Have you been in a leadership meeting where every conversation eventually returned to the same conclusion?
“We need to become an AI company.”
I have.
Two or three years ago, nearly every executive conversation I was part of came back to AI. Sometimes the pressure came from the board or investors. Sometimes it came directly from the CEO and leadership team. But eventually, the mandate reached every part of the organization.
Marketing needed a stronger AI narrative. Sales needed to talk about AI with customers. Technology needed new capabilities and infrastructure. Product needed to put AI at the center of the roadmap.
As a product leader, the natural response is to translate that ask into something the product organization can deliver.
Which AI capabilities should we build? Where should we add copilots, recommendations, summaries or automation? How quickly can we get something into customers’ hands?
That translation is understandable.
It may also be solving the wrong problem.
Because when a CEO or board says, “We need to become an AI company,” they may not primarily be asking for more AI features.
They may be asking for a new growth and valuation story.
A category is an investment thesis
When investors value a company, they do not compare it with every other business. They place it in a category and identify companies with similar business models, growth profiles, economics and risks.
For the past two decades, being a SaaS company communicated a recognizable economic model: recurring revenue, strong gross margins, predictable retention and the ability to grow revenue faster than costs.
Those characteristics gave investors a way to estimate how today’s revenue might eventually become durable cash flow.
But many SaaS markets have matured. Growth has slowed. Competition has increased. AI has also created a more uncomfortable question: how much of the value currently captured by traditional software will remain defensible?
This is why the pressure to become an AI company is not simply about participating in in a technology trend. It is about convincing the market that the company can create a different - and more valuable - future.
Why the category affects valuation
High-growth software companies are commonly valued using a multiple of current or expected revenue. That multiple is a compressed judgment about the future quality of the business.
At the time of writing, companies in the public BVP Nasdaq Emerging Cloud Index trade at an average of approximately 7.6 times revenue.
Among the exceptional private companies in Bessemer’s 2025 Cloud 100, AI companies commanded an average multiple of 24 times ARR, compared with 19 times for their non-AI peers.
This does not mean that adding AI to the company’s positioning automatically earns another five turns of ARR in valuation.
It means investors are willing to set higher expectations for companies they believe can capture the AI opportunity: faster growth, access to larger markets, stronger competitive advantages and greater future cash generation.
Bessemer also notes that the gap between AI and non-AI valuations is compressing and may prove temporary as AI becomes common across software.
The label is already losing its power.
The underlying performance is not.
Most SaaS companies are not becoming AI companies
Adobe has embedded Firefly across Photoshop, Illustrator, Express and Acrobat. It has also introduced standalone Firefly products, enterprise offerings and generative-credit packages.
The important change is not simply that Adobe can say its products contain AI. The company is beginning to isolate and measure the revenue attributable to it.
In its second-quarter 2026 results, Adobe reported that AI-first ARR had surpassed $500 million and had grown approximately threefold year over year.
That gives investors evidence that AI can produce incremental demand and revenue inside an established software portfolio.
But it does not automatically make Adobe an AI-native company.
Investors will still ask whether that revenue is large enough to change Adobe’s overall growth trajectory, whether it is genuinely incremental and whether its economics remain attractive as usage increases.
Intercom is making a deeper change.
Its Fin AI Agent is priced at $0.99 per outcome. Instead of charging only for access to software or another customer-service seat, Intercom charges when the AI completes defined work.
That changes the unit of value.
Fin is not compared only with another software tool. It can also be compared with the cost of resolving the same customer request through human labor.
This potentially gives Intercom access to a larger pool of spending. But it also transfers more responsibility to the product. If Fin does not complete the work successfully, Intercom cannot capture the same value. If model, infrastructure and human-oversight costs become too high, additional usage may not produce attractive margins.
Intercom gains access to a larger economic opportunity by accepting greater accountability for the outcome.
AI-native companies such as Cursor started from a different position. They were designed around AI performing a growing share of the work rather than adding AI to an existing software model.
Established SaaS companies do not inherit that growth trajectory simply by adding similar capabilities.
Most will not become AI companies.
They will become SaaS companies whose future value is increasingly determined by how well they respond to AI.
Some will use AI to defend retention and preserve relevance. Others will create incremental revenue or improve operating margins. A smaller number will change what they sell, how they charge and which market they address.
Only that last group may have a credible case for being evaluated as a fundamentally different kind of business.
What this means for Product
The problem is that “become an AI company” can conceal several very different business objectives.
If AI is supposed to defend the existing business, bundling new capabilities into the current subscription may be rational, even if those capabilities produce little direct revenue.
If the objective is incremental revenue, giving AI away inside existing plans may undermine the strategy.
If the company wants to compete for labor or services spending, a seat-based pricing model may constrain the opportunity. But moving toward outcome-based pricing also makes product reliability and cost-to-serve financially consequential.
If the goal is margin improvement, deploying AI internally may create a better SaaS business. It does not necessarily create an AI business. Expensive customer-facing AI features could even move margins in the opposite direction.
These are not variations of the same strategy.
They require different product investments, pricing models, architectures, metrics and trade-offs.
The most valuable contribution a product leader can make is therefore not to respond with a list of possible AI features.
It is to ask leadership to complete the sentence:
We need AI because it will change __________.
Our growth rate?
Our addressable market?
Our retention?
Our pricing power?
Our operating margins?
Our competitive position?
Our valuation story becomes credible only when the answer produces evidence in the business.
Who owns that evidence?
The mandate also creates an accountability problem.
Product owns adoption. Technology owns model performance. Finance owns margins. Sales owns monetization. Marketing owns the AI narrative.
Each function can achieve its stated objective while the company fails to create additional value.
Product can launch successful AI capabilities. Marketing can reposition the company. Sales can include AI in every customer conversation. Technology can improve model performance.
And the company’s growth, retention, margins and market position may remain unchanged.
Someone still has to determine whether these individual outcomes add up to a different business.
That is the responsibility senior product leaders cannot avoid.
“Become an AI company” is not a product strategy. It is an ambition to change the future the market believes the company can create.
Until leadership makes that future explicit, Product cannot make a coherent investment decision.
So the next time you hear, “We need to become an AI company,” resist the instinct to translate it immediately into a roadmap.
First ask:
What must become economically different about our company if we succeed?
Until next week,
Elena Leonova
Executive product & business-strategy leader
I work with senior product leaders, Directors, and VPs to help them master product strategy when decisions are high-stakes, ambiguous, and made at scale - where trade-offs matter and the cost of getting it wrong is real.
This newsletter reflects the thinking behind my work across:
• Product Executive education - From PM to Product Executive (Maven cohort)
• Advisory & coaching - product strategy and executive decision-making
• Writing & research - including my forthcoming book The Art of Platform Products
Maven cohort: https://maven.com/elena-leonova/from-pm-to-product-executive
LinkedIn: https://www.linkedin.com/in/elenleonova
Website: https://elenleonova.com