Artificial Intelligence is an unavoidable phenomenon that influences a lot of our everyday decision-making. Particularly so in technology-focused areas of financial services, which has created an ‘AI pressure’ within every sub-sector:
- In the first half of 2026, around $21.4 billion was invested in AI-related fintech deals across 800 transactions.
- Around 51% of IRs have embedded AI into their day-to-day lives.
- Only 2% have no plans to use AI whatsoever.
Given the value that generative AI (GenAI) and Natural Language Processing (NLP) tools can provide, many executives have considered them a no-brainer to take the sting out of cumbersome and high-frequency tasks.
For funds, they are especially effective for any work that never reaches an investor or causes backlogs within IR workflows. This could include summarising documents, providing script drafts for earning preparations, commentaries and internal management reports.
Advisors have already found that they save four hours or more weekly by utilising AI for client communications. So too do Large Language Models (LLMs) fuel chatbots that deliver immediate FAQ answers to investors themselves via hedge fund websites.
Still, concerns remain. Beyond grey-area regulatory issues, 65.1% of organisations do not measure AI return, with Gartner predicting that 40% of agentic AI projects will likely be terminated by the end of 2027.
Does AI offset administrative burdens, or provide extra booby-traps? The answer may not be realised for another few years of AI adoption yet. But this is how we see the immediate AI horizon, prevalent for fund managers looking to boost their expertise.
AI-Generated Content Limitations
Many of us will be overly familiar with ChatGPT, Claude, and Gemini in our daily lives, due to GenAI applications being largely democratised.
Yet as welcome as these are for pre-editorial work – drafting email subject lines or planning sequences, for example – if they are left unchecked they can create piles of indiscernible AI slop across internet-based channels.
In an investing context, an AI could present unreliable machine-generated ideas that do not truly understand complex macroeconomic effects on real people.
When the fund management industry is treated with scepticism by many – but also valued as ‘highly professional’ by institutional allocators or affluent investors – GenAI applications have to be used credibly to uphold genuine fund-to-investor relationships.
Sticking to the Script
NLP outputs are not something to take at face-value. This is because they are utilised for very singular jobs. They rely on a well-crafted prompt to suggest a result as close to the analyst’s intended requirement as possible.
AI models assess swathes of data that they are trained on; excellent for surfacing statistics and key findings from huge research reports, but lacking the psychological bandwidth to discern investment strategies.
A good illustration of this (as we have all encountered) involves a chatbot conversation becoming stilted before a proper answer is given. Oftentimes, it is far easier to gain from speaking to a resourceful human. AI works through a predetermined logic, and is unable to deviate.
This can be harmful for the asset-raising process if a chatbot sways an investor away from an upsell or an opportunity that they never thought about. An in-person IR would be able to pick up on an investor’s organic cues.
Data Quality
AI systems require expert implementations to sync with a hedge funds’ vast amounts of existing data. Only then can their results produce accurate ‘assimilated’ results. Otherwise, scarce numbers of data and unconnected platforms restrict AI’s pattern recognition capabilities to leverage investor topics of note, or make relevant suggestions to IRs.
The Importance of Contextual Language
Human linguistics are inherently tied to their context. This has worked in the favour, and to the detriment, of the investment space which features plenty of idiosyncratic terminology (from asset classes to “risk exposure” to “alpha generation”, etc.)
An NLP cannot fathom contextual nuance such as this. Any generated text in an investor-facing disclosure or factsheet may lack coherence or be laced with incorrect grammar and meaning. This can wreck compliance efforts and result in dire regulatory consequences.
In instances where models are trained on English-language data alone, that will produce culturally inappropriate or sensitive content for multi-regional marketing materials. Likewise, regional slang or uses of irony and sarcasm will be complete gobbledygook for LLMs and prone to misinterpretation. AI is far from subtle, and largely matter-of-fact and straightforward; not ideal when delivering bad market news or approaching experienced allocators with language that is not patronising.
Ultimately, only experienced hedge fund marketers can guesstimate the true intentions of their ideal investors’ (a stretch even for the most successful funds). A prospect’s portfolio history may be factored in by an AI, but not their past disappointments or wins, nor their emotional reasons for making a professional or personal allocation.
