Altvia partnered with UserEvidence to survey 187 alternative investment professionals in September 2026 on market pressure, LP expectations, technology, and AI. The full report publishes in November, 2026. Here is an early look at some of the findings.
AI adoption is real, but uneven
Firms are spread across the maturity curve, and most are past the pilot stage.
The other 39% are still at the pilot stage or earlier.
The barriers aren’t about the models
When we asked what stands in the way of adopting or expanding AI, the top five answers were:
Security or confidentiality concerns: 49%
Integration with existing systems: 48%
Trust in AI outputs and accuracy: 47%
Lack of in-house skills or expertise: 40%
Data quality or readiness: 37%
None of these is about how capable the models are, they’re about safety, connection, trust, and who can use the tools. Only 16% cited unclear return on investment.
The barrier changes as firms mature
The biggest obstacle is not the same at every stage. Early on, the problem is skills. At the pilot stage, it is connecting AI to existing systems. Once AI is in production, security and confidentiality take over.
Security concern jumps from 23% of firms still exploring to 55% of firms running pilots, and stays above 50% from there.
Skills concerns fade as firms gain experience, falling from 57% of early-stage firms to 32% of those in production. The same three themes appear throughout: who can use the tools, whether the guardrails hold, and whether the data is connected and current. Ryan Keough explores why in 3 Things to Learn from the Automobile When Deploying AI.
Ambition is running ahead of the foundation
91%expect to spend more on AI.53%are investing in the data it depends on.
The firms that are investing report more progress:
Investing in data infrastructure
Not investing
AI in production or embedded
69%
52%
Technology helps them keep pace
82%
66%
“Without quality data, AI can’t do its job,” says Lisa Lewis, Industry Product Manager at Altvia. “Data infrastructure is the next big Achilles’ heel, but unlike launching a pilot or establishing a policy, it’s often a multi-year commitment that nobody gets credit for.”
LP expectations are rising across the board
Respondents reported higher LP expectations over the past 12 months in every area we measured:
LP expectation
Share reporting an increase
Transparency into fund performance
87%
Speed of response to requests
86%
Frequency of reporting and updates
85%
Self-service access to data
84%
Detail and granularity in reporting
82%
What’s next
The full report goes deeper on how these patterns vary by firm size and role, and on what firms are doing about them. Subscribe to get the report.
How Investor Engagement Manager Enables Firmwide Investor Relationships
Summary: Most AI in investor relations delivers value only to the people with the expertise to operate it. Turning that into a strategic advantage the whole firm holds, and one that accelerates, takes three things in place at once. The automobile needed the same three, and took thirty years to assemble them.
By Ryan Keough
For AI to actually move an organizational objective such as investor relations, fundraising, or the strength of every LP relationship a firm holds, three elements have to be in place at the same time.
There is a version of AI success that firms are quietly settling for, and the awkward thing about it is that it works. It produces genuinely useful output, but only for the people who know the data, know how to ask, and can tell a good answer from a plausible one.
That is a real skill. It is also unevenly distributed, and today’s AI is almost entirely dependent on it. The value a firm gets is capped by how many people it has who hold that expertise, and the colleague at the next desk gets something worse without being able to say why.
The three elements
That is a set of capable individuals, not a capable firm. It does not compound, because the value lives in the operators rather than in the organization. And it is not fixed by training, because the dependency is structural: a system that only works in expert hands will only ever reach as far as your experts do.
One. It has to be accessible to the entire team. Usable by everyone who touches the relationship, not dependent on an expert user who knows how to work it.
Two. The guardrails have to be in place. Safe, reliable and governed, which is what allows a team to move fast rather than what slows it down.
Three. It has to have the essential inputs. Clean, connected, current data, arriving continuously, without anyone hand-crafting it.
Any two without the third leaves you with a pilot. All three together is a different thing entirely.
The automobile is the proof
The car is the clearest example of all three arriving, and of how little the machine alone was worth until they did.
