Over the past year and a half, we have all been wowed by amazing examples of Generative AI, created by clever and amusing prompts, and seemingly constant product updates and releases. Further, enterprises are adopting GenAI at speed; research from earlier this year estimates that up to 38% of enterprises (1,000+ employees) are actively implementing the technology.

But beyond the headlines, things look a little different. While employees are leveraging GenAI to increase productivity on individual and team levels, enterprises are frequently not yet using Gen AI in a structured manner across their organizations. Further, the data that is leveraged to train GenAI models has significant limitations, and as a result, the gains being made are not yet living up to their potential.

In this post, we will take a look at the broad types of GenAI on offer today, why many of the most high-profile offerings have some missing pieces in terms of enterprise use cases, and introduce why tacit knowledge is a vast untapped opportunity for GenAI in enterprises today.

1. General GenAI: improving productivity with publicly available data

General GenAI tools, such as ChatGPT, Gemini, and Meta AI, have commanded the lion’s share of media attention. And in the enterprise, they can boost both individual and team productivity. For example, your marketing, customer support, and engineering teams may use GenAI tools for support emails, to generate creative ideas and story headlines, or to quickly code.

Nonetheless, there are limits to what these kinds of tools can provide.

As they are trained on publicly-available data, they miss key context from within your organization.
The wide range of data sources is both a feature and a bug, since it means that output can be general and sometimes unclear.
Sources are not provided, which can be an issue if users want to verify a particular piece of information.

While general GenAI tools can be valuable tools to help employees with a range of tasks, these limitations need to be taken into account when looking at their uses across the organization.

2. Enterprise GenAI: contextual output for your organization

Enterprise GenAI tools, such as Microsoft Copilot, provide users the ability to improve productivity and creativity, and level up their skills in similar ways to general GenAI tools.

For example, Copilot can be used to automate customer support responses, provide real-time support in live chat, and help automate and personalize sales processes and marketing efforts. However, these tools have the added advantage of leveraging data from internal documentation, meaning that generated output is context-specific. Further, enterprise-grade security and permissions remove the risks of sensitive data being exposed. This makes it compelling and potentially more useful for enterprises to adopt in a structured manner.

Nonetheless, there is still a big missing piece in the source data.

Institutional knowledge is made up of two parts; explicit knowledge, and tacit knowledge. Explicit knowledge is straightforwardly expressed and shared, through SOPs, reports, and other documents. By contrast, tacit knowledge is knowledge, skills, and abilities an individual gains through experience that is often difficult to put into words or otherwise communicate. It includes insights, intuitions, and ideals of employees, which makes it difficult to document in many traditional knowledge capture formats. But it’s incredibly important. According to some studies, tacit knowledge makes up as much as 80% of employee knowledge.

3. Enterprise GenAI that captures tacit knowledge

To reiterate a key point from above; both general and enterprise GenAI tools can be effective drivers of productivity within your organization. But tacit knowledge is a missing piece to what these tools can capture, and due to this, enterprises are missing out on a key opportunity.

To expand a little on what tacit knowledge can involve, here are some examples from different departments.

How capturing tacit knowledge for GenAI can unlock value

Capturing tacit knowledge around a specific goal is where big quantifiable gains can be made.

For a hypothetical example, consider a large organization which has a brain drain risk, as their tenured workforce means hundreds or thousands of employees look to retire or reduce their working hours within a short period of time. Enterprise GenAI could help capture some of the lost explicit institutional knowledge, provided it is already documented. But it can’t help capture the 80% of tacit knowledge.

However, if the organization could leverage an AI solution that would allow them to design a workflow to capture the specific institutional knowledge — both documented and undocumented — they want from these workers at scale, they would have a highly effective way to reduce the risks of brain drain. And this can be measured in a range of ways, from faster employee onboarding, to improved customer retention and reduced labor costs.

How Sugarwork helps capture tacit knowledge and turn it into business value

This is where Sugarwork comes in. Sugarwork enables organizations to build their GenAI processes around their specific goals and challenges, and capture and share institutional knowledge — both explicit and tacit — at scale.

Here’s how it would work in the example above, where an organization is facing a significant number of retiring employees in a short period of time.

1. Tenured leaders and employees transfer targeted knowledge on video using conversation templates.

Through the platform, tenured leaders and employees are paired with learners based on roles and functional expertise. Expert-developed questions are deployed for each pair or grouping, spanning topics of key relevance to your organization. These knowledge-sharing conversations are conducted on video (e.g., Microsoft Teams, Zoom, Google Meet) and are captured and summarized by the platform.

2. Admins track progress through an admin dashboard.

HR admins and managers are able to effectively monitor and track the progress of knowledge capture and sharing down to individuals, and quickly assess if key topics are covered and/or pairs or groups should be switched up.

3. Existing employees access knowledge as needed, with conversational AI.

New and/or existing employees — not only those involved in the knowledge share — can interact on an as-needed basis with captured knowledge in two key ways: via documents of insights and summaries generated by Sugarwork’s LLM, and a conversational interface that enables employees to quickly find relevant information.

Enterprises gain most value from GenAI when they leverage tacit knowledge

To unlock the productivity gains offered by GenAI in a structured way across the organization, enterprises need to be in the driver’s seat; identifying their own challenges, leveraging tools that can capture and share tacit knowledge, and measuring the impact. Organizations taking this approach with Sugarwork have seen compelling results. For example, one publicly-listed company saved $750K in labor costs through a restructuring process through a targeted approach to capturing and sharing institutional knowledge.

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Own Your Knowledge

Unlocking tacit knowledge: the next frontier in GenAI for enterprises