Building a Precision PE Firm
Author : Simon Williams, Founder at WovenLight
Developing the Necessary Talent, Processes and Culture for Success
By harnessing the power of data, analytics and AI, asset managers and their portfolio companies will unlock new opportunities and differentiate themselves in a competitive market.
Embracing this evolution will position the private market ecosystem to deliver superior outcomes for investors and stakeholders alike. At every stage of the investment cycle, firms can have a material impact on — and make a difference to — the performance trajectory of their portfolios. We call this ‘Precision PE’.
Critically, if they are to achieve this, they must invest in talent, refine their processes and foster a culture that embraces data and AI-driven innovation.
To fully realise the potential of data, analytics and AI in portfolio value creation, sponsors must foster the right talent, processes and culture — across both their investment teams and portfolio companies alike.
Talent with Data Acumen
“Talent density is often more important than absolute amount of talent” Sam Altman
We view AI as augmentation, not replacement, for private markets performance improvement. Many voices in the industry and media focus on how a mix of ‘big data’, artificial intelligence and machine learning will automate jobs — and then companies — out of existence. We take a more positive long-term view, believing that embedding AI into the investment lifecycle will give space for more thoughtful investment decisions, more efficient workflows and distinctive value creation, which in turn delivers growth and sustainability, protecting businesses and livelihoods.
In order to achieve this, private equity firms will need to adapt to harness the benefits of diverse teams, skillsets and cultures, spanning the worlds of finance and technology.
They will need to recruit talent with strong data literacy, analytical and engineering skills who can extract actionable insights from complex datasets and turn them into operational capabilities that are simple to adopt and future resilient. This is hard, and the profiles required may well come from different pools than the traditional MBA and investment banks.
In order to operate cross-functional teams comprising data scientists, domain experts and investment specialists, they will need to establish a culture of continuous learning, data-enabled decision-making and ensure access to the latest technologies.
This is not easy.
This is perhaps best described in “Legacy” where James Kerr delves into the leadership principles that have contributed to the success of the New Zealand All Blacks rugby team and emphasises the value of creating a culture of continuous learning and adaptation.
Adaptive Learning Processes
“Start where you are, use what you have, do what you can” Arthur Ashe
Critically, this is about augmentation not replacement. As in the first industrial revolution when factories augmented humans’ muscle power, the AI-powered tools will augment synthesis, experimentation and decision-making, but won’t replace the need for skilled human judgment.
For humans and machines to work together effectively, re-imagining how private markets processes work is essential. The obvious steps are still obvious, namely adopting agile methodologies to promote iterative decision-making and enable rapid responses to market changes, together with implementing data governance frameworks to ensure data quality, privacy and security, thereby establishing a strong foundation for data-driven strategies.
However, there are two less obvious lessons we’d observed over past 15 years of building QuantumBlack and WovenLight.
First, the power of compounding. Build assets that compound. Every time you do something, capture the ‘exhaust’ of lessons and experience to make it easier next time. Likewise eliminate problems, responsibilities and obligations that compound. Compound ROI is a powerful decision-criteria for evaluating data and AI initiatives. Repeatability is a scaling device, so actively look to build consistency and codification in your activities.
Second, when you spot a flywheel, pay close attention. Positive feedback loops come in many powerful forms and operate at various speeds: self-reinforcing network effects, ever-improving cost structures, complementary revenue streams, etc. People misperceive the value of data as providing better insights, but the most valuable use of data is as a feedback loop. Make sure you build these feedback loops into your investment lifecycle, ideally keeping them as short as possible.
Cultivating a Performance Culture
“Coaching is 30% tactics, 70% social competence” Julian Nagelsmann
To attract the talent required to make use of these game-changing technologies requires a culture of innovation and continuous learning, encouraging teams to embrace data-driven approaches and AI-driven insights.
Leaders must lead by example, showing a willingness to experiment with new technologies, being receptive to data-backed ideas and even embrace different economics across the firm.
Culturally, the organisation must embed the use of analytics at varying scales, akin to how you can zoom in or out on Google maps, turning different layers on or off to answer different questions.
Whilst different audiences may need distinct views for their questions, they should consistently rely on the same foundational data, albeit with varying details, similar to how cartographers think about generalisation.
Alongside this, firms should recognise the inherent paradox of data; it’s not all created equally and can be regarded both as an exercise in truth alongside an exercise in narrative. Given this reality the firm’s culture should start to anchor on making little bets, naturally adopting the principles (if not the math) of Bayes probability theorem; attaching a degree of probability to every decision and then update this initial belief as new objective information becomes available.
In high-performing cultures the beliefs are a living system; always learning, always modifying, such that they can harness effective patterns-of-play that adapt as the context changes, as it always does.
