Democratising knowledge

The ability to harness and utilise research insights effectively can be a game-changer for organisations. However, the challenge often lies in making these insights easily accessible and actionable for everyone within the company. Addressing this challenge, a design and research team embarked on an innovative project to create an insight assistant using OpenAI, PlugBear, and Slack. This assistant was designed to democratise knowledge and facilitate insight-driven decision-making across the organisation.

The Challenge: siloed knowledge and inefficient access

Before the development of the insight assistant, the organisation faced several challenges:

1. Siloed knowledge: research insights were often confined to specific teams or departments, leading to a lack of shared understanding and missed opportunities for cross-functional collaboration.

2. Inefficient access: employees found it difficult to locate and utilise relevant research insights promptly, impacting their ability to make informed decisions.

3. Manual processes: the process of gathering, distributing, and updating research insights was manual and time-consuming, leading to delays and potential information gaps.

The solution: an insight assistant

To overcome these challenges, the design and research teams decided to create an insight assistant leveraging OpenAI’s capabilities, PlugBear for integration, and Slack as the communication platform. The goal was to create a seamless and intuitive system that could democratise access to insights across the organisation.

Step 1: Leveraging OpenAI for insights

OpenAI’s language models were integral to the development of the insight assistant. The models were trained to understand and summarise research reports, extract key insights, and answer questions posed. By harnessing the power of natural language processing (NLP), the team ensured that the assistant could provide accurate and contextually relevant information.

Step 2: Integrating with PlugBear

PlugBear, a tool designed for easy integration and automation, was used to connect OpenAI’s capabilities with the organisation’s existing data repositories and Slack. This integration allowed the insight assistant to access a vast amount of research data, analyse it in real-time, and deliver insights directly to employees within their Slack workspace.

Step 3: Implementing in Slack

Slack was chosen as the primary platform for deploying the insight assistant due to its widespread use within the organisation. By integrating the assistant into Slack, the team ensured that employees could easily access insights without needing to switch between different tools or platforms. The assistant could be interacted with through simple commands or natural language queries, making it user-friendly and accessible to all employees, regardless of their technical proficiency.

Key features of the AI assistant

• Real-time insights: employees could request real-time insights on specific topics or projects, ensuring they had the most up-to-date information at their fingertips.

• Summarisation: the assistant provided concise summaries of lengthy research reports, saving employees time and effort.

• Search and retrieval: users could search for past research reports and insights using natural language queries, making it easy to locate relevant information.

• Proactive alerts: the assistant could proactively alert employees about new research findings or updates relevant to their projects or areas of interest.

• Collaboration: the assistant facilitated cross-functional collaboration by making insights accessible to all teams, breaking down silos and encouraging knowledge sharing.

Impact for the organisation

1. Democratised knowledge: by making research insights accessible to everyone, we democratised knowledge across the organisation, ensuring that all employees could benefit from the latest findings and data.

2. Enhanced decision-making: with easy access to relevant insights, employees were able to make more informed and data-driven decisions, leading to better outcomes for projects and initiatives.

3. Improved efficiency: the automation of insight retrieval and summarisation reduced the time spent on manual processes, allowing employees to focus on higher-value tasks.

4. Increased collaboration: the assistant fostered a culture of collaboration and knowledge sharing, as teams could easily access and discuss insights relevant to their work.