Accessible. Early automobiles were for mechanics and enthusiasts: difficult to start, harder to maintain, and priced for the wealthy. The Model T mattered because an ordinary person could afford it and operate it1. Mass value required mass accessibility, not a better engine.
Guarded. Then the roads. Early roads had no lane markings, no stop signs, no signals, no right-of-way conventions and no requirement that the driver knew how to drive. The control layer was invented piece by piece over two decades. Center lines appeared in several states between 1911 and 1917, the first stop signs and signals around 1914 and 1915, and national uniformity only with the first Manual on Uniform Traffic Control Devices in 1935.2 Until then, driving at volume was dangerous rather than useful.
Fueled. And the fuel. Crude oil does not run an engine; gasoline came out of a refining process, as a byproduct of making lamp kerosene, and was so plentiful that refiners burned it off.3 It was never scarce. It simply was not where the driver was. Before filling stations you bought it in cans from a pharmacy or a blacksmith, and planned long trips by telephoning ahead.4 Quality varied with whatever crude it came from, and the property that actually mattered, knock resistance, had no agreed measure until the octane scale arrived in 1930.5
A car nobody could drive, on roads nobody could survive, running on fuel nobody could find, is a hobby. All three together built an industry.
And what the three delivered together was not a better car. It was access. Ordinary people could travel routinely and arrive safely, which changed where they could live, where they could work, and how far a business could reach. Freight moved by road. Rural isolation eased. A machine that had been an enthusiast’s curiosity became infrastructure an entire society ran on, and the people who had it could do things the people who did not simply could not do.
The elements were the enabler. The advancement was the point.
The same three, in our clients’ own numbers
We asked 187 private markets professionals in September what is blocking them.6 The answers map onto those three elements almost exactly.
THE ELEMENT
IN THE CAR ERA
IN AN AI SOLUTION
BARRIER IN OUR SURVEY
Accessible to everyone
A car an ordinaryperson could afford,start and drive, not amachine for mechanicsand enthusiasts.
Value available to the whole team without an expert operator in the middle.
In-house skills: 40%
Guardrails in place
Signs, painted lines, barriers, and rules of the road, so speed was survivable.
Permissions, traceability, auditability, and human approval, so the firm can move fast.
Security 49%, trust 47%
The essential inputs
Refined fuel, available where you needed it, at consistent quality.
Clean, connected, continuously replenished data, delivered without manual effort.
Integration 48%, data readiness 37%
Security 49%, integration 48%, trust in outputs 47%, in-house skills 40%, data readiness 37%. Not one of the top five is a model problem. Every one is one of the three elements being absent.
It shows where firms are stuck: 19% informal or exploring, 20% running structured pilots, 42% in production, and only 19% with AI embedded in day-to-day work. Four in ten have not reached production at all. The middle of that curve is crowded with firms that have proven something works for somebody and cannot get it to work for everybody.
Investor Engagement Manager was built around these three elements rather than around a model. Each one is a deliberate design decision, and the combination is what turns AI from a capability into an advantage a firm actually holds.
How IEM puts the three together
ONE · ACCESSIBLE TO THE ENTIRE TEAM
IEM changes how the work gets done, and it does it for everyone.
The expertise the current generation depends on is not something IEM asks the user to supply. The intelligence is surfaced by the system and embedded in it, rather than drawn out of it by someone who knows how. There is no prompt to compose, no need to know where the data lives, no judgment call about whether an answer can be trusted, and nothing a person has to understand about how AI works in order to benefit from it. IEM surfaces what changed across every LP relationship, explains what is driving it, and puts a recommended next step in front of the person who covers it.
That design choice does something a better model cannot. Because the experience is the same for everyone, the quality of preparation is the same for everyone. The associate, the IR lead and the partner all start from the same place. Value stops being a function of who is good at the tool, which means it scales with the size of the team rather than with the number of experts on it.
TWO · GUARDRAILS THAT LET THE TEAM MOVE FAST
A system that moves first has to meet a higher standard, and IEM is built around four commitments.