What separates human-penned and GenAI content is artisanal, thoughtful writing that marks a caring and trusted expert. Even AI’s typical phrasings (“it’s not just x – it’s y!”) are easily identifiable these days, which can reduce the authenticity of offending funds that publish GenAI content without maintaining an editorial eye.
GenAI can save time to feed today’s content-distribution monster, so long as all results – especially for client-facing materials – are fed through the most effective filter: the fund marketing ‘editor’!
AI-Assisted Personalisation
AI’s ability to crunch down information may lead it to homogenise content and create an echo chamber of simplified information. This can have a few negative ramifications, namely that segmented investor types may receive unsuitable ‘one-size-fits-all’ content once a model disregards diverse products that actually meet an investor’s needs.
Similarly, a marketer may lazily see AI as a way to auto-fill an email salutation. But this does not change a newsletter’s content to be applicable to that one person, or their wider recipient group. And where long-dormant or inactive investors exist, these profiles may skew outcomes as an AI model deems them relevant due to sample selection bias.
A hybrid approach of AI and human marketer can be far more advantageous however, achieving well-tailored fund materials and insights:
- Recycling: AI models fed with a fund’s internal repository data can quickly resurface on-brand, strategic content to be reused in recurring geopolitical climates.
- Investor sentiment: Systems can identify investor sentiment through analysing industry reports, current media articles, and by monitoring trends on social media.
- Dynamic content: AI can auto-fill emailers and website pages with customised content based on individual LPs’ online touchpoints, signposting the correct messaging to use at any one point in time.
- Sales tools: Automations running under predefined rules can calculate lead scores across multiple channels, and trigger further actions for sales or IR teams.
- Predictive tools: AI models assess vast behavioural datasets to raise patterns where redemption or upselling opportunities lie, and signal prospects’ defining interests.
AI Search Visibility
SEO can be a turbulent topic these days. The once-upon-a-time must-have webpage promotion strategy that has been rocked, like many other things, by AI.
While SEO would rank websites on their keyword relevance, even Google’s homepage plays second fiddle to the headline act: an AI Search box.
- AI Search works as an overview of what the engine deems the most important information based on a search input.
- AI tools collate a range of fund information into one neat package, so that a user can find links to blogs, FAQs and landing pages in just a few clicks.
- It boosts discoverability in an age of convenience, where AI Search trumps deep research for digital-first investors conducting their initial due diligence.
Essentially, AI Search maximises an investor’s user experience. Particularly any prospective LPs enquiring about specific strategies, or even first-time investors looking for expert guidance about where to start with alternative funds.
Therefore, AI Search can be leveraged by firms to become the sector-specific authority in their niche. To do so, funds must double down on their USP through repeated messaging across a website and ‘own’ their own slice of the industry, so that their current information can be represented accurately by web-crawling AI agents.
This also goes some way in addressing over 50% of GPs’ huge difficulty in articulating strategy. By thinking of the specifics that differentiate a fund, and making it a habit to feature this across digital real estate, discoverability also increases.
Then again, funds need to be consistent beyond being cited once by AI Search. Research shows that ChatGPT churns almost half of its search results weekly while Google’s AI Mode is only slightly more stable. Only 0.3% of citations come from paid sources.
So, despite its growing popularity, one-time AI Search wins do not necessarily replace thoughtful, planned content cadences that are specifically designed for a fund’s intended audience.
Compliance Concerns
Even before AI changed the ways that funds draft financial communications, marketing operated under strict local and global regulatory compliance.
Messaging parameters are constrained, with repercussions dished out for failing to fairly and transparently present benchmarking graphs, performance disclosures and risk warnings. Elsewhere, using automated channels to send communications across borders that have varying financial disclosure rules can be a dangerous game.
At the same time, the amount of data an advanced marketing system can ingest is only increasing in line with AI’s data processing power. This presents hedge fund marketers with the problem of protecting investors’ sensitive data under Europe’s Markets in Financial Instruments Directive (MIFID) II and the General Data Protection Regulation (GDPR).
