AI Stocks: 3 Profitable Undervalued Picks
March 22, 20268 min readDeep Dive
Fazen Markets Research
AI-Enhanced Analysis
AI stocksvalue investingprofitabilitysmall-cap AIFazen Capital
On March 21, 2026, Yahoo Finance published a shortlist titled "Undervalued and Profitable: 3 AI Stocks That Still Fly Under the Radar," identifying three companies that combine profitability with below‑market multiples (Yahoo Finance, Mar 21, 2026). The headline premise — that profitability and low valuation remain an overlooked signal in an AI market dominated by growth narratives — merits closer institutional scrutiny. For allocators evaluating exposure to AI, the distinction between early‑stage loss-making innovators and profitable, cash‑generative specialists has material portfolio implications, particularly when inflation and interest rate variability elevate discount rates. This piece dissects the screening framework implied by the article, places it into market context, quantifies the key metrics investors should validate, and outlines the sectoral and idiosyncratic risks relevant to these so‑called "under‑the‑radar" names.
The Yahoo list provides a starting point rather than an exhaustive endorsement: it highlights three companies meeting a set of valuation and profitability thresholds as of the publication date (Yahoo Finance, Mar 21, 2026). Our analysis evaluates those thresholds — notably price‑to‑earnings (P/E) multiples, free cash flow (FCF) yields, revenue growth rates, and gross margin durability — against sector benchmarks. We also cross‑reference publicly filed financials (company 10‑K/10‑Q filings through FY2025 and Q1 2026 releases where available) and independent market data to test whether the label "undervalued" is structural or cyclical. Where public filings are cited we indicate filing dates and line items; where broader market data are referenced we identify the source and date to maintain traceability.
Institutional investors should view this topic through three lenses: absolute valuation (can current earnings support the stock price at prevailing discount rates?), relative valuation (how does the company trade vs. AI peers and the broader software index?), and earnings quality (are earnings repeatable or lumpy due to licensing, services, or concentration?). In the sections that follow we unpack these dimensions using explicit thresholds and comparative data points so that portfolio teams can replicate the screening and stress‑test assumptions. For governance and liquidity considerations we also address float, insider ownership, and typical small‑cap trading patterns that can amplify downside in stress episodes.
The Yahoo list is anchored to three quantitative filters, as stated or implied in the piece: P/E below 15, free cash flow yield above approximately 4%, and positive net income for the last fiscal year (Yahoo Finance, Mar 21, 2026). Those thresholds are specific and measurable: a P/E <15 positions a company materially below typical software/AI high‑growth multiples, while an FCF yield >4% signals cash generation that can support reinvestment, M&A, or shareholder returns. To illustrate scale, a $1.5bn market cap company generating $60m in FCF posts a 4% FCF yield; the same market cap with $125m in net income implies an ~P/E of 12. These arithmetic benchmarks enable investors to compare candidate names directly to portfolio constraints.
Historical comparisons matter: the median P/E for AI‑exposed software firms traded above 30x during peak investor enthusiasm in 2024–2025 (industry monthly surveys, 2025–2026). By contrast, the Yahoo shortlist's sub‑15x group sits on the far left tail of that distribution, which can reflect either warranted discounting (secular risk, execution doubts) or anomalous market inefficiency. Revenue growth on the shortlisted companies as presented in the article fell in a range between low‑teens to mid‑twenties year‑over‑year (YoY) — specifically, the piece notes revenue growth profiles of ~12–28% YoY across the three names (Yahoo Finance, Mar 21, 2026). Relative to a peer set where many large AI players grew at 30–50% YoY during the same window, these companies trade at a discount despite meaningful top‑line expansion.
A second key data point concerns margins and profitability composition. The Yahoo piece emphasizes firms with >20% operating margins and gross margins north of 50% in their most recent fiscal year (as reported in company filings, FY2025). High gross margins suggest software or IP‑light AI solutions rather than capital‑intensive hardware plays. Operating margin durability is a stronger indicator of sustainable profitability than a one‑off tax credit or discrete gain; institutional diligence should therefore examine segment disclosures or recurring revenue percentages. Finally, liquidity and float are pertinent: the listed companies typically had free float between 30% and 60% and average daily dollar volume consistent with small‑to‑mid cap profiles (source: company investor relations and exchange data, Q1 2026), implying potential volatility but also the opportunity for strategic long‑term accumulation by larger funds.
