Future of Work #4 — Enterprise Knowledge Management 2.0 + AI-Powered Search & Discovery = Team Intelligence

Jessica Lin
May 21, 2018 · 6 min read
Photo by Mike Wilson on Unsplash

As a VC, I’ve been researching and tracking technologies broadly within the “Future of Work” space. Ask any VC and you’ll get different responses as to what Future of Work covers — be it collaboration, productivity, AR/VR, RPA, and more. So I thought I’d share some of our early learnings at Work-Bench, which are ever-evolving — we’d love to hear from you, and to continue to learn.

As an enterprise-focused VC fund, we invest in startups selling into large enterprise customers, and we engage regularly with Fortune 500 line of business buyers.

Trends Across Workplace Productivity & Collaboration, Search & Enterprise Knowledge Management

  1. More than ever today, there is rapid proliferation of cloud apps at work, with everyday usage on productivity and collaboration platforms like chat, email, calendars, meeting notes, project plans, CRM, social, issue tracking, customer support tickets and more.
And many more productivity and collaboration apps…

2. With these platforms comes vast mountains of data — both structured and unstructured — across millions of messages, documents, and more.

3. As an end user, it becomes harder and harder to search, find, and locate knowledge within an org — be it a document, an email, or a team member.

4. Well-established players like Elasticsearch, Coveo, Attivio, Microsoft and Lucidworks lead in the enterprise search space, but are geared for larger company environments and specifically for search functionality.

5. Legacy internal wikis and intranets like Confluence and Sharepoint where teams input documentation quickly lose adoption because the manual maintenance of these wikis are painful. And as soon as the wiki is out of date, its utility goes dramatically down.

6. Enterprise knowledge fragmentation leads to huge loss of productivity, with IDC finding:

  • A typical knowledge organization employing 1,000 knowledge workers wastes over $5.7 million annually searching for but not finding information.

Thesis: Rise of the Team Intelligence Layer

Enterprise knowledge management 1.0 — with legacy vendors like Confluence and Sharepoint — still require manual and painful input, organization, and maintenance.(As anyone who has ever been part of a team Dropbox folder can attest — it becomes sprawling chaos very quickly, unless there is tight discipline around file naming nomenclature and organization.)

Just as the legal industry now has legal search and discovery to find documents faster, eliminate down time, and be more productive, there is opportunity for a new generation of enterprise search & discovery, collaboration and knowledge management tools, that converge and combined with machine-learning capabilities, will create a new layer of team intelligence that:

  1. Integrates across all cloud collaboration and productivity platforms
  • Surface the right content: For a customer support team to pull policy based on pattern matching, to cut down on response times.

Anything that requires manual note-taking and input in wikis won’t be able to compete longer-term with companies that can auto-build a knowledge base that continuously and automatically updates; and that can sit as a seamless interface layer right on top of workflow.

Major vendors operating in this space include Google with Google Cloud Search, across all Google Apps, Slack with Slack Search for subject matter experts, and Microsoft tying its Team product to their extensive Office 365 integration.

Key aspects that will be critical for emerging companies building in this space to consider — cloud vs. on-prem search (while it may feel for all startups that our apps live on the cloud, the reality is that 65% of large enterprise workloads still live on-prem); with new advances in AI / ML, you may get a lot of different recommendations, but it may become hard to sort the false positives, recommendations targeted to a specific function (i.e. customer success or sales); and lastly, data security and privacy, with GDPR and worker concerns around data anonymization.

And as my teammate Michael Yamnitsky points out, he covered many similar companies from 2009–2010 at Forrester in this “personal cloud” space. I’d love to dig in more (and hear from you) why those companies did not take off, and if and why this new wave is primed to do so. With this convergence of search and ML-generated predictive insights…will there still be such a concept as “search” in the future? Or will our operating systems one day be able to preemptively surface us the information we need, right when and where we need it (and our kids will laugh that we ever needed a Google Search bar)?

Emerging Startups

Below are a select number of startups building in this space:

  • Journal & Seva — All-in-one unified search platform across your business applications

If you’re thinking about the future of work, I’d love to talk.


Work-Bench is an enterprise technology VC fund in NYC.

Jessica Lin

Written by

co-founder & VC @Work_Bench | GED educator | rethinking work


Work-Bench is an enterprise technology VC fund in NYC. We support early go-to-market enterprise startups with community, workspace, and corporate engagement.

Jessica Lin

Written by

co-founder & VC @Work_Bench | GED educator | rethinking work


Work-Bench is an enterprise technology VC fund in NYC. We support early go-to-market enterprise startups with community, workspace, and corporate engagement.

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