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The broad-based adoption of digital technologies has emerged as one of the most powerful and disruptive forces in industry over the past decade, driving a fourth industrial revolution in which entire sectors are being reshaped and business processes transformed.
As often happens with changes of this magnitude, those slow to join the fold have been left at a competitive disadvantage. We’ve now seen once-dominant incumbent organizations lose ground to their more digitized competitors in nearly every industry. In the asset-management space, there’s little doubt that institutional investors face the same peril. Banking offers a glimpse of what institutional investors might face as nimble new fintechs offering mobile alternatives to services such as payments, loans, and deposits chip away at incumbents. Other large asset managers, such as pension plans and general accounts for insurance, are likely to experience disruptions and performance impacts if they remain behind the digital curve.
The good news is that we’re still at the beginning stages of this new era for large investors. Those that embrace digital and analytics as necessary instruments to augment decision making will have an enormous advantage over those that continue to rely on personal judgment and incomplete data. Already, the adoption of digital technology at scale is creating a new breed of investors who are faster and better at identifying and evaluating opportunities.
In this article, we’ll describe the nature of the opportunity for investors and explain how to get started—while avoiding common pitfalls.
Digital and analytics: Practically made for investors
Digital technologies profoundly alter how humans interact with each other, objects, machines, and systems and rewrite the division of labor between humans and machines. For investors, tasks performed from back to front offices can be streamlined by using digital applications.
Artificial intelligence and other advanced analytics stand at the forefront of digital. Such technologies enable the analysis of massive amounts of data to generate predictive insights at a speed and scale well beyond what humans alone can achieve. Investors already are using analytics and various data sources, such as Bloomberg, to support their investment decision process, but many are only starting to leverage more advanced analytics powered by AI. The wide availability of ever-cheaper data and computing power means that AI can enable investors to analyze more data far faster than previously possible.
AI-enabled automation can also help investors perform repetitive tasks faster and at lower cost. Many investment processes are repeatable. Tedious, redundant analyses, for example, can now be carried out by computers, allowing humans to focus on what they do best: evaluating those machine-generated insights to challenge their investment theses and factor in idiosyncratic risks that AI will not capture.
The cost advantages of digital technologies increase the more they are used. Once the investment in technology has been made, organizations can expand the technology’s use with zero marginal increase in costs. Developing the ability to generate fresh investment insights with greater efficiency will prove critical for investors to remain relevant and competitive in the years ahead.
Early returns from the field
While it’s still early days, there is ample evidence that digital and analytics can provide investors with a competitive advantage. Early adopters are using analytics to support portfolio managers across multiple asset classes. Some firms are using AI to analyze hundreds of nonconventional data sources to help them derive a basket of stocks with a higher likelihood of outperformance. A study by McKinsey of more than 1,000 investors found that those leveraging analytics had a 5.3 percent gain in return on investment capital (ROIC) over those that relied on a more traditional approach.
In addition, there’s evidence from adjacent sectors. Man Group, a UK-based hedge fund with $154 billion in assets under management, is at the forefront of using AI to generate returns. It is continuing to push the frontiers through the Oxford-Man Institute of Quantitative Finance (OMI), a research institute it co-founded with Oxford University in 2007. The OMI brings together experts from academia and industry in a wide variety of disciplines. They use AI, machine learning, and other technologies to generate insights into markets and develop new tools for financial decision making. In real estate, using AI to forecast rent at the street-corner level has led to stronger performance in data-rich markets such as the United States. During the COVID-19 crisis, even port-traffic recovery emerged as an area where AI was better suited to look at all available data to predict where and when traffic would resume.
Despite these successes, some investors shun new technologies because they are wed to the belief that making investment decisions or enhancing the value of a private-equity investment is more art than science. Still others may fear the threat that AI poses to their jobs and compensation.
The reality is more nuanced. Successful use of digital and AI requires a clear understanding of the limits and strengths of the technologies—as well as the limits and strengths of the humans who must wield them. Both have essential roles.
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Getting started—while avoiding common pitfalls
Realizing the full potential of digital and analytics requires a radical change to culture and mindset. Organizations must shift from a culture that thinks in terms of individual use cases and achieving immediate returns on technology investments to a culture in which entire investment and operational processes (such as hiring, performance evaluation, and general-partner-agreement compliance) are reengineered to fully leverage technology.
To reshape the organizational culture as well as the actual technology required for full-scale adoption of digital technologies, successful companies focus on strategy, capabilities, and execution (exhibit). There are pitfalls in each of these areas, and we call them out here so investment institutions can avoid them.
Strategy
While it’s important to be strategic rather than tactical, that doesn’t mean every part of the organization should be transformed immediately. To ensure impact is created early in the transformation, it is best to start with the transformation of a single business domain before moving to another part of the organization—a full portfolio such as value investing or corporate bonds, for example—where investing teams are already open to reconsidering their approach in order to deliver stronger performance. Although functions such as partner-relationship management, human resources, risk, or finance are also great candidates, demonstrating investment impact is more likely to galvanize the organization, build belief in the benefits of digitization, and create the excess returns required to fund future efforts.
Strategic pitfalls to avoid
Assuming leadership knows how to direct digitization efforts. Make sure decision makers get the training and background they need to lead the digitization effort effectively. Leaders with only limited technology literacy can have misconceptions about the power and potential of AI and other digital technologies, which can cause organizations to work on the wrong problems or adopt solutions that are already outdated. Executives must recognize that the risks of disrupting their internal investment processes with digitization are far lower than the risks of not adopting technology at all and losing their competitive advantage in the long run.
