使用AI和NLP消除组织孤岛

伊曼纽尔Helbert

2024年2月15日

对话指纹的背后是人工智能和自然语言处理技术, 一个可以优化信息分配的新概念

One of the greatest challenges within organisations – especially large and multinational entities – is how to circulate information.  然而,, being able to distribute the right information to the right person is central to building competitiveness and can even prove a game-changer.

So, we started with a small team to investigate whether artificial intelligence (AI) and natural language processing (NLP) technologies could be used to help determine and visualise the emergence and propagation of information. 本质上, we wanted to know if we could identify within an organisation w在这里 employees were discussing the same topic but had no overlap with each other – a clear indicator of the siloing that can impede information sharing in the workplace.

Respecting privacy laws and maintaining corporate confidentiality were paramount. But this also meant that we needed to devise a method that allowed us to identify topics of conversation without having any knowledge of the content of the conversation.

这就是我们创造对话指纹这个概念的方法.

跨越孤岛的方法

今天有许多可用的通信方式, but we believe this new approach of conversation fingerprinting complements those existing tools.

Tools like communities – such as networks of experts – and internal events that encourage the development of cross-functional networks. T在这里 are also internal social networks and discussion groups which can simplify the exchange of information.

These tools work because they generate closeness and trust between members and make it possible to tackle cross-functional subjects with a broader range of skills than a single project team might provide. 但它们受到自身构成方式的限制, 包括哪些成员, and which topics are discussed – all of which may remain compartmentalised within the same silo or geographical area.

用图形表示这些协作工具也不容易. And this aspect of visualising information flow – knowing w在这里 information is generated, 它如何传播/不传播，谁消费它——是关键.

事实上, visualising is at the heart of graph-based theories such as Social Network Analysis (SNA) and Organisational Network Analysis (ONA). These theories can be used to model and analyse interactions within an organisation, with each employee representing a node in the graph and edges between nodes representing interactions. They are behind consumer and business applications such as web page ranking and purchasing recommendations and can be used to detect communications roadblocks or adjust teams to optimise efficiency.

The upside to these theories is that they can show who is connected to whom and how often, 甚至估计交换的信息量. 通过在组织图上建模通信图, it is possible to identify ways to improve productivity by changing the structure of the organisation to match the structure of communications flows.

仍然, these graph-based theories remain limited by the fact that they only consider statistical data relating to communications traffic and nodes. 这并不能解释对话的语义内容, on the assumption that the information exchanged is naturally linked to the tasks and missions of each team.

谈话指纹

通过在我们的研究中引入AI和NLP技术, 我们了解到，我们可以改变这一点，发现新的视角.

那么，这个对话指纹的概念是什么? 本质上, 它是两个雇员之间交换信息的唯一标识符, 每条信息都以指纹的形式编码. In applying the concept, our aim was to assess the similarities between the fingerprints. 

通过将这些流程与对话图相关联, it became possible to visualise the branches through which information was propagated, 以什么样的速度和强度. 

更好的是, it became possible to see similar conversations emerging in parts of the graphs that were not connected to each other. 

后者是社区管理和推广的一个重要方面. It enables different entities working on similar subjects to be brought together, t在这里by encouraging cross-disciplinary interaction and optimising talents and energies.

从这里到哪里

这项研究很有前途. Not only has it validated the relevance and performance of the solution's architecture, but it has also opened up new areas of use – such as intelligent search – making it possible to find specific information buried in a stream of conversation. 

And t在这里’s an environmental benefit to this more frugal AI approach when compared to large language models (LLMs). 虽然生成式人工智能最近成为头条新闻, 这项技术被证明是一个巨大的能源用户. 

Combining AI and NLP technologies offers a less expensive and more controlled alternative, 同时尊重公司和员工资料的保密性. And that makes conversation fingerprinting even more compelling as a tool to forge high-performing communities that drive competitiveness.