Why talent risk is a business issue — not just an HR issue. A MESSAGE FROM HEDBERG SEARCH As we move into the final quarter of 2026, companies are planning for growth, technology changes, new opportunities, and what comes next. But every strategy ultimately depends on people. The right leadership team can help an organization execute its strategy, navigate change, and build long-term value. A leadership gap, unexpected departure, or missing capability can have the opposite effect. That makes talent risk something business leaders should be thinking about before a critical position becomes vacant. 1. CAN YOUR TEAM EXECUTE YOUR GROWTH STRATEGY? A strong business plan is only as effective as the people responsible for executing it. As companies expand, enter new markets, or adopt new technology, their talent requirements can change. Leaders should ask: Do we have the capabilities we will need? Where are our leadership gaps? Are we too dependent on one or two key individuals? What happens if a critical executive leaves? These aren’t questions to ask only when someone resigns. They are questions worth asking before the need becomes urgent. 2. PLAN FOR WHAT’S NEXT Succession planning isn’t simply about retirement. Executives leave. Organizations change. Businesses grow. New leadership capabilities become necessary. The companies that prepare aren’t necessarily predicting exactly who will leave. They’re thinking ahead about what they will need when circumstances change. Building an experienced leadership pipeline takes time. So does finding, evaluating, and onboarding senior talent. The worst time to discover a leadership gap is when it is already affecting business performance. 3. LOOK BEYOND THE JOB TITLE One of the most important questions in executive hiring isn’t: “What title do we need?” It’s: “What capabilities do we need?” A future leader may need experience managing growth, navigating change, developing teams, building client relationships, or leading through technological transformation. The right candidate isn’t necessarily someone who has held the exact same title. It’s someone whose experience, capabilities, leadership approach, and perspective align with where the organization is going. That distinction is particularly important in specialized areas such as investment management, wealth management, asset management, family offices, and private markets. 4. TALENT CAN AFFECT BUSINESS VALUE Talent becomes especially important during periods of transition — including growth, mergers and acquisitions, leadership changes, and potential ownership transitions. Leadership depth matters. So does employee retention. So does having a clear understanding of who is responsible for critical functions. A company preparing for its next stage should not wait until a transaction or transition is underway to start thinking about these issues. Strong leadership planning is part of long-term value creation. 5. RETENTION MATTERS TOO Hiring great people is only part of the equation. Keeping them matters just as much. During periods of growth or change, organizations need to understand what keeps their key leaders engaged and committed. Compensation and incentives can play a role. So can meaningful responsibility, professional growth, recognition, culture, and a clear understanding of where the organization is headed. There isn’t a single retention strategy that works for every company. Understanding the people who are critical to the organization’s future is the starting point. THE HEDBERG SEARCH PERSPECTIVE Executive search should begin with the business need, not simply the job description. Before identifying candidates, it is important to understand: Where is the organization going? What capabilities will it need? What leadership gap needs to be solved? What kind of person can make an impact in that environment? At Hedberg Search, this perspective is especially relevant across investment management, wealth management, asset management, family offices, private markets, and financial services. The goal isn’t simply to find someone who looks impressive on paper. It’s to identify leadership talent whose experience and capabilities align with the organization’s next chapter. Talent risk is business risk. Companies that think ahead about leadership, succession, capabilities, and retention give themselves more options when change arrives. Because ultimately, a business strategy is only as strong as the people responsible for bringing it to life.   THE RIGHT PEOPLE. THE RIGHT LEADERSHIP. THE RIGHT NEXT STEP.   Scott Hedberg Founder & President 

In last month’s issue, we framed AI as an inevitable shift in investment management, less about hype, more about practical advantage. This month, we move from theory to application. The key question for most investment professionals is no longer “Is AI relevant?” but rather: “How can I actually use this in my day-to-day research process?” The answer is already taking shape across leading firms, and it centers on speed, scale, and sharper insights. 1. Accelerating Fundamental Research At its core, investment research is about synthesizing large volumes of information into a coherent view. AI dramatically compresses the time required to do this. Tasks that traditionally took hours or days can now be completed in minutes: ► Summarizing earnings transcripts across multiple quarters ► Extracting key themes from management commentary ► Identifying shifts in tone, language, and guidance For example, instead of manually reviewing 10 years of earnings calls for a single company, AI tools can surface: ► Changes in capital allocation priorities ► Evolving margin pressures ► Management’s consistency (or inconsistency) over time This doesn’t replace the analyst’s judgment, but it allows them to spend more time interpreting insights rather than gathering them. 2. Enhancing Competitive Analysis Understanding a company in isolation is no longer sufficient. The edge increasingly comes from relative insight, such as how a company compares to peers across multiple dimensions. AI enables: ► Rapid peer group comparisons across financials, strategy, and positioning ► Automated identification of differentiators (pricing power, cost structure, growth drivers) ► Real-time tracking of competitor announcements, filings, and sentiment Instead of building static comp sheets, analysts can now dynamically assess: ► Which companies are gaining share, and why ► Where margins are expanding or compressing across a sector ► How strategic narratives differ between competitors This shifts competitive analysis from periodic to continuous. 3. Identifying Industry Trends Earlier One of the most valuable applications of AI is its ability to detect patterns across large, unstructured datasets. Traditional research often relies on lagging indicators: quarterly filings, earnings releases, and industry reports. AI expands the aperture by incorporating: ► News flow and press releases ► Supply chain signals ► Hiring trends ► Regulatory developments By aggregating and analyzing these inputs, AI can highlight emerging trends earlier than traditional methods. For instance: ► A surge in job postings for a specific technical skill across multiple companies ► Increasing mentions of a new technology in earnings calls ► Subtle shifts in language around demand or pricing Individually, these signals may seem insignificant. Collectively, they can point to meaningful inflection points. 