Given AI’s vague or differing governance structures per region, a continued lack of standardisation can lead to any generated materials failing the more concrete legislative rules outlined by leading authorities.
AI can quickly become a thorn in a firm’s side if accountability is not taken around how it is utilised for creating content:
- Compliance checks: Any content generated in real-time to personalise offerings runs the risk of bypassing pre-distribution reviews and approvals by the compliance team.
- Regulator-ready formats: Customised presentations and pitches must be designed in line with specified formats that GenAI could ignore, potentially violating terms.
- Model collapse: If an AI model’s pattern-matching deteriorates, inappropriate investment products may be offered to unsuitable investor bases.
- Fair use: An investors’ risks and benefits have to be balanced in light of financial regulations, away from the persuasive ‘positive’ spin an NLP will prioritise.
While AI compliance is building, a firm’s model risk management is key: checking systems, validating how AI arrives at its outcomes (to inform marketing and sales decisions), and keeping records of AI application usage in light of supervisory audits.
With oversight paid to how AI could alter the sentiment of investor communications, firms can gain confidence knowing where to sign-off tasks left to the technology, and be honest about their AI usage to boost the trustworthiness of the financial sector (which has not always had the best track record!).
Global AI Guidance (At A Glance)
The United States
The Securities and Exchange Commission’s general AI rules for SEC-registered firms and advisors come under the umbrella of its Marketing Rule. It places the responsibility for transparent, fair, representative communications on the firm (and not the technology it uses), whether or not AI was involved in its creation.
AI applications must be supervised in line with applicable reviews by the Financial Industry Regulation Authority (FINRA), including Rule 3110 that defines a firm having a “reasonably designed supervisory system” tailored to its business, including AI model risk management, data privacy and integrity.
FINRA has recently proposed changes to its Rule 2210 framework, modernising in line with inevitably high GenAI usage within fund marketing. It looks to switch from principal-based approvals toward a risk-based system according to a businesses’ size, and the complexity of the products or services they promote.
The European Union
Attempting to unify an AI legal framework across its 27 Member States, the EU also aims to set the international watermark for AI governance. It sets out risk-based rules for AI developers and deployers, including nine prohibited practices (such as social scoring and AI-based deception), while stressing transparency in its intended use and the implementation of explainable models.
United Kingdom
The Financial Conduct Authority’s (FCA) AI rules pertain to its Consumer Duty standards, to protect customers and for firms to put their needs first, including the way that products are designed and information is relayed to investors.
While no new rules for AI tools are being built on existing recommendations, autonomous AI for financial promotions will still risk failing its COBS 4.2 rule that communications be “fair, clear, and not misleading.”
The FCA points to the continuous monitoring of AI-assisted promotions to ensure that humans are proactively in-the-loop and substantiate any claims before publication. It continues to monitor the long-term impact of AI on financial services through its Mills Review.
Investor Concerns
Being ‘on board’ with the AI wave differs per person. Where some view AI as being very beneficial to their lives, some deem it utterly untrustworthy wherever it is used.
In fact, many younger investors see AI chatbots as a valuable investment advisor over and above the views of professional press journalists or firms. Equally, a lot of allocators will not be confident in a machine’s advice, or its confidential use of their data, especially for something as important as portfolio construction.
If their trust is compromised, they will seek more personal firm-to-investor connections elsewhere. And despite institutional investors’ growing technological methods for finding and benchmarking various fund strategies, the emotional intelligence and competence needed for actual investment will still lack across NLPs compared to human advisors.
Investors themselves are not algorithms. A quality AI tool may help a generational pool of prospects, yet it is not a be-all-end-all for an entire investor base who can likely spot where GenAI has played a part within the fund marketing process. Even if its usage has not been disclosed (as it should.)
The Next Steps for AI Exploration
A staged approach to implementing AI helps usher in newfound confidence, towards the point that a fund marketing stack can be automated then continuously reviewed by its named human controllers to be as effective as possible.
Here are some recommendations for careful and compliant AI adoption:
- Preserve a fund’s value: what a fund stands for – fair and guided investment strategies that help their audience – must always be the foundation before any foray into digitalisation.
