Business

How to Run a Successful Business with Family and how Zaf Baker does it

Published

6 years ago

September 13, 2020

Keep family and business separate. We’ve all heard this sort of advice about how it is deadly and dangerous to involve family and friends in our business as it is bound to lead to collisions, fights, and in some cases, permanent destruction of both the relationships in the business. However, we have seen many instances in which family and friends are able to do business together and still drive.

A good example of this is Zaf and Adam Baker, brothers who run both a property business and a car dealership. The two have been in business for years and have seen mammoth success and are able to balance being both siblings as well as business partners, proving it can be done. Obviously, the situation between the Baker Brothers is not always the norm as there are instances of people who have had relationships destroyed by bringing in family and friends into business. If you are considering this route, it can be done.

First, it is important to look into the relationship itself. A quick peek at Baker’s social media will show that he and his brother are very close and according to recent interviews, they have been very close from a young age. If your relationship between you and your family member or friend is already tumultuous, it will only be heightened due to the pressures of running a business together. If you are thinking of starting a business with someone, make sure it’s someone you already have a good relationship with.

When you do find this person you have a good relationship with and want to go to business with, make sure it is a slow transition in the beginning. The Baker brothers did not start their empire off the bat. Instead, they began their career with a wholesale car dealership and then transitioned it by expanding the business into real estate. Starting with a single project or business venture will give you and your family member the chance to get to know each other as business people as opposed to siblings or otherwise. This means that many of the clashes and teething problems and other issues will be sorted out in the beginning as opposed to popping up later and causing bigger issues.

When you begin working with a family member, make sure that all the rules and roles are defined. For example, Zaf Baker is known as the more outgoing of the two brothers and has a very prolific social media presence which also promotes their business. When you are starting your business venture with a family member, decide ahead of time who is going to do what and make sure that each person is allowed to do their work without consent interference. Especially if there is an age difference or seniority, it is easy for one party to feel slighted. Instead, if each person is given a defined role and not constantly hovered over, the business will likely thrive.

Furthermore, business and pleasure time should also be clearly defined. In the confines of your business, it should be very clear that you are partners first and foremost and ensure that each partner works professionally as though they were working for or with a stranger. Outside the office, however, try your best to keep the personal relationship alive by engaging in the activities you have done prior. One of the issues that many people often have when working with a family member or friend is that they either lose their business partner by trying to maintain the relationship or lose their friend by trying to keep things professional in the workplace.

The key is to find a balance between the two for all involved. A quick look at Baker’s Instagram to grow will show you that the two brothers play as much as they work. His Instagram has shots of them traveling around the world, meeting some of the biggest celebrities in the world, engaging in many hobbies and partying. They also often seen with other members of the family traveling which shows that their relationship has not been harmed by the business partnership.

Finally, it is important to acknowledge when this sort of relationship is not feasible. It must be acknowledged that not every relationship can work in a professional setting and this is perfectly fine. There is no benefit in trying to force it if the flexibility does not exist. If it does exist, however, make sure to apply all the above rules to ensure the best possible results not just for the business but also for the relationship that is in question.

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

1 day ago

April 16, 2026

Matthew Wiles

AI in asset management explained at its most basic level is this: using machine learning, data modeling, and automation to make faster and more accurate investment decisions. The applications vary widely across asset classes, fund strategies, and operational functions. Understanding where AI creates real value separates productive adoption from expensive experimentation.

Asset managers now face a data environment far larger than any human team can process manually. Market signals, company filings, macroeconomic indicators, alternative data sources, and portfolio monitoring all generate information continuously. AI tools process that information at scale. They surface patterns that traditional analysis would miss or find too late.

AI in Asset Management Explained Across Core Investment Functions

AI delivers the most measurable results when applied to specific investment functions rather than deployed as a general capability. The clearest applications sit in portfolio construction, risk management, and credit analysis.