Setting the wrong goal. Many organizations misunderstand the real value of digital technologies and set about using it to replace humans rather than to enhance human decision making. On the other hand, sophisticated investors have seen their portfolio performance in equities improve by more than 50 basis points by allowing AI to identify a basket of stocks where portfolio managers should focus their attention. The right goal is most often to pursue digital and AI technologies as a means of human augmentation rather than replacement.
Getting too narrow. Some investors focus on addressing smaller, individual issues with software that isn’t well integrated into the overall investing process. As a result, they often end up launching dozens of small initiatives that, taken together, do not materially move the needle for the organization. Investment decision makers need to take a holistic, strategic view of the opportunities new technologies present.
Sophisticated investors have seen their portfolio performance in equities improve by more than 50 basis points by allowing AI to identify a basket of stocks where portfolio managers should focus their attention.
Capabilities
Bringing leadership and practitioners into the process
Success with digital and analytics will ultimately depend on whether leadership and practitioners buy into the new solutions and ways of working. Both groups should be part of the process—and receive specialized training as the first pilot ramps up.
Leaders: The executive team needs a common understanding of digital technology and its related terms. Training should also focus on helping leaders champion agile development projects, finding the optimal balance between guidance and autonomy to allow innovative yet practical ideas to flourish.
Core practitioners: For speed and efficiency, multiple core-practitioner cohorts (data scientists, data engineers, translators, product owners) should be trained in parallel so the organization has the resources to move to new opportunities over time. Delivering these trainings in mixed cohorts and regrouping the various roles to address real business problems will help the organization expand its pipeline of projects while building the necessary bench to accomplish the digitization journey.
The broader organization: From the start of the digitization journey, the central team—that is, the group responsible for driving the AI agenda and setting up the core analytical practices—should roll out an organization-wide digital-literacy program to help employees at all levels understand the rationale behind the effort and enable them to interact effectively with the central team to identify goals and end products.
To enable a long-term technology transformation, firms will need to set up the right internal capabilities (see sidebar, “Bringing leadership and practitioners into the process”). Scoring some early wins can inject momentum into the overall transformation, so these early efforts must be well supported. They should also fit into an overall strategic foundation that can ultimately support scaling of digital and analytics throughout the organization. Some of these capabilities include the following:
- Technology: Technology tops the list of key capabilities. For at-scale digital deployment, technology should ideally be cloud based to enable agility and foster innovation. Firms also need tooling that can enable continuous solution delivery and post-deployment monitoring (such as MLOps).
- Data: The approach to data is also critical. Firms need a vision and strategy that provide visibility on the full life cycle of data, from acquisition to insight consumption, and a data architecture and data governance that are supported by IT.
- Talent: The new talent model is much more tech heavy, with teams of highly technical specialists guided by a few seasoned investors who have the judgment and experience to make the best use of the machine-generated insights. This requires new processes to recruit from a wider array of backgrounds. The revised HR strategy must adapt compensation and incentives for the differing needs and aspirations of tech candidates. For instance, as a systematic asset manager, Man Group invested in a broad range of talent from different scientific backgrounds. These recruits are fully integrated into investment teams, which means there is no clear distinction between technical (for example, data scientists) and investment (for example, portfolio managers) talent.
- Agile: Finally, to rapidly develop, test, and iteratively improve tech solutions, firms will need a new operating model that encourages deep customer input and collaboration with all key business functions. This means empowering delivery teams with quick decision making, adaptive learning, and greater autonomy.
Capability-building pitfall to avoid
Shiny-object syndrome. In some cases, investors get caught up in pursuing technology for its own sake, rather than leveraging a practical approach that is fit for purpose. Successful companies keep their eyes on the prize. Democratizing access to data, building interfaces for ease of consumption, embedding analytics, or using automation to focus high-performing resources on the tasks that add the most value will serve companies far better than building out the latest tech without a clear, practical application. Many investors overinvest in developing sophisticated AI models, for example, rather than weaving AI into the fabric of their organization by making code available and easy to use, driving adoption, and putting in place the capabilities, infrastructure, and data foundation needed to scale.
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Execution
Adapting the organizational model to maximize adoption of technology by end users in the organization may be the most challenging part of a digital transformation, often representing half of the total effort and investment.
Doing so successfully involves three steps:
- Establish a robust protocol for rolling out analytics solutions to the front line by identifying the best opportunities and co-developing solutions and adoption processes with the investment professionals.
- Define a framework for ongoing monitoring of analytics solutions to enable improvement as issues arise.
- Align key stakeholders to ensure accountability for reinforcing adoption and ownership of new processes lies with the business, not IT.
Execution pitfall to avoid
Leaving end users on the sidelines. The practice of putting frontline users at the center of technology delivery helps organizations seamlessly integrate insights from data and analytics into the investment process. For example, BlackRock’s Systematic Active Equity team uses human insights, data, technology, and mathematics to drive adoption of digital and analytics tools across the core business processes and achieve better results for customers.
Industries are being fundamentally disrupted by digitization, and the investment space is no exception. However, basic digital technologies will eventually erode the scale advantage for investors, making speed the primary differentiator between competitors—speed that AI can help provide. There will likely be a dispersion of returns that favors players with AI-augmented workforces at the expense of those that continue to invest with less sophisticated tools. We believe a window of opportunity exists for investors to build both the foundational and advanced technological capabilities they need to be on the winning side. Now is the time to act.
