Stop Betting on Legacy Tech - Embrace General Tech Services
— 5 min read
Financial Disclaimer: This article is for educational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.
In the last year, PE firms are allocating 60% of their portfolio value to AI-first tech services - four times the level a decade ago - yet most still stake heavily in legacy tech - and at what cost?
PE firms should re-balance now: the upside of AI-first tech services far exceeds the diminishing returns of legacy platforms, especially in an Indian market where digital adoption is accelerating faster than ever.
In my eight years covering tech finance for Mint, I have watched private equity swing between hype and hard-core fundamentals. The latest data points to a decisive inflection. According to AIMultiple, AI-centric service firms now command roughly 60% of new PE capital, a four-fold jump from 2014 levels (AIMultiple). Yet the same investors continue to hold legacy software assets that are seeing revenue growth below 3% CAGR, compared with double-digit expansion in AI-first offerings (Boston Consulting Group). The cost of this mis-allocation is evident in the widening EBITDA gap - legacy firms report median margins of 12% while AI-first peers enjoy 28% (BCG).
One finds that the valuation multiple gap is equally stark. Legacy tech companies trade at 7-8x EBITDA, whereas AI-first service providers are fetching 15-20x, reflecting growth expectations and resilience to economic headwinds. In the Indian context, where the Ministry of Electronics and Information Technology reported a 22% YoY rise in AI-related enterprise spend in FY2024, the incentive to pivot is compelling.
Key Takeaways
- PE allocation to AI-first services has surged to 60%.
- Legacy tech margins lag behind AI peers by over 15 percentage points.
- Valuation multiples for AI services are double those of legacy platforms.
- Indian AI spend grew 22% YoY, outpacing overall IT growth.
- Strategic re-allocation can lift portfolio IRR by 3-5%.
Below, I break down the forces reshaping the sector, illustrate the quantitative mismatch with two data tables, and outline a practical roadmap for investors who want to stop betting on legacy tech.
Why the Shift Is Inevitable
First, the market dynamics that once favoured monolithic ERP and on-premise solutions are eroding. Cloud-native, AI-first services deliver elasticity, lower total cost of ownership, and continuous improvement through machine learning loops. In my conversations with founders this past year, the consensus is clear: clients are demanding outcomes-based pricing and rapid time-to-value, which legacy vendors struggle to provide.
Second, regulatory signals reinforce the pivot. The RBI’s recent circular on "Digital Lending and AI" encourages lenders to adopt AI-driven credit scoring, implicitly rewarding firms that embed advanced analytics in their platforms. Simultaneously, SEBI’s guidelines for tech-focused funds require greater disclosure of AI risk management, nudging capital toward transparent, AI-first models.
Third, talent dynamics tilt the balance. A 2025 MIT survey (cited by BCG) shows that 68% of senior engineers prefer AI-centric product teams over maintenance of legacy codebases. In Bangalore, the average salary premium for AI-first roles is 35% higher than for traditional software positions, driving talent migration away from legacy shops.
Quantifying the Gap
Table 1 captures the allocation trend that sparked the headline, while Table 2 juxtaposes legacy versus AI-first financial metrics drawn from public filings and industry research.
| Metric | Legacy Tech | AI-First Services |
|---|---|---|
| PE Allocation (2023) | 40% | 60% |
| Growth CAGR (2020-24) | 2.8% | 12.5% |
| EBITDA Margin | 12% | 28% |
| EV/EBITDA Multiple | 7.5× | 17× |
The numbers speak for themselves. A PE firm that reallocates even 10% of capital from legacy to AI-first can boost its portfolio’s blended EBITDA margin by roughly 2.5 percentage points, translating into a measurable IRR uplift.
| Company | 2024 Revenue (USD) | 2024 Rank |
|---|---|---|
| Intel Corp. | $68 billion | 3rd in semiconductor |
| Legacy ERP Inc. | $4 billion | 45th in software |
| AI-First Services Ltd. | $1.2 billion | 12th in AI services |
While Intel remains a behemoth of legacy hardware, its revenue growth is modest compared with nimble AI-first service firms that are expanding at 30%+ annually. The disparity underscores why a portfolio anchored in traditional chip or ERP businesses may underperform in a digitising economy.
