Work-Bench VC Jonathan Lehr And Team On Enterprise Tech Trends To Watch

I first met Jonathan Lehr shortly after he and Jessica Lin cofounded Work-Bench almost a decade ago to help early-stage enterprise tech startups in New York get the funding, support and connections they need to do business with corporate customers. The venture capital firm has since invested in more than 50 companies, including Cockroach Labs, Socure, Dialpad and Spring Health. (Here’s a piece that my colleague Alex Konrad wrote after they raised a $100 million third fund last year.)

So I asked the Work-Bench General Partner to tell us what’s on his radar for 2023 as part of our new CIO insights series. In addition to his own perspective, he shared some thoughts from some of his team members and a founder that he’s backed.

On Cybersecurity

Security leaders are caught between two major challenges. On one hand, companies continue to place greater focus on the performance of the CISO organization in light of the ever growing impact of cybersecurity threats. On the other hand, boards are demanding higher ROI with cut cybersecurity spending. In 2023, more and more CISOs will use automated performance management tools to rationalize their budget and consolidate measurements of their security program and achieve improved performance. – Shirley Salzman – Cofounder and CEO, SeeMetrics

From CISOs we’ve spoken with, we’re seeing them leverage more automation across their security program. This is especially true in areas like governance, risk, and compliance, where security leaders would prefer their security analysts focus on higher order work rather than mundane, manual, but still necessary tasks. Take for example third party risk and vendor due diligence. Piles of vendor security questionnaires can be processed by AI, which can unlock greater scale of coverage for vendor risk and is a force multiplier for security teams with little resources. – Kelley Mak, Work-Bench

On Cloud and DevOps

Reliability has emerged as the next DevOps frontier for CIOs across the Fortune 500. Whether a mobile banking app, an ecommerce site, or streaming media, downtime not only is detrimental to a company’s brand but also hurts their bottom line. In an age of cloud-hosting and increased microservice architectures, traditional incident management tools are standing in the way of reliability. New tools and approaches are emerging to go beyond simply alerting you to a problem, to actually helping you remediate, better communicate, and learn from incidents in order to improve your organization’s reliability. This is an area for which we’ve seen significant corporate spend in 2022, and we expect it to continue being a priority budget item for CIOs even as many other parts of cloud spend get slashed. – Jonathan Lehr, Work-Bench

On Data and ML

One of the top-of-mind challenges for the data and engineering leaders we’ve engaged with tends to be centered around enabling visibility into their critical systems and enforcing real-time risk communication about any anomalies. Given the proliferation of modern data architectures, and sheer volume of data that is being stored and processed across distributed systems, proper data management (data collection, transformation, governance, privacy and availability) is critical to the enterprise. From financial services to highly regulated industries, there is an urgency for businesses to react to “bad data” in a near-real manner to prevent any incidents or outages on the customer end. In 2023, we expect to see increased awareness around the need to implement proper data monitoring and observability guardrails and adoption in sophisticated solutions that enable the enterprise to react proactively to data issues across their batch and stream processing environments – Priyanka Somrah, Work-Bench

With the recent excitement around foundational models, organizations should consider how they can leverage in-house data for optimized model performance for mission-critical use cases. For many organizations we’ve spoken with, generic pre-trained models are only scratching the surface in terms of value added benefits for enterprise companies. Generic models will expand machine learning’s reach, but the most impactful models and business outcomes will come from leveraging a company’s internal data. Unfortunately, for many organizations, this data is stored disparately across multiple file stores and is kept in a black box as it usually contains sensitive personal health & identity information. In 2023, we expect to see technologies that enable the long tail of machine learning through new model deployments, optimized model performance, and contrastive learning. – Daniel Chesley, Work-Bench