- Start small and build up a stack: AI tools are far-reaching for tasks large and small. Trial what works (and what does not) under your firm’s level of AI expertise and available resources to ‘do more with less’.
- Maintain data hygiene: ensure that investor data is current to avoid sending automated communications to unsubscribed or dormant contacts, and to train AI models on up-to-date intelligence for more accurate outcomes.
- Engage with regulators: local authorities can share AI use case recommendations, helping firms avoid falling foul of increasing scrutiny and ever-changing legislation.
- Establish best practice: document which AI tools are used, what they are used for, the humans that are accountable for it, and any risks they may pose.
- Craft an AI compliance culture: educate staff through AI-based training sessions with internal and third party experts, boosting AI skills wholesale.
Practical AI Tools to Use Today
Routine Tasks
Previously manual checklist items that can be left to AI allow marketers to improve their campaign strategies, including:
- Summarising insights from research papers or regulatory reports.
- Generating pitch meeting briefs based on recent investor activity.
- Drafting email subject lines and body copy.
- Structuring email sequences, commentaries and outlooks.
- Transcribing an executive’s speaking events.
Chatbots
Website interactions can take place via on-page chatbot triage, answering simple questions or directing investors to relevant pages and IR representatives, rather than being fully automated conversations which are often confusing and frustrating.
Sentiment and Compliance Tracking
Check spelling inaccuracies, market sentiment and potential disclosure risks for articles, social posts, email correspondence, and CEO letters before they get sent out.
Predictive Analytics
AI’s ‘suggestions’ based on pattern recognition can point out active investors across digital touchpoints, missed opportunities, reactivated leads, and optimal contact times that marketers and IRs can choose to pursue.
Data Integrations
With an AI-backed CRM assimilating nonlinear investor data in one place, marketers can:
- See their most popular investment topics and regions.
- Visualise individual digital behaviours.
- Track online journeys toward conversions.
- Report campaign success in real-time according to user-set KPIs.
A/B Testing
Segmenting email nurture sequences per investor types, risk attitudes and experiences helps assess how certain messaging variations lead to open rates, unsubscribes, downloads, and meetings created.
Sales Triggers and Lead Scoring
Set thresholds can identify which investors have accrued ‘scores’ to become prime targets for sales teams, knowing that the prospects are engaging strongly and regularly with fund content.
A Deep Data-Dive Into Allocator Behaviours
The ProFundCom platform is able to identify how hedge funds’ digital marketing efforts are performing, tracking touchpoint data across their channels and investor bases.
These insights – combined with industry research into all things email compliance, deliverability, GenAI usage, and popular investment strategy – presents a holistic picture of what is driving (or holding back) fund marketing in 2026 and beyond.
Notably, we are seeing the impact that AI content and email tools are having on fund-to-investor relationships, and which channels fund marketers should optimise to create more asset-raising opportunities.
Email & Deliverability Statistics
- 38%: email open rates for funds
- 20% of sends, or around half, of opens are made by ‘genuine’ humans
- 4% click through rate (CTR) of sends
Serving A Bespoke Audience
The reported figure for open rates reflects emails sent to opt-in LP and allocator lists, which are usually small and targeted at fund managers. This may explain why this marginally beats the financial services average of between 25% and 30%.
The figures may also be inflated by Apple Mail Privacy Protection (MPP), which pre-fetches tracking pixels irrespective of whether a recipient reads the message or not.
The CTR figure also appears higher than the 2% to 3% average of financial emails, given that the existing investor recipients have signed up to receive factsheets or letters. Prospective emails to ‘cold’ contacts would have highly reduced figures.
Email Service Providers and AI
The remaining reported opens can be attributed to institutional security scanners, such as Mimecase or Proofpoint, while Apple MPP makes up most of the rest.
Apple Mail and Gmail together account for almost 90% of all email opens. Outlook – a popular choice for institutional allocators – only amounts to just over 5%.
More than ever, funds are having to grant special attention to the pre-open experience. Sender names and subject lines are now being scanned by AI inbox management tools, including Gmail’s inbox summaries and Apple’s previews, before they reach any investors’ eyes.
