The anticipation surrounding AI’s potential is palpable among private market firms, prompting a reevaluation of how this transformative technology could revolutionize various aspects of their operations. Yet, to fully harness AI’s capabilities, firms must first attain a certain level of digital maturity.
At Holland Mountain, we have long utilized our Digital Maturity Scale to guide clients through their operational decisions, helping them become more digitally adept. With the advent of AI, we have integrated specific updates and changes to this scale to reflect the evolving technological landscape.
This article delves into how private market firms can effectively use the Digital Maturity Scale and the changes brought by AI that firms should be aware of to harness its potential fully.
Holland Mountain’s Private Markets Digital Maturity Scale
The digital maturity scale is a pivotal benchmarking tool, enabling firms to gauge their position on the digital transformation spectrum. It proves particularly useful for private capital firms, facilitating comparisons with industry counterparts and validating operational decisions during growth phases.
Holland Mountain has employed the digital maturity scale for nearly a decade to guide firms through digital transformation initiatives. Despite rapid industry advancements, the scale has remained relatively unchanged, providing a consistent measure of digital progress.
Exploring the Six Pillars of Digital Maturity
Pillar Framework
The scale comprises six pillars, ranging from “At Risk” to “State-of-the-Art.” Each pillar is underpinned by foundational elements of the digital journey—people, process, technology and data, and AI. Segmenting digital maturity into these sub-categories aids private capital firms in identifying specific areas for enhancement, guiding their operational strategies for the forthcoming years.
Linear Progression
The maturity scale is linear, and achieving higher maturity levels depends on previous digital projects. For instance, firms must implement the essential core systems before a data platform, enhanced BI, and AI tools can be leveraged.
The Evolution of Digital Maturity in Private Markets
Progress Over Time
Over the years, we have observed our clients advancing up the maturity scale. Previously, the primary focus was on transitioning from Excel to core systems. Now, with these core systems established, there is a growing emphasis on refining processes and managing data to maximize the returns on technology investments. Simultaneously, efficient operating models and processes empower private capital firms to adopt new technologies effectively.
Current Status and Goals
Currently, most firms have reached the “Established” stage and are striving towards the “Optimized” stage. Their efforts are particularly focused on enhancing data capabilities, data governance, BI reporting, implementing AI point solutions and other value-add elements, which become feasible once core systems are robust.
Benchmarking Digital Transformation
Avoiding the hype
Ultimately, the scale provides a benchmark and a clear direction for firms, steering them clear of the hype and headlines generated by technology vendor sales initiatives.
Cultural Impact
Simultaneously, culture often represents the largest obstacle for firms aspiring to progress through the stages. The scale facilitates a deeper awareness of a firm’s internal position and enhances understanding of its future direction in terms of the operational roadmap.
Integrating AI into Digital Maturity
AI has not transformed the fundamentals of the digital maturity scale; core systems and processes remain crucial even in an AI-enhanced world. If we look at AI across the stages in the scale, it shows us that firms are not required to reach the “State-of-the-Art” stage to begin leveraging AI effectively.
The evolution of GenAI has made the application AI much more accessible. Vendors are incorporating AI tooling in their offerings, meaning that managers operating at “Emerging” and “Established” stages can also benefit from the efficiencies of AI.
Managing Data in the AI Era
Significance of Data Management
One of the most significant challenges in harnessing the benefits of AI is data capabilities. Private capital firms must possess robust data management capabilities underpinned by core systems that provide accurate and relevant data. Implementing a data platform, like Holland Mountain’s ATLAS, helps firms connect and reconcile data from systems, service providers and feeds, providing a single source of consolidated truth.
Firms need more than just public data when integrating AI, and therefore the quality of their proprietary data directly influences the outcomes of AI applications. Consequently, the quality of AI usage is intrinsically tied to a firm’s data capabilities.
AI and Data Governance
AI underscores the importance for private capital firms to establish robust data governance frameworks even before AI implementation. The governance of unstructured files and it’s associated meta data is equally important as the data management for structured systems. This requirement is not merely a one-off task but a sustained commitment to maintaining data quality.
The true value of AI and a data platform lies in their ability to integrate and synthesize information, allowing data science to effectively drive insights and decisions.
People and Culture in Digital Maturity
New Roles and Talent Needs
In terms of hiring, this shift alters the profile of potential new roles, necessitating the addition of, AI, data governance, data science and business intelligence expertise. These roles are pivotal in helping firms ascend the maturity scale and providing the necessary support to internal teams, enabling them to get the maximum value for their operations.
Cultural shifts
Private capital firms must not only consider recruiting new talent in various data domains but also embed the significance of data within their firm’s culture. It’s vital that existing employees understand the value data offers not only to their individual roles but also to the broader team.
At a broader level, private capital firms invest in technology during the earlier stages of their digital maturity. As they progress, the focus shifts towards the people and processes that will ultimately maximize the value generated by these technological investments.
Choosing the Right Technology Partners in an AI-Driven World
The rapid pace of change within the technological landscape is challenging. It underscores the importance of carefully selecting partners who are innovative and positioned for growth. Private capital firms face a significant risk of investing in systems that may not adapt or evolve.
Particularly in the realm of AI, numerous solutions are already available from both new providers and existing ones that are integrating AI features. Seeking guidance to navigate the selection process and mitigate risks is recommended.
AI Vendors’ Landscape in Private Markets
Get a 360 view of key AI use cases and vendors for Private Equity in our latest article.
So, How to Step Up AI Maturity?
To move up the AI maturity level, it is important to stay open to new ideas, test them, and focus on quick wins with established use cases. These are essential strategies.
With AI being relatively early in the application phase in private markets, it is advised to pursue projects that demonstrate value to the business early before moving on to larger AI initiatives.
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