Portfolio Construction and Factor Modeling With AI

Traditional portfolio construction relies on return and correlation assumptions built from historical data. AI-driven portfolio tools go further. They process real-time market data, alternative signals, and macroeconomic inputs simultaneously. This surfaces factor exposures that static models miss.

Machine learning models in portfolio construction can:

Identify non-linear relationships between asset classes that correlation matrices do not capture
Adjust factor weightings dynamically as market conditions shift rather than on a quarterly rebalancing schedule
Flag concentration risks before they appear in standard risk reports
Model tail scenarios using a broader range of historical stress periods than traditional value-at-risk models allow

James Zenni, founder and CEO of ZCG with over 30 years of capital markets experience, has built the platform’s investment approach around the principle that better data and faster analysis produce better outcomes. That view shapes how AI capabilities get deployed across ZCG’s private equity, credit, and direct lending strategies.

Credit Analysis and Private Markets AI Applications

Credit analysis in private markets has historically depended on periodic financial reporting and relationship-based deal intelligence. AI changes that model. Lenders using machine learning tools now monitor borrower health continuously rather than waiting for quarterly covenant tests.

Specific credit applications include:

Cash flow pattern analysis that identifies revenue deterioration weeks before it shows up in reported financials
Supplier and customer relationship mapping that flags single-source dependencies and concentration risks
Covenant monitoring automation that tracks hundreds of credit agreements simultaneously and alerts teams to early warning signs
Loan pricing models that incorporate current market spread data and comparable transaction history

These capabilities compress the time between identifying a problem and taking action. In credit, that time advantage directly affects loss rates and recovery outcomes.

AI in Asset Management Explained Through Risk and Compliance Applications

Risk management and regulatory compliance represent two of the highest-value AI applications in asset management. Both functions involve processing large volumes of structured and unstructured data under time pressure.

How AI Transforms Risk Monitoring in Asset Management

Traditional risk monitoring produces reports at set intervals. AI-powered risk systems run continuously. They flag anomalies in position data and monitor correlated exposures across a portfolio. Alerts fire when market conditions shift beyond defined thresholds.

The practical risk management applications include:

Real-time portfolio stress testing against live market inputs rather than end-of-day snapshots
Liquidity modeling that accounts for position size relative to market depth across multiple scenarios
Counterparty exposure monitoring that aggregates risk across instruments, custodians, and trading relationships
Regulatory reporting automation that reduces manual preparation time and lowers the risk of filing errors

ZCG applies these capabilities across its approximately $8 billion in AUM. The platform was founded 20 years ago. It built its investment infrastructure around systematic data analysis and operational discipline.

AI for Operational Efficiency in Asset Management Firms

Beyond investment decisions, AI delivers significant value in fund operations. Back-office functions like reconciliation, reporting, and compliance documentation consume substantial resources at most asset management firms.

AI tools applied to fund operations include document processing systems. These extract and verify data from offering documents, side letters, and subscription agreements automatically. Reconciliation tools flag breaks between custodian records and internal systems automatically. Investor reporting platforms generate customized materials from structured data inputs, reducing the manual production time significantly.

ZCG Consulting (“ZCGC”) advises operating companies across more than a dozen sectors on operational improvement programs, including technology-driven process redesign. Those operational efficiency principles translate directly to asset management back-office functions.

Applying AI to Asset Management: Limitations Firms Must Address

AI in asset management explained fully must include the limitations. Models trained on historical data perform poorly when market regimes change. Overfitting produces tools that work in backtests but fail in live environments. And AI outputs require experienced interpretation to avoid acting on statistically significant but economically meaningless signals.

The ZCG Team approaches AI adoption with the same discipline it applies to investment underwriting. Every tool requires a defined use case and a measurable success metric. A review process keeps experienced judgment in the decision chain. That framework prevents the common failure mode where AI adoption generates activity without improving outcomes.

Firms that treat AI as a capability layer on top of sound investment processes generate sustainable advantages. Those that treat AI as a replacement for process discipline find the technology amplifies existing weaknesses. It rarely corrects them.