Inflated ARR in AI Startups: How to Truly Evaluate Them

The context: why AI startups' ARR metrics are under scrutiny

In May 2026, TechCrunch published an in-depth analysis This is a practice that is now widespread in the AI ecosystem. Some startups inflate their Annual Recurring Revenue metrics to build compelling growth narratives. Furthermore, the venture capitalists who fund them are often aware of this distortion. However, they continue to publicize those numbers.

The phenomenon is not new in the tech world. However, in the era of artificial intelligence, it takes on particular contours. In fact, the business models of AI startups are often hybrid: they combine software licenses, professional services, API credits, and pilot contracts. Consequently, the very definition of “recurring revenue” becomes elastic and manipulable.

For Italian SMEs operating in B2B or retail, this scenario has direct implications. In particular, it concerns anyone considering adopting AI tools, choosing technology partners, or integrating third-party platforms into their workflows.

The Numbers That Matter: How ARR is Distorted in the AI Sector

ARR, or Annual Recurring Revenue, measures annualized revenue from recurring contracts. It is a standard metric in traditional SaaS. However, in the AI context, it undergoes various forms of manipulation.

The first technique is the’Aggressive annualization. A startup with a monthly contract of 10,000 euros declares an ARR of 120,000 euros, even though the contract only lasts three months. Therefore, the number looks solid, but it does not reflect any multi-year commitment from the customer.

The second technique concerns the inclusion of Professional services. Consulting, implementations, and training are counted as recurring revenue. Conversely, by definition, they are not: they are project-based.

The third distortion involves the pilot agreements. Some startups also count proof-of-concept projects as ARR, which often do not convert into definitive contracts. Therefore, the real conversion rate remains hidden from outside observers.

According to an analysis by Gartner on AI Startup Metrics, more than 40% of companies in the sector use non-standard definitions of ARR in their public communications. Furthermore, less than 20% clearly specify which revenue components are included in their calculations.

Strategic Reading: What It Means for Those Choosing an AI Provider

B2B SMEs often find themselves having to evaluate AI solution providers without having the analytical tools of venture capital funds. However, there are observable signs even without access to certified financial statements.

The first sign is about the Base customer composition. A startup with 500 low-ticket paying customers is structurally more sound than one with 5 enterprise pilot contracts, even if the stated ARR is identical. Therefore, it is appropriate to ask how many active customers pay monthly, not just the total number.

The second element to check is the Net Revenue Retention (NRR). This metric measures how much existing customers spend over time. An NRR above 100% indicates organic growth. Conversely, an NRR below 90% signals high churn, regardless of the reported ARR.

The third criterion is the contractual structure. Prepaid annual contracts indicate real commitment from customers. Instead, monthly agreements that can be canceled at any time significantly reduce the solidity of recurring revenue.

In this sense, Harvard Business Review has dedicated an analysis in the assessment of AI vendor stability. The article emphasizes how operational due diligence is more important today than a vendor's media reputation.

The Hidden Risk for SMEs: Dependence on Fragile Platforms

Adopting an AI tool from a startup with inflated metrics exposes SMEs to concrete risks. In particular, the main risk is service disruption. If the startup runs out of funds or is acquired, existing contracts can be terminated with minimal notice.

We of SHM Studio We have observed this scenario in at least three cases over the past eighteen months. Client companies had integrated third-party AI tools into their processes. digital marketing e SEO. Subsequently, vendors shut down or pivoted the product. As a result, an urgent migration was necessary, with unplanned costs and disruptions.

Besides this, is there a risk of technological lock-in. Some AI platforms build deep dependencies into business workflows. Therefore, switching vendors becomes expensive even when the service deteriorates. Consequently, the initial vendor evaluation must also include the ease of exiting the contract.

Finally, there's the reputational risk. Publicly associating with a startup that later turns out to have inflated its metrics can damage the client company's credibility, especially in B2B contexts where trust is a critical asset.

Operational Implications: A Checklist for Evaluating AI Vendors

Based on these elements, it is possible to build a structured evaluation process. Below are the main criteria we suggest applying before signing any contract with an AI vendor.

• Request ARR breakdownHow much comes from prepaid annual contracts, how much from monthly ones, and how much from non-recurring professional services.
• Check the number of active customersnot only the historical total, but how many are paying today and with what frequency.
• Ask for Net Revenue Retention: A value higher than 100% is a positive indicator of the product's reliability.
• Analyze the financial runwayHow many months of operation does the current liquidity guarantee, regardless of stated revenues.
• Evaluate the exit clauses: notice periods, data portability, API availability in case of service closure.
• Check the concentration of the base customer: If the ARR’s 50% comes from just one or two customers, the risk of volatility is high.

These criteria apply to both the selection of tools for AI management in companies, both to the selection of platforms Google Ads campaigns o LinkedIn campaign with intelligent automation components.

What no one says openly: the role of VCs in myth-building

TechCrunch's investigation raises an issue that goes beyond creative accounting. Venture capitalists are not unwitting victims of these practices. On the contrary, they often actively encourage them.

The mechanism is simple. A fund that has invested in a startup has an interest in maximizing its valuation at the time of exit. Therefore, spreading growth narratives based on generous ARR serves to pave the way for subsequent rounds or high-multiple acquisitions. Thus, the secondary market absorbs valuations that would not withstand rigorous analysis.

According to McKinsey in its State of AI 2025, the pressure to show rapid growth has led many organizations to favor vanity metrics over real value indicators. Furthermore, the competition for talent and media visibility amplifies this effect in the AI sector more than in any other tech vertical.

For SMEs, this means operating in a market where public information about vendors is structurally distorted. Therefore, independent due diligence isn't an accessory option: it's an operational necessity.

SHM Studio's Perspective: How We Guide Clients' Technology Choices

SHM Studio supports Italian SMEs in selecting and integrating digital technologies, including AI solutions applied to web development, SEO copywriting and marketing automation. In this context, critical vendor evaluation is an integral part of our consulting process.

Specifically, when evaluating tools to integrate into client workflows, we systematically apply the criteria described in this article. Additionally, we monitor vendor financial stability over time, not just at onboarding. This way, we reduce the risk of needing to manage urgent migrations or unplanned service disruptions.

For companies considering adopting AI tools or wanting to review their technology stack, the SHM Studio Blog offers regular updates on these types of dynamics. Furthermore, the team is available for a personalized assessment through the page contacts.

Finally, for those who manage businesses digital marketing With AI components, we suggest periodically reviewing contracts with automated platform providers. Therefore, it is useful to set internal alerts when a vendor changes pricing, modifies APIs, or announces funding rounds with valuations that are anomalous compared to reported revenues. These signals, read together, offer a more reliable picture of an AI startup's true health than any press release.