AI in the private equity market: How investors can discover promising companies earlier

How investment firms use artificial intelligence to identify the next high-growth companies

The competition for young companies begins long before the financing round

In the private equity market, success isn't solely determined by how much capital an investment firm can raise. Equally important is its ability to identify promising companies early, accurately assess their development, and establish strong relationships with their founders in a timely manner. However, relevant information is often scattered across company websites, recruitment platforms, trade publications, databases, and internal meeting notes. Artificial intelligence promises to aggregate this information more quickly and help investment teams use their limited attention more effectively.

One example of this is the collaboration between the growth investment company Highland Europe and the AI ​​provider Unframe. A customized application, internally called "Highland Brain," is designed to make new companies visible, prioritize potential investments, and better leverage existing team knowledge. This approach demonstrates how AI can support the search for investment opportunities and the maintenance of existing contacts—especially in a market where attractive companies are highly competitive and investment decisions are often prepared long before a funding round.

However, a faster search doesn't automatically translate into a better investment strategy. Public signals like staff growth or new customer relationships can provide important clues, but in themselves, they aren't proof of economic success. The crucial question is whether the additional information leads to better discussions, more informed selection, and attractive long-term investments. The central question, therefore, isn't whether AI can analyze more companies, but whether it helps investors use their capital more effectively.

Those who only discover the best companies during the sales process are often already too late

Competition in private capital markets is not solely determined by the amount of available fund volume. Equally important is the ability to identify promising companies early on, correctly assess their development, and establish a solid relationship at the right moment. Capital can be raised, data can be purchased, and numerous analytical tools are available to many market participants simultaneously. What remains scarce, however, is the ability to derive better decisions from incomplete information in a timely manner.

Artificial intelligence addresses precisely this bottleneck. Its economic benefit lies less in spectacular forecasts than in the systematic linking of information that was previously scattered across company websites, HR platforms, specialist articles, databases, emails, and internal meeting notes. By meaningfully combining this information, investment firms can focus their attention more effectively and discover companies that slip through conventional search filters.

The collaboration between growth investment company Highland Europe and AI provider Unframe illustrates this transformation. At its core is a customized application that analyzes company signals, prioritizes investment opportunities, and integrates the team's existing expertise into a shared workflow. Internally dubbed the Highland Brain, the system thus combines two functions: it supports the search for previously uninvested companies and improves decisions regarding which existing contacts should be followed up and when.

However, one crucial limitation from an economic perspective is that a better search engine does not automatically translate into a better investment strategy. Only when additional information actually leads to better discussions, more informed selections, and economically attractive investments does a tangible added value emerge. The relevant question, therefore, is not whether AI can analyze more companies, but whether it improves the quality of the capital employed.

A growing transaction market does not solve the structural problems

The importance of more efficient investment processes becomes clear when looking at the current state of the private equity industry. After several challenging years, the global buyout market recovered significantly in 2025. The value of recorded investments increased by 44 percent to US$904 billion. At the same time, the number of transactions decreased by 6 percent to 3,018. This growth was therefore concentrated in larger deals and did not represent a uniform recovery across the entire market.

This distinction is crucial. A rising transaction volume can create the impression that capital can once again be invested without difficulty. In reality, the total value says little about the breadth of the market and whether smaller or medium-sized investment firms have easier access to attractive companies. A few very large acquisitions can skew the statistics, while competition for other high-quality investments remains just as intense.

This tension is also evident on the sales side. The global value of recorded buyout exits increased by 47 percent in 2025 to US$717 billion. In contrast, the number of sales and other recorded exits declined by 2 percent to 1,570. At the same time, approximately 32,000 companies with an estimated value of US$3.8 trillion remained in the respective portfolios. The average holding period at the time of exit was approximately seven years.

The figures refer to the buyout segment and should not be equated with the entire private investment market. Private markets also include, among other things, growth capital, venture capital, private debt financing, infrastructure, and real estate. Nevertheless, the buyout trend illustrates a relevant economic context: long-term capital commitment, demanding valuations, and uneven returns increase the pressure to prepare investment decisions more carefully.

This connection is also significant for growth investors like Highland Europe, even if their investment models are not identical to classic leveraged acquisitions. Those who finance companies at high valuations need convincing growth, robust competitive advantages, and realistic future divestment options. A larger number of potential investments is only helpful if it results in a better selection.

Why the search for a company is becoming an independent value-creation function

The search for investment opportunities is often treated as a preparatory activity. The actual investment process seems to only begin with due diligence, valuation, and contract negotiation. This view underestimates how strongly the quality of subsequent decisions is already determined by the observed business universe.