Secure. IEM reads inside the firm’s existing permissions rather than building a second access model beside them. What it surfaces to any individual stays bounded by what that person could already see.
Traceable. Every alert, assessment and draft ties back to the record it came from. Nobody is asked to accept a number on faith.
Auditable. What was surfaced, what was recommended, who saw it, who approved it and what was done, all reconstructable afterward.
Human-led. Nothing consequential reaches an investor or changes a record until a person approves it.
The instinct is to read that list as friction. The roads say otherwise. Lane markings and signals did not restrain traffic; they are the reason traffic could move at volume and at speed without constant collision. Guardrails are what make speed possible, and they are the difference between a firm piloting AI in one corner and a firm running it everywhere.
THREE · THE INPUTS THAT MAKE IT WORK
No firm in private capital is short of data. It accumulates as a byproduct of the work, whether email, documents, fund reporting or notes, and like crude in the ground, none of it runs anything in the state it arrives in. It has to be refined: resolved, connected, deduplicated, put in context. Most of it never is. It sits where it landed.
Firms tend to judge their data by whether the CRM looks populated, which is the equivalent of grading fuel by specific gravity: easy to measure, and not the property that determines whether the engine runs.
Altvia’s underlying data management and data model is what turns that raw material into an enabler rather than a constraint, and the governing principle is that refining cannot depend on the user. That is the lesson of every CRM adoption program ever run: if the value requires people to faithfully enter data first, the value does not arrive. So we handle it three ways.
1. We refine the data you already have. Existing records cleaned, connected and put right, so the firm’s history is usable from day one rather than after a migration project. Nobody starts with an empty tank.
2. We embed data consumption, so nobody hand-crafts the fuel. Integrations and imports keep other systems feeding in automatically. The picture stays current without anyone copying between tools or maintaining a second record by hand.
3. We consume unstructured email automatically. Email is the richest and least governed record of an LP relationship. It is captured, cleaned, organized and recorded into the CRM without user involvement, and made available to IEM without anyone filing anything.
The common thread is that none of the three require a person to be diligent for the system to work. That matters more than it sounds. A system reading only what people remembered to type is reading a fraction of the relationship, and the fraction is biased toward whoever is most dutiful about paperwork, which is rarely the person closest to the investor.
This is how siloed data becomes organizational intelligence: refined, pumped into the tank, and available to everyone entitled to see it.
No one person holds the whole LP relationship. With the inputs handled this way, the firm holds it, even when the individual who built it has moved on.
From changing the work to changing what the firm can do
Each element on its own is worth something and capped. Accessibility without guardrails is a fast car on an unmarked road. Guardrails without accessible value is governance over a pilot nobody uses. And either of those without clean, continuous inputs is an engine with nothing in the tank.
Together they change the work itself. Coverage stops depending on how many relationships a team has the bandwidth to work. Preparation arrives assembled rather than assembled by someone. The quiet relationship that was drifting gets surfaced while there is still time, and the re-up picture arrives months before a launch rather than during the raise.
But the change to the work is the smaller half of it.
Mass access to the automobile did not simply make individual journeys easier. It changed what was possible for the people and the businesses that had it, and it did so at the level of the whole enterprise rather than the single trip. The same thing happens here. A firm where every person works from the same quality of preparation, where coverage no longer tracks bandwidth, and where institutional memory survives the departure of the person who built the relationship, is not performing the old job more efficiently. It is operating at a level its competitors cannot reach by working harder, because working harder is precisely what the constraint always was.
That is a strategic advantage, not an operational improvement. And unlike an operational improvement, it accelerates.
Every interaction connected makes the next connection sharper. Every proactive pass improves the one after it. The firm’s accumulated history becomes an asset a competitor starting today cannot simply buy, because the only way to acquire it is to have been compounding it already. The gap does not stay the same size; it widens in favor of whoever assembled the three elements first.
That is the difference between deploying AI and gaining ground because of it.
Launching with all three
Investor Engagement Manager is available today, with the data refined and continuously replenished, the guardrails built in rather than promised, and the value accessible to every person on the team without an expert in the middle.