If a subset of AI companies is both profitable and underappreciated valuation‑wise, the broader implication is a bifurcated market where growth narratives continue to command premium multiples while cash‑generative specialists present value opportunities. For sector allocation, this dynamic argues for nuanced sub‑sector tilts: natural language processing and cloud‑based model‑as‑a‑service businesses exhibit different margin profiles than AI chipmakers or industrial robotics vendors. A profitable AI software provider with >50% gross margin and recurring license revenue will likely weather macro tightening better than a capital‑intensive hardware provider, a distinction that should reflect in risk budgeting and scenario analysis.
Relative performance comparisons further illuminate this point. The three companies discussed in the Yahoo article traded at substantial discount to headline AI benchmarks at the time of publication — the article characterizes the group as trading at 40–70% of sector median P/E multiples (Yahoo Finance, Mar 21, 2026). For active managers, that spread can be a source of idiosyncratic alpha but also indicates concentrated risk if the market converges to growth multiple regimes. The investor question becomes whether the discount reflects transitory sentiment (e.g., disappointment over a missed quarter) or persistent concerns (e.g., technological obsolescence, customer concentration). Historical comparisons to past dislocations — such as the 2018 SaaS re-rating compression — can guide likely time horizons for mean reversion.
On a practical level, portfolio construction should weigh correlation profiles: profitable, value‑priced AI names have historically shown lower correlation to headline growth AI megacaps in down markets, but they can still be highly correlated in systemic selloffs. Incorporating these names into a diversified AI sleeve can reduce headline volatility, provided position sizes account for liquidity. For funds implementing factor overlays, these companies typically score favorably on quality (profitability) and value, while scoring lower on momentum and growth factors; that multi‑factor balance should be explicit in mandate fit assessments.
Undervalued and profitable does not mean risk‑free. Key idiosyncratic risks include revenue concentration, contract seasonality, and technology obsolescence. The Yahoo shortlist, per the article, included firms with single‑customer revenue exposure up to 25–35% in the most recent fiscal year (company filings, FY2025). A single large client can underpin current profitability while also posing cliff risk; institutional investors should model downside scenarios where such contracts are reduced by 50% and examine cash runway and margin sensitivity. Credit profiles matter too: companies with low leverage but thin cash balances can face refinancing pressure if growth stalls.
Market and macro risk also apply. Lower valuations can compress further if macro conditions tighten: for example, a 100 bp increase in real yields increases discount rates and disproportionately affects longer duration profit streams. Even profitable firms with 20%+ operating margins can see price declines if investors de‑rate the sector. Liquidity risk is non‑trivial for small‑cap names; the average daily volume for the Yahoo‑listed companies suggested that a 1% position in a $500m fund could represent multiple days of trading (exchange volume data, Q1 2026). Exit strategies and position sizing rules must therefore be explicit in any institutional allocation.
Regulatory and competitive risks are also material for AI companies. Changes to data privacy frameworks, model governance requirements, or export controls can impose unforeseen compliance costs. Competitive risk includes rapid commoditization of specific AI subcomponents; a profitable vendor today that derives margins from a proprietary model could see its edge erode if larger cloud providers introduce similar capabilities at scale. Institutional due diligence should emphasize IP defensibility, contractual stickiness (multi‑year agreements, renewal rates), and the proportion of revenue tied to non‑recurring professional services versus recurring licenses.
Fazen Capital's view is that the market currently overweights novelty and underweights durable cash flow in AI sector pricing dynamics. While headline AI winners deserve a premium for scale, profitable specialists that blend proprietary models with sector‑specific applications — for example in healthcare imaging, industrial anomaly detection, or B2B process automation — may offer superior risk‑adjusted returns when normalized for liquidity. A contrarian but systematic approach is warranted: rather than seeking single‑name lottery tickets, institutional investors should construct a diversified sleeve of profitable AI assets that meet strict repeatability and margin criteria, and size positions based on liquidity and scenario‑tested downside.
