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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Sure Valley Ventures says it shared the AI workflows it uses on AgenticInvestor because it expects generic prompts and repeatable workflows to become widely available. In Barry Downes’s view, a venture firm’s lasting edge comes instead from its accumulated data, judgment, access and relationships with founders. The playbook is described as a way to help investors spend less time on process and more time on decisions and founder support—not as a replacement for human judgment.
What Sure Valley Ventures says it shared
In a 29 September 2026 article in The AI Journal, Barry Downes, Managing Partner of Sure Valley Ventures (SVV), says the firm open-sourced internal workflows used on AgenticInvestor. The workflows support company evaluation, preparation of investment materials and management of the investment process.
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The article does not identify a public code repository or an open-source license, and it does not include implementation instructions. Its account therefore explains the decision to share the playbook, but does not establish that readers can download or run the workflows.
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Why give the workflows away?
Downes’s case is that practical AI infrastructure can raise the productivity floor across venture capital. If routine work takes less time, investors can devote more attention to assessing opportunities and helping founders. SVV chose to share workflows it uses internally rather than treat the workflows themselves as a lasting competitive moat.
That decision rests on a distinction between tools that can be copied and advantages that take time to build. Downes argues that generic prompts and workflows will become commoditized, while accumulated data, investment judgment, access to opportunities and founders’ trust will remain scarce. This is his view of what creates durable advantage in venture capital, not an independently established industry finding.
Where AI fits into the investment process
The article describes AI as support for recurring, multi-step work rather than just isolated chatbot requests. Its examples span research and administration across a deal’s lifecycle:
- Summarizing due-diligence materials and identifying potentially relevant deals.
- Screening companies and preparing diligence packs.
- Managing founder follow-ups and keeping CRM records accurate and consistent.
In this framing, an agentic workflow connects tasks into a repeatable process. It can prepare or organize information, but the investment decision still calls for human evaluation. Downes puts the boundary plainly: “I firmly believe that AI is here to support, not replace, human judgement and to clear the path to focus on the decisions that actually generate outsized returns.”
What results SVV reports—and what they establish
SVV says company screening became twice as fast, and describes substantial time savings in preparing diligence packs and managing founder follow-ups, along with marked improvements in CRM accuracy and consistency. The article provides no baseline timings, sample size, study design or measurement method. These are SVV-reported outcomes, not independently verified benchmarks, and they should not be assumed to predict results at another firm.
The article also attributes two adoption figures to an unnamed source: 34% of European venture firms reportedly use AI to summarize due-diligence materials, and 26% to identify relevant deals. It gives no source organization or year, so those figures cannot be checked against a named survey from the article and should not be treated as verified industry-wide statistics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the decision means for other venture firms
SVV’s rationale offers a useful way to assess an AI workflow without assuming that adopting AI automatically improves investment decisions. The relevant question is whether a workflow makes recurring work more dependable and frees people to exercise judgment where it matters.
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- Look beyond one-off prompts. Consider whether the work involves a sequence of tasks that can be made repeatable, such as turning diligence materials into an organized pack.
- Make checking part of the workflow. Faster summaries or CRM updates are useful only if people can review them and correct errors or inconsistencies.
- Fit the work to existing processes. Follow-ups and record updates need to fit the firm’s CRM habits and responsibilities, not create a parallel process that goes unchecked.
- Keep consequential decisions with people. AI can help organize evidence; it should not be confused with the experience and judgment involved in deciding whether to invest or how to support a founder.
Those considerations follow from the use cases and argument in Downes’s article; they are not a comparison of specific software products or a claim that every venture firm needs the same workflow.
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