The automobile needed thirty years to assemble those three elements, and the assembling is what cost the time. The findings are published now. There is no reason for a firm in private capital to repeat the experiment.
In the first post of this series, we argued that every AI strategy has to answer four questions: what advantages does AI create, what are the risks, what is the integration plan, and in what sequence should that plan be executed?
The first two questions, what the advantages are and what the risks are, have answers specific to each firm. This post expands on the advantages and risks for the relationship that matters most to a GP: the one with the LP.
What AI can do for the GP/LP relationship
The obvious answers to the question of AI advantages are already familiar:
Draft an LP email
Read a year of interactions and estimate how healthy a relationship is.
Summarize a call and flag an overdue follow-up
Fill in the fields nobody filled in
These are useful, and every serious platform will offer some version of them. They are also generic AI use cases with a private markets vocabulary applied. A firm that stops there has bought a faster version of work it already does.
The strategic advantage is the firm’s capacity to hold LP relationships at depth. Today that capacity is set by headcount and memory. A team of three can know forty relationships well, and the rest get the newsletter. Institutional knowledge like who cares about what, what was promised in the last meeting, and which contact is the actual decision-maker, lives with the people who happen to cover the account. And then it’s lost when they leave the firm.
The advantage AI can deliver, if it’s built for this purpose, is that this deep relationship context, the kind of context that isn’t typically found in an account record, is housed and understood across the firm, all the time. Partner-level judgment can be applied on any of them at the moment it matters.
Why LP relationship depth matters more between fundraises
Capital is concentrating with managers LPs already know. Bain’s 2026 Global Private Equity Report finds that 53% of LPs are constrained from making new commitments because capital they already pledged has not been called, up 15 points in a year. The number of buyout funds reaching a close fell 23%. In this market, the relationship a firm carries into the fundraise decides most of the outcome before the first meeting.
The benefits of a deeper relationship compound in ways that show up across the private markets business:
An LP who knows the firm re-ups, and at a larger allocation
Diligence moves faster, because the DDQ confirms what is already established rather than examining it
First closes get anchored, which pulls the rest of the roster forward
Co-investment and separate account conversations get a serious hearing
A continuation vehicle has real support behind it when the firm needs one
A difficult quarter comes with room, and with people who speak well of the firm to the consultants and peers who shape the next fundraise
Every one of those outcomes is earned in the quarters when no fund is in market and where relationship capital is built through interactions like reporting delivered on time, questions answered fully, and follow-ups kept.
How LP relationships are lost between fundraises
It’s in these same quarters where relationships are lost, and they’re lost quietly. Most of the LPs a firm loses are still on the list when the fundraise opens. They took the calls, they received every report, there’s nothing in the file that indicates anything changed. Then the commitment comes back smaller, or as a polite note that this cycle is a pass.
This kicks off an internal process where the team tries to dissect the moment it went wrong. Was it a quarterly report that landed late during the LP’s reallocation? A champion who moved to a different mandate and a successor who never got the call? A follow-up from the annual meeting that stayed in a drafts folder?
Commitments recede before anyone declines. A firm can lose an LP before the fundraise begins and still has to earn every commitment when it does.
Why CRM and a disciplined IR team stopped being enough
Ten years ago, a good CRM and a disciplined IR team was a system that worked. Both still matter, what’s changed is the shape of the business around them. Firms added continuation vehicles, co-investment sleeves, separately managed accounts, and new strategies to stay relevant to the same LPs. Each addition brought its own reporting calendar, its own LP segment, and one more place for the story to diverge.
In Altvia’s interviews with IR teams this year, the recurring theme was learning about an important LP change later than they wanted and not having a system that would have surfaced it. They all knew the data was somewhere within the firm, but nobody had time to find it.
The risk question: the LP relationship answer
The generic risks are leakage and fabrication, and every vendor addresses them. The specific risks within an LP relationship define what the tools have to do.
