Work-Bench VC Jonathan Lehr and team on business technology trends to watch

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Jonathan Lehr, co-founder and managing partner of Work-Bench

Courtesy of Work-Bench

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

So I asked the general partner at Work-Bench to tell us what’s on his radar for 2023 as part of our new CIO Insights series. In addition to his point of view, he shared some thoughts from some of his team members and a founder that he supported.

On computer security

Security leaders are caught between two big challenges. On the one hand, enterprises continue to pay more attention to the performance of the CISO organization in light of the ever-increasing impact of cybersecurity threats. On the other hand, boards of directors require a higher ROI with a reduction in cybersecurity spending. In 2023, more CISOs will use automated performance management tools to streamline their budget and consolidate their security program metrics for better performance. – Shirley Salzman – Cofounder and CEO, SeeMetrics

From the CISOs we spoke to, we see them leveraging increased automation in their security program. This is especially true in areas like governance, risk, and compliance, where security leaders would rather have their security analysts focus on higher-order work rather than mundane, manual, but still necessary tasks. Take third party risk and vendor due diligence for example. Stacks of vendor security questionnaires can be processed by AI, which can unlock a greater scale of coverage for vendor risk and is a force multiplier for under-resourced security teams. – Kelley Mak, workbench

On cloud and DevOps

Reliability has emerged as the next DevOps frontier for Fortune 500 CIOs. Whether it’s a mobile banking app, an e-commerce site or a streaming media site, downtime is not only bad for the a company’s brand, but also hurt its bottom line. In an era of cloud hosting and ever-increasing microservice architectures, traditional incident management tools hinder reliability. New tools and approaches are emerging that go beyond simply reporting an issue to actually help you resolve it, communicate better, and learn from incidents to improve your organization’s accountability. This is an area where we have seen significant business spending in 2022 and we expect it to continue to be a top budget item for CIOs even as many other parts of cloud spending are cut. – Jonathan Lehr, workbench

On data and ML

One of the biggest challenges for data and engineering leaders that we’ve engaged with tends to center around enabling visibility into their critical systems and applying real-time risk communication on any anomalies. Given the proliferation of modern data architectures and the sheer volume of data that is stored and processed on distributed systems, proper data management (data collection, transformation, governance, privacy, and availability) is critical to business. From financial services to highly regulated industries, there is an urgency for businesses to react to “bad data” in near-realistic ways to prevent any customer incidents or disruptions. In 2023, we expect to see increased awareness of the need to implement proper data monitoring and observability systems and adopt sophisticated solutions that enable the business to proactively react to data issues in their batch processing environments and streams – Priyanka Somrah, Work-Bench

With the recent enthusiasm for foundational models, organizations should consider how to leverage internal data to optimize model performance for mission-critical use cases. For many organizations we spoke to, generic pre-trained models are just scratching the surface in terms of value-added benefits for businesses. Generic models will broaden the reach of machine learning, but the most impactful models and business outcomes will come from using a company’s internal data. Unfortunately, for many organizations, this data is stored disparately across multiple file repositories and is kept in a black box as it usually contains sensitive personal health and 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, workbench

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