A fund can only invest in companies it knows and to which it has access. If interesting providers remain outside its focus, they won't appear in even the most thorough subsequent review. The search process thus directly influences the pool of companies from which investments can be made.

Traditionally, this selection is made from personal networks, recommendations, industry events, company databases, and manual research. Each of these methods is valuable. However, together they do not automatically produce a complete or consistent market overview. Well-known founders, well-connected consultants, and companies with a strong media presence often receive more attention than less visible competitors.

This can lead to significant distortions, particularly in the European market. Different languages, regional trade publications, and specialized industries make it difficult to obtain a unified overview. A successful provider of industrial software can be firmly established in its customer segment without being regularly featured in the usual technology media.

AI-powered search can close such gaps, provided it actually taps into different information spaces. It must not only find more of what is already known, but also open up new search avenues. The economic benefit then arises from better market coverage and not merely from faster processing of the same databases.

The crucial bottleneck is attention

Investment teams operate under a severe capacity constraint. There are more companies, news stories, and potential contacts than people can carefully process. Therefore, the scarce resource is not just information, but qualified attention.

Every additional research effort consumes time. Every initial consultation potentially pushes another aside. Even an interesting company initially incurs costs before it's clear whether it fits the strategy at all. An investment firm must therefore not only identify promising companies but also allocate its limited processing capacity effectively.

This is where the economic core of a prioritization engine comes into play. Its purpose is to improve the order of tasks. A company with high inherent appeal might currently be less urgent than a somewhat smaller provider with whom there is a specific reason for discussion. Conversely, an upcoming funding round might be of little relevance if its business model or size doesn't align with one's own strategy.

A useful prioritization therefore combines several dimensions: strategic fit, quality of available information, anticipated business development, relationship history, and time urgency. These factors should not be indiscriminately merged into a seemingly objective overall score.

The distinction between attractiveness and timing of contact is particularly important. An excellent company can be worth monitoring continuously without needing to be actively approached. Conversely, a suitable occasion for conversation can justify contact without necessarily constituting an investment recommendation.

Highland Europe combines market research and relationship management

Highland Europe focuses on growth companies in Europe and Israel. The focus is on ambitious founding teams and product-oriented companies, including those in software and technology-driven consumer markets. The operational challenge lies in identifying relevant companies from a large and dynamic market environment and building relationships before a financing process narrows the available options.

The solution employed primarily addresses the discovery of new companies. To this end, various indicators are combined: workforce developments, information about potential customer acquisitions, public company news, and other signals regarding business performance. This integrated analysis is crucial because a single data point often remains ambiguous.

The second part concerns the existing contact database. Companies already recorded in the CRM are not sorted solely by static characteristics. Previous interactions, notes, and the course of conversations are also factored into the classification. The application is designed to help leverage existing knowledge for future actions.

The publicly described design includes personalized company lists, transparent evaluations, and concise research documentation. Three to five easily understandable reasons are provided for each recommendation. The system also incorporates user feedback, such as when a company is added, deferred, or rejected.

The interesting point is not that an algorithm creates a ranking. Such rankings have existed for a long time. What's new is the closer connection between ongoing market monitoring, internal expertise, and concrete workflows. A research tool is evolving into a supporting infrastructure for the entire investment team.

Growth signals are indications, not proof

The economic quality of such a system depends significantly on how carefully it interprets observable signals. Employee growth is a good example. An increasing number of employees can indicate growing demand. However, it can also be a sign of aggressive upfront financing that has not yet generated viable revenue.

Conversely, a stable workforce is not automatically a negative. A software company can increase its productivity and generate significantly more revenue with a stable team. Especially with AI-driven business models, a mechanical equation of staff expansion with business success would be problematic.

The same applies to customer acquisition. A publicly named large company may be an important reference customer. However, this does not necessarily mean that a large, recurring, and profitable contract already exists. There are significant economic differences between a pilot project, a limited rollout, and company-wide use.

The attitude of experienced managers is also ambiguous. It can enable professionalization and growth. At the same time, it can indicate that existing structures are no longer working or that a company needs to be realigned after a difficult period.

A reliable analysis therefore considers combinations and temporal sequences. Repeated customer acquisitions, targeted staff expansion, and a verifiable product expansion together paint a different picture than isolated announcements. Nevertheless, the limitation remains: Public signals do not replace financial data, customer interactions, or an examination of the actual contract quality.

