How a user-centric discovery can lead to transformative solutions

Discover how a user-centric discovery process helped uncover hidden challenges, redefine priorities, and deliver transformative solutions that created lasting impact.

What is a user-centric discovery?

A user-centric discovery is an approach in project development that prioritises understanding the needs, challenges, and workflows of end users from the outset. By engaging with users directly through activities such as workshops, interviews, and process mapping, this method ensures that the solutions designed are aligned with real-world problems rather than assumptions. It focuses on identifying pain points and inefficiencies in existing processes, allowing for the development of practical, efficient, and user-friendly tools. A user-centric discovery reduces the risk of failure as it creates a process of continuous user feedback and iteration, thus creating solutions that are usable and technically viable.

Benefits of a User-Centric Discovery Process

1. Increased Efficiency: By addressing specific pain points this auditing company will face, such as reducing the time spent on manual data entry and report writing, the proposed AI solutions are set to improve productivity for auditors.

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2. Improved User Satisfaction: The auditors and compliance teams will benefit from tools that are built around their needs. These tools not only simplify their workflows but also reduce frustration.

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3. Faster Adoption of New Technology: When solutions are built around user needs, they are more likely to be adopted. A user-centric approach ensures that the tools are intuitive and easy to use, reducing the learning curve and increasing the likelihood of success when new technologies are introduced.

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4. Lower Risk of Failure: Focusing on users during project discovery reduces the risk of developing solutions that don’t work in practice. By continuously testing ideas with users, our client can identify potential issues early and make adjustments before full-scale implementation.

Case study

This methodology was utilised in an AI discovery project run by QuantSpark for an auditing company. The purpose of the project was to analyse existing processes identifying areas of inefficiency whereby AI could be utilised to improve and reduce costs of core processes and ensuring the company remained compliant. In order to produce impactful recommendations, user needs and pain points were at the forefront of every decision. By focusing on the people who will ultimately use the system, the client is better positioned to implement solutions that are practical, efficient, and scalable.

Understanding the existing technical landscape

In the early stages of the AI Discovery project, QuantSpark conducted workshops with key user groups, including auditors, compliance teams, and the technology department. An initial deep dive with the technology team offered a high-level view of the audit reporting process, shedding light on the current tools in use and their limitations. This session not only revealed the broader technology landscape but also provided insights into existing strategies.

With this understanding of the tools, processes, and gaps, QuantSpark was well-positioned to conduct focused workshops with auditors and compliance members. These sessions aimed to uncover the root causes of inefficiencies, moving beyond surface-level issues to understand why these challenges persist in day-to-day operations.

Discovery workshops: Understanding the key pain points

To accurately identify inefficiencies, the workshops focused on two key activities: Mapping out the current workflow as team members walked through their processes and conducting interview-style Q&A sessions.

An important insight that emerged from these sessions was that auditors often relied on offline resources, requiring additional manual work to integrate information into their internal systems. This led to inconsistencies, wasted time, and a significant number of corrections.

Pain point gathering

This and many other insights drawn directly from the users highlighted how the existing systems had added constraints to their workflow rather than simplifying it. To create refined solutions that address the core problems the QuantSpark team carried out an Affinity Mapping Exercise, a collaborative method for organising and grouping related ideas or data into themes, helping teams identify patterns and insights from complex information.

Without these discovery activities, such issues may have gone unnoticed, and the project may have moved forward without addressing the core pain points that were making users’ jobs more difficult. Understanding these workflows and the real challenges faced in daily operations provided the foundation for more impactful solutions.

Solutionising: Aligning solutions with user needs

One of the key outcomes of QuantSpark’s discovery phase with this auditing company was the identification of several recommendations and consequently Proof of Concept (PoC) opportunities, which were based directly on user pain points. By listening to auditors and compliance teams, the project team were able to identify areas where AI could streamline processes and save time.

To approach this holistically, QuantSpark refined the "As-Is" process map, which was created based on several discovery sessions, into a "To-Be" process map. The "As-Is" map captured the challenges users face in their day-to-day tasks, while the "To-Be" map illustrated a more efficient workflow made possible by the implementation of AI solutions. For example, the utilisation of AI to perform basic compliance checks, reducing the feedback loop between the auditor and compliance team, and in turn, reducing the operational cost of the process.

Solutionising: Incremental development based on user feedback

A user-centric approach to development ensures that progress is phased and incremental. This is why the initial focus on Proof of Concepts (PoCs) reflects a commitment to ongoing user engagement. In the AI discovery project, the RICE prioritisation framework—based on Reach, Impact, Confidence, and Effort—was used to evaluate and rank solutions, helping the team identify which ones would provide the highest value and should be prioritised.

The solutions with the highest value were recommended as POCs which will then be tested with users to assess technical feasibility and gather feedback on usability and effectiveness. By starting with a small group, specific features, and well-defined metrics, QuantSpark can help the client iterate, refine, and enhance solutions before scaling them to full production.

Conclusion

This AI discovery project is a clear example of the power of a user-centric approach. By listening to users, identifying their pain points, and proposing solutions that directly address those challenges, QuantSpark can provide clients with tools that will improve efficiency, enhance compliance, and ultimately, make life easier for their users.

The phased development, starting with PoCs, shows a commitment to constant iteration based on user feedback, ensuring that the final product will meet the real needs of those who use it every day.