Evaluating the Fortinet AI strategy means separating genuine product-level revenue from broad marketing claims. Every cybersecurity vendor now labels something "AI-powered," but the real question for investors is whether that label translates into measurable business results. To figure that out, you need to look at specific product lines, capital expenditure patterns, competitive positioning against peers, and how much of a company's total revenue actually ties back to artificial intelligence capabilities rather than traditional firewall and network security sales.
Key takeaways
- Fortinet's AI capabilities are embedded across its product portfolio rather than sold as standalone AI products, which makes isolating "AI revenue" difficult from the outside.
- The company's custom ASIC chip approach (FortiASIC) gives it a hardware-level differentiation that most software-only competitors lack, but it also ties AI performance to proprietary silicon.
- Compared to Palo Alto Networks and CrowdStrike, Fortinet's AI narrative leans more toward operational efficiency and threat detection speed than toward platform-level AI transformation.
- Capital expenditure on AI-related R&D is a better signal of commitment than marketing language. Investors should track R&D as a percentage of revenue over time.
- The majority of Fortinet's business remains rooted in traditional network security appliances and subscriptions, with AI functioning as an enhancement layer rather than a separate revenue stream.
What does Fortinet AI strategy actually look like inside the product portfolio?
Fortinet has taken what you might call the "AI everywhere" approach. Rather than launching a distinct AI product with its own SKU and pricing page, the company has woven machine learning and AI-driven threat intelligence across its Security Fabric platform. FortiGuard Labs, the company's threat research arm, uses AI models to analyze billions of security events and update threat signatures. FortiAI (formerly Cybereason-adjacent capabilities) handles things like malware detection and network anomaly identification.
Here's what that means practically: when a customer buys a FortiGate firewall or subscribes to FortiSASE, they're getting AI-enhanced features baked in. The AI isn't a line item on the invoice. It's part of the value proposition that justifies renewal rates and upsell opportunities.
Security Fabric: Fortinet's integrated architecture that connects its various security products (firewalls, endpoint protection, SD-WAN, SASE) into a unified platform. AI and automation run across this fabric rather than sitting in one standalone product. For investors, this matters because it means AI value shows up in retention and expansion metrics, not a separate revenue line.
This embedded approach has a trade-off. It makes the AI story harder to quantify from the outside, which is exactly why the FTNT AI investment thesis requires more digging than a simple revenue breakout.
How much Fortinet AI revenue can you actually identify?
Short answer: Fortinet doesn't break out AI-specific revenue, and that's not unusual in cybersecurity. Neither do most of its competitors in a clean, auditable way. What you can do is look at proxy metrics.
The first proxy is service revenue growth. Fortinet's business has two main buckets: product revenue (hardware appliances) and service revenue (subscriptions, support, FortiGuard security services). The AI-enhanced capabilities primarily live in the service and subscription layer. If service revenue grows faster than product revenue over time, that's a signal that customers are paying for the software and intelligence layer, which is where AI lives.
The second proxy is R&D spending. Companies that are serious about AI don't just talk about it in earnings calls. They spend on it. You can track Fortinet's R&D expense as a percentage of total revenue and compare it to prior periods. A sustained increase suggests real investment, not just a rebrand.
The third proxy is customer metrics. Average revenue per customer, net retention rates, and the mix of large enterprise deals versus SMB customers can all hint at whether AI-enhanced products command pricing power.
Net retention rate: The percentage of recurring revenue retained from existing customers after accounting for upsells, downgrades, and churn. A net retention rate above 100% means existing customers are spending more over time. For AI-driven platforms, high retention suggests the AI features are sticky enough to justify continued spending.
None of these metrics give you a clean "Fortinet AI revenue equals X dollars" number. But together, they paint a picture of whether AI is a real business driver or a PowerPoint slide.
