Business

Jobs That May be Under Your Radar

Published

4 years ago

September 29, 2022

According to the U.S. Bureau of Labor, the average worker can expect to sit nearly 45% of the work day. On the surface, that doesn’t sound so bad. However, what isn’t considered is the effect that having a college degree has on that percentage.

Despite lengthy research, there simply isn’t much data on how much people tend to sit at work if they have a college degree versus not having one. However, looking at specific occupations does show data.

Jobs including accounting, business, and tech tend to lead to workers sitting anywhere from 70-80% of the time.

In fact, with an exception to a few areas where a degree is required, most of the post-college workforce appears to be in a position where they spend most of their day at a desk.

For some, this is not an issue. For many others, it can lead to increased stress, dissatisfaction at work, weight gain, and a repetition of tasks that get old after a few days. So why do people continue to work in these environments? Part of it may be our tendency to follow the crowd, and college programs often funnel their graduates to these kinds of jobs.

What if someone wants to break away from the norm? There are certainly options, and here are just three of them.

Coaching

Coaching a sport can be one of the most satisfying and productive jobs that exist. On top of the satisfaction of helping athletes improve their skills, depending on the coach, it can also serve as a workout and a way to stay active.

This option can be especially good in unique sports such as rowing, pole vault, or Irish dance. Many potential clients/athletes may not know about these opportunities, but once word gets out, there may be a lot of interest. Moving up in these specific fields is much easier than trying to go the route of a football or basketball coach. If a rowing team is looking for a coach, and you’ve got the experience, you may end up in a small candidate pool for a great job.

Run an Excursion

Everyone loves excursions while on vacation. It’s a market that’s growing every year, and with the right equipment and skills, it’s very possible to have success here. The best part is that almost no matter where you go, the market will be there.

In a tourist area like Orlando, Florida, so many people go that despite a lot of excursion options, opportunity is still there. On the flip side, in a small town in Kansas, the market may be small, but there won’t be any competition.

The key is to be unique. If close to a desert, a dune buggy adventure will catch a lot of people’s attention. If there are already a lot of those excursions available, have a romantic candlelight dinner under the stars. The possibilities are endless. If you decide that you want an excursion that will keep you up on your feet and active, that’s totally up to you.

Start a Business

Starting a company can be stressful and overwhelming, especially with zero experience. One key is to utilize resources and not pretend that you know how to do everything. Just as you wouldn’t have a plumber frame a house, a dentist perform brain surgery, or an engineer file your taxes, running everything for your business alone will likely not be successful.

Odds are, you may be able to do the business part, but utilizing resources for other areas can help make a business successful.

What does this have to do with not sitting all day? Similar to the excursion idea, starting your own business means choosing your hours, and the work style. You may decide that 7-10 AM is a great time to do all the paperwork and desk-related tasks, take a break from 10-11 AM, and then spend 11-4 PM doing active tasks related to the business. You can decide to work late at night and keep the mornings open.

With few exceptions, a self-business allows you to work when, where, and how you want.

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

3 hours 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.