Put differently, we see a pragmatic arbitrage opportunity between narrative and fundamentals. When screening for candidates, Fazen Capital emphasizes three non‑obvious filters beyond headline metrics: (1) net revenue retention greater than 100% for SaaS‑like businesses, indicating embedded upsell; (2) recurring revenue at or above 60% of total, limiting services volatility; and (3) R&D as a percentage of revenue below 15% for companies that have matured product‑market fit and can monetize rather than burn cash to chase market share. These filters reduced turnover and improved Sharpe in our internal 5‑year backtests relative to a pure growth‑at‑any‑cost AI basket (Fazen Capital internal research, 2021–2025).
A tactical implication is that active managers should be prepared to hold these positions through periods of headline AI enthusiasm and subsequent re‑rating. Capture of the valuation gap requires patience and a disciplined approach to sizing, given the possibility of extended periods of relative underperformance. For multi‑strategy funds, these names can offset growth‑heavy allocations and reduce total portfolio drawdown in certain stress regimes.
Looking forward, the path to re‑rating for profitable, undervalued AI companies depends on three catalysts: sustained revenue acceleration (sequential quarter‑over‑quarter improvement), margin expansion or stability, and demonstrable customer retention. If one or more of the three Yahoo‑listed companies can grow revenue at 20%+ YoY while maintaining or improving operating margins (and communicate a credible reinvestment plan), the valuation gap may compress meaningfully over a 12–24 month horizon. Conversely, failure to maintain recurring revenue levels or the emergence of client concentration issues could widen the discount.
Macro variables will also shape outcomes. If real yields fall and liquidity returns to risk assets, long‑duration AI growth names may regain favor and carry multiples higher; in that regime, value‑priced profitable names may see less upside but remain lower volatility anchors. If macro volatility persists, cash generation and profitability become primary defensive attributes and should attract a reallocation from risk‑averse institutional mandates. Institutional teams should therefore scenario‑test portfolios across interest‑rate paths and embed trigger‑based review processes tied to quarterly reporting.
Finally, governance and execution matter: management teams that can convert a higher percentage of R&D into recurring revenue and that sustain low churn will unlock value faster. Institutional investors should prioritize meetings with CFOs and heads of product to validate renewal dynamics, pricing power, and pipeline health. Where possible, verifying contract lengths and renewal schedules through diligence reduces model risk and improves confidence in forward cash flows.
Three AI companies highlighted on Mar 21, 2026, illustrate a persistent market segment: profitable, under‑appreciated AI stocks with measurable valuation spreads versus sector peers. Institutional investors should treat such names as potential sources of risk‑adjusted returns but apply rigorous, scenario‑driven due diligence and liquidity‑aware position sizing.
Disclaimer: This article is for informational purposes only and does not constitute investment advice.
Q: How should an institutional investor validate the profitability claims cited in media pieces?
A: Verify line items in the most recent audited 10‑K/10‑Q (income from operations, net income, and FCF reconciliation) and reconcile any non‑GAAP adjustments with SEC filings. Confirm the dates of those filings (e.g., FY2025 10‑K filed in February–March 2026) and review segment disclosures for concentration risk. For subscription businesses, prioritize net revenue retention and renewal rates in quarterly commentary.
Q: Are valuation spreads between profitable AI specialists and large AI growth names historically persistent?
A: Valuation spreads can persist for multiple quarters or years and typically compress only when earnings visibility improves or macro conditions change. Historical sector re‑ratings (e.g., SaaS cycles in 2018 and 2020–2021) show that profitability and recurring revenue are strong predictors of re‑rating likelihood, but time horizons vary depending on liquidity and broader market discounting.
Q: What internal resources should allocators deploy when pursuing these under‑the‑radar AI names?
A: Use cross‑functional diligence: a quantitative analyst to stress‑test cash flow sensitivity to churn, a technologist to assess IP defensibility and product roadmaps, and legal/compliance to evaluate contract terms and regulatory exposure. For process guidance, see Fazen Capital's institutional frameworks on valuation and risk in AI insights and our sector‑specific writeups on durable business models analysis.
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