Fortinet's custom ASIC approach and why it matters for AI positioning
One of the more underappreciated aspects of the Fortinet AI strategy is its custom silicon. Fortinet designs its own ASICs (application-specific integrated circuits) called FortiASIC, which power its FortiGate appliances. These chips are purpose-built for security processing tasks like packet inspection, encryption/decryption, and increasingly, AI-driven threat analysis.
Why does this matter? Because running AI inference at the network edge requires processing power. Most competitors rely on general-purpose CPUs or offload to cloud-based AI processing. Fortinet can run certain AI workloads directly on the appliance, which means faster detection with lower latency. For customers with strict data sovereignty or performance requirements, this is a real differentiator.
The risk? Custom silicon is expensive to develop, and it locks Fortinet into a hardware-centric model when the industry is shifting toward cloud-native and software-defined architectures. If the market moves entirely to cloud-delivered security (SASE, SSE), Fortinet's ASIC advantage could become less relevant. The company is building out its cloud portfolio, but hardware remains a big part of the story.
You can explore Fortinet's financial profile on Rallies.ai to track how the product vs. service revenue mix has evolved over time.
How does FTNT artificial intelligence compare to Palo Alto Networks and CrowdStrike?
This is where things get interesting. All three companies market AI aggressively, but their strategies are fundamentally different.
Palo Alto Networks has gone all-in on what it calls "platformization," trying to consolidate customers onto a single platform (Strata, Prisma, Cortex) with AI running across all three. Palo Alto's XSIAM product is explicitly positioned as an AI-driven security operations platform. The company has been more willing to sacrifice short-term product revenue (through free platform access deals) to drive long-term subscription adoption. Its AI narrative is the most aggressive of the three.
CrowdStrike built its entire business on AI from day one. The Falcon platform uses machine learning models trained on its proprietary threat graph, which processes trillions of security events. CrowdStrike's AI story is arguably the most "native" because the company never had a pre-AI legacy business to migrate from. Its challenge is maintaining growth rates as it scales into adjacent markets like cloud security and identity.
Fortinet's AI positioning is more pragmatic and less flashy. The company emphasizes operational efficiency, cost-effectiveness, and breadth of coverage across its Security Fabric. Fortinet's typical customer gets AI as part of an already cost-competitive package, rather than paying a premium specifically for AI capabilities. This makes FTNT artificial intelligence harder to market as a growth catalyst but arguably more defensible in a price-sensitive market.
If you want to compare these companies side by side, the Rallies.ai Vibe Screener lets you filter cybersecurity stocks by financial metrics and thematic exposure.
What does this mean for investors evaluating FTNT AI?
The competitive comparison reveals a positioning gap. Palo Alto and CrowdStrike can more easily point to AI as a revenue growth driver because their go-to-market strategies are structured around it. Fortinet's AI is more of an enabler than a headline product. That doesn't mean it's less valuable. It means the investment case for FTNT AI relies more on margin expansion and customer retention than on a single high-growth AI product line.
Capital expenditure: follow the money behind the Fortinet AI strategy
Marketing budgets can say anything. Capex and R&D tell you what a company actually believes in. When evaluating whether Fortinet's AI ambitions are real, here's a framework to apply.
- R&D as a percentage of revenue. Track this over multiple periods. A company that's genuinely building AI capabilities will typically show sustained or increasing R&D investment. If R&D stays flat while AI marketing ramps up, that's a yellow flag.
- Capex on data infrastructure. AI model training and inference require compute power. Look for capital expenditure related to data centers, cloud infrastructure, or partnerships with cloud providers. Fortinet's ASIC development also falls into this bucket.
- Acquisition activity. Has the company acquired AI-focused startups or talent? Acqui-hires and tuck-in acquisitions of ML/AI teams signal real commitment. Fortinet has made selective acquisitions in this space, though less aggressively than Palo Alto Networks.
- Patent filings. This is a less common angle, but patent activity in machine learning, automated threat response, and AI-driven network analysis can indicate genuine technical development rather than marketing repackaging.
You can find R&D and capex data in Fortinet's public filings. For a quicker look at financial trends, the FTNT research page on Rallies.ai pulls together key metrics in one place.