The greatest data treasure often lies within one's own CRM

Many investment firms already possess extensive internal information. The problem is that this information is only of limited use. Meeting notes, email histories, and personal assessments exist in various formats and are not systematically linked in daily practice.

It is precisely this unstructured content that can be particularly valuable from a business perspective. A note might state that a founder currently doesn't require capital but would like to discuss the matter again after entering a new market. Another entry might point to an unresolved product issue or an upcoming personnel decision.

Such information describes conditions and timeframes that are rarely found in standardized databases. Its significance only becomes clear in context. For example, a later expansion announcement can make a months-old meeting note relevant again.

An AI system can make these connections visible and provide the responsible team member with a reasoned suggestion. The added value then arises not from new external information alone, but from its connection with existing relational knowledge.

However, this knowledge base also requires quality standards. A personal assumption should not retrospectively appear as an established fact. An old assessment should not be perpetuated indefinitely. The date, author, context, and uncertainty of a note must be preserved so that institutional memory does not become an institutionalized misconception.

Individual experiential knowledge becomes a learning-oriented work process

Over years, individual investment professionals develop a knack for identifying promising companies. This knowledge is often only partially documented. It lies in their preferred questions, their assessment of founding teams, and their ability to distinguish between weak and strong growth signals.

This creates a scaling problem for an organization. As long as such assessments remain largely tied to specific individuals, other team members benefit only to a limited extent. New employees have to learn many things anew. When experienced individuals leave, some of the knowledge is lost.

A shared AI application can help make certain decision-making patterns visible and reusable. However, it needs to capture more than just a simple "like" signal. Whether a company was rejected because of its business model, size, location, or rating is crucial for the next recommendation.

The quality of the learning process therefore depends on the quality of the feedback. If a company is merely put on hold because the team currently lacks the processing capacity, it would be wrong to conclude from this that there is a fundamental aversion to the business model.

A high-performing system should therefore distinguish between professional assessment and organizational behavior. Not every inaction implies an investment decision. This differentiation is challenging, but essential if the application is to achieve more than simply perpetuating past work habits.

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When personalization becomes a digital echo chamber

Adapting to an investment strategy is a significant advantage over standardized tools. At the same time, it creates a risk: A system can become increasingly adept at suggesting precisely those companies that match the team's existing preferences.

This is efficient at first glance. However, it can narrow the focus on new business models. Investors who have primarily financed rapidly growing software companies with specific sales structures might systematically undervalue other value creation models.

A particularly problematic aspect is the confusion between familiarity and quality. A company appears convincing because it resembles well-known success stories. Conversely, an unusual technology or a less familiar market segment seems risky, even though it might offer a more attractive risk-reward ratio.

Therefore, a good search architecture deliberately requires room for deviations. Besides companies with a high strategic fit, it should also be able to highlight cases that do not conform to the usual pattern but become interesting due to understandable signals.

Such openness is not a technical detail, but a question of investment culture. The organization must decide whether it uses AI only to implement existing beliefs more efficiently or also to specifically question these beliefs.

Early action creates options, but not an automatic price advantage

Early contact can improve an investor's negotiating position. It allows them to get to know the company over a longer period, observe its development, and build a relationship before a formal financing need arises.

This head start, however, does not equate to a guaranteed valuation discount. Well-informed founders know their alternatives. An attractive company can command a high price even without a comprehensive process. Furthermore, economic advantages arise not only from the purchase price, but also from the quality of information and access to it.

The real benefit of early contact lies in additional options for action. An investor can wait and see how things develop, clarify specific questions, and better assess what kind of support is actually needed. Conversely, they can recognize early on that a company, despite a positive public image, doesn't fit their own strategy.

This option value is difficult to measure, but economically relevant. Those who only enter a short-term financing round must assess under time pressure what other market participants may have been pursuing for years.

AI can facilitate access to such early relationships. However, the relationship itself remains a human endeavor. Founders make decisions not only based on analytical skills, but also on trust, reliability, and the quality of personal collaboration.

More research capacity can improve the quality of the conversation

A frequently underestimated effect of automated research lies in the preparation for meetings. When basic information is already compiled, an investor needs to spend less time on questions that can be answered from publicly available sources.

This allows the conversation to move more quickly to the actual business topics: customer loyalty, willingness to pay, product differentiation, sales bottlenecks, or capital allocation. Good preparation thus improves not only internal efficiency but also the impression made on the company.

This is relevant for founders because the number of potential investors is not the same as the number of helpful contacts. Those who have clearly done their homework and understand the specific connections can differentiate themselves from superficial contact requests.