Under the SEC Marketing Rule, a communication that offers advisory services to prospective or current private fund investors can be considered an advertisement and carries the obligations that follow. The line between an ordinary LP update and an advertisement is not always obvious. A system that drafts for IR therefore has to stop before the send and wait for a person to take the next step.
Additionally, every figure carries the firm’s reputation, so a number with no visible source is a number nobody can defend in the meeting. Partners want to know what the model can access and where the data goes. In one beta conversation this year a partner returned to that question three separate times.
And the IR and fundraising function is starting from behind: in Altvia’s forthcoming 2026 Private Markets Survey, investor relations and fundraising respondents report the lowest AI maturity of any function, a mean of 3.1 on a 0 to 5 scale, and the lowest confidence in AI accuracy, 29% against 47% of all respondents. A strategy that hands them a general-purpose assistant and a training session has misjudged both the advantage and the risk.
A Standard Emerges
Put the advantage and the risks together and a standard emerges:
AI for the LP relationship holds the full relationship in one place, with the deal and fund context that shapes it, so the firm’s knowledge of an LP no longer depends on who covers the account.
It captures that relationship with less manual effort each quarter, because a system that depends on someone remembering to update it inherits the failure it was meant to fix.
It brings the right slice of the relationship to the right person at the right moment, with the source shown.
And it proposes while a person approves.
That standard is what Altvia means by a GP Engagement Platform. Stay tuned! The next post in this series delves into the third question: what’s the plan, in practice?
Sources
Bain & Company, Global Private Equity Report 2026, Fund-raising section (published February 22, 2026). https://www.bain.com/insights/outlook-gaining-traction-global-private-equity-report-2026/
U.S. Securities and Exchange Commission, Investment Adviser Marketing, Release No. IA-5653 (the Marketing Rule, Rule 206(4)-1 under the Investment Advisers Act of 1940). https://www.sec.gov/rules-regulations/2020/12/investment-adviser-marketing
An AI strategy in private markets has to answer four questions. What advantage does AI create in productivity, innovation, and quality? What risks come with it? What is the specific plan to integrate it? And in what sequence should that plan be executed, and how fast? For a general partner (GP), the answers turn on fundraising, investor relations, and deal sourcing, because that is where the firm’s longest relationships are and its highest compliance exposure.
The four questions an AI strategy has to answer
Every effective AI strategy answers four questions for its stakeholders:
What advantages can AI offer in productivity, innovation, and quality?
What risks and vulnerabilities come with integrating it, across the full range of use cases a firm might attempt?
Given honest answers to the first two:
What is the specific plan to integrate AI?
And in what sequence should that plan be executed, and how fast?
The four questions apply to any organization in any industry. The answers do not. Where private markets differ is in how the answers affect fundraising, investor relations, and deal sourcing.
Question 1: What are the advantages in productivity, innovation, and quality?
Sure, the generic answer centers around faster drafting, summarized records, and cleaner pipelines. Those are helpful, they’re tablestakes, and they improve the workflow without improving the decisions. The advantage that matters most to a GP is continuity of context across a fund cycle.
Bain’s 2026 Global Private Equity Report finds that 53% of limited partners (LPs) are constrained from making new commitments because capital they already pledged has not been called, up 15 points in a year, while the number of buyout funds reaching a close fell 23%. In a market that tight, an LP going quiet is expensive, and it is hard to see day to day.
It shows up as an LP that receded quietly during the quarters before launch — a commitment lost before the work began. Nobody decides to disengage; the engagement just thins. What changes the outcome is a system that reads the drift, explains what is driving it, and puts it in front of the person who covers the relationship while there is still time to do something about it.
Context retention is the difference between AI that works and AI that does not
MIT’s NANDA initiative found in its 2025 State of AI in Business report that 63% of executives want AI that retains context, and it named the ability to build on what happened before as the core barrier separating successful deployments from the rest.
Bring that into a GP’s world. A successful deployment retains the context of an LP relationship that spans years, vehicles, and people. It also learns from that relationship, and gets sharper with every interaction.