4. Unlocking Alternative Data Alternative data has long been discussed, but historically difficult to operationalize. AI is changing that. Investment teams can now more effectively leverage: ► Web traffic and consumer behavior data ► Satellite imagery ► Transaction-level datasets ► Social and sentiment analysis The challenge has never been access. It’s been interpretation. AI tools can structure and contextualize these datasets, allowing analysts to: ► Identify trends that are not yet reflected in financials ► Validate (or challenge) management narratives ► Build more forward-looking views Used correctly, alternative data becomes less about novelty—and more about confirmation and edge. 5. The Speed Advantage Perhaps the most immediate and tangible benefit of AI is speed. Consider the difference: ► Screening hundreds of companies manually vs. AI-assisted filtering in seconds ► Reviewing a 200-page industry report vs. extracting key insights instantly ► Monitoring a portfolio vs. receiving real-time, prioritized alerts Speed alone is not an advantage. What you do with it is. The firms gaining traction are those using AI to: ► Cover more ground without sacrificing depth ► Respond faster to new information ► Reallocate time toward higher-value thinking In many ways, AI is not changing what analysts do. It is redefining where they spend their time. 6. From Information to Insight Here’s a critical distinction: AI is exceptionally good at processing information, but it does not replace judgment. The role of the investment professional remains: ► Framing the right questions ► Interpreting outputs within context ► Making decisions under uncertainty AI can surface patterns. It can highlight anomalies. It can accelerate workflows. But conviction still comes from experience, perspective, and disciplined thinking. The most effective professionals will not be those who rely on AI, but those who integrate it seamlessly into their process. Closing Thoughts We are still early. Most firms are in experimentation mode: testing tools, refining workflows, and determining where AI fits within their investment process. But one trend is clear: The gap between AI-enabled and traditional research approaches is widening. This is not because AI replaces the skill, but because it amplifies it. For investment professionals, the opportunity is straightforward: Start small. Apply AI to a single workflow. Build familiarity. Then expand. Because the question is no longer whether AI will shape investment research. It already is.

Smarter Research. Better Decisions. Artificial intelligence has quickly become one of the most discussed topics across industries, including investment management. Yet for many professionals, the conversation still feels unclear: a mix of excitement, skepticism, and uncertainty about what AI actually means for their work. Most investment professionals today fall into what I would call the “curious but cautious” category. They recognize that something important is happening. But they are also asking reasonable questions: Is this real value or just hype? Will it replace analysts? How does this actually apply to investment decision-making? The reality is far more practical, and far more relevant, than most headlines suggest. The Investment Industry Is Drowning in Data The volume of information available to investors has grown exponentially over the past decade. Earnings calls, filings, research reports, industry publications, alternative data sources, economic indicators, and real-time market information create an environment where the challenge is no longer access to data. It is processing and interpreting it effectively. Human cognitive capacity has limits. Time is finite. Complexity continues to increase. This is precisely where AI becomes valuable. What AI Actually Is (In Practical Terms) At its core, modern AI is a combination of advanced pattern recognition, language processing, and probability modeling. It does not “think” like a human. Instead, it excels at identifying patterns across large amounts of information, summarizing content, and generating structured insights quickly. A useful comparison is the adoption of spreadsheets decades ago. Excel did not replace financial professionals. It dramatically increased their productivity and analytical capability. AI is following a similar trajectory but with much broader applications. AI as an Augmentation Tool, Not a Replacement One of the most common concerns is whether AI will replace investment professionals. The evidence suggests the opposite. Investment management remains fundamentally a judgment-driven profession. Experience, intuition, domain expertise, and contextual understanding still matter enormously. However, professionals who effectively use AI tools will likely gain a meaningful advantage over those who do not. Here’s a simple way to think about it: AI will not replace investment professionals. But investment professionals who use AI may replace those who don’t. Early Practical Use Cases in Investing AI is already being applied in ways that are directly relevant to daily workflows. Some examples include: ► Earnings Transcript Analysis ► Summarizing multiple quarters or years of management commentary in minutes ► Industry and Competitive Research ► Rapidly scanning large amounts of information to identify trends, risks, or strategic positioning ► Document Review ► Analyzing credit agreements, filings, or research materials more efficiently ► Market and Sentiment Monitoring ► Tracking changes in language, tone, and emerging narratives across markets ► Idea Generation and Screening ► Accelerating early-stage research to identify areas worthy of deeper analysis None of these replace judgment. They accelerate insight generation. Why AI Matters Now There are three primary forces driving AI adoption in investment management: 1. Data Explosion The volume and complexity of information continues to increase. 2. Computing Power Advances in technology allow tools to process information at unprecedented speed. 3. Accessibility AI tools are no longer limited to large institutions. They are becoming available to professionals at all levels. This combination creates a tipping point. AI is moving from experimental to practical. The Strategic Implication Historically, investment advantage has come from better information, better analysis, or better judgment. AI has the potential to enhance all three. It allows professionals to: ► Process more data ► Identify patterns faster ► Challenge assumptions more effectively ► Focus time on higher-value thinking The firms and individuals who learn to integrate these tools thoughtfully into their workflows may gain a meaningful competitive edge over time. Importantly, this is not about replacing human expertise. It is about amplifying it. Looking Ahead Artificial intelligence in investment management is still in its early stages. The tools will continue to improve, and best practices will evolve. But the direction is increasingly clear. AI is becoming another tool in the professional toolkit, much like spreadsheets, databases, and analytics platforms before it. Those who begin exploring and experimenting now will likely be better positioned as the technology matures. Next Month’s Preview We will explore how AI is already transforming investment research workflows and how analysts are using it to dramatically accelerate insight generation. Scott #AI #BusinessStrategy #InvestmentManagement #HedbergSearch