However, an automatically generated research profile must not create a false sense of security that the data does not support. An incorrectly assigned customer or a misunderstood personnel change can jeopardize a conversation. Therefore, the responsibility for preparation remains with the human.

The productive division of labor is as follows: The machine collects and structures information, while the investor reviews and analyzes it in greater depth. The greater the economic significance of a piece of information, the less likely it is that it should be accepted unchecked from an automatically generated summary.

The business case needs more than a convincing demonstration

The economic benefit of an AI application cannot be measured solely by its speed or the quality of a demonstration. Investment companies need a multi-stage success measurement process.

At the operational level, the question is how much time is actually saved on research and compilation. At the next level, it's necessary to examine whether additional relevant companies are discovered and whether recommendations lead to qualified discussions.

Only then can it be assessed whether the improved search also changes the investment pipeline. The sheer number of new entries is not what matters. A database filled with unsuitable companies incurs follow-up costs and can scatter attention more than before.

A simplified calculation illustrates the scale of the effect. If ten investment employees each save four hours per week, and this reduction in workload is accumulated over 45 working weeks, 1,800 hours will be freed up annually. Assuming internal total costs of €120 per hour, this corresponds to a calculated capacity value of €216,000.

This model calculation does not describe actual results at Highland Europe, nor does it guarantee cost reductions. Free time only becomes economically beneficial when it is used productively. If it is merely consumed by additional internal coordination or a larger number of unpromising meetings, the effect remains limited.

Proving better returns takes years

Between an improved recommendation and a realized return on investment lies a long chain of events. A company must be contacted, mutual interest must develop, due diligence must be passed, and financing must be available on reasonable terms. Subsequently, it must prove its economic viability.

Therefore, it would be premature to conclude that faster research will automatically lead to higher fund returns. Publicly described product features and positive user experiences do not yet prove a causal return advantage. No reliable, publicly proven additional investment returns or long-term return effects have been demonstrated for specific applications.

A sound evaluation should therefore use intermediate indicators. These include the relevance of newly discovered companies, the proportion of recommendations that were actually followed up, and the quality of the resulting discussions. Equally important is the question of how often the system overlooked companies that were later identified as interesting independently.

Comparisons must also take similar conditions into account. Different market phases, industries, and team capacities can skew results. A better year for technology investments should not automatically be interpreted as a success of the new software.

The appropriate perspective is therefore neither euphoric nor cautious. Operational advantages can be verified relatively quickly. Claims of superior investment results require significantly longer observation and more careful benchmarks.

Rapid prototyping shortens the economic learning curve

A key feature of the collaboration is the rapid transformation of an idea into a usable application. The described development process envisions a functioning prototype within one to two weeks. This allows for early testing of whether the planned support is relevant in actual daily work.

This is economically valuable because the uncertainty of a software project doesn't only become apparent after a long development cycle. Users can identify early on whether the chosen company is convincing, whether important information is missing, and whether the proposed prioritization aligns with their workflow.

However, a prototype is not a complete production proof. Continuous operation requires robust interfaces, defined permissions, stable data processing, and controlled changes. In addition, ongoing maintenance, cost control, and the handling of erroneous results are essential.

The sensible conclusion, therefore, is not that complex enterprise software can always be completed in just a few days. Rather, the time until the first verifiable benefit can be shortened. This reduces the risk of working for a long time on a system that is technically unsuitable.

Especially in a fast-paced AI market, this capability can be more important than having the most extensive initial feature set. The key is to learn effectively early on and then scale reliably.

Tailor-made AI is changing the classic software decision

For a long time, companies faced a clear choice: buy standardized software or have a custom application developed. Standard products were usually available more quickly, but required adjustments to their own processes. Custom developments were a better fit, but were often expensive and required a lot of maintenance.

AI-powered platforms are attempting to push this boundary. Reusable technical components are combined with customer-specific data, decision rules, and workflows. This allows the application to be more tailored to the individual user without requiring a complete rebuild.

Highland Europe's differentiation lies precisely in this approach. Existing standard tools provided information, but did not adequately reflect the shared working methods and specific understanding of prioritization. In contrast, the new application connects external data with internal systems and continuous team feedback.

This presents the provider with a challenging business model question. The more customized a solution becomes, the greater the potential benefit for the customer. At the same time, the risk increases that implementation and support will require too much manual effort.

The model only becomes economically attractive when individual customization is based on a largely reusable technical foundation. Otherwise, revenue may increase, but scalability does not necessarily follow suit.

The AI ​​provider's key performance indicators require precise classification

The development of Unframe itself also illustrates the interest in such operating models. In May 2026, a $50 million funding round led by Highland Europe was announced. At the same time, the company reported a total contract value of more than $100 million within less than twelve months and a net revenue commitment of 400 percent.