The measurable gain is less rework
In conversations I have had with Heads of IR, the advantage they actually measure is the reduction in rework and double-checking. An AI system that produces a figure a partner then has to go and verify has not improved the decision, and it has not saved anyone any time.
Question 2: What are the risks of AI in private markets?
Here too, the generic answer holds true: data leakage and fabricated output. And every AI vendor addresses those. But the risks specific to private markets are regulatory and reputational before they are technical. Data leakage and fabricated output apply to every industry, and every vendor addresses them. GPs must also assess three risks specific to their own firm:
Regulatory (SEC Marketing Rule). A page that reaches an LP is an advertisement, and it carries the obligations that come with that.
Compliance. An LP email, a quarterly letter, or a data-room summary sent without a person reviewing it is a compliance event.
Reputation. Every figure that reaches an LP carries the firm’s reputation, and a number with no visible source is a number nobody can defend in the meeting.
Partners repeatedly ask what the AI model can access and where the data goes. In our 2026 Private Markets Survey, security becomes the top barrier once firms reach production, displacing the skills and integration concerns that dominate earlier stages. In private markets, that barrier has a compliance officer’s name on it.
The structural risk: the function that owns the LP relationship is the least equipped to evaluate AI
There is a structural risk at play as well. In Altvia’s forthcoming 2026 Private Markets Survey, investor relations and fundraising professionals trust AI accuracy less than any other function: 29% against 47% overall. They name a lack of in-house skills as their top barrier. A strategy that hands this function a general-purpose tool and a training session has misjudged the risk.
Why general-purpose AI answers are not sufficient for private markets
Horizontal CRM, document, and marketing platforms answer the first two questions generically for every industry at once. A GP’s answers rely on things those platforms do not account for: commitments that attach to a subsidiary’s vehicle and roll up to a parent, side letters whose obligations come due on their own calendar, feeders and look-through investors, the reporting cycle, and the person who covers the relationship. The benefit of a system, and its risk, lives in those specifics.
This is where fundraising, IR, and deal sourcing have room to move ahead. The best answers to the first two questions will come from people who understand the LP relationship, working with tools built around it, on the platforms the firm already runs.
Question 3: What is the specific plan to integrate AI?
A plan is not a list of tools. It settles three things: who builds it, where it runs, and what it has to guarantee regardless of the first two. The first two are execution decisions. The third is a property of the system itself, and it is the one most plans leave out. Once the first two questions have honest answers, each of the three has a fairly obvious one.
Four questions a GP has to answer for themselves
1. Who can build and continue to develop the right solution? The NANDA study found that purchased solutions from specialized vendors, paired with partnerships, succeeded about 67% of the time, while internal builds succeeded about one-third as often. The sharpest gap was in financial services. For a firm whose IR function already names in-house skills as its top barrier, building in house compounds the very problem the project is meant to solve.
2. Where’s the data coming from? AI-ready data does not mean a new warehouse. It means one current representation of the LP relationship spanning the CRM, the investor portal, the data room, and fund reporting all captured with less manual effort each quarter and read by the AI where it already lives. Gartner projects that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. Most of those projects will not fail at the model. They will fail at the data underneath it.
3. Who’s accountable? It’s a trick question, you are! That’s why it’s so important that every output shows provenance and its paper trail. The system proposes; a person approves. This is not a feature to negotiate at procurement. It is the condition that lets a compliance officer, a partner, and an IR associate say yes to the same system.
4. Build or Buy? The build case fails on economics. The cost that sinks it sits underneath the model. Provenance on every output, entitlements that match the firm’s sharing rules, an audit trail a compliance officer will accept, and change control on models and prompts maintained every quarter thereafter. None of that differentiates one GP from another, and all of it has to be right before a single output reaches an LP. A specialist carries that cost across many firms and improves it with every deployment. A single firm carries it alone, for as long as the system runs.
Building is the right call when three conditions hold together: the workflow is genuinely proprietary, the governance stack already exists and is actively maintained, and the team is staffed to keep it current as models change. Few firms clear all three. When two hold and the third does not, the third is what fails, and it fails after the system is already producing work that reaches LPs.
