methodologies. Have open conversations within your team about how your current practices might need to evolve in the face of AI's potential.