What percentage of Fortinet's business is actually AI-driven?
This is the question every investor asks, and the honest answer is: nobody outside Fortinet's finance team knows for certain, and even they might struggle to draw a clean line.
Here's why. When a FortiGate appliance uses AI to detect a zero-day threat, is that "AI revenue" or "firewall revenue"? When a FortiGuard subscription updates its threat database using machine learning models, is the subscription fee "AI revenue" or "security service revenue"? The answer is both, which means any estimate of "AI-driven percentage" involves subjective line-drawing.
What we can say with reasonable confidence is that the majority of Fortinet's revenue still comes from traditional cybersecurity products and services. Network firewalls, SD-WAN appliances, and security subscriptions make up the bulk of the business. AI enhances these products but hasn't replaced or redefined them in the way that, say, generative AI has reshaped parts of the software industry.
A reasonable mental model: think of Fortinet's AI as the engine upgrade in an existing car, not a new car entirely. The vehicle (cybersecurity platform) is the same, but it runs better, faster, and with more intelligence. Investors who buy FTNT expecting a pure-play AI story will likely be disappointed. Those who see AI as a margin and retention tailwind on a fundamentally sound cybersecurity business may have a more accurate view.
Red flags and blind spots in any AI revenue narrative
This isn't specific to Fortinet. It applies to every company claiming AI-driven growth. Here's what to watch for.
- "AI-powered" without specifics. If a company says its product is AI-powered but can't explain what model architecture it uses, what data it trains on, or what measurable outcome the AI delivers, be skeptical.
- Reclassifying existing features. Some companies rebrand rule-based automation or basic statistical models as "AI" to ride the hype cycle. Look for evidence of genuine machine learning investment, not just terminology changes.
- AI bookings vs. AI revenue. Bookings (contracts signed) can include multi-year deals that haven't generated revenue yet. Revenue is what actually hit the income statement. Some companies emphasize AI bookings growth while AI revenue remains modest.
- Customer concentration. If AI revenue comes from a handful of large contracts, that's riskier than broad-based adoption across thousands of customers.
These aren't accusations aimed at Fortinet specifically. They're a checklist for any AI investing thesis. Apply them equally to every cybersecurity vendor making AI claims.
How to evaluate the FTNT AI investment thesis yourself
If you're building a position in Fortinet or just researching the cybersecurity AI space, here's a practical process.
- Read the earnings call transcripts. Search for mentions of AI, machine learning, FortiAI, and automation. Count how often management provides specific metrics versus general statements. Specificity is a good sign.
- Track the service revenue mix. Pull the revenue breakdown from quarterly filings. Calculate service revenue as a percentage of total revenue across multiple periods. A rising trend supports the AI-as-value-driver thesis.
- Compare R&D intensity. Calculate R&D spend as a percentage of revenue for Fortinet, Palo Alto Networks, and CrowdStrike. This gives you a relative sense of investment commitment.
- Monitor competitive wins and losses. Industry analyst reports (Gartner Magic Quadrant, Forrester Wave) evaluate vendors on AI and automation capabilities. Changes in positioning over time reflect real product development, not just marketing.
- Assess the ASIC roadmap. New FortiASIC generations that include dedicated AI processing cores would signal that Fortinet is doubling down on hardware-accelerated AI. If ASIC development stalls, the competitive moat narrows.
For a faster starting point, you can use the Rallies AI Research Assistant to pull financial data and run comparative analysis on FTNT and its peers.
Try it yourself
Want to run this kind of analysis on your own? Copy any of these prompts and paste them into the Rallies AI Research Assistant:
- I want to understand Fortinet's AI strategy beyond the marketing — are they generating real revenue from AI products, how does their AI positioning compare to Palo Alto Networks and CrowdStrike, and what percentage of their business is actually AI-driven vs. traditional cybersecurity?
- What's Fortinet's AI strategy? Are they actually making money from AI, or is it mostly future promises?
