In 2026, AI is already embedded in the investor’s day-to-day, so we asked six investors to describe, in plain terms, what AI has changed about their daily work and where they see it adding genuine value.
What emerged is a consistent picture: AI has become the industry’s most effective time arbitrage tool, compressing hours of rote analytical work into minutes and redirecting human attention toward the decisions that actually matter.
Below, we have pinpointed five insights from experienced financial experts who are living this shift in real time, to understand exactly what AI has changed, what it hasn’t, and what comes next.
Investor research done in minutes
The most universal change is in research and document analysis. Ivaylo Penev is the CEO of ELANA Fund Management, one of Bulgaria’s largest non-banking financial groups with over 30 years of history, describing a workflow that would have been unimaginable two years ago.
“Going through a few hundred pages IPO prospectus or yearly filing has now been reduced to extracting the important part for us with the proper prompts without the need to go through all of the other non-essential parts,” he says. “Deep industry research has always been hard to obtain and we can rely on quick, but meaningful summaries in a very short time.”
Dilan Sisu from e2vc, a fund that operates across emerging European markets, frames the same shift in terms of access: “Tasks that used to take days, such as competitive mapping, market research, or understanding technical architectures, can now be done much more comprehensively and in a fraction of the time.” The implication is not just efficiency but depth – investors can now cover ground that was previously impractical to explore.
“The process of moving from initial curiosity to a well-formed point of view has become much more fluid,” she adds.
From tools to live intelligence
The need to process more data, faster, without losing analytical depth is precisely the problem that a new wave of fintech startups is being built to solve. Miroslav Pavlov, co-founder of an AI investment research platform, describes a founding insight that will sound familiar to anyone who has spent time in the industry: “While AI is clearly here to stay and major players like OpenAI, Anthropic, and Google continue to advance generative AI toward “superintelligence” – I believe the future lies in vertical AGI: domain-specialized intelligence designed to master specific industries.”
Edge Hound is a product built for the exact the workflow investors are already trying to construct manually. His long-term vision:
“Over time, AI will take on a significant share of investment analysis and fundamentally reshape how capital is allocated. Much of today’s traditional research reports and investment decks will become automated, allowing investors to focus more on strategy, creativity, and long-term vision rather than manual data processing.”
That thesis has attracted serious institutional attention. Paul Bakunovicz, a former senior leader with nearly three decades at Citi bank, cuts to the heart of why the retail investment gap matters. “Retail investors face a structural disadvantage: persistent information overload, fragmented data, and decision-making that is too often driven by noise rather than signal,” he says. “Edge Hound truly brings clarity, discipline, and structure to how individuals interpret markets, which directly addresses that gap.”
Deal flows meet the algorithm
At the more operationally aggressive end of the spectrum, Christo Peev from Space Tree Ventures has gone further than most. He runs every inbound deck through an AI pipeline that extracts market size assumptions, benchmarks traction against comparable companies, and stress-tests business models across multiple growth scenarios.
“Tools like Claude are insanely good at parsing long data rooms, spotting inconsistencies between narrative and metrics, and generating follow-up questions that would normally take hours of manual work,” he says. He also uses Kingo AI as a daily execution layer to track investor conversations and surface the highest-value follow-ups. “This compresses initial screening from days to minutes and shifts my time toward conviction building and founder assessment.”
On where AI adds the deepest value, Peev points to pattern recognition across early-stage signals that are notoriously hard to read:
“AI can correlate these with historical outcomes and surface leading indicators of breakout or failure much earlier.”
Veselina Markova, partner at Eleven Ventures – the fund has made 170+ investments since 2012 and backed companies including unicorn Payhawk, takes a similarly integrated approach, though with a sharper focus on what AI frees her up to do. “Investment memos, which usually take too much time, now require less and less time because AI handles the routine tasks, allowing me to spend more energy on the creative part and actual decision-making,” she says.
She has also developed tailored workflows to support portfolio companies with business development opportunities, and her CRM now handles note-taking and drafting agendas as a baseline function. “It significantly improves my speed.”
Not everyone, however, is rewriting their entire workflow around AI. Bogan Iordache from Underline Ventures — a Bucharest-based early-stage fund founded in 2022, increasingly active across CEE — offers a more grounded perspective. “We’ve been using AI for multiple day-to-day tasks, but I would not say AI has fundamentally changed our routine,” they say. The use cases are real but specific: research, summarisation, faster identification of relevant sources, and scenario brainstorming. Critically, the outputs are treated as hypotheses, not conclusions. “We use the information to construct hypotheses, which we then check with industry reports, customer interviews, etc.”
Their sharpest observation cuts to a structural limit that all early-stage investors eventually hit: “In early-stage investments, a lot of the investment decisions are based on data that is not digitally available, and AI can only identify exciting areas, rather than make or suggest decisions.”
The public market filter
Zhana Hristova from BEAM, Bulgarian Stock Exchange, manages the exchange’s dedicated segment for growth-stage companies, brings a more measured perspective from the public markets side.
“Over the past year, AI has become more like a high-speed filter for me. It helps process large amounts of information, compare data sources, and flag inconsistencies much faster than before.”
She is deliberate about what this does and doesn’t change: “This saves time and makes the initial screening more efficient, but it hasn’t changed the core discipline of investing — you still need judgment and the ability to ignore market noise.”
Her clearest point of value is not in her own decision-making but in what AI can do for the companies she invests in. “For me, the real advantage isn’t AI telling me what to buy, but helping the companies we invest in become more efficient and profitable,” she says. “In the end, investment decisions still belong to people who understand the bigger picture.”
The conviction gap
Five different investment contexts and one consistent conclusion: AI is most useful in the space between raw information and a formed point of view, as it synthesizes, maps, flags, and drafts. But it does not decide.
Veselina Markova puts it plainly: “While the data is processed by AI, the critical thinking remains entirely ours. I don’t see it as a replacement for an investor’s intuition, but rather as a way to be ‘augmented.’ It gives us a cleaner, more data-backed foundation to stand on, but the final conviction always comes from the human element.”
Ivaylo Penev reaches for a telling analogy: it’s like having “the perfect ‘poker face’ zero emotions ultra rational guy sitting on your team”.
Dilan Sisu identifies the downstream effect most clearly: “By reducing the friction around accessing and processing information, AI allows investors to focus more on judgment, evaluating founders, understanding their insight, and assessing whether they are building something truly differentiated.”
In CEE, this matters more than in more mature ecosystems. AI is partially compensating for structural information gaps that have historically made the region harder to analyze. The picture that emerges is not of AI replacing the investor, but of the investor becoming harder to replace precisely because they now have better tools.
This article is part of The Recursive’s ongoing coverage of the investment ecosystem in Southeast Europe.