Sales teams should be guided by metrics including CTRs, reply rates and document downloads as signs that content is hitting with LPs, rather than open rates or signals from MPP-protected inboxes that are rather woolly in identifying genuine investor intent.
Human vs AI Content
- 30% of people who open monthly factsheet or letter PDFs read to the end
- 30% of opens occur on mobile devices
- 30% of recipients use AI tools to query found materials over reading them directly
Document Formats
PDFs are still a popular choice for transmitting financial updates, branded graphs and appropriate disclosures and risk warnings in one single document.
The fact that less than a third read to the end of emails indicates a preference for scanning the first page of commentaries or skipping straight to performance charts. It is interesting, too, that institutional readers are largely relying on desktop for any fund documents that include graphical tables, PDF formats included.
This information is vital for funds to double down on the popular constituent parts of fund material in order to keep interest across lengthy nurture campaigns. Out of 80% of advisors that do distribute market commentary, a mere fifth follow it up with tailored campaigns.
Are AI Advisors Taking Over?
The data is conclusive that recipients are split equally between those who utilise AI to summarise documents, and those that absorb the entirety of the content they have been sent.
Over half of investors (a figure likely on the increase as we speak) ‘ask’ AI before reaching out to advisors, and to research investments using search tools. Around 48% cite it as a way to organise their thoughts for pre-meeting research discussions.
So while AI content is ranking as an influential source of knowledge over traditional news and email, only a small 3% portion would replace human advisors with AI. Clearly human-generated expert content still holds sway for allocation decisions after initial, online-only due diligence.
Hedge Fund Engagement Trends
- 35% of website visitors return within 30 days
- 45% of registrants attend webinars and investor calls
- >20% of investor intention share is directed at multi-strategy accounts
Existing and Prospective Investors
The majority of ‘returning’ activity across fund websites involves accessing gated investor portals and conducting monthly Net Asset Value checks. This is a sign that revamping the online experience for existing investors is just as valuable as crafting buyer journeys for future prospects.
For both of those segments, online events – both educational sessions or one-to-one communications – seem to be popular for those that do register. However, this number accounts for only 6% of all invites.
Calculating the ROI of online events should therefore be determined based on the follow-up actions of those that attended, or those that signed up but did not attend, including:
- Download figures for emailed video replays
- CTRs of linked content (articles, additional landing pages) sent in follow-up materials
- The number of subsequent meetings booked with IRs
Global LP Sentiment
The good news is that investor sentiment is largely positive for the hedge fund sector. In one case, 175 allocators across 18 countries deployed a net $26.8 billion in the first half of 2026. That is double the figure from the year prior.
This is, however, in spite of investors only allocating 5% to hedge funds after initial meetings. This means that managers should build website content, emailers and events centred on the strategies that investors are getting excited about, across various regions.
Europe (49%) and APAC (48%) are most enthusiastically pursuing hedge funds, and North America is shortly behind at 42%. The latter was also the jurisdiction that launched the most funds.
Fund Strategy Breakdown
- Multi-strategy: More than 20% of investors’ share is directed at multi-strategy funds, and many firms have ensured momentum through consolidations and external capital strategies.
- Private Markets: Nearly a third of affluent investors hold private market investment. 67% are affluent millennials, and 56% of non-holders are planning to allocate in the next year. Only 11% of Baby Boomers report holdings.
- Equity: This strategy accounts for two-thirds of all developing hedge funds and is riding high on AI’s popularity. Many equity managers in Asia have seen significant returns given the region’s focus on physical AI infrastructure.
- Macro: Macro funds make up for over 10% of developing funds under growing demand, second only to multi-strategy in investor’s intentions.
Looking ahead to 2027…
There are plenty of positives hedge fund managers can take away from investor movements made in 2026. Securing growth relies on marketers making use of their LPs’ behavioural data, and constructing campaigns directly attuned to what they need.
As the macroeconomic landscape fluctuates, leading hedge fund marketers will adopt strategies to build out streams of unique and valuable educational content, potentially leading to long-term investor relationships that make a real difference to every party involved.