- Compare Fortinet, Palo Alto Networks, and CrowdStrike on R&D spending, service revenue growth, and AI-specific product capabilities. Which company has the strongest AI moat?
Frequently asked questions
Does Fortinet break out AI revenue in its financial reports?
No. Fortinet does not report a separate AI revenue line in its public filings. AI capabilities are embedded across the Security Fabric platform and included in product and service revenue. Investors need to use proxy metrics like service revenue growth, R&D spending trends, and management commentary to estimate the AI contribution.
What is FTNT artificial intelligence used for in its products?
Fortinet uses AI and machine learning primarily for automated threat detection, malware analysis, network anomaly identification, and security operations automation. FortiGuard Labs trains AI models on billions of security events to generate real-time threat intelligence. These capabilities are distributed across FortiGate firewalls, FortiSASE, FortiEDR, and other products in the portfolio.
How does Fortinet AI revenue compare to CrowdStrike's?
Direct comparison is difficult because neither company isolates AI revenue cleanly. CrowdStrike's entire Falcon platform is built on AI-native architecture, so in a sense, all of CrowdStrike's revenue is "AI revenue." Fortinet's AI is an enhancement layer on a broader product portfolio that includes hardware appliances. The better comparison is R&D investment intensity and service revenue growth rates, which give a relative sense of AI commitment.
Is Fortinet's custom ASIC chip an AI advantage?
FortiASIC gives Fortinet the ability to run AI inference workloads directly on its security appliances, which reduces latency and improves performance for on-premises deployments. This is a meaningful advantage for customers who need edge processing. The risk is that as the industry shifts toward cloud-native security delivery, hardware-based AI processing may become less of a differentiator over time.
What percentage of Fortinet's business is AI-driven?
There's no public figure for this, and any estimate requires subjective judgment about what counts as "AI-driven." The majority of Fortinet's revenue comes from network security appliances and subscriptions that have been enhanced with AI but are not fundamentally new AI products. A conservative view is that AI is a feature multiplier across the existing business rather than a standalone growth segment.
How should investors evaluate FTNT AI claims?
Focus on three things: R&D spending trends (is the company actually investing?), service revenue growth (are customers paying for the AI-enhanced layer?), and earnings call specificity (does management cite concrete AI metrics or just use buzzwords?). Comparing these signals across Fortinet, Palo Alto Networks, and CrowdStrike gives you a relative benchmark for the cybersecurity AI space.
Is Fortinet a good AI stock?
Fortinet is primarily a cybersecurity company with AI capabilities, not an AI company that does cybersecurity. Whether that makes it a "good AI stock" depends on your investment thesis. If you want direct AI revenue exposure, pure-play AI companies may fit better. If you want a cybersecurity business with AI as a competitive and margin tailwind, Fortinet's positioning may be worth researching. Always do your own due diligence and consult a financial advisor before making investment decisions.
Bottom line
The Fortinet AI strategy is real but embedded. AI runs through the company's products as an enhancement layer rather than a standalone revenue stream, which makes it harder to quantify but potentially more durable than a single flashy AI product. Investors evaluating FTNT AI should track service revenue mix, R&D intensity, and competitive positioning rather than waiting for a clean "AI revenue" line that may never appear in the financials.
If you're researching AI-driven cybersecurity investments, building a framework for separating substance from marketing is the most valuable skill you can develop. Explore more AI investing analysis and guides to sharpen that framework across the entire sector.
Disclaimer: This article is for educational and informational purposes only. It does not constitute investment advice, financial advice, trading advice, or any other type of advice. Rallies.ai does not recommend that any security, portfolio of securities, transaction, or investment strategy is suitable for any specific person. All investments involve risk, including the possible loss of principal. Past performance does not guarantee future results. Before making any investment decision, consult with a qualified financial advisor and conduct your own research.
Written by Gav Blaxberg, CEO of WOLF Financial and Co-Founder of Rallies.ai.
