Introduction-
Every era has its defining inflexion point.
The 19th century had the steam engine.
The 20th century had the internet.
The 21st century now has Artificial Intelligence — and this shift is already proving larger, faster, and more economically consequential than its predecessors.
From boardrooms to balance sheets, AI isn’t just another technology wave — it’s becoming the operating system of modern business. And for lenders, investors, and private credit players, it’s quietly rewriting how risk is assessed, how value is measured, and where the next decade’s capital flows will be created.
AI Is the New Opportunity Engine
McKinsey’s Superagency report signals what many leaders already feel in real time:
- $4.4 trillion in annual productivity upside from enterprise AI.
- 92% of companies plan to increase AI investments in the next 3 years.
- Only 1% have fully implemented AI across workflows.
- Employees are using AI 3x more than leadership estimates.
- 47% of C-suite leaders admit their AI adoption is too slow.
What Leaders Need to Understand
AI isn’t a tool upgrade. It’s a capability shift.
Erik Brynjolfsson puts it sharply:
“This is a time when you should be getting benefits — and hoping your competitors are only experimenting.”
Employees already know the shift is happening.
Leadership, however, is still catching up:
- C-suite leaders are 2.4x more likely to blame employee readiness than their own alignment.
- Yet employees use GenAI 3x more than leaders assume.
- 48% of employees say training is the #1 barrier — not willingness.
The workforce is ready.
Leadership needs to catch up — and fund the transformation with intention.
The Science Is Scaling — And So Is the Economic Impact
Within two years, AI capabilities have expanded at a pace no industry has ever seen:
- Google’s Gemini 1.5 grew from 1 million tokens to 2 million in months.
- Model accuracy, reasoning, and contextual depth have doubled quarter over quarter.
- Hardware innovation (NVIDIA’s $4T ascent) is now as critical as software innovation.
Sundar Pichai captured the moment clearly:
“AI is more profound than fire or electricity.”
We’re building systems that will outperform top-tier human expertise in more domains, with compounding improvements every 90 days.
This is no longer automation — it’s augmentation at an institutional scale.
The Third Order of Work: The Next Human Promotion
As AI absorbs repetitive and rules-based tasks, human work is entering what researchers call the Third Order of Work:
- First Order: Manual tasks
- Second Order: Cognitive tasks
- Third Order: Compound, creative, strategic, judgment-driven tasks
This transition will redefine operating models, decision cycles, and value creation.
For private credit, this shift changes everything — how lenders evaluate companies, how management teams operate, and how risk is priced in a world where human + AI collaboration becomes the new enterprise standard.
Beyond the Tipping Point
A look at sentiment across roles reveals a powerful truth:
- 94% of employees know GenAI tools.
- 99% of C-suite leaders know them.
- But leaders believe only 4% of employees use AI for 30% of work — the real number is 3x higher.
This disconnect is becoming a competitive risk.
Companies with aligned leadership + AI-literate teams will outpace the market at multiples.
Millennials (35–44), now the managerial core of corporate India, are emerging as the largest AI-skilled demographic:
- 62% report high AI proficiency
- Far more than Gen Z (50%) or baby boomers (22%)
They will drive enterprise-level AI adoption — if leadership empowers them.
Trust, Risk, and the New Governance Mandate
Although employees expect job changes, they trust their employers more than big tech, universities, or startups:
- 71% trust their company to deploy AI ethically.
International C-suite leaders are also more aggressive:
- 55% of Indian executives expect >10% revenue lift from AI
- Global average: 31%
- US: 17%
The winners will be organisations that manage AI responsibly but boldly — balancing governance with innovation.
Key risks to manage:
- Leadership misalignment
- Cost inconsistency
- Workforce redesign
- Supply-chain dependence
- Explainability and model risk
This is not a tech project. It’s a strategic rewiring of the enterprise.
In some industries, employees are cautious
· aligning leadership,
· addressing cost uncertainty,
· workforceplanning,
· meeting the demand for explainability.
What This Means for Private Credit and Corporate Finance
“Learn from yesterday, live for today, hope for tomorrow”
-AlbertEinstein, theoretical physicist
Here’s where the shift becomes extremely relevant for a private credit firm like BMGP:
1. Business Model Transformation Will Drive Capital Demand
Companies modernizing with AI will require:
- Working capital for execution
- Capex for infrastructure upgrades
- Debt restructuring for transformation cycles
- Bridge financing for efficiency-led transitions
AI adoption = new credit demand cycles.
2. AI-Enabled Companies Become Lower-Risk Borrowers
AI-driven enterprises show:
- Better forecasting accuracy
- Higher revenue per employee
- Lower operational risk
- Faster decision cycles
- More transparent data trails
For lenders, this means cleaner underwriting and more confidence in long-term repayment.
3. Credit Underwriting Itself Is Being Rewritten
AI enables:
- Real-time cashflow modelling
- Sector-specific risk benchmarks
- Pattern-based borrower screening
- Dynamic covenant monitoring
The private credit firms that adopt AI early will underwrite faster, price more accurately, and scale smarter.
4. New Asset Classes Will Emerge
Expect rapid growth in:
- AI transformation financing
- Data infrastructure credit
- AI-enabled SaaS revenue financing
- Digital operations refinancing
- Cross-border AI expansion debt (India ↔ UAE)
The firms that recognize this shift early will capture premium yield opportunities.
The Leadership Requirement: Rewire or Fall Behind
McKinsey’s Rewired Framework outlines the six pillars leaders must build:
- Roadmap
- Talent
- Operating model
- Technology
- Data
- Scaling
This is not optional anymore.
AI is the biggest corporate restructuring driver since globalization — and the companies that delay are already falling behind competitors who are aggressively transforming.
Conclusion
Einstein once said:
“Learn from yesterday, live for today, hope for tomorrow.”
AI pushes us to do all three.
Yesterday teaches us that every major technological shift reshapes economic power.
Today demands bold, strategic adoption.
Tomorrow belongs to the companies — and lenders — who see AI not as a threat, but as a multiplier.
For business leaders and private credit players alike, the question is no longer “Is AI important?”
It’s “How fast can we integrate it into strategy, capital, and execution?”
The next decade of corporate growth — and private credit opportunity — will be written by the companies that answer that question with conviction.
