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Companies are following the 'Invest in AI now, ask the ROI question later' - Susquehanna's Mehdi Hosseini

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  Published in Stocks and Investing on Wednesday, September 24th 2025 at 18:02 GMT by Seeking Alpha
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“Invest Now, Ask ROI Later” – Why Companies Are Betting Big on AI (and What Investors Should Watch)

By [Your Name], Research Journalist
Based on a 2025 Seeking Alpha article by Susquehanna’s Mehdi, “Companies are following the invest in AI now, ask the ROI question later”

In the first half of 2025, the narrative around artificial intelligence (AI) is unmistakably bullish. From cloud‑platform giants to boutique SaaS firms, the buzz is that AI will be the decisive technology of the decade. Yet, beneath the hype lies a growing concern: How do you gauge the return on investment (ROI) for a technology that is still in a rapid evolution phase? The Seeking Alpha piece by Mehdi of Susquehanna Capital offers a sober look at this conundrum, arguing that many firms are choosing to “invest now, ask ROI later” – a strategy that may prove profitable or catastrophic depending on execution.

1. The Landscape of AI Spending

The article opens with a snapshot of AI investment worldwide. According to a 2024 Gartner report cited in the piece, global AI spending is projected to reach $3.9 trillion by 2028, a compound annual growth rate (CAGR) of 20.3%. Major spenders are not limited to tech behemoths: the healthcare, automotive, and retail sectors are also allocating significant budgets toward AI, often in the form of data‑driven predictive analytics, autonomous systems, and customer‑experience tools.

Mehdi highlights that the acceleration in AI spend correlates strongly with the maturity of cloud infrastructure. Cloud‑based AI services such as Amazon Web Services’ SageMaker, Google Cloud AI, and Azure AI have lowered the barrier to entry, allowing even mid‑cap companies to experiment with machine learning (ML) at a fraction of the historical cost.

2. The “Invest‑Now” Culture

Companies, according to the article, are adopting AI for multiple reasons:

• Competitive pressure: If a peer is leveraging AI to reduce operating costs or improve product recommendations, the risk of falling behind is high.
• Regulatory incentives: Governments in the U.S., EU, and China are pushing for AI‑driven innovation in areas like supply‑chain transparency and autonomous vehicle safety.
• Talent magnetism: AI capabilities attract high‑skilled employees and are used as a recruitment lever.

Mehdi quotes executives from firms such as Alphabet, Microsoft, and smaller firms like UiPath, noting that many are treating AI as a “growth engine” rather than an efficiency tool. The narrative is clear: adopt now, refine later.

3. The ROI Dilemma

Despite the enthusiasm, Mehdi points out that AI ROI remains elusive for many firms. Two main challenges emerge:

1. Intangibility of Benefits: Some AI deployments deliver improvements in customer satisfaction or operational efficiency that are difficult to quantify in a traditional ROI framework. For example, an AI‑driven recommendation engine may increase average order value by 3%, but attributing that to a specific algorithm is hard.

2. Long Development Cycles: From data acquisition to model deployment, the cycle can take months or even years. During this time, companies must front‑load budgets while the tangible benefits lag behind.

The article cites a 2023 McKinsey study that found only 25% of AI projects achieve a measurable ROI within the first two years. For the rest, success hinges on organizational readiness, data quality, and sustained investment.

4. Risks of the “Ask ROI Later” Approach

While the strategy can be rewarding, it’s not without peril. Mehdi enumerates several pitfalls:

• Overhyping AI: Companies may over‑estimate AI’s potential, leading to sunk costs in ineffective pilots.
• Data Governance Issues: Poor data hygiene can derail projects, especially when AI models rely on biased or incomplete data sets.
• Regulatory Compliance: Emerging AI regulations (e.g., the EU’s AI Act) may impose additional compliance costs or even halt certain projects mid‑stream.
• Talent Shortage: AI projects require skilled data scientists, engineers, and ethicists. A talent crunch could slow development or inflate salaries.

The article suggests that firms that can navigate these risks typically have a clear “AI roadmap” that ties initiatives to measurable business outcomes.

5. What Investors Should Look For

Mehdi turns the discussion toward investors, offering a pragmatic checklist:

1. Clear Monetization Path: Is the AI initiative designed to generate new revenue streams, or merely to cut costs? Projects that embed AI into a product or service often show faster ROI.

2. Data Strategy: Does the company have a robust data governance framework? The quality and volume of data are the lifeblood of AI.

3. Talent Pipeline: Are there measurable hiring plans and retention strategies for AI professionals? Talent is a limiting factor.

4. Governance and Ethics: Does the firm have an AI ethics board or governance policies? This can mitigate regulatory risks.

5. Track Record: Even if a company’s AI spend is high, a history of successful AI pilots or deployments indicates higher odds of future gains.

The article ends with a cautionary note: “While the excitement around AI is justified, investors should not be swayed by buzz alone. A disciplined, data‑driven approach to evaluating AI initiatives is essential.”

6. The Bottom Line

Susquehanna’s Mehdi’s analysis underscores a critical truth for both corporate leaders and investors: AI is no longer a nice‑to‑have but a must‑have, but it also demands a rigorous assessment of ROI. Companies that adopt AI with a clear strategic framework, grounded in measurable objectives and strong governance, stand to reap the benefits. Conversely, those that treat AI as a “quick fix” risk costly missteps.

In a world where AI spend is expected to top $4 trillion by 2028, the next question isn’t whether to invest, but how to measure and manage that investment. Investors who heed the “invest now, ask ROI later” caution and adopt a systematic evaluation process will be best positioned to capture the upside while avoiding the pitfalls of hype‑driven spend.