Question 4: In what sequence should the plan be executed, and how fast?
Sequence first because most firms have it backward, and because the sequence is what sets the pace.
Our survey shows the top barrier shifting as firms mature: skills at the informal stage, integration during pilots, security in production, and return on investment only once AI is already embedded. ROI arrives last. Which means most firms are years into the work before they can say what the work was worth.
Reverse the order.
Start with the return. Choose one use case with an outcome you can measure, and define the measure before the pilot begins rather than after it. If you cannot say what success would look like as a number, you do not have a pilot. You have an experiment.
Require the vendor to demonstrate approval rules, audit trail, and data boundaries. Settle approval rules, the audit trail, and data boundaries while the stakes are still small, so security shows up as a solved problem rather than a blocker at the worst possible moment.
Select a system that already sits where the work happens, so integration is not a project you run. Run inside the systems the team uses now — CRM, investor portal, data room — rather than beside them. Integration stops being a project.
Let the skills barrier resolve itself. The skills gap that stalls informal-stage firms is largely an artifact of building rather than partnering. Answer Question 3 correctly and this one mostly disappears.
That sequence also answers the velocity question. Move quickly through the pilot and deliberately into production. A pilot’s only job is to produce a number, and that should take weeks, not quarters. Production is where the compliance exposure becomes real, and that deserves time. Firms that invert this, like a cautious year-long pilot followed by a fast rollout, take the longest to learn anything and carry the most risk when they finally do.
Pilot-stage firms are already choosing their anchor. In our same survey, 82% of firms at the pilot stage rank LP relationship visibility among their top three objectives, ahead of deal flow, compared with 56% of all other firms.
That is where the industry is placing its first serious AI bet. And because the pilot becomes the system a firm scales, the pilot stage is where that bet is effectively decided.
What is next in this series
Over the next several weeks we will take these questions in turn: the problem the LP relationship poses and the standard AI has to meet, the plan in practice, and what our survey says about sequence. The framework stays the same throughout. The answers are ours, and they are specific to private markets.
Frequently asked questions
What should a private markets AI strategy answer first? It should answer what advantage AI creates in productivity, innovation, and quality, and what risks come with it. Only after those two are answered honestly does a plan, and a sequence for executing that plan, become possible. For a GP, both answers are specific to fundraising, investor relations, and deal sourcing.
What are the biggest AI risks for a private equity firm? Beyond data leakage and fabricated output, three risks are specific to private markets: SEC Marketing Rule exposure, because a page that reaches an LP is an advertisement; compliance exposure from a draft sent without human review; and reputational exposure from any figure that reaches an LP without a visible source.
Should a GP build AI in house or buy from a specialist? MIT’s NANDA research found that organizations that purchased solutions from specialized vendors , paired with partnerships, succeeded about 67% of the time , while internal builds succeeded about one-third as often, with the widest gap in financial services.
What does AI-ready data mean in private markets? AI-ready means one current representation of the LP relationship — across the CRM, the investor portal, the data room, and fund reporting — captured with less manual effort each quarter and read by the AI where it already lives. Gartner projects that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data.
In what order should a GP roll out AI, and where should it start? Start with one use case whose outcome you can measure, and define the measure before the pilot begins. Govern before production, run the pilot inside the systems the team already uses rather than beside them, and partner rather than build so the skills gap does not become the gate. In Altvia’s 2026 Private Markets Survey, 82% of pilot-stage firms rank LP relationship visibility among their top three objectives.
About the author
Sebastian Holst is Chief Product Officer at Altvia, where he leads product strategy for the platform private markets firms use to run fundraising, investor relations, and deal sourcing. This is part one of a four-part series on AI strategy in private markets.
Methodology: Altvia 2026 Private Markets Survey, 187 respondents. Pilot-stage figures rest on 38 respondents and investor relations and fundraising figures on 21. Treat both as directional. All percentages are shares of respondents.