These figures describe strong commercial momentum, but they should not be confused with one another. The total contract value is not equivalent to realized annual revenue. It may include obligations over longer contract terms.

Even a net sales retention rate of 400 percent must be interpreted in context. When calculated appropriately, it indicates a significant expansion of business with an existing customer cohort. However, on its own, it provides no conclusive information about profitability, implementation costs, or the sustainability of this growth.

For a sound assessment, further information would be necessary: ​​for example, the contract durations, the composition of the customer base under consideration, and the distinction between recurring platform revenues and project-related services.

This conceptual precision is particularly important in the AI ​​market. High demand and rapid expansion can indicate a viable business model. However, they do not replace the examination of how much recurring economic revenue remains after the provision and support of the solutions.

The dual role as investor and customer creates close relationships and conflicts of interest

Highland Europe is both an investor and a user of Unframe. This situation can have advantages. The investor experiences firsthand how quickly the company develops, how it reacts to problems, and whether the product is used in everyday life.

The provider gains a demanding reference customer with direct access to decision-makers. This collaboration can help develop features under realistic conditions and incorporate expert feedback more quickly.

At the same time, this close relationship limits the significance of positive assessments. An investor has a vested interest in the successful development of their portfolio company. Therefore, a customer opinion arising from this relationship cannot be equated with an independent impact assessment.

A professional organization should consciously distinguish between these two roles. The investment thesis may be convincing even though a specific use case still needs improvement. Conversely, a helpful product may be used without necessarily confirming every growth assumption made by the provider.

This separation protects both sides. It allows for open criticism of the product and prevents operational decisions from being justified solely on the basis of stakeholder interests.

Data protection and control are integral parts of the economic benefits

As soon as an AI system processes CRM notes, emails, and confidential company information, security becomes a business-critical requirement. It's not just about technical availability, but about protecting relationships and sensitive information.

An investment company needs clear rules about which data may be processed, who can access results, and how information remains separate between clients. Equally important are deletion options, traceable logs, and the control of connected external services.

Special attention should be paid to updating the underlying models. Improved performance is welcome, but changing models can alter expenditures and priorities. Such changes should be reviewed before being implemented uncritically in sensitive workflows.

External content also poses risks. A system must distinguish between imported material and permissible instructions. Company websites or documents must not be able to control internal processing simply because they contain certain wording.

Security is therefore not a secondary cost factor. It determines whether an application can be widely used at all. A high-performance solution to which the team, for legitimate reasons, does not entrust confidential data, will remain economically underperforming.

The lasting advantage does not arise from the language model alone

When powerful AI models are accessible to many market participants, their mere use can hardly create a lasting competitive advantage. The same applies to publicly available company information.

Differentiation arises from the combination of data quality, industry-specific interpretation, internal relationship knowledge, and consistent application. A company can read the same message as its competitors and still draw a different conclusion because it has a better understanding of the market and its past developments.

Furthermore, there is an organizational effect. A well-maintained system can ensure that information is not only available but also reaches the responsible person in a timely manner. This difference between knowledge and action is greater in many organizations than the difference between two analytical models.

However, the advantage of rapid signal detection can also diminish. If numerous funds react to similar settings, customer reports, and financing news, competition for the same companies intensifies.

Then the value increases of those insights that are more difficult to standardize: technical assessment, understanding of industrial procurement processes, classification of customer economics, and the ability to provide concrete support to founders in the next stage of development.

AI is changing the division of labor, not responsibility

The obvious consequence of such a development is not the end of human investment work. Rather, its focus shifts. Manual data collection and repeated summaries become relatively less important. Interpretation, critical review, and relationship building gain in importance.

This presents a particular challenge for younger employees. Routine tasks have often been a learning path through which market knowledge was built. When AI takes over these tasks, organizations must consciously create other opportunities to develop economic judgment.

Experienced investment professionals, however, should not view the new technology merely as a more convenient information service. They must explain their decision-making criteria more precisely and address the question of where their existing patterns might be misleading.

The responsibility remains human. Prioritization is not an investment decision, a research profile is not a company due diligence, and early contact is no guarantee of a successful investment.

The realistic benchmark for AI in private markets is therefore ambitious but clear: it should make relevant companies more visible, improve the handling of uncertainty, and enable informed decisions. Its economic value does not lie in generating as much activity as possible. It lies in focusing scarce attention where good information translates into viable relationships, and viable relationships into better investments.

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