In today’s enterprise landscape, AI holds enormous promise — but harnessing that potential is still a work in progress. While organizations are eager to scale AI efforts, many struggle to put it to use in a way that meaningfully enhances business outcomes. The issue isn’t just about access to the technology — it’s about how organizations align AI with decision-making, data readiness, and human expertise.

Rapid growth, fragmented systems, and pressure to move quickly often lead teams to make critical decisions based on incomplete or disconnected data. Without a strong data foundation and clear operational alignment, the value of AI can be diminished — or missed entirely. Integrating AI into daily workflows takes more than new tools; it requires rethinking how data is used across the business.

To explore how companies are closing this gap — and why success with AI depends as much on people as it does on platforms — Tim Veil, Principal at Two Bear Capital, sat down with Fivetran to share how his team is helping organizations move from assumptions to insight and build smarter, more connected data strategies.

Incomplete data undermines business value

One of the most persistent disconnects in organizations — whether a fast-moving Series A startup or a global enterprise — is how often highly consequential decisions are made with little backing from data. Teams are often under pressure to move fast, meet deadlines, and deliver outcomes. In these high-velocity environments, decisions are frequently made with incomplete data and, worse, based on flawed assumptions.

Veil explains this pattern is far too common: “There’s always an impending deadline… and what I have observed many times over is that people are often making decisions with incomplete data. They are making a lot of assumptions — and many times, unfortunately, not the world’s best assumptions — about what they think the data is telling them.”

When businesses rely on guesswork rather than insight, they risk misaligned strategies, ineffective campaigns, and missed opportunities. The solution? Centralizing data from multiple sources and integrating it into everyday workflows. When analytics are surfaced in a clear, accessible way, data naturally becomes part of the decision-making process — not an afterthought.

“The more we can begin to infuse data into those conversations, the better decisions people are making, the better outcomes we get,” Veil says. The goal isn’t to slow down innovation; it’s to make speed and accuracy work together by grounding quick decisions in trustworthy insights. In fast-moving organizations, that shift can make all the difference.

When off-the-shelf isn’t enough, custom data connectors matter

For organizations navigating complex data ecosystems, off-the-shelf integration tools are often a great starting point. But they don’t always cover the full range of systems businesses rely on — especially when proprietary platforms or industry-specific tools come into play.

Veil encountered this firsthand. “Data lives all over the place,” he explains. “When you can pull that data into a single place, you just have the ability to make better, more informed decisions.” Veil wasn’t even thinking about AI yet — just the fundamentals of consolidating critical information across tools like Salesforce, Affinity, JIRA, and Carta to power executive-level reporting.

At Two Bear and across its portfolio companies, the challenge was clear: how to pull data from a diverse set of systems — some with standard integrations, others with no connectors at all — and bring it into one centralized view. The goal was to build a single pane of glass for investor reporting and operational insights, without relying on error-prone, hand-coded pipelines.

While Fivetran provided out-of-the-box connectors for many core systems, Veil needed more flexibility to go further. That’s when he explored the Fivetran Connector SDK, which allows teams to build custom connectors that follow the same standards and automation as Fivetran’s native options.

With the Connector SDK, Two Bear was able to integrate platforms like Carta and Standard Metrics into their data pipeline without writing manual scripts. The result? A fully unified view of their portfolio performance, investor metrics, and fundraising activities — all powered by a pipeline that’s scalable, reliable, and tailored to their needs.

AI is transformational, but don’t overlook the power of the workforce

AI offers an unparalleled ability to retrieve and synthesize information, radically enhancing human insight and decision-making and fundamentally reshaping how companies operate and compete. And yet, many organizations still aren’t using AI effectively. Too many are missing opportunities to automate workflows, enhance decision-making, or improve efficiency with tools that are already available today.

The truth is, AI isn’t a future concept — it’s a present-day tool, and non-generative types of AI have been around for decades. The most successful use cases are the ones that amplify human capability, not attempt to replicate or replace human insight — because ensuring AI systems deliver reliable, ethical, and business-aligned outcomes means human oversight is essential. Even the most advanced models can produce errors, reflect bias, or misinterpret context — making it critical to keep people in the loop. Rather than replacing human judgment, AI should serve as a co-pilot, with human experts providing supervision, validation, and course correction. That’s why successful organizations approach AI adoption as a gradual, iterative process — embedding quality control measures and refining automation workflows over time. This human-centered strategy not only increases trust in AI systems but also ensures they evolve in ways that align with real-world goals and values.

At the end of the day, tools matter. But so do people who operate tools. And building a deep “library” of real-world experiences — whether technological or interpersonal — is just as critical to long-term success as any algorithm.

Building a strong data foundation

Closing the AI readiness gap isn’t just about adopting the latest models or tools — it’s about establishing the right foundation. As more organizations seek to unify fragmented data landscapes, the ability to build custom connectors — without sacrificing automation — becomes a competitive advantage. Without a centralized view of the business, even the most advanced AI strategies will fall short. Fragmented systems, siloed insights, and manual workarounds all introduce friction at the very moment companies need speed, accuracy, and scale.

As Tim Veil’s experience shows, when organizations invest in integrating data from every corner of their tech stack — even when that means building custom solutions — they unlock new levels of visibility and agility. More importantly, they empower people across the business to move from assumptions to informed action.

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