Jessica Lin
Nov 6, 2018 · 3 min read
Photo by rawpixel on Unsplash

At Work-Bench, we divide and conquer our investment areas in enterprise software as follows:

I often get asked what Future of Work means from an investment perspective, which for us at Work-Bench has spanned from unified communication & sales intelligence; AI for meeting scheduling, AR for industrial workers; AI for mental health; and next-gen customer success platform.

A core part of our investment thesis at Work-Bench is understanding painpoints directly from enterprise buyers to identify massive and timely market opportunities. We then play matchmaker with leading startups based on our research.

Below are some top of mind areas and representative companies in each.

I would love to connect if you’re a startup building in this space, or if you’re a corporate executive evaluating new tools and platforms for adoption.

Procurement

As a general trend, we are seeing greater expectations for our software to generate and derive additional insight — across verticals like compliance, incident response for security, industrial operations, and more. This layer of intelligence is expected to help drive operational efficiency, productivity, visibility on spend and cost savings, and reduce manual workflows. The same is now being seen in procurement, with buyers are no longer settling for sub-par experiences from legacy tech, as they now expect tech-enabled platforms to drive vendor intelligence, cost-savings and efficiencies.

With the rise of a younger generation of procurement specialists will also bring greater demand for better experience from the procurement tools and software that they use, which has historically been manual, painful, and centered around legacy tooling, or even still conducted primarily on paper.

Intelligent Documentation

A commonly bemoaned painpoint we hear from developers and product designers is the consuming amount of time they spend in documentation of their work, which still requires tedious manual input and writing.

There has been talk for some time now on intelligent or automated documentation, both for internal knowledge management and also external docs for users, beyond existing API documentation platforms like Read the Docs, Apiary, and Swagger.

I recently came across great writing by Tom Johnson on trends in the field of technical writing, and his prediction that developers will actually be writing more docs, not less:

“Hyperspecialization has become only more acute with each passing year. Because of this specialization, I think more engineers and other specialists will be writing docs — because the information is so technical and specialized, tech writers will have a hard time developing the content. Instead, technical writers might play more general roles with content and more specialized roles in editing, publishing, and curating content.” (link)

Compensation & Sales Incentives

Increasing legislative emphasis on equal pay laws (here and here) make compensation processes a rising priority for companies to figure out (many of whom still operate in Excel). This is especially a painpoint for fast-growth companies, especially those who start reaching maturity levels requiring review and calibration of total comp.

Beyond real-time proprietary data (which at the moment lives in expensive datasets owned by consultants) and unifying workflows, the first order problem of accessing data and from there creating benchmarks and transparency is an opportunity to then unlock previously unreachable personalized compensation analytics, insights, and context.

Related to this is sales incentives and commissions — which has historically been dominated by Xactly and CallidusCloud; and/or homegrown customized Excel spreadsheets owned by sales ops and FP&A teams. What happens when you can bring in an intelligent optimization layer across designing and adjusting incentives mid-quarter or mid-year, visibility and recommendations for cross sell/upsell opportunities, as well as pricing and discount levels based on analysis of deal characteristics?

If you are an enterprise startup building in any of these spaces or a corporate executive working with platforms and tools in these areas, I’d love to chat.

For more enterprise investment research across machine learning, cybersecurity, cloud-native infrastructure, and future of work, check out our Work-Bench blog.

Work-Bench

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

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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