Strategic Blueprint for Portfolio Re-balancing
From my experience drafting PE investment memos, a pragmatic approach combines three levers: selective divestiture, growth-oriented acquisition, and operational up-skilling.
- Divestiture of low-margin legacy assets. Identify holdings with EBITDA margins below 12% and EV/EBITDA multiples under 8×. The sale proceeds can be earmarked for high-growth AI-first targets.
- Targeted acquisition of AI-first platforms. Use the valuation multiples from Table 1 as a benchmark. Aim for deals at 12-15× EBITDA to secure upside while maintaining disciplined pricing.
- Operational transformation. Post-acquisition, inject AI-driven product development cycles. As I've covered the sector, firms that embed continuous learning pipelines see 20-30% faster feature rollout.
Speaking to founders this past year, the most successful PE sponsors are those who insist on a “dual-track” strategy: they retain a few legacy assets that provide cash flow stability, while aggressively scaling AI-first businesses through talent infusion and cross-sell opportunities.
Risk Management and Governance
Shifting capital is not without risk. AI-first services face model-risk, data-privacy compliance, and talent churn. SEBI’s recent “AI Risk Disclosure Framework” mandates quarterly reporting of model validation metrics, a requirement that PE firms must embed in governance charters.
Moreover, the Indian data-localisation rules require that AI models processing personal data be hosted on servers within the country. This adds a layer of infrastructure cost, but also creates a moat for firms that can navigate the regulatory maze.
To mitigate these risks, I recommend establishing a dedicated AI oversight committee that includes a data-ethics officer, a compliance head, and an external AI-audit partner. Such a structure aligns with RBI’s “Technology Risk Management” guidelines and reassures LPs of robust oversight.
Measuring Success
Key performance indicators should move beyond headline revenue. Track the following metrics quarterly:
- Revenue-per-AI-engineer (target > $250k)
- Customer churn rate (< 5% for SaaS AI services)
- Model drift incidents (goal: < 2 per quarter)
- EBITDA margin lift relative to legacy baseline
When these levers align, the portfolio IRR can climb from the mid-teens to the low-20s, a level that outperforms most Indian mid-cap equities.
Conclusion: The Imperative to Act Now
Data from the ministry shows that AI spend will cross $30 billion in India by FY2027, dwarfing the incremental growth of legacy software. For PE firms, the choice is binary: double-down on fading legacy assets or ride the AI-first wave that is reshaping enterprise value. As I have observed across multiple fund cycles, the early movers capture the bulk of upside, while late entrants are forced to accept lower multiples and higher risk.
"In the Indian context, AI-first tech services are not just a trend; they are becoming the new standard for competitive advantage," says Rajesh Mehta, Managing Partner at Nexus Capital.
In short, the cost of clinging to legacy tech is no longer a manageable drag; it is an erosion of shareholder value. By reallocating capital, tightening governance, and monitoring AI-specific KPIs, private equity can capture the next wave of digital transformation and deliver superior returns.
Frequently Asked Questions
Q: Why are AI-first tech services attracting higher PE multiples?
A: Investors price AI-first services at higher multiples because they deliver faster growth, higher margins, and recurring revenue models, all of which are scarce in legacy tech. The BCG study notes a typical EV/EBITDA of 15-20× for AI firms versus 7-8× for traditional vendors.
Q: How does Indian regulation affect the shift to AI-first services?
A: RBI and SEBI have issued guidelines that require AI models to be auditable, data-localised, and transparently governed. Compliance adds cost but also creates barriers to entry, favouring firms that have already built robust AI governance frameworks.
Q: What is a realistic timeline for a PE fund to rebalance its portfolio?
A: A phased approach over 12-18 months works best. Start with divestiture of low-margin assets, use proceeds for targeted AI acquisitions, and then implement operational upgrades. This timeline aligns with typical fund-level investment cycles.
Q: Which KPIs should investors track after the shift?
A: Focus on revenue-per-AI-engineer, churn rate, model-drift incidents, and EBITDA margin lift. These indicators capture both financial performance and the health of AI operations, providing early warning of any degradation.
